THE CONTROLLING FACTORS INVOLVED IN BIOMASS ...

THE CONTROLLING FACTORS INVOLVED IN BIOMASS AQUEOUS PRETREATMENT:

FUNDAMENTALS TO APPLICATIONS

By

LIBING ZHANG

A dissertation submitted in partial fulfillment of

the requirements for the degree of

DOCTOR OF PHILOSOPHY

WASHINGTON STATE UNIVERSITY

Department of Biological Systems Engineering

JULY 2016

© Copyright by LIBING ZHANG, 2016

All Rights Reserved

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To the Faculty of Washington State University:

The members of the Committee appointed to examine the dissertation of LIBING ZHANG

find it satisfactory and recommend that it be accepted.

Bin Yang, Ph.D., Chair

Shulin Chen, Ph.D.

Manuel Garcia-Perez, Ph.D.

Melvin Tucker, Ph.D.

Hong-fei Wang, Ph.D.

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ACKNOWLEDGEMENTS

I would like to thank the agencies for funding my research through research grants,

including: Jet Fuel Production from lignin in Remote Locations – DOD DARPA YFA ,

Pretreatment of Sub-millimeter Biomass Particles – DOE-SBIR Phase I & II, Upgrading Lignin

to Aromatic Hydrocarbons- DOE-NREL, EAGER: Aqueous Phase Catalytic Processing of

Lignin to Hydrocarbons- NSF, and Catalytic Production of Aviation Fuel Hydrocarbons from

Lignocellulosic Biomass-Derived Lignin – Sun Gant- DOT. Also, I would like to thank the

Chinese Scholarship Council for providing the important Graduate Student Scholarship for me to

complete my thesis.

I sincerely appreciate my advisor, Professor Bin Yang, for his continued encouragement and

advice throughout these years. I would also like to acknowledge my committee members Dr.

Hongfei Wang, Dr. Melvin Tucker, Dr. Manuel Garcia-Perez, and Dr. Shulin Chen for their

supports in my research and PhD studies.

I am grateful to Drs. John R. Cort, Zheming Wang, Li Fu, Luis Velarde, Zhou Lu, Satish

Nune, and Shunli Chen from Pacific Northwest National Laboratory; Drs. Yuqiao Pu and Art J.

Ragauskas from BioEnergy Science Center; Dr. Shi-You Ding from Michigan State University;

and Dr. Jim Dooley and Lead Mechanical Engineer David Lanning from Forest Concepts, LCC

for their support in my PhD studies. Also, I would like to thank Marie S. Swita, Steve Jordan,

Daniel T. Howe, and Teresa Lemmon for helping me with my PhD research. I would like to

acknowledge Dr. Jie Xu and Mr. Dmitry Gritsenko from University of Illinois at Chicago for

their help in mass transfer modeling development.

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I’d like to specially thank the Biological Systems Engineering program at Washington State

University for providing me with the platform to develop research and leadership skills.

Additionally, I would like to give my appreciation to all my research colleagues for their helpful

discussions on the many research topics we collaborated on and valuable sharing of their career

development.

At last, I really want to thank my family members for their endless love and support,

especially my parents, Fuqing Zhang and Shuming Liang, for their encouragement and my

beloved husband, Dan C. Berghofer, for his continuous support.

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THE CONTROLLING FACTORS INVOLVED IN BIOMASS AQUEOUS PRETREATMENT:

FUNDAMENTALS TO APPLICATIONS

Abstract

by Libing Zhang, Ph.D.

Washington State University

July 2016

Chair: Bin Yang

Of all sustainable resources, only lignocellulosic biomass can be transformed into organic

fuels and chemicals that can integrate well with our current transportation infrastructure due to

the inherent convenience, cost, and efficiency advantages of these organic fuels over current

fuels. However, the high cost of biofuel conversion technologies poses challenges in large-scale

commercialization. Pretreatment is the most expensive operation unit that is responsible for

disrupting the naturally recalcitrant lignocellulosic biomass to provide reactive intermediates for

production of renewable biofuels and bioproducts through biochemical or thermochemical

processes. A better fundamental understanding of biomass pretreatment is essential to bring

biofuels to market.

The goal of this dissertation is to study the dissolution chemistry of hemicellulose, cellulose,

and lignin for hardwood and softwood during flowthrough and batch pretreatment at

temperatures of 140 °C to 270 °C, initial pHs (pH 2-12), and flow rate of 0-25 mL/minute. The

complete solubilization of hardwood was achieved to produce insoluble lignin with high purity

and mild structural modification (i.e. changes of side chains and Cβ-C5). However, ~30%

softwood lignin was found to be undissolvable and was collected for analysis the first time.

Based on wet chemistry and NMR results, unlike hardwood, dilute acid flowthrough

pretreatment of softwood led to vigorous C-C5 recondensation resulting in undissolvable lignin

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structures. On the other hand, High Resolution Sum Frequency Generation Vibrational

Spectroscopy (SFG-VS) was developed to selectively and in-situ characterize cellulose for the

first time and new structural information was observed. The implementation of the simulation of

aqueous pretreatment of cellulose using a heated fluid test bed on cellulose and dynamic SFG-

VS analysis indicated a cellulose recrystallization hypothesis, leading to new insights on the

effectiveness of biomass pretreatment. In addition, Total Internal Reflection (TIR)-SFG-VS was

invented and tested to selectively characterize the cellulose surface of Avicel and the cellulose Iβ

crystalline particles. At last, the results of the effects of the biomass comminuting approach on

aqueous pretreatment indicated the associations among particle sizes, cutting methods, mass

diffusion and sugar yields in pretreatment and enzymatic hydrolysis. The findings of this

dissertation provide important fundamental insights to establish the effective biomass

pretreatment.

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TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS ........................................................................................................... iii

ABSTRACT .................................................................................................................................... v

TABLE OF CONTENTS .............................................................................................................. vii

LIST OF TABLES ....................................................................................................................... xvi

LIST OF FIGURES ..................................................................................................................... xix

CHAPTER ONE

INTRODUCTION .......................................................................................................................... 1

1.1 Biochemical conversion of biomass to bioethanol ................................................................ 2

1.2 The essential role of aqueous biomass pretreatment ............................................................. 4

1.3 The key problems in aqueous pretreatment ........................................................................... 5

1.4 Thesis objectives and hypothesis........................................................................................... 6

1.5 Thesis organization ................................................................................................................ 8

CHAPTER TWO

FUNDAMENTALS OF BIOMASS AQUEOUS PRETREATMENT ..........................................11

2.1 Abstract .................................................................................................................................11

2.2 Introduction ..........................................................................................................................11

2.3 Plant cell wall macrostructure ............................................................................................. 12

2.4 Biomass aqueous pretreatment research progress ............................................................... 13

2.4.1 Hot water and dilute acid pretreatment ......................................................................... 16

2.4.2 Alkaline pretreatment .................................................................................................... 18

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2.4.3 The flowthrough aqueous pretreatment ........................................................................ 19

2.4.4 Summary of biomass degradation chemistry in aqueous pretreatment ........................ 23

2.5 Process characterization of flowthrough aqueous pretreatment .......................................... 28

2.5.1 Cellulose characterization in aqueous pretreatment ..................................................... 28

2.5.2 Lignin production and characterization in aqueous pretreatment ................................. 31

2.6 Preprocessing of feedstock for aqueous pretreatment ......................................................... 45

2.7 Conclusion ........................................................................................................................... 48

CHAPTER THREE

CHARACTERIZATION METHOD DEVELOPMENT FOR CRYSTALLINE CELLULOSE

USING HIGH RESOLUTION BROADBAND SUM FREQUENCY GENERATION

VIBRATIONAL SPECTROSCOPY IN BIOFUEL ..................................................................... 49

3.1 Abstract ................................................................................................................................ 49

3.2 Introduction ......................................................................................................................... 50

3.3 Materials and methods ......................................................................................................... 57

3.3.1 Cellulose samples.......................................................................................................... 57

3.3.2 HR-BB-SFG-VS ........................................................................................................... 58

3.3.3 Polarization Null angle measurement with picosecond scanning SFG-VS .................. 60

3.3.4 Atomic force microscopy (AFM).................................................................................. 60

3.4 Results and discussion ......................................................................................................... 61

3.4.1 Traditional Raman in cellulose characterization ........................................................... 61

3.4.2 Polarization Null Angle (PNA) testing ......................................................................... 62

3.4.3 HR-BB-SFG-VS spectra of cellulose Iα, Iβ and Avicel samples .................................. 64

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3.4.4 Morphology of cellulose Iα and cellulose Iβs from AFM image .................................. 69

3.4.5 Unique O-H signatures for cellulose Iα and cellulose Iβ .............................................. 71

3.4.6 CH Spectral features for cellulose Iβ samples from different sources .......................... 74

3.4.7 Characterization of Avicel probed by HR-BB-SFG-VS ............................................... 77

3.5 Conclusion ........................................................................................................................... 79

3.6 Acknowledgements ............................................................................................................. 80

CHAPTER FOUR

TOWARD DYNAMICAL UNDERSTANDING OF FUNDAMENTALS OF PRETREATMENT

AND ENZYMATIC HYDROLYSIS OF CELLULOSIC BIOMASS VIA A SFG-VS ............... 82

4.1 Abstract ................................................................................................................................ 82

4.2 Introduction ......................................................................................................................... 84

4.3 Materials and methods ......................................................................................................... 91

4.3.1 SFG-VS set-up .............................................................................................................. 91

4.3.2 Design of SFG-VS geometries to observe cellulose surface layers .............................. 92

4.3.3 The fluid heating cell system development................................................................... 94

4.3.4 Cellulose sample preparation and heating program ...................................................... 95

4.3.5 Molecular observation of cellulose structural changes in enzymatic hydrolysis using

SFG-VS .................................................................................................................................. 96

4.4 Results and discussion ......................................................................................................... 97

4.4.1 Structural characterization of cellulose surface ............................................................ 97

4.4.2 The characterization of thermal behaviors of dry cellulose by SFG-VS .................... 105

4.4.3 Structural changes of wet cellulose during heating process ........................................110

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4.4.4 The different SFG-VS signals of dry and wet Avicel bulk crystals .............................113

4.4.5 Study of the temperature dependence of dry Avicel surface layers .............................114

4.4.6 Molecular structural changes of cellulose in enzymatic hydrolysis using SFG-VS ....116

4.5 Conclusion ..........................................................................................................................119

4.6 Acknowledgements ........................................................................................................... 121

4.7 Supplementary materials ................................................................................................... 122

CHAPTER FIVE

CHARACTERIZATION OF FLOWTHROUGH SOFTWOOD AQUEOUS PRETREATMENT

UNDER NEUTRAL AND ALKALINE PH AT ELEVATED TEMPERATURES .................... 129

5.1 Abstract .............................................................................................................................. 129

5.2 Introduction ....................................................................................................................... 129

5.3 Materials and methods ....................................................................................................... 132

5.3.1 Feedstock .................................................................................................................... 132

5.3.2 Flowthrough pretreatment system............................................................................... 132

5.3.3 Flowthrough pretreatment of pine wood chips ........................................................... 133

5.3.4 GC/MS analysis .......................................................................................................... 135

5.3.5 Enzymatic hydrolysis on pretreated whole slurries .................................................... 135

5.3.6 2-D 1H -13C HSQC NMR of ball milled pine wood lignin and pretreatment obtained

RISL ..................................................................................................................................... 135

5.4 Results and discussion ....................................................................................................... 136

5.4.1 pH changes of pretreatment solvent after pretreatment .............................................. 136

5.4.2 Hemicellulose removal and hemi-sugars recovery ..................................................... 139

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5.4.3 Cellulose removal and glucose recovery .................................................................... 140

5.4.4 Lignin removal and recovered soluble lignin yield .................................................... 142


RISL ..................................................................................................................................... 145

5.4.6 Pretreatment reactions kinetics at pH 6.4-12.0 ........................................................... 147

5.5 Conclusion ......................................................................................................................... 151



CHAPTER SIX

CHARACTERIZATION OF LIGNIN DERIVED FROM WATER-ONLY AND DILUTE ACID

FLOWTHROUGH PRETREATMENT OF POPLAR WOOD AT ELEVATED

TEMPERATURES ...................................................................................................................... 155

6.1 Abstract .............................................................................................................................. 155

6.2 Introduction ....................................................................................................................... 156


6.3.1 Materials ..................................................................................................................... 159

6.3.2 Flowthrough pretreatment of poplar wood ................................................................. 159

6.3.3 Composition analysis of the solid residues and determination of lignin removal ...... 160

6.3.4 Ultraviolet visible spectroscopy (UV-Vis) quantification of RSL .............................. 161

6.3.5 Recovered insoluble lignin (RISL) isolation and purity measurement ....................... 161

6.3.6 Gel permeation chromatography analysis of molecular weight of flowthrough lignin

.............................................................................................................................................. 162

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6.3.7 Py-GC/MS analysis of ReL ........................................................................................ 162

6.3.8 Fourier transformed infrared (FTIR) spectroscopic analysis of ReL.......................... 163

6.3.9 Characterization of RSL after flowthrough pretreatment by GC/MS ......................... 163

6.3.10 RISL structural characterization by 2-D 1H -1C NMR HSQC NMR ........................ 163


6.4.1 Lignin removal and recovery in aqueous phase .......................................................... 165

6.4.2 Lignin purity determination of the isolated RISL ....................................................... 168

6.4.3 Py-GC/MS spectroscopic analysis of untreated poplar wood and pretreated solid

residues ................................................................................................................................ 170

6.4.4 FTIR spectroscopic analysis of ReL ........................................................................... 172

6.4.5 Characterization of the RSL by GC/MS ..................................................................... 174

6.4.6 2-D NMR of Ball-milled poplar lignin and RISL ....................................................... 175

6.5 Conclusion ......................................................................................................................... 179



CHAPTER SEVEN

REVEALING THE MOLECULAR STRUCTURAL TRANSFORMATION OF HARDWOOD

AND SOFTWOOD IN DILUTE ACID FLOWTHROUGH PRETREATMENT ..................... 185

7.1 Abstract .............................................................................................................................. 185

7.2 Introduction ....................................................................................................................... 185


7.3.1 Materials ..................................................................................................................... 189

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7.3.2 Dilute acid flowthrough pretreatment of pine wood ................................................... 190

7.3.3 Structural characterization of pretreatment recovered insoluble lignin by 2-D 1H-13C

HSQC NMR ......................................................................................................................... 191

7.3.4 Solid state CP/MAS 13C NMR characterization of dilute acid flowthrough pretreated

poplar and pine lignin .......................................................................................................... 192

7.3.5 Gel permeation chromatography molecular weight analysis of the flowthrough derived

pine lignin ............................................................................................................................ 192

7.3.6 Enzymatic hydrolysis .................................................................................................. 192


7.4.1 Delignification and cellulose removal of poplar and pine wood with dilute acid

flowthrough pretreatment ..................................................................................................... 193

7.4.2 Sugar recovery in dilute acid pretreatment of pine wood ........................................... 195

7.4.3 Characterization of pretreatment recovered insoluble lignin by 2-D 1H-13C HSQC

NMR .................................................................................................................................... 197

7.4.4 Solid state CP/MAS 13C NMR characterization of residual pine lignin in pretreated

solid residues ........................................................................................................................ 199

7.4.5 GPC analysis of the flowthrough derived lignin......................................................... 204

7.5 Conclusion ......................................................................................................................... 205



CHAPTER EIGHT

HOT WATER AND DILUTE ACID PRETREATMENT OF SUB-MILLIMETER BIOMASS

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PARTICLES ................................................................................................................................ 212

8.1 Abstract .............................................................................................................................. 212

8.2 Introduction ....................................................................................................................... 213


8.3.1 Materials ..................................................................................................................... 216

8.3.2 Batch tubular pretreatment .......................................................................................... 218

8.3.3 Digestibility evaluation of pretreated whole slurries by enzymatic hydrolysis .......... 220

8.3.4 Kinetic modeling of biomass degradation .................................................................. 221


8.4.1 Mass transfer modeling of hot water and dilute acid pretreatment of corn stover and

poplar wood ......................................................................................................................... 223

8.4.2 Effects of particle sizes and cutting approaches on mass transfer coefficients........... 226

8.4.3 Experimental results of hot water and dilute acid pretreatment of corn stover and

poplar wood ......................................................................................................................... 230

8.4.4 Optimization of the Douglas fir hot water and dilute acid pretreatment conditions ... 233

8.4.5 Pretreatment results of Douglas fir with various particle sizes and cuttings approaches

.............................................................................................................................................. 239

8.4.6 Hot water and dilute acid pretreatment of poplar wood ............................................. 244

8.4.7 Sugar yields from hot water and dilute acid pretreatment of poplar and corn stover and

sequential enzymatic hydrolysis .......................................................................................... 247

8.5 Conclusion ......................................................................................................................... 250


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CHAPTER NINE

CONCLUSIONS AND FUTURE PERSPECTIVES ................................................................. 256

Future work ............................................................................................................................. 261

Achievements and perspectives ............................................................................................... 263

APPENDIX ................................................................................................................................. 265

REFERENCES ........................................................................................................................... 288

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LIST OF TABLES

1. Table 2.1 Literature reviews of hemi-sugars recovery and total glucose yield operated under

flowthrough system; mono refers to monomeric sugars while oligo represents oligomeric

forms of sugars…………………………………………...…………….…………………….21

2. Table 2.2 Lignin characterization methods and targeted properties………………………....43

3. Table 3.1 Peak position and relative peak intensity parameters from curve fitting

using Lorentzian lineshape profiles (as in Equation 3.3 in the main text) of (a) Avicel, (b)

cellulose Iα from alga Valonia ventricosa (Glaucocystis (nostochinearum)), and (c) cellulose

Iβs from red reef tunicate and (d) Halocynthiaroretzi tunicate within wavelength of 2700 to

3050 cm-1 and 3200 cm-1 to 3450 cm-1………………………………………………………68

4. Table 3.2 Comparison of C-H, O-H peaks areas and their ratios of Avicel, cellulose Iβ

(different sources) and cellulose Iα .........................................................................................79

5. Table S4.1 Peak positions, amplitudes, and widths after curve fittings of SFG-VS spectra via

Lorentz profile convoluted with a Gaussian intensity distribution method in (a) TIR-SFG-VS

spectra of Avicel surface layers, (b) SFG-VS sepctra of Avicel bulks, (c) TIR-SFG-VS

spectra of cellulose Iβ surface layers, and (d) SFG-VS spectra of cellulose Iβ bulks within the

wavelength of 2800 to 3750 cm-1...........................................................................................122

6. Table 5.1 Distribution of the detected linkages of ball milled pine lignin and RISLs. BML:

ball milled wood lignin; A: RISL collected at pH of 11.0 and pretreatment severity of 6.1; B:

RISL collected at pH of 12 and pretreatment severity of 5.8. The percentages of inter

linkages of BML were based on all detected peaks………………………………………...146

7. Table S5.1 Main lignin 2-D 1H-13C Cross-peaks assignments in the HSQC

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Spectra………………………………………………………………………..……….…….153

8. Table 6.1 FTIR spectra band assignments………………………..…………….…………..174

9. Table 6.2 Relative proportions of major structural linkages and S, G, H percentages in ball

milled lignin and flowthrough derived RISL; A: RISL collected at 240 °C, 25 ml/min, 10

min, with 0.05% (w/w) H2SO4; B: 270 °C, 25 ml/min, 10 min, with water-

only………………………………………………………..……………………...………...177

10. Table S6.1 Major GC/MS detected aromatic compounds in hydrolysates……..………….182

11. Table S6.2 Assignments of main lignin 1H- 13C cross-peaks in the HSQC Spectra of the

RISLs……………………………………………………………………….……...……….184

12. Table 7.1 General lignin difference in hardwood and softwood………………….…...…..186

13. Table 7.2 The linkage distribution of ball-milled and flowthrough-derived poplar wood and

pine wood lignin (flowthrough pretreatment conditions: 240 °C, 0.05% (w/w) H2SO4,

10min)………………………………………………………………………………...…….199

14. Table 7.3 Selected chemical shifts and signal assignments in a 13C NMR

spectrum………………………………………………………………………….…………202

15. Table 7.4 The molecular weight of pine lignin (flowthrough pretreatment at 240 °C, 0.05%

(w/w) sulfuric acid, and 10 min)………………….………………………………………..204

16. Table S7.1 Enzymatic hydrolysis of flowthrough pretreated pine wood whole

slurries………………………………….……………………………………...………… 206

17. Table S7.2 Assignments of main lignin 1H- 13C cross-peaks in the HSQC Spectra of the

pretreatment recovered insoluble lignin………………………………………..………….209

18. Table 8.1 Characteristics of feedstock used in the study………………………….……….217

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19. Table 8.2 Selected pretreatment conditions performed in the study……………….…...…219

20. Table 8.3 Some unit volume of biomass solids (biomass density)……….…………...….. 222

21. Table 8.4 Kinetic modeling parameters (physical mass transfer coefficients) of xylan, glucan,

and lignin removal with consideration of mass transfer (a) 1% sulfuric acid pretreatment with

residence time of 0 to50 min at 140ºC and (b) hot water pretreatment with residence time of 0

to 30 min at 190ºC………………………………………………………………….……....227

22. Table 8.5 Sugar yields from pretreatment and enzymatic hydrolysis…………..……….…247

23. Table A1 Pyrolysis products of residual lignin derived from aqueous flowthrough

pretreatment by Py-GC/MS ……………..……………………………………...…………267

24. Table A2 The distribution of hydrodeoxygenation (HDO) products of flowthrough derived

lignin and the selectivity of HDO conversion with noble metal catalyst

matrix…………………………………………………….……………………...………….271

25. Table A3 Some acid diffusivity of various sized and cut biomass feedstock…………….286

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LIST OF FIGURES

1. Figure 1.1 Simplified process flowchart of biochemical conversion process for production of

ethanol from cellulosic biomass and cost distribution……………………………...…………4

2. Figure 1.2 (a) SFG-VS system and (b) flowthrough reactor……………....……………….....7

3. Figure 2.1 Plant cell wall structure and major chemical components matrix…………….....13

4. Figure 2.2 The aqueous degradation of major biomass components under acidic and alkaline

conditions; (a) cellulose, (b) hemicellulose, (c) lignin………………………………………27

5. Figure 3.1 (a) Raman spectra of cellulose Iα from alga Valonia ventricosa (Glaucocystis

(nostochinearum)) and (b) cellulose Iβ from Halocynthiaroretzi tunicate in the frequency

regions of 300 to 1600 cm-1 and 2500 to 3700 cm-1………………………………...………62

6. Figure 3.2 PNA for different visible polarization angles of cellulose Iα from alga Valonia

ventricosa (Glaucocystis (nostochinearum)), cellulose Iβ from Halocynthiaroretzi tunicate,

and Avicel using scanning SFG system at 2956 cm-1………………………………….……63

7. Figure 3.3 (a) Experimental HR-BB-SFG-VS spectra of Avicel, cellulose Iβ from

Halocynthiaroretzi tunicate, cellulose Iβ from red tunicate, and cellulose Iα from alga

Valonia ventricosa (Glaucocystis (nostochinearum)) at wavelengths of 2700 cm-1 to 3450 cm-

1. All spectra intensities were normalized and presented on the same intensity scale. (b)

Spectra for two cellulose Iβs after peak fitting via Lorentz profile convoluted with a Gaussian

intensity distribution method; (c) Spectra for cellulose Iα (left axis) and Avicel (right

axis)……………………………………………………...………………………………..…66

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8. Figure 3.4 Comparison of AFM images of (a) Avicel, (b) cellulose Iα from alga Valonia

ventricosa (Glaucocystis (nostochinearum), (c) cellulose Iβ from red tunicate and (d)

cellulose Iβ from Halocynthiaroretzi tunicate. The scale of all images is 2 x 2µm……...….70

9. Figure 3.5 HR-BB-SFG-VS spectra of cellulose Iβs from Halocynthiaroretzi tunicate and red

reef tunicate between wavenumber of 3100 cm-1 to 3500 cm-1………………………..…….72

10. Figure 3.6 HR-BB-SFG-VS spectra of cellulose Iβ from Halocynthiaroretzi tunicate and

cellulose Iα from alga Valonia ventricosa (Glaucocystis (nostochinearum)) between

wavenumber of 3200 cm-1 to 3450 cm-1……………………………………………………..73

11. Figure 3.7 Difference of cellulose Iβs from red reef tunicate and Halocynthiaroretzi tunicate

by HR-BB-SFG-VS…………………………………………………………………….……74

12. Figure 3.8 HR-BB-SFG-VS spectra of Avicel and cellulose Iα from alga Valonia ventricosa

(Glaucocystis (nostochinearum)) (a) and cellulose Iβ from Halocynthiaroretzi tunicate (b),

respectively, between wavelengths of 3200 cm-1 to 3450 cm-1…………………………...…77

13. Figure 4.1 a) TIR-SFG-VS design on cellulose surface observation and b) non TIR-SFG-VS

on cellulose bulk characterization. IR and VIS refers to infrared and visible 532nm beams,

respectively. c) total internal reflectance concept; d) simulation of SFG intensity with the

change of TIR and non TIR SFG-VS geometry, in which b) refers to non TIR SFG-VS while

TIR represents TIR-SFG-VS……………………………………………………………..….92

14. Figure 4.2 Cellulose heating high temperature fluid cell systems……………..……………94

15. Figure 4.3 SFG-VS spectra and peak fittings via Lorentz profile convoluted with a Gaussian

intensity distribution method of Avicel surface layers (red color) and bulk crystallines (blue

color) within wavelength of (a) 2800 to 3000 cm-1 and (b) 3000 to 3750 cm-1; Dots

xxi

represented experimental data while lines represented curve fittings. All intensities were

calibrated at the same voltage (1000 v) for the SFG-VS measurements…………………...97

16. Figure 4.4 SFG-VS spectra and peak fittings via Lorentz profile convoluted with a Gaussian

intensity distribution method of Iβ surface (red color) and bulk (blue color) within

wavelength of (a) 2800 to 3000 cm-1 and (b) 3000 to 3750 cm-1; Dots represented

experimental data while lines represented curve fittings. All intensities were calibrated at the

same voltage (700 v) for the SFG-VS measurements……………………..……………….103

17. Figure 4.5 Cellulose crystalline structural changes under different temperature during

heating process…………………………………………….……………………………….106

18. Figure 4.6 The recrystallization of dry cellulose polymer after cooling down from heating

process ………………………………………………….…………………………….…....108

19. Figure 4.7 Cellulose crystalline structural changes of wet Avicel heating

process...………………………………………………………………………………...…..111

20. Figure 4.8 Recrystallization process during cooling process of wet Avicel…………….....112

21. Figure 4.9 Impact of water on SFG-VS characterization of Avicel ...……………..…..…..113

22. Figure 4.10 Temperature dependence of Avicel surface layers characterized by SFG-VS (up:

structural changes of dry Avicel surface layers during heating process; down: heated dry

Avicel surface layers cooling down to room temperature)…………………………..……..114

23. Figure 4.11 SFG-VS spectra of the decay of enzymatic hydrolysis to study enzymatic

hydrolysis kinetics................................................................................................................117

24. Figure 4.12 SFG-VS characterization of Restart samples from interrupted enzymatic

hydrolysis ……………………………………………………………………………....….118

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25. Figure S4.1 Whole spectra of Avicel and Iβ bulks and surface layers a) Avicel; b) Iβ (black:

blank control; blue: bulks; red: surface layers)…………………………………………..…124

26. Figure S4.2 Demonstrations of various molecular structures of Avicel surface layers and

relatively stable Iβ structure……………………………………………………..…….……125

27. Figure S4.3 TIR-SFG-VS spectra of (a) Avicel and (b) cellulose Iβ under two polarizations,

(red color) SSP and (Blue color) PPP; s-, s-, p- polarization corresponding to the polarization

of the optical fields of the SFG signal, visible and IR beams, respectively. p polarization is

defined when electric field vector parallel to the plane of incidence within the incident plane

formed by the incident beam direction and the surface normal, and s polarization is

perpendicular to the incident plane………………………………………………………....126

28. Figure S4.4 Cellulose structure (two glucose units) and different molecular vibrations......127

29. Figure S4.4 Heat disruption and recrystallization hypothesis……………………………...127

30. Figure 5.1 pH of the pretreatment hydrolysates at different severities after the hydrolysates

were cooled down to room temperature, pretreatment conditions: pH 6.4-12, 200-270°C, and

0-10 min……………………………………………….……………………………….…...137

31. Figure 5.2 Hemicellulose removal and hemi-sugars recovery in stage 1 (pretreatment only,

pH 6.4-12, 200-270°C, and 0-10 min)……………...………………………………………139

32. Figure 5.3 Cellulose removal and glucose recovery (Stage 1: pretreatment; Stage 2:

enzymatic hydrolysis), pretreatment conditions: pH 6.4-12, 200-270°C, and 0-10 min…...140

33. Figure 5.4 Lignin removal and recovered soluble lignin yield, pretreatment conditions: pH

6.4-12, of 200-270°C, and 0-10 min………………………………………………………..142

34. Figure 5.5 GC/MS results of the pretreatment hydrolysates collected at pH of 12 and

xxiii

pretreatment severity of 5.2………………………………………..……...………………..144

35. Figure 5.6 2-D 1H-13C HSQC NMR spectra of ball milled pine lignin and pretreatment

obtained RISLs at 11.0 and 12.0. (a,b) ball milled pine wood lignin, (c,d) RISL derived from

pH of 11.0 and pretreatment severity of 6.1, (e,f) RISL derived from the pH of 12.0 and

pretreatment severity of 5.8……………………………………………..……………….…145

36. Figure 5.7 Proposed biomass degradation mechanisms of softwood in aqueous flowthrough

pretreatment at initial pH 6.4-12, (a) hemicellulose; (b) cellulose; (c) lignin……...………149

37. Figure S5.1 The HPLC analysis of degradation products in pretreatment hydrolysates at pH

of 12.0 and pretreatment severity of 6.1…………………………….………….…………..152

38. Figure S5.2 Main detected lignin linkages…………………………..……………………..154

39. Figure 6.1 Scheme for lignin recovery and analytical analysis in this study………..…….165

40. Figure 6.2 Lignin removal and recovery as RSL and RISL. (a) water-only and (b) 0.05%

(w/w) H2SO4 flowthrough pretreatment (temperature: 160-270 °C, time: 0-12min, flow rate:

25mL/min)……………………………………………………………………………….…166

41. Figure 6.3 The RISL purity determined by AcBr method. (a) water-only; (b) 0.05% (w/w)

H2SO4 at the flow rate of 25 ml/min………………………………………….....………….168

42. Figure 6.4 Py-GC/MS results of untreated poplar wood (control) and solid residues after

flowthrough pretreatments. (a) Untreated poplar wood and solid residues pretreated at

240 °C, 0.05% (w/w) H2SO4 for 10 min (pretreatment severity ~5.0); (b) Untreated poplar

wood and solid residues pretreated at 270 °C, water-only for 10 min (pretreatment severity

~6.0)......................................................................................................................................170

43. Figure 6.5 Effects of pretreatment severity on S, G and H unit removal by water-only and

xxiv

0.05% (w/w) dilute acid pretreatment (poplar wood control sample: G:S:H= 7:11:2)……171

44. Figure 6.6 Characterization of ReL in pretreated solid residues by FTIR (800-2000 cm-1).

Control: Untreated Poplar wood; a) Solid residues from 0.05% (w/w) sulfuric acid

pretreatment at 240 °C for 2.6 min; b) Solid residues from water-only pretreatment at 230℃

for 3.8 min; c) Solid residues pretreated with 0.05% sulfuric acid at 240℃ for 5 min……173

45. Figure 6.7 2-D 13C - 1H HSQC correlation NMR spectra of aliphatic regions (left column)

and aromatic regions (right column), (a, b) Ball milled poplar wood lignin; (c, d) 240 °C,

0.05% (w/w) H2SO4 for 10 min (pretreatment severity ~5.0); (e, f) 270 °C, water-only for 10

min (pretreatment severity ~6.0)…………………………………………..……………….176

46. Figure 6.8 Plausible lignin β-O-4 cleavage and condensation reaction pathways in

hydrolysates…………………………………………………………………………...……179

47. Figure S6.1 Assigned interunit linkages of lignin, including different side-chain linkages,

and aromatic units: (A) β-O-4 aryl ether linkages; (B) resinol substructures (β-β′, α-O-γ′, and

γ-O-α′ linkages); (C) phenylcoumarane substructures (β-5′and α-O-4′ linkages); (D)

spirodienone substructures (β-1′ and α-O-α′ linkages); (G) guaiacyl units; (G′) oxidized

guaiacyl units with an Cα-ketone; (S) syringyl units; (S′) oxidized syringyl units with a Cα

ketone; (E) p-hydroxybenzoate substructures; (F) alcohol end groups; (K) cinnamaldehyde

end groups……………………………………………………………………………...…..183

48. Figure 7.1 Lignin and cellulose contents in pretreated solid residues of poplar and pine wood

after the flowthrough pretreatment (200-270°C, reaction time of 2-10min, and 0.05% (w/w)

H2SO4)………………………………………….…………………..………………………193

49. Figure 7.2 Sugar yields of pine wood at 0.05% (w/w) sulfuric acid flowthrough pretreatment

xxv

under different severity log R0 (a) Cellulose recovery; (b) Hemi-sugars recovery………...195

50. Figure 7.3 Characterization of (a,b) ball milled pine lignin; (c,d) flowthrough pretreatment

recovered insoluble pine lignin from liquid phase by 2-D 1H-13C HSQC NMR. (pretreatment

conditions: 240 °C, 0.05% (w/w) sulfuric acid, and 10min)……………………………….197

51. Figure 7.4 Solid state CP/MAS 13C NMR spectra of a) pine residual lignin; b) pine

pretreatment recovered insoluble lignin; c) poplar pretreatment recovered insoluble lignin

under 240°C with 0.05% (w/w) H2SO4 for 10min………………………………..……..….200

52. Figure 7.5 Possible recondensation mechanisms of pine lignin during dilute acid

pretreatment………………………………………………………………………...………203

53. Figure S7.1 Assigned interunit linkages of lignin, including different side-chain linkages,

and aromatic units: (A) β-O-4 aryl ether linkages; (B) resinol substructures (β-β′, α-O-γ′, and

γ-O-α′ linkages); (C) phenylcoumarane substructures (β-5′and α-O-4′ linkages); (D)

spirodienone substructures (β-1′ and α-O-α′ linkages); (G) guaiacyl units; (G′) oxidized

guaiacyl units with an Cα-ketone; (S) syringyl units; (S′) oxidized syringyl units with a Cα

ketone; (H) hydroxyphenyl groups; (E) p-hydroxybenzoate substructures; (K)

cinnamaldehyde end groups……………………………………………………...…………210

54. Figure S7.2 GPC analysis of lignin molecular weight of a) poplar flowthrough pretreatment

recovered insoluble lignin, b) pine wood flowthrough pretreatment recovered insoluble

lignin, c) pine wood flowthrough derived residual lignin under 240°C with 0.05% (w/w)

H2SO4 for 10min……………………………………………………………………...….....211

55. Figure 8.1 Kinetic diffusion modeling (equation 8.6, 7, 8) of pretreatment behaviors of major

components in six feedstock 1% (w/w) H2SO4 pretreatment at 140ºC with residence time of

xxvi

0-50 min including (a) xylan dissolution, (b) glucan dissolution, and (c) lignin dissolution,

and hot water pretreatment at temperature of 190ºC with residence time of 0-30 min

including (d) xylan dissolution, (e) glucan dissolution, and (f) lignin

dissolution…………………………………………………………………………….....….224

56. Figure 8.2 Biomass removal trend of Douglas fir in hot water and dilute acid pretreatment

(a) Biomass removal; (b) lignin removal; (c) cellulose removal, pretreatment severities 2.1-

4.5………………………………………………………………………………..…………235

57. Figure 8.3 Total sugar yields from Douglas fir (1% H2SO4), left axis is Absolute (hemi-

sugars+glucan)/hemi-sugars/glucan recovery (from 100g dry biomass) while right axis is

Relative (hemi-sugars +glucan) /hemi-sugars/glucan recovery (% of maximum), Stage 1:

sugar release during pretreatment (measured after posthydrolysis); Stage 2: sugar release

during enzymatic hydrolysis; Stage 1+2: sugar release during pretreatment and enzymatic

hydrolysis, 100 mg protein Ctec2 (93 FPU) with 20 mg Htec2/g (glucan +

xylan)…………………….…………………………………………………………..…….237

58. Figure 8.4 Biomass and major components removal of Douglas Fir (140°C, 1% (w/w)

sulfuric acid, 50min), Absolute biomass removal (from 100g dry biomass), crumbler (CB)

and hammer cut (HM)……………………………………………………………………..239

59. Figure 8.5 Recovery of Douglas fir sugars through dilute acid pretreatment (1% (w/w)

sulfuric acid), Stage 1: sugar release during pretreatment (measured after posthydrolysis);

Stage 2: sugar release during enzymatic hydrolysis; Stage 1+2: sugar release during

pretreatment and enzymatic hydrolysis, 20 and 200 mg protein Ctec2 with 4 and 40 mg

Htec2/g (glucan + xylan)………………………………....……………………...…………241

xxvii

60. Figure 8.6 Recovery of Douglas fir sugars through hot water and enzymatic hydrolysis (a)

biomass and lignin removal; (b) sugar recovery; Stage 1: sugar release during pretreatment

(measured after posthydrolysis); Stage 2: sugar release during enzymatic hydrolysis; Stage

1+2: sugar release during pretreatment and enzymatic hydrolysis, 20 and 200 mg protein

Ctec2 with 4 and 40 mg Htec2/g (glucan + xylan)...……………………………….………243

61. Figure 8.7 (a) Biomass removal, (b) Cellulose removal, (c) Lignin removal, (d) Manan and

xylan removal, and (e) sugar yields of poplar in dilute acid pretreatment, 140°C, 1% (w/w)

sulfuric acid, 40min, and enzymatic hydrolysis (72 hours), 100 mg protein Ctec2 with 20 mg

Htec2/g (glucan + xylan)…………………………………………………………………...245

62. Figure 8.8 Biomass removal, sugar yields of green poplar in hot water pretreatment, 190°C,

hot water, 15min (a) biomass/major components removal, (b) combined sugar yields after

pretreatment and enzymatic hydrolysis, 100 mg protein Ctec2 with 20 mg Htec2/g (glucan +

xylan)………………………………………………………………………………………246

63. Figure S8.1 Left) Crumbler M24 research prototype machine used with 4.8 and 1.6 mm

cutters, Right) Research scale Crumbler machine with 0.8 mm cutters (by ForestConcept,

LCC)…………………………………………………...…………..………...……………..251

64. Figure S8.2 Total sugar yields from Douglas fir of (a) 0.05% wt sulfuric acid (b) Water only,

left axis is Absolute (hemi-sugars+glucan)/hemi-sugars/glucan recovery (from 100g dry

biomass) while right axis is Relative (hemi-sugars +glucan) /hemi-sugars/glucan recovery (%

of maximum), Stage 1: sugar release during pretreatment (measured after posthydrolysis);

Stage 2: sugar release during enzymatic hydrolysis; Stage 1+2: sugar release during

pretreatment and enzymatic hydrolysis…………………..…………………….…………..254

xxviii

65. Figure S8.3 Hot water pretreatment of poplar wood chips for 5,10,15,25, and 30 min (left to

right), particle size of 1/8′′-16 mesh and cut by Crumbler…………………………………255

66. Figure A1 SFG-VS characterization of (a) Avicel 2600-3600cm-1; (b) Avicel 1000-1600cm-1;

(c) Cellulase and BSA 2600-3800 cm-1; (d) Cellulase and BSA 1500-1800 cm-1, Note: (A)-

Avicel without a flat CaF2 window on top of the sample; (B)-Avicel with a flat CaF2 window

on top of the sample………………………..............................................................………265

67. Figure A2 Isolation processes of the native and pretreatment derived

lignin ……………………………………………………………………………………….270

68. Figure A3 GC/MS determination of water/dilute acid soluble lignin fragments from 240°C

with 25 ml/min flow of 0.05% sulfuric acid………………………………………………..272

69. Figure A4 Determination of cellulose crystallinity before and after flowthrough pretreatment

by XRD………………………………………………………………………...…………...273

70. Figure A5 Our flowthrough system……………………………………….……………….274

71. Figure A6 Application of SFG-VS in observing pretreated biomass substrate and in

comparison with XRD results………………………………………………………..……..275

72. Figure A7 Designed measuring cell for mass transfer coefficients, (a) reported device (b)

designed device……………………………………………………………………….……279

73. Figure A8 Calibration of conductivity (a) Sulfuric acid (b) NaOH………………………281

74. Figure A9 Fractional uptake of NaOH, Douglas fir 1/8’’-16mesh, CB substrates………..285

xxix

DEDICATION

This dissertation is dedicated to my husband, parents and other

family members who provide love and support.

1

CHAPTER ONE

INTRODUCTION

The limitation of supply of fossil fuels can cause an energy crisis, which is why so much

research is on attractive and renewable energy resources. Along with hydro, geothermal, wind,

and solar energy, cellulosic biomass based biofuels are among the most promising and emerging

renewable energy sources that can have a pervading impact on energy use structures, especially

in transportation fuels. The biofuels produced from cellulosic biomass can enhance energy

security, reduce greenhouse gas emissions, improve global competitiveness, and create job

opportunities 1. The U.S. Department of Agriculture (USDA) and Department of Energy (DOE)

estimated that about 1.3 billion dry tons of cellulosic biomass could be available annually, which

is equivalent to 1.5 billion barrels of traditional petroleum 2. This amount of bio-oil can have

major effects on energy use structures in the U.S. without causing food shortage 3, 4. Currently,

E10, E85, and E20–E40 represent 10%, 85%, and 20-40% bioethanol blending with conventional

gasoline, respectively, while B20 indicates 20% biodiesel blending with petroleum refined diesel.

The research and commercialization of biofuels receive policy support from the U.S.

government. For example, the DOE’s Bioenergy Technologies Office has supported biorefinery

development such as INEOS Bio’s Indian River BioEnergy Center, POET-DSM’s Project

LIBERTY, and the Abengoa’s Bioenergy Biomass of Kansas. DuPont has implemented a

cellulosic ethanol biorefinery in Nevada, Iowa supported by government funding and

collaboration with National Renewable Energy Laboratory (DOE; Growing America’s Energy

Future: Bioenergy Technologies Office Successes of 2014). Biofuels have received considerate

attention from the public.

2

Among different types of biofuels, bioethanol production has been popular since the 1990s,

but it was only produced from corn grains back then. Nowadays, ethanol or butanol, which can

be produced from the fermentation of starches and carbohydrates, is considered to be the most

promising forms of biofuels to replace petroleum gasoline. The U.S. Secretary of Energy

proposed that 30% (~60 billion gallons) of transportation fuel should be ethanol, indicating the

need for about 60 billion gallons per year to meet the annual demand of 140 billion gallons of

gasoline. However, even though ethanol production from corn and grains has doubled from 4.5

to 7.5 billion gallons, the ethanol production is not enough to satisfy the growing demand for

transportation fuels 5. Therefore, cellulosic bioethanol can have a significant impact in helping to

meet the demand of the transportation fuel market. However, establishing full commercialization

of bioethanol is still a work in progress. The main challenge is the relatively high operation cost

of bioethanol production.

1.1 Biochemical conversion of biomass to bioethanol

After the energy crisis of the 1970s, the DOE initiated the Office of Alcohol Fuels to support

the increase of ethanol production. In the past few decades, the cost of cellulosic ethanol was

reduced from $4.00 to 1.2-1.5$/gallon 6. However, this price is still not low enough to compete

with the petroleum market. The main technique necessary to trigger this improvement is the

biochemical conversion process of biomass by biotechnology (Trichoderma reesei discovered

during World War II) 7. Biochemical conversion is one of the two main conversion R&D

processes (biochemical and thermochemical) of the DOE and Office of Energy Efficiency and

Renewable Energy (EERE) biomass program, which benefits greatly from advancements in

biotechnology 8. The biological conversion of cellulosic biomass through enzymatic hydrolysis

3

of cellulose and fermentation of sugars offers the potential for higher yields, higher selectivity,

lower energy costs, and milder operating conditions than thermochemical processes 9. The goal

of biochemical conversion R&D is to reduce the cost of biomass to fermentable sugars and other

usable intermediates, and further conversion of these intermediates to liquid transportation fuels

or other bioproducts 8. Thus, the main study focus of biochemical conversion is reducing the

cost.

A techno-economic analysis, particularly at large scale production to reduce the investment

risks, is required for commercialization of bioethanol production. Several techno-economic

analyses were carried out by the National Renewable Energy Laboratory 9-13, which gained

strong experience that can lower the risks to biofuel commercialization. In these analyses,

biochemical conversion to bioethanol is considered as one of the closest biofuel techniques to

commercialization. Also, the biochemical conversion of biomass includes mainly feedstock

preparation, pretreatment, enzymatic hydrolysis, and fermentation, presenting a cost distribution

of ~30%, ~20%, 12%, and ~10%, respectively (Figure 1.1). Therefore, pretreatment is the most

expensive operation process, and it impacts the cost of both upstream and downstream processes

14. As mentioned, the high cost of biochemical production of cellulosic ethanol is the major

challenge, thus, the reduction of pretreatment cost is significant to make cellulosic ethanol

competitive with starch ethanol and gasolines.

4

Figure 1.1 Simplified process flowchart of biochemical conversion process for production of

ethanol from cellulosic biomass and cost distribution

1.2 The essential role of aqueous biomass pretreatment

The direct biological treatments of cellulosic biomass to bioethanol produce a low yield of

bioethanol because lignocellulosic biomass is recalcitrant towards chemicals and microbiological

treatments due to its naturally complex structure and chemical composition. The sugar yield of

biomass of enzymatic hydrolysis is very low without pretreatment, which is not economically

feasible 15. Pretreatment can improve bioethanol yield, which refers to the process of opening

biomass structures to increase accessibility, reduce biomass recalcitrance, and produce more

fermentable sugars. The biomass pretreatment not only produces sugars for biochemical

conversion, but it also produces sugars, lignin or other chemical intermediates for

thermochemical, biological and catalytic upgrading to valuable materials, chemicals and fuels,

which improves biorefinery economics.

5

Biomass pretreatment methods have been categorized into chemical, physical, biological,

and thermal-chemical methods 14. In order to compete with the conventional fossil fuel market,

the sugar price should be, at most, 5 cents/lb to make bioethanol cost-effective. However, the

latest sugar price was projected to be 6.4 cents /lb in 2002 by National Renewable Energy

Laboratory (NREL) in the process design and economics study utilizing co-current dilute acid

pre-hydrolysis and enzymatic hydrolysis for corn Stover 11. To achieve such a low sugar cost, the

pretreatment technique should be oriented to low cost, which also affects the cost of feedstock

preprocessing and other downstream processes. The aqueous pretreatment based on water only or

dilute acid/alkali is a promising option to achieve low-cost-pretreatment due to the cheap and

recyclable steam, substantial industrial experience on steam treatments, and long history of

development. Many pretreatment reviews have been published in the past ten years 1, 5, 16-27, and

they have summarized the most significant achievements in biomass aqueous pretreatment

including pretreatment reactors, kinetics, process overview, economics, and biomass chemistry.

Thus, many efforts have focused on aqueous pretreatment in the past decades 17, however, more

efforts are still needed to advance pretreatment and reduce the cost of pretreatment to accelerate

commercialization .

1.3 The key problems in aqueous pretreatment

The insufficient studies of key fundamental issues of aqueous pretreatment result in a high

cost of aqueous pretreatment. Despite the importance of aqueous pretreatment technique, the

process characterization of aqueous pretreatment is poorly understood. Although both the

recovery of sugars and the removal of lignin are frequently investigated as pretreatment targets,

the mechanisms are not well understood. The effects of temperature, pH, and time on the

6

aqueous pretreatment performance are not well defined which might add extra cost on the

reactor, chemical and steam. Due to the limitation of cellulose analytical methods, cellulose

crystalline structure is rarely characterized in aqueous pretreatment. Without a clear

understanding of cellulose crystalline structural change during aqueous pretreatment, it is

difficult to develop strategies to improve sugar yields and reduce enzyme loading. This impedes

developing low-cost sugar production. The conversion of lignin to valuable materials, fuels, and

chemicals has received considerate attention in recent years. However, little effort has been made

to characterize lignin derived from aqueous pretreatment. This poses challenges on lignin

utilization and valorization which couldn’t provide extra credits and reduce the cost. Also, the

insufficient understanding of lignin chemistry in aqueous pretreatment impedes achieving the

high yield of sugars which increased sugar cost.

1.4 Thesis objectives and hypothesis

Against the above background, the goal of this thesis is to develop greater insight into the

key fundamental knowledge of aqueous pretreatment. The tools developed to pursue this

knowledge are SFG-VS and a flowthrough reactor (Figure 1.2). The specific research objectives

of this thesis are mainly as follows:

1. Study the key fundamental issues in aqueous pretreatment by process characterization of

the developed aqueous flowthrough pretreatment technique.

2. Study effects of pH, temperature, and reaction time on flowthrough aqueous

pretreatment.

3. Develop cellulose characterization tools and characterize original cellulose and structural

transformation in aqueous pretreatment.

7

4. Produce and characterize lignin from the aqueous pretreatment of poplar and pine wood

to study lignin characteristics and solubilization mechanisms for potential utilization. The

aqueous pretreatment derived lignins have been provided to upgrade to jet fuel, for

carbonization to carbon based materials to produce electrodes, and for biological

fermentation to produce bio-lipids but these are not the focus of this thesis.

5. Identify the most promising substrate particle sizes and cutting approaches (e.g. Shards,

Cubic prismatic) for aqueous pretreatment. Develop applicable mass transfer modeling to

describe solvent diffusion into biomass and better aqueous pretreatment kinetics.

Figure 1.2 (a) SFG-VS system and (b) flowthrough reactor

The main hypothesis corresponding includes:

1. The high yield of sugars and usable lignin (e.g. high purity, low molecular weight, and

mild structural modification) are produced by the aqueous flowthrough pretreatment

when elevated temperatures are applied at whole pH range because flowthrough is

fractionating both softwood and hardwood and moving products in a short time to avoid

further reactions.

8

2. The basic cellulose structure and structural transformation by the effects of aqueous

pretreatment heating temperature and media at the molecular level can be studied by

SFG-VS system with/without a cell to mimic aqueous pretreatment because SFG-VS is

in-situ and selectively characterizing crystalline structure of cellulose.

3. The fundamental issues of aqueous pretreatment in lignin properties, behaviors, and

chemistry can be understood by the flowthrough pretreatment system because the

flowthrough system can separate lignin (e.g. residual, soluble and insoluble lignins) from

carbohydrates for characterizing.

4. The energy-saving biomass particle sizes and cutting methods for aqueous pretreatment

can be studied by developing a kinetic modeling with mass transfer effects on hot water

and dilute acid pretreatment of various particle sizes and cutting methods because the

particle sizes and sharps of biomass particles affect the diffusion of pretreatment solvent

into biomass.

1.5 Thesis organization

The background and motivations of this thesis have been introduced in chapter 1. The

purpose of the rest of this chapter is to establish the organization of the thesis’ content. Chapter 2

reviews the fundamental knowledge and recent research progress of aqueous pretreatment. To do

a great job, one must first sharpen one’s tools. Due to the limitations demonstrated by traditional

methods of studying cellulose crystalline structure, chapter 3 describes the SFG-VS technique in

characterization cellulose crystalline structure to assist in cellulose characterization in aqueous

pretreatment. To investigate more detailed cellulose crystalline structure, High Resolution

Broadband SFG-VS (HR-BB-SFG-VS) is developed to characterize four cellulose crystals from

9

well-known sources. This work has been published in the journal Cellulose 28. This work lays the

foundation of the application of SFG-VS in studying cellulose structural characterization in

aqueous pretreatment.

In chapter 4, a high temperature liquid flowthrough cell with fluid pathways, heating system,

and temperature controller are used under the SFG-VS system to dynamically monitor cellulose

crystalline structure change with effects of high temperatures and chemicals, which simulates

practical aqueous pretreatment process. To study the cellulose crystalline surface layers, the laser

input angles and geometry (prism) are developed for the first time to observe the molecular

structure of cellulose crystalline surface layers. Also, the enzymatic hydrolysis process is

characterized by SFG-VS to demonstrate the capacity of SFG-VS in studying enzymatic

hydrolysis process.

Chapter 5 summarizes the effects of neutral and alkaline pH, and pretreatment severities on

pretreatment sugar yield and lignin recovery of softwood as well as the change of pH in aqueous

pretreatment. The softwood pretreatment kinetics and product distribution under neutral and

alkaline pH are investigated. Chapter 6 will take a closer look at hardwood lignin removal

mechanisms to reveal the depolymerization and repolymerization mechanisms of lignin in

aqueous pretreatment. The enhancement of sugar yield in flowthrough aqueous pretreatment as

well as lignin removal and properties (e.g. yield, purity, and molecular weight) are also well

documented in this chapter. Chapter 7 compares softwood and hardwood lignin removal

mechanisms and reveals their different recalcitrance in aqueous pretreatment. The different

hardwood and softwood structural transformations were revealed. This work is significant in

guiding pretreatment strategies on various feedstock, the utilization of lignins from various

10

sources and mixed feedstock. Feedstock preprocessing can have a significant impact on

pretreatment performance and biorefinery costs. In chapter 8, the pretreatment and enzymatic

hydrolysis behaviors of variously shaped and sized biomass are assessed to evaluate the

feasibility of energy saving shapes and sizes. Also, mass transfer modeling will be discussed to

determine the optimum particle sizes and cutting methods. At last, chapter 9 will cover the most

significant findings, conclusion, and perspectives of this thesis.

11

CHAPTER TWO

FUNDAMENTALS OF BIOMASS AQUEOUS PRETREATMENT

2.1 Abstract

Biomass pretreatment is required in the biochemical conversion of biomass to bioethanol.

The pretreatment process can assist enzymatic hydrolysis in producing high yield of stable sugar

intermediates for fermentation. Besides, new development of catalytic, biological, and

thermochemical valorization of lignin in the production of hydrocarbons has generated attention

to reactive lignin intermediates. It is expected to achieve a whole fractionation of biomass to

produce high yield of sugars and lignin and a clear fundamental understanding of cellulose and

lignin properties, behaviors, and chemistry in aqueous pretreatment. In this chapter, the research

progress of aqueous pretreatment is described. The recent research progress and characterization

of cellulose and lignin are summarized in aqueous pretreatment. At last, the preprocessing of

feedstock for aqueous pretreatment is reviewed.

2.2 Introduction

The aqueous pretreatment consists of a variety of pretreatment methods. In this thesis, it

refers to steam based pretreatment such as hot water, dilute acid, and alkaline pretreatment. The

advantages of aqueous pretreatment are the cheap and recyclable steam, the substantial industrial

experience using steam treatments, and the long historical development of aqueous pretreatment.

This chapter will first explore the research progress of aqueous pretreatment including hot water,

dilute acid, and alkaline pretreatment. The biomass degradation chemistry in aqueous

pretreatment will also be presented. Additionally, the application of flowthrough reactor system

in aqueous pretreatment will be discussed. The cellulose and lignin characterization and

12

chemistry in aqueous pretreatment will be reviewed as well. The aqueous pretreatment has

accumulated a great amount of publications. The chapter only covers key research references and

reviews for readers to pursue.

2.3 Plant cell wall macrostructure

Plant photosynthesis is the biological process for plants to convert solar energy into organic

biopolymers known as cell wall polysaccharides; primarily cellulose, hemicellulose, and lignin,

which all contain carbon and energy for plant survival. Only few microorganisms are capable of

decomposing these polymers, e.g. E. coli (a bacterium), Saccharomyces cerevisiae (yeast), and

Zvmomonas mobilis (bacterium). Lignin is believed to not only keep the mechanical strength of

the plant cell wall, but it is also thought to make carbohydrates resistant to microbial attack.

Besides, cellulosic biomass also comprises some amount of minerals, oils, free sugars, starches,

and other compounds. The chemical and physical characteristics of plant cell walls have a great

impact on their accessibility to enzyme and chemicals’ treatments, thus, the structural studies of

these organic biopolymers (e.g. cellulose and lignin) are crucial to resolving their interaction

with enzymes to improve hydrolysis efficiency. The plant cell wall microstructure is shown in

Figure 2.1, which shows the embedding of cellulose in hemicellulose and lignin to form a

complex matrix. Four main chemical bonds are observed in lignocellulosic biomass, namely,

ether, ester, C-C, and hydrogen bonds. The bonds in individual cellulose, lignin, and

hemicellulose are presented in Figure 2.1. In addition, these three biopolymers share chemical

bonds with each other that form a tight cell wall microstructure. The bonds for connecting

hemicellulose and lignin polymers are ether, ester, and hydrogen bonds, while cellulose and

lignin are connected by ether and hydrogen bonds. The hydrogen bonds with hemicellulose are

13

much weaker than intra and inter-chain hydrogen bonding network in cellulose since

hemicellulose lacks primary alcohol functional groups 16. In addition, cellulose and

hemicellulose are only connected by hydrogen bonds. Besides, lignin has covalent bonds with

polysaccharides, which causes three biopolymers bond tightly 16. The distribution of chemical

bonds in plant cell walls is important for pretreatment method design and degradation chemistry.

Also, the aqueous hydrolysis of these bonds is one of the mechanisms to break down the

biopolymers.

Figure 2.1 Plant cell wall structure and major chemical components matrix

2.4 Biomass aqueous pretreatment research progress

The successful pretreatment method design should have all the following key attributes 17, 21,

14

29-31:

1. Limited size reduction of biomass to decrease energy cost in biomass prehandling

2. Minimal and inexpensive chemical use

3. Low cost reactors (minimizing volume, pressure, temperature, and reaction time)

4. Compatible to downstream conversion

5. Reactive intermediates upgraded to value-added products to increase plant avenues

6. High solid loading operation to get high concentration of product stream

7. Easy products transportation

8. Fast heat transfer

9. Low capital and operational costs

10. High product yield and selectivity

To date, biological methods are hard to control, presenting poor products selectivity and long

residence time 17. Physical methods were not effective in achieving high sugar yields, and the

total pretreatment cost was high 32. Concentrated chemical pretreatment is deficient in high

chemical cost and tedious chemical recovery procedures. Aqueous pretreatment is the most

promising potential option that can achieve all the attributes described above. Aqueous

pretreatment heats biomass with steam, which is the simplest and cheapest pretreatment option

17. The aqueous based pretreatment methods are defined to be the process of heating cellulosic

biomass in water to convert structural biomass major components to reactive intermediates that

can be further biologically, catalytically, or thermochemically converted into fuels, chemical, and

biomaterials 17. Thus, the aqueous pretreatment occurs in pure water with or without the slight

addition of catalytic chemicals. The final products (e.g. sugars) from aqueous pretreatment of

15

biomass are not simply obtained for the purpose of biological conversion to ethanol. The

products can also be reactive intermediates for subsequent thermochemical and catalytic

upgrading, e.g. reactive lignin for hydrodeoxygenation upgrading to hydrocarbons, or converting

sugars to chemicals. Thus, the pretreatment methods can be diverse, depending on their various

purposes.

Many efforts have been devoted to biomass aqueous pretreatment in the past ten years,

which have been reviewed 14, 19, 20, 27, 33. Also, biomass aqueous pretreatment has been well

introduced and summarized in the book titled ′Aqueous Pretreatment of Plant Biomass for

Biological and Chemical Conversion to Fuels and Chemicals′ 17. Several leading aqueous

pretreatment methods, mainly acid and alkaline based aqueous pretreatment, have been

intensively investigated. The most extensively studied pretreatment was performed by the

Biomass Refining Consortium for Applied Fundamentals and Innovation (a national consortium

of universities and the DOE National Renewable Energy Laboratory) to use the same corn

stover, poplar wood, and switchgrass as feedstock and enzyme sources in ammonia, lime,

controlled pH, sulfur dioxide, and dilute sulfuric acid pretreatments 34-36. The advancement of

pretreatment techniques in recent years has achieved more than 95% of hemicellulose recovery,

which originally presents 20-30% in biomass (arabinose, galactose, glucose, mannose, and

xylose-hemi-sugars). However, the removal and recovery of cellulose and lignin are still limited,

which results in high enzyme dosage and poor utilization of carbohydrates and lignin. Secondly,

although the formation of acetic acid by autohydrolysis of biomass in hot water pretreatment can

act as a catalyst to biomass hydrolysis reaction, these sugar degradation products (e.g. furfural,

acetic acid, and HMF) can potentially inhibit a subsequent fermentation process. At last, the

16

degradation products from lignin inhibit downstream enzymatic hydrolysis and fermentation

efficiency.

In summary, it is important to find a pretreatment method that can fractionate biomass and

separate biomass degradation products into three streams (hemicellulose, lignin, and cellulose) or

two streams (sugar and lignin) with limited sugar degradation loss for high yield of ethanol

production and lignin utilization. The organosolv pretreatment was reported to achieve complete

separation of biomass components into solid lignin, an aqueous stream and pure cellulose 18.

However, this method suffers from the challenge of recovering pretreatment solvents. The

flowthrough reactor system can also achieve this goal by exiting sugars and solubilized lignin out

of the reactor after hydrolysis and depolymerization to avoid further sugar degradation loss.

2.4.1 Hot water and dilute acid pretreatment

In early 1898, sulfuric acid was used in the hydrolysis of cellulose and hemicellulose in

biomass to produce sugars; however, a high concentration of acid was used, which resulted in the

high operation cost and challenge in acid recovery. Currently, dilute sulfuric acid pretreatment is

greatly favored since it can recover more than 90% of the theoretical maximum sugars with more

monomeric sugars compared to less than about 65% in autohydrolysis, which occurs when

biomass is pretreated with water only. Several dilute acid pretreatment reviews were published to

summarize the pretreatment process and conditions, reaction kinetics, reactor systems, and

economics 26, 27, 33. The acidic reagents may be mineral or organic acids, such as hydrochloric,

sulfuric, phosphoric, peracetic, oxalic, and/or maleic acid. The liquid hot water is also a dilute

acidic method since the pH of water decreases with temperature. For example, under the same

pressure and ionic strength, pKw decreased with increasing temperatures until 250°C. Above

17

250°C, pKw increased along with increased temperature (Refers to Release on the Ionization

Constant of H2O". International Association for Properties of Water and Steam. August 2007.).

Thus, water acts as an acid in high temperature 37-39. At temperatures between boiling (100°C)

and critical temperature of water (374°C), water is less polar because temperature disrupts

hydrogen bonding of water, which provides a good solvent for pretreatment. The water/steam

treatment has been highly favored and gained a lot of experience in the pulp and papermaking

industry. The reactors used for this process in the industry include batch Masonite gun and the

continuous screw fed STAKE II reactor. Thus, dilute acid pretreatment technique gets support

from mature pulping industry. However, the challenge is to control the sugar degradation loss

and achieving high yield of sugars with low enzyme loading.

Hot water pretreatment is performed at temperatures of 200–230°C for up to 15 min. Hot

water pretreatment can achieve 40-60% of the total biomass solubilization with 4–22% of the

cellulose, 35–60% of the lignin, and all of the hemicellulose 16. A dilute acid pretreatment (0-2%

sulfuric acid) at temperatures of 140°C-280°C using different reactor systems was operated to

achieve more than 95% of hemi-sugars recovery and more than 80% of glucose yields followed

by enzymatic hydrolysis with over 90% of the hemicellulose as monomeric sugars. Steam

explosion pretreatment adds a sudden physical vapor pressure release of reactor to the hot

water/steam treatment of biomass, which increases enzymatic digestibility of pretreated

substrates. Controlled pH pretreatment was developed to reduce sugar degradation to obtain as

high as oligomeric hemi-sugar to reduce the effects of released organic acids in the sugar

degradation loss. However, the sugar degradation in hot water and dilute acid pretreatment is still

hard to control.

18

2.4.2 Alkaline pretreatment

Alkaline pretreatment is an important pretreatment method because of its unique

performance. In 1927, the Austrian chemist Skrabal published the importance of H+ and OH- in

biomass hydrolysis by indicating that the H+ and OH- concentration directly influenced

degradation of major biomass compounds. The study of the impact of OH- on biomass hydrolysis

occurred earlier than H+ because alkali had been used in the pulping industry for decades. The

Kraft process (NaOH 53% and sodium Na2S 21%; ~170°C; ~120min) was invented by Carl F.

Dahl in 1879 in Danzig, Germany. Alkalis can efficiently remove lignin to produce cellulosic

pulps. Nowadays, high concentration of alkali is used in pretreating biomass at low temperature

and pressures at ambient conditions for hours or days - to increase enzymatic hydrolysis sugar

yield. Several factors in alkaline pretreatment can have a great impact on pretreatment efficiency.

For example, in ammonia-based pretreatment methods, the ammonia loading, water loading,

temperature, time, blow down pressure, and number of treatments all have great impacts on

pretreatment performance 40. The aqueous ammonia swells the cellulose and causes a possible

transformation of the crystal structure at very mild conditions compared to other pretreatment

methods. Additionally, the common features of alkaline pretreatments can improve enzymatic

digestibility of biomass with the production of controllable sugar degradation loss (e.g. HMF and

furfural) 16. Meanwhile, the removal of the hemicellulose acetyl group is significant because it

reduces the inhibition of acetyl groups on fermentation, which can be achieved by alkaline

pretreatment 41. Ammonia Fiber Expansion (AFEX) was also performed to enhance enzymatic

hydrolysis with its benefits of fiber expansion. Ammonia Recycled Percolation (ARP) passes an

aqueous ammonia solution (usually 5-15 (w/w) %) through a column reactor packed with

19

biomass at temperatures between 80-180°C. The alkalis (e.g. ammonia, lime, and NaOH) can

depolymerize lignin, which reduces non-effective binding of lignin to enzymes (e.g. cellulase)

thereby improving the sugar yield of enzymatic hydrolysis. The alkaline pretreatment has

reached its bottleneck in achieving its low cost of commercialization.

The alkaline pretreatment increases biomass internal surface area, which exposes cellulose

for enzymes during the enzymatic hydrolysis process. Moreover, although the alkali is

ineffective at the removal of hemicellulose, the alkalis can solubilize lignin and break the bonds

between lignin-carbohydrates complex (LCC). Significantly, it can swell the cellulose crystalline

structure as mentioned before. Furthermore, the alkali can transform cellulose I to other

polymorphs such as Na-cellulose I and cellulose II, which are easily digested by enzymes as it

requires less activation energy to break down cellulose 42, 43. However, the fundamental

mechanism behind cellulose transformation in alkaline pretreatment is still not well investigated.

Thus, a reliable cellulose characterization method and study of cellulose crystalline structure,

especially on the molecular level, are vital to the development of alkaline pretreatment

techniques.

2.4.3 The flowthrough aqueous pretreatment

Recently, the flowthrough pretreatment reactor system has gained considerate attention due

to its capacity to fractionate biomass to produce higher yield of sugars with limited sugar

degradation loss than in the batch reactor system. This continuous pretreatment concept was

reported early in 1983 and pioneered by Bobleter and co-workers 44-47. The flowthrough

apparatus was described in a previous review on aqueous pretreatment, but it is still in its infant

stage 48. The current understanding of the flowthrough reactor system is that it can rapidly heat

20

up with a steam injection and cool-down within a short amount or/period of time 27 with a flow

of liquid passing through a packed bed of biomass, resulting in biomass hydrolysis 33. Also, the

flowthrough reactor provides the ideal mass and heat transfer by directing heating source-steam

with tubular reactor and fluidized sand bath instead of the oil/salts bath. It was reported that the

flowthrough can ensure that the same chemical reacts with biomass in pretreatment by pumping

a continuous pretreatment solution 27. Thus, the flowthrough reactor system is a promising tool to

study the interaction between chemicals and biomass and get accurate kinetic reaction data to

fundamentally study the biomass aqueous solubilization process.

Several pretreatment studies using flowthrough reactor have been reported. The hemi-sugars

recovery and total glucose yield operated under flowthrough system was examined (Table 2.1).

These previous studies showed higher sugar yield compared to the batch pretreatment. The dilute

sugar stream is a technical concern to the ones who want to commercialize, and it is raised when

discussing flowthrough pretreatment. A recent flowthrough pretreatment study reported a lower

minimum ethanol selling price than dilute acid, hot water, and steam explosion in a batch reactor

49. The extensive heat recovery, similar to an oil refinery, can be integrated, thereby sugar

dilution is no longer a limiting factor 49. In the early stage of flowthrough pretreatment study,

people focus on hemicellulose recovery while cellulose and lignin behaviors are untapped. Also,

the sugar (e.g. monomeric and oligomeric) releasing patterns and enzymatic hydrolysis

efficiency of pretreated slurries are not well understood. Additionally, little has been reported on

the chemistry of the major components of biomass, especially lignin chemistry, although people

have recently started to pay attention to lignin properties derived from flowthrough system.

21

Table 2.1 Literature reviews of hemi-sugars recovery and total glucose yield operated under flowthrough system; mono refers to

monomeric sugars while oligo represents oligomeric forms of sugars

Feedstock Reactor Temp.,

°C

Flow rate,

ml/min

Time,

min

Yield, % original hemi-sugars or

glucose

Lignin

Removal

Reference

Hemi-sugars Glucose

(Stage 1)

Glucose

(Stage 2)

Corn stover Hastelloy C-

276 tubing

(14.3 mL)

200, 2 or 25

Water

10 95 N/A N/A N/A 50

Cellulose Flow reactor

(3.6 mL)

295 10 Water 12 N/A 80mono+

20oligo

N/A N/A 51

Bamboo,

chinquapin(hardwood), and

Japan cedar(softwood)

Flow reactor

(3.6 mL)

180 for

20min

and

5°C/mi

n to

285

10 Water 60 >95 >95 N/A N/A 52

Corn stover 3.8 mL VCR

fitting

200 10 Water 16 90 <5 N/A 50 53

Corn stover 16.8 mL

tubular

200 10.5

Water

8 82 N/A N/A 30-40 54

Corn stover 3.8 mL 316

stainless steel

VCR fitting

180 0-10 (0.05

or 0.1

wt %

acid)

4-16 10-100 N/A N/A <50 55

Corn stover 14.3 mL 316

stainless steel

VCR fitting

180-

215

0-25 (0-

0.1 wt%

acid)

20 25-100 <5 50-96 10-70 56

22

Six woody and four

herbaceous

Hastelloy 2-

276 tubing

percolating

reactor

200-

230

1

water

0-15 100 (90

mono)

4-22 N/A 35-60 57

Sugar-cane bagasse and

leaves

316 stainless

steel 250 mL

190-

230

400-

500water

0.75-

4

100 (80

mono)

<10 N/A >60 58

Poplar Hastelloy 20.5

ml

200-

280

10-62.5

(water or

0.05% wt

acid)

2-10 90-100 10-100 Whole

slurries

to 100%

mono

60-100 59, 60

Poplar, sugarcane, bagasse 56 mL 316

stainless steel

180-

225

30 (water) 8-28 60-98 35-95 N/A 60-80 61

Miscanthus 20.5 ml

Hastelloy C

276

160 25 (water) 10-68 N/A N/A N/A Lignin

characteri

zation

performed

62

13C-enriched corn stover

stems

stainless steel

tube (D12.7

×L152mm)

170 20 (water) 0-80 <80 <20 N/A <50 63

Populus

trichocarpa×Populus

deltoides

stainless steel

tube (D12.7

×L152mm)

180 20 (water) 3-12 20-80 <5 N/A 20-55 64

Populus

trichocarpa×Populus

deltoids and Cellulolytic

Enzyme Lignin (CEL)

stainless steel

tube (D12.7

×L152mm)

140 or

180

20 12-

192

(wate

r)

N/A N/A N/A 5-75 65

23

In summary, the aqueous pretreatment played a significant role in biomass deconstruction

involving acids and alkalis. The high cost of aqueous pretreatment is still the main challenge for

commercializing. The purpose of the conventional pretreatment approaches is to increase the

digestibility of pretreated residual cellulose and hemicellulose. The substantial cellulose in the

residual solid residues resulted in high dosage use of enzymes, which are too expensive, resulting

in a cost of 0.17$ per gallon ethanol (9% production cost among total cost) 9, 11. The new concept

developed in our group is to fractionate the whole biomass, which can recover maximum high

yield of fermentable sugars (almost 100% of hemi-sugars and glucose) in the pretreatment stage

only, which potentially reduces enzyme dosage and achieves utilization of other usable by-

products (e.g. lignin) to increase profits. However, some aspects of this process, such as the

pretreatment process kinetics, mass transfer effect on pretreatment performance, cellulose

crystalline structural change in the process, and the characterization of derived lignin for

producing value-added products still requires further fundamental understanding. The

flowthrough reactor system can enable the study of these fundamentals.

2.4.4 Summary of biomass degradation chemistry in aqueous pretreatment

Biomass degradation chemistry is one of the fundamentals of aqueous pretreatment. It

possesses several reaction pathways. H+ and OH- concentration in pretreatment solution have a

great impact on the distribution of final products. The biomass in acidic conditions undergoes

mainly acid hydrolysis and dehydration reactions, and biomass degradation in alkaline conditions

includes peeling off reactions, termination stopping reactions, alkaline scission, and oxidative

alkaline degradation. Acidic and alkaline route conversion of biomass to bioproducts showed

potential abundant value-added products (See Figure 2.2). Firstly, cellulose hydrolysis can occur

24

under both acidic and alkaline conditions to produce monomeric and oligomeric sugars. Under

acidic conditions, sugars undergo further dehydration to form 5-HMF. Meanwhile, the sugars can

have reverse aldol and condensation reactions to generate aldehydes and humin respectively 66.

Under alkaline conditions, sugars suffer from severe peeling off reactions to form small organic

acids and aldehydes 67. Secondly, hemicellulose hydrolysis also can occur under both acidic and

alkaline conditions to produce sugars. The acidic degradation of sugars under acidic conditions

forms aldehydes (e.g. pruvaldehyde, glyceraldehyde, glycolaldehyde, acetaldehyde and

formaldehyde) and organic acids by reverse aldol reactions similar to sugars derived from

cellulose. Additionally, the alkaline degradation of sugars from hemicellulose mainly includes

various organic acids (e.g. glyceric, glycolic, formic, acetic, lactic and oxalic acids). At last, the

lignin chemistry under acidic conditions was reported to be predominantly centered about

depolymerization reactions via rupture of the β-O-4 linkages forming Hibbert ketones and

repolymerization by acid catalyzed condensation between the aromatic C6 and a carbonium ion

at Cα 68, 69, Cβ-C5 60, Cα-C5 70, and C4-O-C5. Under alkaline conditions, C5-C5 can be

generated through radical coupling reactions of lignin fragments. Also, enol ether structure is

easily formed the cleavage of β-aryl ether bonds in phenolic arylpropane units 71. However, the

chemistry showed is still not well defined, especially the cellulose and lignin degradation

mechanisms.

27

Figure 2.2 The aqueous degradation of major biomass components under acidic and alkaline

conditions; (a) cellulose, (b) hemicellulose, (c) lignin 60, 67, 71-74

28

2.5 Process characterization of flowthrough aqueous pretreatment

2.5.1 Cellulose characterization in aqueous pretreatment

Cellulose is the main target of aqueous pretreatment; thus, the characterization of its

structural and chemical changes is important. The hydrolysis of structural polysaccharides is still

the rate and cost limiting step in the conversion of lignocellulose into fuels. Although the

development of more efficient enzyme systems can help to resolve this issue in the long term, the

studies of the cellulose crystalline structure, surface accessibility, and interaction with enzymes

improves cellulose hydrolysis in the short term. Also, the study of cellulose structure can help

achieve the development of an effective cellulase enzyme system because the detailed cellulose

molecular structure is crucial to investigate its interaction with various enzymes.

2.5.1.1 Conventional characterization methods of cellulose

Cellulose is the most abundant and water insoluble biopolymer in the biomass cell wall

matrix. The growing and arrangement of plant amorphous and crystalline structures are affected

by species and environment, which makes cellulose structure from various sources complex and

different. Therefore, Cellulose from various sources possesses different degrees of

polymerization, crystallinity, surface structure, pore size, and heterogeneity of crystalline and

amorphous (or less crystalline) structures. The cellulose structure has been studied since the early

1900s. The bacterial and tunicate cellulose microcrystals have been studied as standard crystals

for investigating native cellulose crystalline structure. Thus, when developing a new tool to

characterize cellulose, one should always starts from these classical and standardized crystals.

The characterization of native cellulose in the plant cell wall is difficult due to the

interference of other matrix polymers (mainly hemicelluloses and lignin interlinks). Many

29

analytical tools have been seen employed to study cellulose structure recently. Advanced

imaging techniques, such as atomic force microscopy (AFM), have been used to observe the

cellulose surface structure. Also, other spectroscopic tools have demonstrated useful tools in the

characterization of cellulose molecular structure. These analytical tools of cellulose mainly

include X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Raman,

Nuclear Magnetic Resonance (NMR), Neutron Fiber Diffraction, and computational calculation

and models. XRD has been used in studying cellulose crystalline structure for several decades.

Many studies have used XRD to investigate the cellulose crystal molecular and hydrogen

bonding network. In XRD analysis of cellulose, the crystallinity of cellulose samples can be

calculated by peak height ration, peak deconvolution, and amorphous subtraction methods.

However, crystallinity calculation depends on the method applied 75 and is not consistent in

different pretreatment studies. XRD has also been used in studying the lateral thermal expansion

coefficients (TECs) of cellulose crystals in the lateral direction up to 250°C to determine the

structural transformation of cellulose crystals to high temperature states 76. X-ray diffractograms

of cellulose crystals can be used to determine clinic phases of cellulose polymorphs as well as

crystal dimensional parameters (e.g. a, b, c crystal length, α, β, and γ crystal orientation angles)

77. In addition, Synchrotron X-ray and Neutron Fiber Diffraction in assistance with

computational calculation was also used to study hydrogen bonding and the unit cell parameters

(e.g. crystallographics, helical parameters n and h, glycosidic torsion angles φ=O-5---C-5—C-

1—O-1, ψ= C-5–C-1–O-1–C-4′ (φ, ψ), crystal density and dimensional parameters) in cellulose

two basic polymorphs 78, 79.

30

FTIR and Raman were used to determine the crystallinity of cellulose; however, the

calculation method was based on the peak ratio, which was inconsistent depending on the

selection of peaks 80. Also, the assignments of FTIR and Raman were not clear since the crowd

peak distribution 81, 82. NMR is another method of characterizing cellulose structure that can

identify C1-C6H in cellulose structure. Also, Iα and Iβ cellulose crystalline phases were first

demonstrated by solid-state NMR (13C CP/MAS). 13C NMR can detect and study C1 to C6.

However, the cellulose peaks in NMR spectra were hard to be distinguished from other polymers

in whole cell wall samples, e.g. hemicellulose and lignin, because of their complexity and

overlapping signals 83. NMR has also been used in cellulose amorphous or surface structural

study. However, the peak assignments of cellulose amorphous and surface regions have not yet

been well resolved 75.

2.5.1.2 New technique to characterize cellulose in aqueous pretreatment

In the characterization of cellulose in aqueous pretreatment, most studies focus on the

change of cellulose chemical compositions, degree of polymerization (DP) of cellulose, and

crystallinity. The molecular structure characterization has rarely been carried out; thus, the

cellulose crystalline structural transformation in aqueous pretreatment is not well understood.

The main limitation of these spectroscopic tools (NMR, FTIR, and Raman) is interference from

carbohydrates. Besides the spectroscopic tools, Scanning Electron Microscope (SEM) and AFM

were applied to image surface structure at the sub-nanometer resolution and morphological

information 84, but their primary application in cellulose characterization in aqueous pretreatment

is the observation of cellulose surface and crystal disruption. Also, the cellulose crystallinity

31

analysis is not always consistent when applying different calculation methods. The unclear

cellulose structure is a hurdle to understanding cellulose behaviors in aqueous pretreatment.

Due to the limitation of current cellulose characterization in aqueous pretreatment,

developing a new cellulose characterization method is needed to shred a light on a clear

understanding of cellulose characteristics in aqueous pretreatment. Also, the dynamic monitoring

of the structural transformation of cellulose during heating to mimic aqueous pretreatment is a

necessary foundation to achieve high C6 sugars yields as well as to help understand interaction

of cellulose crystalline cellulose with enzymes. Currently, the new emerging cellulose structural

characterization method is scanning SFG-VS (6cm-1) 85 as well as the high resolution SFG-VS

with a resolution of 0.6cm-1 28. The SFG-VS method can selectively characterize cellulose

crystalline structure without the interference of other biopolymers in biomass. Thus, the SFG-VS

tool has great potential to study cellulose structural change during biomass aqueous pretreatment.

2.5.2 Lignin production and characterization in aqueous pretreatment

Lignin is the most abundant natural source of aromatics, which are attractive as important

carbon precursors. The utilization of the second abundant biopolymer lignin in cellulosic

biomass to produce value added products can improve biorefinery economics. The conversion of

lignin to high value products can offset the high cost of pretreatment, which pushes the

biochemical conversion forward to commercialization. One of the Biochemical Conversion R&D

goals is to convert lignins from biomass into fuels, chemical intermediates, or materials but not

simply combusted to heat and utility supplies 8. The U.S. Energy Security and Independence Act

of 2007 mandates that 79 billion liters of cellulosic biofuels be produced annually by 2022, and,

for that to occur, about 62 million tons of lignin will be produced 86, 87. More importantly, besides

32

the lignin residues burned to meet the demand for the internal energy use, technologies are

needed to convert remaining lignin residues to value added products 87. The conversion of

carbohydrates to valuable products (e.g. ethanol, carboxylic acids, levulinic acid, glycerol, and

sorbitol) is well developed, but the utilization of lignin is not well performed. However, research

on lignin valorization has gained tremendous attention in the past five years. The aqueous

pretreatment can provide various lignin sources with different lignin chemical compositions and

structural characteristics (e.g. molecular weight, solubility, yield, purity, and functionality) for

valorization. The linkages abundance, monolignol types, molecular weight, and amount of lignin

are varied in different engineered biomass species. Thus, the characterization of various lignin in

aqueous pretreatment is very important for lignin valorization.

2.5.2.1 The significant role of lignin in aqueous pretreatment

Different biomass species present different lignin content, composition, and structural

information. For example, hardwood contains about 10% less lignin than softwood lignin;

eucalypt wood consists more syringyl units dominant in β-O-4' followed by β-β' linkages,

whereas the spruce shows more guaiacyl units and linked by mainly β-O-4' and phenylcoumaran-

type bonds 88, 89. Almost all lignin is linked to polysaccharides with covalent bonds, mainly

hemicellulose 90. Additionally, hemicellulose and lignin polymers are ether, ester, and hydrogen

bonds linked while lignin and cellulose have fewer bonds. Therefore, hemicellulose is like the

glue between lignin and cellulose. The separation of lignin from biomass requires the links

between polysaccharides to break down, which aids the difficulties of selective isolation of

certain lignin intermediates. Additionally, the challenges of studying native lignin include the

isolation process, interference of polysaccharides, and analytical method limitations. The lignin

33

isolation and purification methods usually caused structural modification on the lignin

macromolecular matrix. The dioxane/water extracted lignin was known as Bjorkman lignin or

ball milled wood lignin, which represents the native lignin prototype. This lignin extraction

method was believed to cause the least significant structural modification on native lignin;

however, the yield was up to 30%, as reported 91.

Lignin was demonstrated as the most recalcitrant compound in biomass aqueous

pretreatment. Lignin removal can not only improve pretreatment efficiency but also reduce

inhibition on subsequent enzymatic hydrolysis and fermentation processes. However, complete

lignin removal was reported insufficient for cellulose accessibility to enzymes as expected,

which was proposed to attribute to the aggregation of cellulose microfibrils 92. Thus, a detailed

study of lignin behaviors and sugar yields in aqueous pretreatment is not clear and still needed.

In aqueous pretreatment of biomass, the isolation of lignin can alter several lignin

characteristics, including depolymerization, repolymerization, molecular weight, solubility,

functionality, and interunit linkages. In addition, no reported tools are able to selectively and in-

situ observe lignin structure yet, which makes the lignin structure remain undefined.

Comprehensive reviews of the chemistry and structural properties of lignin were reported 89, 93-97.

Thus, different lignin isolation processes result in the diversity of lignin sources in molecular

weight, purity, solubility, and structural characteristics, which have a great impact on the

availability of lignin substrates, conversion to different value products, and economics.

The challenges of lignin production from cellulosic biomass are the complex three

dimensional structure, various and random interunit linkages, low product selectivity, and the

effects caused by bonded polysaccharides Besides, native lignin isolation, lignin has been

34

produced by several industrial processes, including pulping processes. Recently, only three lignin

sources have been available in the commercial market, namely, Kraft, lignosulfonate, and

organosolv lignin 98. These lignins make good comparisons to aqueous pretreatment derived

lignins in characteristics and market potential. These data of commercial lignin markets can help

with the development of aqueous pretreatment derived types of lignin. To explore further

utilization of lignin derived from aqueous pretreatment, the consistent characterization of lignin

intermediates and compared their properties to commercialized lignin are significant.

2.5.2.2 Lignin production and characterization in aqueous pretreatments

The aqueous pretreatment can cause the change of lignin structure and characteristics such

as the molecular weight, solubility, phenolic hydroxyl groups, aliphatic hydroxyl groups,

methoxyl groups, and inter-units linkages of lignin. The lignin characterization in aqueous

pretreatment is important to gain a fundamental understanding of aqueous pretreatment and

effective for producing lignin intermediates for different applications.

2.5.2.2.a Lignin derived from hot water and steam explosion pretreatment

The chemistry and structural transformation of lignin under hot water pretreatment are

similar to dilute acid pretreatment. This is because the pH of water decreases with increasing

temperature resulting in lightly acidic solutions. Also, releasing organic acid from acetyl groups’

hydrolysis from hemicellulose can result in the decreasing of pH of the pretreatment media. The

hot water flowthrough pretreatment was studied on Miscanthus and the mechanism was proposed

as the selective hydrolysis of β–O–4 sub-structures together with cinnamic and/or coumarylate

ester linkages cleavage followed by oxidative cleavage at C–C position, in conjunction with the

preferential removal of S-lignin during the delignification process 62. Little softwood lignin’s

35

chemistry or structural transformation is unknown. Thus, a detailed study of softwood lignin

chemistry and how it’s different from other species is significant.

Steam explosion pretreatment suddenly releases reaction pressure in steam gun/reactor to

degrade lignin. The recovery of lignin in steam exploded liquid is carried out by acidifying the

liquid to pH 2-3 to precipitate lignin followed by washing and filtration. The yield is about 70%

16. The steam explosion derived lignin has a wide range of polydispersity 4-38, while the average

molecular weight is about 3000Da 99. The reaction mechanism of lignin during steam explosion

was proposed by Li et al. 68, which described the generation of organic acids through the initial

hydrolysis of acetyl groups in hemicellulose, which leads to the formation of a carbonium ion

that can react with benzylic groups to eliminate ROH groups. Simultaneously, the carbonium ion

causes cleavage of β-O-4 inter-unit and causes the formation of Hibbert Ketones through

acidolysis reactions. However, besides the depolymerization reactions, unlike lignin

condensation C-C position in conventional pulping, the condensation of lignin also occurs in the

electron rich C-2 or C-6 positions during steam explosion. The steam pretreatment using a steam

gun can remove more than 95% of the xylan in the biomass. However, only a limited amount of

lignin is recovered because most of it (>85%) remains in the residual solids.

2.5.2.2.b Acid pretreatment lignin

The dilute acid pretreatment was usually carried out at temperature of 120-210°C with an

acid concentration of less than 4% for 0-60min. Besides the popularity of sulfuric acid,

hydrochloric, nitric, and phosphoric acids were also applied. The chemistry of acid degradation

of lignin is the acidolysis of aryl ether linkages (primarily β-O-4 linkages) and acid catalyzed

recondensation without significant cleavage of any other linkages. Dilute acid or steam explosion

36

pretreatment of biomass led to a decrease of β-O-4 68, 100, 101 as well as a decrease in the

protonated and oxygenated aromatic carbons per aromatic ring 100. Also, the molecular weight

distribution of lignin would be expected to decrease together with repolymerization or

condensation reactions, resulting in a more heterogeneous lignin structure. Several lignin

behaviors were reported in dilute acid pretreatment, namely, a) lignin droplet formation and

redeposit, b) pseudo lignin formation, and c) lignin migrating. Lignin droplets were formed at the

pretreatment temperatures higher than the phase transition temperature (e.g. 120-200°C 102),

which caused lignin to liquefy and coalesce in the pretreatment liquid, and the droplets might

travel in the cell wall matrix and deposit back to the biomass surface 103, 104. The droplets were

then reported to have a higher S/G ratio than the original lignin and showed inhibition on

enzymatic hydrolysis of pretreated slurries by blocking the cellulose surface from enzymes

attack 105. The pseudo lignin was formed by carbohydrates and lignin degradation products such

as furfural 106. The lignin migration and distribution in biomass plant cell walls were observed in

the dilute acid pretreatment system 107. Lignin has the tendency to form spherical droplets, which

causes deposition onto the biomass surface. It was observed that, during dilute acid pretreatment,

lignin droplets generated and stayed on the biomass surface 103, which suggests that the majority

of the lignin did not change significantly after pretreatment. The kinetics of lignin is not well

studied and mainly focuses on hemicellulose hydrolysis.

The utilization of acid pretreatment derived lignin depends on the separation and purification

of pretreatment slurries. Three main separation methods of lignin from acid pretreatment slurries

were reported, namely acid precipitation by gradually adjusting the pH of the solution 108. The

second method is to use various solvents such as organic solvents, enzymes, biomimetic catalysts

37

and ionic liquids to extract lignin 109. The third method is to use membrane technology, which is

good for sieving lignin fractions with various the molecular weights 110.

In high pretreatment severities, sugars suffer from sugar degradation loss. Therefore, the

traditional pretreatment methods tend to protect carbohydrates from degradation and remain in

biomass solid phase by performing low pretreatment severities pretreatment. However, lignin

removal from biomass is insufficient in low pretreatment severities. Substantial lignin remains as

residual lignin in biomass solids together with the majority of cellulose 35, 36. For example, at

0.1% wt sulfuric acid batch tubular pretreatment at 180°C for 20min, 71.7% xylan was removed

from the biomass solid while only 1.5% lignin was removed 56. Therefore almost 98.5% of lignin

and intact cellulose remained in biomass residues, which is not effective for cellulose and lignin

utilization as mentioned above. The direct recovery of this residual lignin from the biomass

residues adds extra cost. Furthermore, the recovery of lignin after enzymatic hydrolysis or

fermentation produced poor quality lignin and a low ethanol production yield.

The development of flowthrough reactor increased aqueous phase lignin recovery while also

ensuring high enzymatic hydrolysis digestibility. For example, by increasing the flow rate to

25ml/min, the pretreatment with 0.1% wt sulfuric acid pretreatment at 180°C for 20min achieved

100% xylan removal and 68.4% lignin removal as well as 30% more digestibility than in the

batch reactor system 56. Furthermore, high temperatures more than 200°C were used in

flowthrough reactor and reported to achieve almost 100% of biomass solubilization, in which

lignin was recovered almost 100% in the aqueous phase 59. However, the characterization of

lignin and cellulose in the aqueous flowthrough pretreatment is not well conducted. Also, the

flowthrough reactor suffers from high water consumption, resulting in a low sugar concentration

38

for downstream production. The lignin derived from flowthrough pretreatment can avoid

contamination of enzymes and chemicals to lignin intermediates, which greatly improves the

efficiency of using lignin. In summary, the flowthrough concept of biomass aqueous

pretreatment can greatly improve lignin recovery, isolation, and utilization.

2.5.2.2.c Alkaline pretreatment lignin

In carbohydrates, sodium hydroxide can disrupt hydrogen bonding network in cellulose and

hemicellulose, resulting in the swelling of carbohydrates. It was also reported that sodium

hydroxide can break the ester linkages between lignin and hemicellulose and the deprotonation

of phenolic groups 111. Sodium hydroxide is effective in lignin removal. Similar to sodium

hydroxide pretreatment, lime pretreatment can also remove lignin and improve enzymatic

digestibility of pretreated substrates 112, 113. However, the price of sodium hydroxide is higher

than other alkalis. For example, lime is less expensive than sodium hydroxide, and the recovery

of lime is easier when it reacts with carbon dioxide and insoluble calcium carbonate in water.

The use of ammonia hydroxide in biomass pretreatment has developed as Ammonia Freeze

Explosion (AFEX) and Ammonia Recycle Percolation (ARP). AFEX treats biomass by applying

ammonia and then suddenly releasing high pressure, which causes both chemical and physical

effects on the biomass 114. The ARP process is an improvement in pretreatment reactor design, in

which a flowthrough column reactor is used. This reactor design can eliminate away biomass

degraded products, which helps achieve high yields of depolymerized lignin intermediates.

However, the drawback of using ammonia is the possible inclusion of nitrogen into the lignin

structure, similar to sulfur being added to Kraft lignin structure 115.

The alkaline pretreatment caused saponification of intermolecular ester bonds crosslinking

39

xylan hemicelluloses and lignin 116. The delignification mechanism of alkaline pretreatment has

been the subject of many studies for years. The lignin depolymerization and isolation are difficult

due to the complexity of the lignin structure and the tendency of lignin to repolymerize.. In

alkaline pretreatment of biomass, the delignification mechanism involves an alkaline oxidative

by OH- anions on biomass caused by single electron/radical reactions, which caused cleavage of

both ether bonds and C-C bonds. The oxidative oxygen can be transformed to superoxide

radicals (-O2) by reacting with phenolic hydroxyl groups under pH>12. Several organic acids

were produced during the reaction. The nucleophilic attack also caused ring opening to produce

dicarboxylic acids. Meanwhile, condensation reactions happened in the C5 positions to have

Biphenyl methane structure 115. Thus, due to the different lignin removal mechanisms in alkaline

pretreatment compared to acid pretreatment, the produced forms of lignin should possess

different properties. The different properties make the tailoring of these lignins necessary for

utilization.

2.5.2.2.d Organosolv pretreatment lignin

The organosolv pretreatment can separate lignin from carbohydrates in biomass. Similar to

organosolv pulping, the biomass can be fractionated completely into lignin, aqueous sugar

stream, and solid cellulose 117. Organosolv pretreatment can solubilize lignin into the

pretreatment solvent or washing liquor. The amount of organic solvents together with water

applied varied in the pretreatment. For example, some studies applied low boiling point

methanol, ethanol, and acetone varied between 50-80%, which is miscible in water. The addition

of mineral acids (i.e., hydrochloric, sulfuric, and phosphoric) and organic acids (i.e., formic,

acetic, and peracetic acids, dimethyl sulfoxide, ethers, ketone, phenols, oxalic, acetylsalicylic,

40

and salicylic) can accelerate lignin delignification 18, 118. In some cases, several high boiling point

alcohols can be used at atmospheric pressure, such as ethylene glycol, glycerol, and

tetrahydrofurfuryl 18. The organosolv pretreatment produces a relatively pure, non-degraded, and

sulfur-free lignin. The drawbacks of the organosolv pretreatment are the high cost of solvents

and the difficulties of solvent recovery. Organosolv lignin is believed to keep even more of the

native lignin structure than ball milled lignin. Organosolv lignin can be precipitated by lowering

the solution pH for recovery as a separated fraction. The organosolv pretreatment is expensive

due to the high cost of solvents and solvent recovery. A process design and economics study

should be carried out to evaluate whether the profits of lignin as by-products converted to high

value products can offset the high cost. Also, the application of organic acid as a solvent might

cause subsequent enzymatic hydrolysis efficiency 119. However, the organosolv lignin is an

important native lignin representative.

The lignin isolation mechanism for alcohol extraction is OH¯, which attacks the linkages

between lignin and hemicellulose such as ester bonds as well as the ether and 4-O-

methylglucoronic bonds. The cleavage of β-O-4 linkages is the major lignin solubilization

mechanism in organosolv pretreatment 115. The cleavage of other ester bonds is also the main

mechanism of organosolv pretreatment (α-O-aryl ether> β-O-aryl ether). However, C-C

condensation reactions were observed in organosolv pretreatment (α-5 condensations) 120. This

C-C condensation position is different from that of other aqueous pretreatment. The lignin

degradation chemistry in organosolv pretreatment was reported 121 to be the initial solvolytic

cleavage of α-O-aryl ether linkages to form Quinone methide intermediate. It was followed by

the nucleophilic substitution in the benzylic position through the application of a SN2 mechanism

41

for the formation of a benzyl carbocation under acidic conditions 17, 121. In organosolv lignin

chemistry 121, 122, β-O-aryl ether linkages were broken by homolytic cleavage to form

formaldehyde and stilbenes. During organosolv pretreatment, a high yield of Hibbert’s ketones

with carbonyl groups were generated. The reactive benzyl carbocation intermediate from both α-

O-aryl and β-O-aryl ether cleavages can condense with another electron-rich carbon atom in a

neighboring lignin unit. The use of organic acids in organosolv pretreatment is different from

alcohols. For example, the hydroxonium ion can be generated from acids, which causes ring

hydroxylation in the lignin structure. The potential of using strong organic acid might have

oxidative reactions or ring opening besides the cleavage of ether bonds of lignin structure.

The molecular weight of organosolv lignin is lower than milled wood lignin which results in

the high solubility of solvents, which makes the lignin easy to use. Meanwhile, organosolv lignin

shows less sulfur content compared to Kraft pulping and lower oxygen content than ball milled

lignin. The hydroxyl (OH) functionalities, such as aliphatic, condensed and uncondensed

phenolics, and carboxylic OH groups in organosolv lignin were widely studied with 31P NMR

spectroscopy. These studies suggested the decreasing of aliphatic OH content while increasing of

the phenolic and carboxylic OH content 123, 124.The utilization of lignins depends on the

downstream industrial process needs on lignin properties. The modification of lignin in the lignin

separation process is required to meet the demand of the purity, solubility, molecular weight, and

polysaccharides contaminations of lignins.

The above description of aqueous pretreatment derived lignin behaviors indicated that the

fundamental understanding of lignin behaviors and chemistry in aqueous pretreatment are not

well performed, especially biomass lignin in the flowthrough system, which can reveal accurate

42

lignin chemistry by separating lignin with fast mass and heat transfer. The valorization of these

aqueous pretreatment derived lignins to materials, chemicals, and fuels relies on the

characterization of lignin during aqueous pretreatment.

2.5.2.3 Current lignin structural characterization methods

Lignin produced in an early stage of the process (i.e. after the biomass aqueous

pretreatment) can become a potential value-added co-product, improve enzymatic digestibility,

and reduce fermentation inhibition 125. Along with cellulose and hemicellulose, lignin presents

15–40% weight and contains the highest specific energy content among other biopolymers in

cellulosic biomass 126. Lignin consists of methoxylated phenylpropane units with three primary

monomers (monolignols): p-coumaryl, coniferyl, and sinapyl alcohols. The biosynthesis of lignin

is carried out by the oxidative coupling of monolignols to form a three dimensional biopolymer

through certain C-O-C and C-C linkages 89. The oxidative coupling is active in O–4, C–1, C–3,

C–5, and C–β, resulting in β–O–4, β–5, β–β, and β–1 formation 89. Due to the diversity of lignin

properties, lignin characteristics depend on different biomass origins, the environmental

conditions of plant growth, and various extraction methods. Lignins with the random formation

of linkages and the three dimensional amorphous structure of lignin are polar polymer, exhibiting

a poor solubility in a polar solvents such as polyolefin 127. Lignin characterization methods

advancement is crucial to track lignin structural modification during lignin recovery from

biomass aqueous pretreatment. Developing an analytical method to selectively characterize

lignin in the whole lignocellulosic material without isolating it is helpful in-situ study of native

lignin structures. Table 2.2 listed major lignin characterization methods applied in lignin

chemical composition and structural studies.

43

Table 2.2 Lignin characterization methods and targeted properties

Lignin characterization methods Target lignin characteristics Reference

Chemical

composition

K-lignin Acid insoluble lignin content; Lignin purity 128

Acetyl Bromide Lignin content; Lignin purity 129

Ultraviolet-visible

spectrophotometry

(UV-Vis)

Soluble lignin content 130

TAPPI method Acid insoluble lignin 131

Kappa number

(KMnO4 consumption)

Purity/lignin content 132

Thermogravimetric

analysis

Thermal stability 133, 134

Elemental analysis

(ICP)

Elemental composition (C, H, O content) 135

Pyrolysis gas

chromatography mass

spectrometry (Py-GC-

MS)

S/G ratio and intrinsic lignin fractions 136

Gel permeation

chromatography (GPC)

The number-average and weight-average

molecular weights (Mn and Mw,

respectively)

137

XPS Concentrations of functional groups, the

valence band structure and O/C atomic ratio

138, 139

Exclusion

chromatography (SEC)

Molecular size distribution 137

GC/GC-MS Soluble lignin identification and semi-

quantification

140

Structural

characteristics

Gel-state 2-D NMR

(HSQC)

Whole plant cell wall 2-D-NMR profiling

studies

83

31P NMR Hydroxyl group content (Aliphatic, phenols,

catechol , syringyl, Guaiacyl, condensed

lignin, carboxylic acid OH)

141

13C NMR qualitative and quantitative understanding of

lignin structure

142

1H NMR Identify functional groups: phenolic

hydroxyl (ArOH), aliphatic hydroxyl

143

44

(AlkOH), and methoxyl (MeO) groups and

quantify carboxylic acids, aromatic

hydrogens, and formyl and methoxyl groups

2-D HSQC NMR Identifying structural units 144

Solid state NMR In situ observe lignin structure without

lignin isolation, lignin content in biomass,

and identify condensed structure

145, 146

FTIR spectroscopy Structure, functionality, and lignin content 147, 148

Raman Structural information (aromatic, syringyl,

and guaiacyl)

149

Acidolysis (at 100 °C

in 0.2 M HCl in

dioxane±water (9:1,

v/v))

Monomeric and dimeric acidolysis products

to assist analysis by gas chromatography

after silylation, include β-O-4, β-5, β-β, β-1,

glyceraldehyde-2-aryl ether, 2-

aryloxypropiophenone, cinnamaldehyde,

cinnamic acid, benzaldehyde, benzoic acid

and quinoid types and selective cleavage of

arylglycerol-ethers and some other types of

labile ether linkages; only structural units

bound by arylglycerol b-ether bonds

137, 150

Thioacidolysis (boron

trifluoride in dioxane±

ethanethiol solution)

5-5 or 5-O-4 bonded dimers, monomeric

compounds to assist gas chromatography

detection after silylation; only structural

units bound by arylglycerol b-ether bonds

151, 152

Permanganate

oxidation (dimethyl or

diethyl sulfate

alkylation then

oxidation)

Oxidize lignin side chain to form carboxylic

acids (Di/Monocarboxylic acids) for gas

chromatography detection (e.g. biphenyls,

catechol and diphenyl ethers)

153

Nitrobenzene and

cupric oxide oxidation

(alkaline solutions at

high temperature)

Assist to spectroscopic studies 154

Ozonolysi Destroy double bonds and the aromatic

rings, leaving the side chains intact in the

form of carboxylic acids

137

NMR spectroscopy is a powerful and non-destructive tool in lignin structural

characterization that is widely used and developed. NMR spectroscopy technology is based on

the intrinsic spin property of nuclei. In a strong magnetic field, some molecule spins align with

45

the field, resulting in a slight net alignment parallel to the field. The energy of the spin is

detected as signal to reveal the information about individual atoms in a molecule and their

environment 155. Since lignin structure has no repeating linkages in the same environment, the

molecular structure can be accurately resolved by NMR. NMR was important in identifying

novel units such as dibenzodioxocins 156. However, the NMR characterization of lignin in

biomass suffers from the overlapping of polysaccharide peaks 83. Also, the quantification of

lignin interunit through either 1-D or 2-D spectra could not give absolute values 157, 158, which

only provides relative proportions of various peaks. 4D-, 5D-, and higher-D-NMR have been

applied to characterize labeled proteins 159. Thus, 3D- NMR will probably be an evolution of

current NMR tools for lignin structural characterization if it can be widely used to resolve

overlap and confusion of 2-D NMR. Besides structural characterization, the characterization of

several other lignin properties such as purity, molecular weight, OH content, and linkages

distribution is significant too since those lignin characteristics are important for further lignin

valorization. For example, the low molecular weight, narrow polydispersity, and total OH

content (aliphatic and phenolic) are beneficial for antioxidant activity 127. However, the majority

of lignin characterization only provides relative proportions or values compared to native lignin

rather than absolute values for each proportion. The lignin characterization methods displayed

above serve important roles in characterizing lignin in aqueous pretreatment to unravel lignin

behaviors and properties in aqueous pretreatment.

2.6 Preprocessing of feedstock for aqueous pretreatment

Mechanical refining before aqueous pretreatment is a critical supplement to aqueous

pretreatment in order to achieve a better fermentable sugars yield. Aqueous pretreatment

46

involves a penetration of catalyst into biomass 160, such as water, acid and alkali, to extract

dissolved biomass components. Such a solvent penetration process involves solvent wetting

biomass, impregnating biomass, and diffusing into biomass. Since the structure of biomass is

porous and heterogeneous 161, solvent diffusion during aqueous pretreatment is greatly

influenced by the pore size of particles and biomass shapes. However, intensive size reduction of

the biomass causes not only the energy-intensive and expensive grinding and milling before

chemical pretreatment, but it also might lead to the losses of carbohydrates during physical

treatment. The feedstock preparation cost represents 30% of the total cost in biological

conversion to bioethanol. To make the ethanol production economically feasible, the energy cost

from preprocessing should be reduced. It can be achieved by balancing the pretreatment

performance while using chips that are as large as possible in the pretreatment 162. The first step

towards this is the fundamental understandings on the effects of particle sizes and shapes of

biomass on pretreatment performances.

Recent studies on the effects of biomass sizes showed inconsistent results. Some studies

reported that smaller sizes led to better pretreatment performance. But some other studies found

the opposing results that those larger sizes can achieve better sugar yields. For example, the

steam explosion pretreatment has been shown to favor smaller sizes 163, while the steam

explosion with SO2 as a catalyst of Douglas Fir showed the best pretreatment sugar yields at size

of 5×5cm rather than smaller sizes (e.g. <0.4mm or 1.5×1.5cm) 164. Some studies on the effects

of biomass chip particle size on pretreatment effectiveness of woody biomass as well as

enzymatic hydrolysis effectiveness for biological processing were also conducted 165.

Furthermore, no studies have ever reported the optimization of biomass chips cutting in terms of

47

energy efficiency and pretreatment performance. The systematic study of particle sizes as well as

biomass cutting methods, which involves mass transfer effects, is important for cost effective

bioethanol conversion. Similar to most of chemical processes in large scale commercialization,

kinetic reactions, heat transfer, mass transfer, and momentum transfer are technical focuses.

Knowledge deficiencies in these four aspects of engineering are a hindrance for pretreatment

commercialization.

Kinetic and mass transfer modeling is a potential tool to study the effects of biomass sizes

and shapes on pretreatment. Saeman proposed the first acid hydrolysis model of cellulose based

on the assumption of a homogeneous two-step first order reaction in 1945. In the model,

cellulose was first converted to glucoses and then degraded to degradation products such as

hydroxymethylfurfural (HMF) 166. Further work found that first-order reactions were not

accurate for heterogeneous biomass degradation. Traditional kinetic modeling is deficient in

revealing true information of biomass reactions. Nevertheless, the heterogeneous structure of

hemicellulose in biomass was shown to have great effects on the reaction rate of hemicellulose

dissolution, in which mass transfer played an important role in pretreatment performance.

Solubility limitations, mass transfer, homogeneous or nonhomogeneous reaction types all

become important parameters to consider for kinetic modeling of aqueous pretreatment.

In summary, biomass particle size and cutting approaches are important factors in energy

effectiveness. Also, lignocellulosic biomass particle size, particle microscopic structure, porosity,

and particle shape have a significant impact on pretreatment solvent diffusion, even on

enzymatic hydrolysis efficiency 167. Thus, the mass transfer modeling is an effective approach to

optimize biomass cutting approaches and particle sizes.

48

2.7 Conclusion

This chapter has mainly reviewed current research status, process characterization, and the

significance of feedstock preprocessing of aqueous pretreatment. The key fundamental issues

and challenges are identified. First of all, the sugars undergo degradation loss with low aqueous

cellulose and lignin solubilization in aqueous pretreatment. The biomass aqueous pretreatment is

not well studied with different pH 35, 36. Although pH is an important factor to pretreatment sugar

yield, its effects on pretreatment performance were not were studied 168. Secondly, much less

effort has been devoted to cellulose structural transformation in aqueous pretreatment or

enzymatic hydrolysis. In addition, lignin structural modification and properties are not well

characterized, which impedes further utilization. At last, the effects of preprocessing feedstock

into different shapes and particle sizes in aqueous pretreatment in energy cost and pretreatment

performance are not well understood. The successful large scale production and

commercialization rely on resolving these key fundamental problems of aqueous pretreatment to

create cost competitive bioethanol versus conventional gasoline, which is the focus of this thesis.

49

CHAPTER THREE

CHARACTERIZATION METHOD DEVELOPMENT FOR CRYSTALLINE

CELLULOSE USING HIGH RESOLUTION BROADBAND SUM

FREQUENCY GENERATION VIBRATIONAL

SPECTROSCOPY IN BIOFUEL

3.1 Abstract

Here we conducted the first sub-wavenumber high-resolution broadband sum frequency

generation vibrational spectroscopy (HR-BB-SFG-VS) study on both the C-H and O-H region

spectra of crystalline cellulose. HR-BB-SFG-VS has about 10 times better resolution than the

conventional scanning SFG-VS and is known to be able to measure the intrinsic spectral

lineshape and to resolve much more spectral details. With HR-BB-SFG-VS, we found that in

cellulose from different sources, including Avicel and cellulose crystals isolated from algae

Valonia (Iα) and tunicates (Iβ), the spectral signatures in the OH regions were unique for

different allomorphs, i.e. Iα and Iβ, while the spectral signatures in the C-H regions varied in all

samples examined. Even though the origin of the different behaviors of the crystalline cellulose

in the O-H and C-H vibrational frequency regions is yet to be correlated to the structure of

cellulose, these results provided new spectroscopic methods and opportunities to classify and

understand the basic crystalline structure, as well as variations, in polymorphism of the

crystalline cellulose structure.

Keywords: Cellulose Iα • Cellulose Iβ • Avicel • High Resolution Broadband Sum Frequency

Generation Vibrational Spectroscopy (HR-BB-SFG-VS)

50

3.2 Introduction

Understanding the cellulosic biomass recalcitrance at the molecular level can be the key in

overcoming the fundamental barrier to make cellulosic biofuels cost-competitive 169. Although

plant cell walls are complex and dynamic, recent advances in analytical chemistry and genomics

have substantially enhanced our understanding, and also highlight how much the knowledge-gap

remains 170. The basic chemical constituents of biomass are well known. The predominant

polysaccharide of biomass is cellulose, which makes up about 30 to 50% of biomass in the form

of linear fibrils composed of approximately 30 to 40 hydrogen-bonded chains of -(1, 4)

glucopyranosides with a native degree of polymerization ranged from several hundreds to over

ten thousands, is the most abundant organic polymer on Earth. In cellulose structures, hydrogen

bond network exists in two conformations, i.e. intra-chain and inter-chain hydrogen bonding.

The intra-chain hydrogen bonds connect 6COH with the 2COH and 3COH with endocyclic

oxygen. Inter-chain hydrogen bonds link 3COH with 6COH 171. Intermolecular hydrogen bonds

are parallel to the glycosidic linkage for both types of chains. Due to its hydrogen bonding

diversity, cellulose can exhibit various supra-molecular structures, including amorphous, para-

crystalline and crystalline structures.

Although based on cellulose crystalline structures, at least four types of cellulose, namely

cellulose I, II, IIII,II, and IVI,II have been reported, cellulose I is the only existing form of natural

cellulose 172. Furthermore, native cellulose I has been shown to be composed of two distinct

crystalline forms, referred to as Iα and Iβ 173. Cellulose Iα is triclinic parallel crystalline with

only one repeating cellobiose in the unit cell while Iβ is monoclinic parallel-down crystalline

structure 174. The relative amounts of I and I have been found to vary significantly among

51

cellulose samples from different origins. For example, cellulose Iα is the dominant form in algae

and bacterial cellulose, while Iβ is found dominant in tunicate and higher plants 173. Cellulose

materials isolated from different organisms and the methods used to isolate them may result in

various different forms with different physiochemical properties, such as crystal size, shape, DP

and crystallinity 175. Previously, cellulose Iα and Iβ have been characterized by CP/MAS 13C

NMR 78, 79, 173, and molecular dynamics simulation. However, these studies only reflected

ordered crystalline and disordered amorphous structure. Other cellulose structures with different

crystalline packing forces and orders have not been studied in details 176, 177. These differences in

crystalline packing forces and orders have a broad impact on a variety of chemical and

enzymatic reactivity with cellulose 178. Thus, investigation of crystalline structural characteristics

in different cellulose polymorphs is a fundamental step to understand crystalline structure

recalcitrant to enzymatic hydrolysis in bioconversion of biomass.

Many cellulose characterization techniques provide capabilities to image and characterize

biomass, including: a) Atomic force microscopy (AFM) which allows researchers to not only

investigate chemical compositions of different layers but also characterize the topographic

structures of the cell wall surface 179, 180; b) High resolution solid state CP/MAS 13C NMR

powered in the quantitative analysis of structural features of cellulose, especially of the

crystalline allomorph and disordered domains. The line-shape spectral fitting analysis of solid

state NMR spectra allows a detailed comparison and characterization of the cellulose supra-

molecular structures and has been extensively used to investigate the structural characteristics of

cellulose 181, 182. Cellulose Iα and Iβ were first identified by 13C NMR 173. However, 13C CP/MAS

NMR results are usually interfered by lignin, hemicellulose and pectin 183.

52

In addition to solid-state nuclear magnetic resonance (NMR), native cellulose

polymorphism, crystallinity, hydrogen bonding, and mechanical strength were also studied by IR

and Raman vibration spectroscopy, and X-ray diffraction (XRD) 184-193. XRD has been widely

applied to quantify and identify cellulose crystalline structure. However, crystallinity index (CrI)

calculated from XRD varied depending on calculation methods 194. Infrared or Raman

vibrational spectroscopy of cellulose was also reported to be advantageous in understanding

cellulose structure among other analytical methods 195. FT-Raman was developed to quantify

cellulose crystalline structures. However, specific spectral signatures of cellulose with different

crystallinity have not been well established using IR or Raman spectra. Moreover, quantified

crystallinity results from different characterization techniques are not always unique or consistent

with each other when using different vibrational peaks. For example, Schenzel et al. employed a

spectral deconvolution method by using bands at 1462 and 1481 cm-1 80, while other researchers

applied different peaks as deconvolution bands thus resulted in inconsistent results. Due to

complex network of cellulose with other polymers as well as different growing environment of

cellulose species, cellulose structural information from these techniques is often interfered by

other amorphous polysaccharide components. Therefore, isolation and purification of cellulose

from biomass are performed before applying these techniques. However, the isolation and

purification process can affect cellulose structures, thus making characterization of native

cellulose difficult. Therefore, to avoid interference of other biopolymers from biomass and

tedious cellulose isolation process, a nondestructive spectroscopic technique that can selectively

probe the cellulose crystallinity is needed for characterization of biomass.

Recently SFG-VS has been used to study the physiochemical properties of crystalline

53

cellulose, as the crystalline cellulose exhibits enormous nonlinear optical response 85, 171, 196-201.

SFG-VS is a second-order nonlinear spectroscopic method that is sensitive to the structural

symmetry of the molecules and molecular assembly such as the cellulose crystalline units.

Therefore, it has shown great potential in studying cellulose microfibril arrangement and

orientation at different stages of plant growth or from different sources 197.

In SFG-VS measurement, usually a visible laser beam with a fixed frequency (𝑣𝑖𝑠) and an

infrared (IR) laser beam with tunable frequency or broadband (𝐼𝑅) simultaneously interact with

the material to generate a signal at the sum of the two frequencies (𝑆𝐹𝐺 = 𝑣𝑖𝑠 + 𝐼𝑅) that

give the vibrational spectra of the material. By tuning the IR frequency, the SFG vibrational

spectra of the interested material can be measured. SFG-VS is distinct from the IR and Raman

spectroscopy because SFG has unique symmetry properties as a second order nonlinear process.

Such symmetry properties require the molecule or molecular assembly in the materials that

generates SFG signal are with broken centrosymmetry, i.e. the material is either a non-

centrosymmetriuc crystalline material or a molecular surface/interface where the molecules are

aligned due to asymmetric forces at the surface/interface 202, 203. For bulk amorphous solids or

liquids, SFG-VS is surface/interface selective, i.e. forbidden for the bulk materials, because the

SFG-VS signals only come from the surface or interfaces. In the past two decades, SFG-VS has

been extensively used to probe molecular surfaces and interfaces 204-206. However, in recent

years, it was also found useful in selective probe or detection of crystalline materials such as

energetic and explosive materials as well as crystalline cellulose 207-209.

The signal of SFG is proportional to the intensity of the visible and IR laser intensity

(𝐼(𝜔𝑉𝐼𝑆) and 𝐼(𝜔𝐼𝑅) ) as well as the square of the effective susceptibility ( 𝜒𝑒𝑓𝑓(2) ) of the

54

material. That is 203, 206, 210,

(3.1)

, which contains all the structural and molecular nonlinear optical information of the

material, is proportional to the number density of the molecules (N) and is a linear function of

the SFG susceptibility tensor that is a structural and ensemble orientational average

( ) of the molecular polarizability tensor , that has a frequency dependence on the

intrinsic vibrational frequency of the molecules in the material 206, 210, 211. One has,

(3.2)

(3.3)

(3.4)

Here is the molecular polarizability tensor that consists a non-resonant contribution

and is frequency dependent on the vibrational frequency (with a homogeneous width

and an inhomogeneous width 2.35 ) of the qth vibrational modes of the molecule 211.

Here (i,j,k) and (i’,j’,k’) represent the axis of the (x,y,z) or (x,y,z) coordinates system for the

laboratory or the molecular frame, respectively. The mode specific amplitude factor is

proportional to the molecular Raman tensor and IR transition dipole vector , where Q

is the normal mode coordinates of the qth mode. Therefore, the SFG response can only come

2

2

SFG eff VIS IRI I I

2

eff

ijk

' ' 'i j k ' ' 'i j k

' ' '

' ' '

ijk s i j k

i j k

N

2

22, ' ' '

' ' ' , ' ' '

IR

qq i j k

i j k NR i j k

q IR q q

ei

' ' ', ' ' '

0

1

2

i j kq i j k

q q qQ Q

' ' 'i j k

, ' ' 'NR i j k q

2 q q

, ' ' 'q i j k

' 'i j

qQ

'k

qQ

55

from the mode that is both Raman and IR active. All these equations not only provide the way to

understand the molecular origin of the SFG spectra, but also provide the frame work to compute

or calculate the SFG response from the individual molecular property (through calculate )

and molecular unit structure and ensemble average (through calculate ). The values of the

and tensors are the result of the microscopic and macroscopic structure and symmetry

properties, thus they can be used to determine the structure and interactions of the molecular and

overall structure of the material.

Since the number of molecules contributing to the SFG-VS signal from the surface or

interface is much smaller than that of crystalline materials, usually the SFG-VS signal from

crystalline materials is at least one order or a few orders of magnitude stronger than that from the

surface. In addition, since the amorphous material generally has no SFG response, SFG-VS is

capable of selectively probe crystalline materials imbedded in the amorphous materials 85, 196.

This is the clear advantage of SFG-VS as a selective probe of crystalline cellulose than IR or

Raman spectroscopy that probes the crystalline and amorphous material indiscriminately. There

is also different polarization dependence between the SFG-VS signal from the surface and the

crystalline materials.

As the symmetry of crystalline material and surface is different, ensemble average of the

SFG responses from the crystalline material and the surface have very different polarization

dependence. Therefore, these properties can be used to distinguish and determine whether the

SFG signal is from the crystalline material or surface when the origin of the SFG signal cannot

be easily distinguished. In SFG-VS, the polarization angle corresponding to the zero (null) SFG

intensity is called the polarization null angle (PNA). In general, the SFG from crystalline

, ' ' 'q i j k

' ' 'i j k

' ' 'i j k ijk

56

material does not have a null angle, while the SFG signal from a surface usually has a clear null

angle (Velarde and Wang 2013b). Therefore, PNA measurement can be used to determine

whether the SFG signal is from the crystalline material or from the surface.

Kim and co-workers using a commercial picosecond scanning SFG-VS system with spectral

resolution about 6 cm-1 successfully demonstrated strong capacity of SFG in detecting crystalline

structure of cellulose 85, 171, 196. Also, density functional theory with dispersion corrections (DFT-

D2) was performed to simulate SFG vibration frequencies of laterally aligned cellulose Iα and Iβ

crystals 212. These works reported on the CH stretching vibrational modes even though the OH

stretching vibrational spectra with the resolution of SFG-VS at 6 cm-1 85, 171, 198, 212.

Recently, our team developed a high-resolution broadband sum frequency generation

vibrational spectrometer (HR-BB-SFG-VS) with sub-wavenumber spectral resolution that can

provide an accurate spectral lineshape and more spectral details in SFG-VS studies with

resolution of 0.6 cm-1 211, 213-215. This development has provided new opportunities in improved

measurement and better understanding of the SFG spectral and the surface/interface structure and

interactions. In this chapter, four different types of cellulose were probed with HR-BB-SFG-VS

in both the CH and OH stretching vibration frequency regions. We chose the samples with

known and well-characterized crystalline structures, such as Avicel, cellulose Iα and cellulose Iβs

from different sources, in order to establish explicit spectral signatures to differentiate the

cellulose Iα and the cellulose Iβ crystalline cellulose forms. The determination of the structural

difference and similarities of two basic polymorphs- cellulose Iα and cellulose Iβ as well as

different sources of cellulose Iβs can help establish solid foundation for developing reliable

methodology and metrics to identify cellulose structural variance and quantify the extent of

57

changes during cellulose chemical treatments or enzymatic hydrolysis. We expect the new

information in HR-BB-SFG-VS spectra to improve in-depth interpretation of basic cellulose

crystal structures. In this chapter, characteristic peaks were determined. Spectral information

provided clues for investigating Avicel composition of cellulose Iα and cellulose Iβ. PNA

measurement was operated to verify the crystal structure signal sources. Results from the current

study will aid in the investigation of complexity and uncertainty of cellulose I polymorphs by

revealing new information on cellulose structures.

3.3 Materials and methods

3.3.1 Cellulose samples

Cellulose crystals (cellulose crystals Valonia and cellulose crystals tunicate) were prepared

according to reported methods 78, 79, 212. Cellulose crystals Valonia (CCV) was isolated from alga

Valonia ventricosa (Glaucocystis (nostochinearum)) cell walls 216. Ethanol extraction, alkali and

acid treatment were carried out in sequence to produce cellulose Iα. 13C NMR analysis results

showed that cellulose Iα fraction was found to be as high as 90% 216. Two cellulose crystals

tunicate (CCT) samples from different sources were used in this chapter. One was produced from

tunicate (Halocynthiaroretzi) cellulose 217. The sample was initially treated with aqueous KOH

and sodium chloride for several times to make it colorless. Then strong sulfuric acid hydrolysis

was carried out at 40ºC for 8 hours with stirring afterwards. The suspension was further

processed to form cellulose microcrystals 186. Another source of cellulose Iβ was isolated from

red reef tunicate (Rhopalaea abdominalis) 218. The red reef tunicates (That Fish Place,

http://www.thatpetplace.com/pet/prod/235370/product.web) were slit open with a sharp knife

and their mantles were washed thoroughly with deionized water. Whole mantles were soaked

http://www.thatpetplace.com/pet/prod/235370/product.web

58

overnight in a 5% (w/v) aqueous KOH solution at room temperature. The mantles were then

rinsed with deionized water and bleached for 6 hours at 70°C with a bleaching solution,

exchanging the used bleaching solution with fresh solution every 2h. The bleaching solution

consisted of 300 mL of chlorite solution (containing 6g NaClO2) mixed with 5mL of glacial

acetic acid. After repeating the KOH/ bleaching treatment four times, the mantles became

completely white and were then washed thoroughly. Avicel PH-101 (11365) was purchased from

Fluka BioChemika (Ireland).

3.3.2 HR-BB-SFG-VS

The details of the experimental setup for sub-wavenumber HR-BB-SFG-VS and FID-SFG

intensity measurement were described previously 211, 215. Briefly, to make HR-BB-SFG-VS

possible, one needs to generate well synchronized sub- 100 fs IR pulses and ~ 100 ps 800 nm

pulses. The sub 100 fs IR pulses provide broad IR spectral coverage and the ~ 100 ps 800 nm

pulse provides the sub-wavenumber spectral resolution. This is achieved by electronically

synchronizing the two sets of Ti: Sapphire oscillators/amplifiers at 1 kHz repetition rate running

at 40 fs and ~ 100 ps pulses at the fundamental (800 nm), respectively. Such system was

constructed with lasers from Coherent, Inc. with synchronized 800 nm pulses with > 3.5 W

power at ~ 90 ps and > 7.5 W at ~ 40 fs, respectively. The overall timing jitter between the laser

pulses from the two amplifiers is estimated to be < 250 fs. In the HR-BB-SFG-VS experiment,

about half of the fundamental output generated by the ps amplifier (~ 90 ps as measured by

cross-correlation) was further attenuated by a variable neutral density filter to ~ 60 µJ/ pulse and

used as the source of VIS radiation. The incident angle of the VIS beam (800 nm) is βVIS=45º and

βIR =56º with respect to the surface normal. The SFG signal was collimated by an achromatic

59

lens and isolated from the VIS and IR beams by the combination of an optical iris, an 800 nm

notch filter, and a 785 nm shortpass filter (Semrock Inc.). The SFG signal was polarization

selected and imaged onto the spectrometer slit (typically open to < 100 µm) of a 750 mm

spectrograph (Andor Shamrock). The SFG signal was dispersed by a 1200 lines/mm grating and

recorded by a thermoelectrically cooled (-80°C) Electron-Multiplied CCD camera (Andor

Newton 971P, back-illuminated) containing a 1600x400, 16 µm2, pixel array. In the HR-BB-

SFG-VS measurement, usually a two-minute exposure for the ssp polarization combination was

used. Here the ssp polarization combination is defined as with s-, s-, p- polarization

corresponding to the polarization of the optical fields of the SFG signal, visible and IR beams,

respectively 213. p polarization is defined when electric field vector parallel to the plane of

incidence within the incident plane formed by the incident beam direction and the surface

normal, and s polarization is perpendicular to the incident plane. Background noise spectrum was

measured by delaying the ps VIS and fs IR pulses by > 1 ns. The spectra were normalized with

the SFG profile from the top surface of a thick Z-cut α-quartz crystal. In comparison to the

spectral resolution of the conventional BB-SFG-VS (~15 cm-1) and the scanning SFG-VS (~ 6

cm-1) in the literature, HR-BB-SFG-VS in this chapter has a spectral resolution of ~0.6 cm-1. As

demonstrated recently, such high order of magnitude improvement of the spectral resolution not

only enables resolving of closely overlapping peaks 215, but also provides accurate and intrinsic

spectral lineshapes that is crucial for the quantitative spectral and coherent vibrational dynamics

analysis of the molecular vibration- at surfaces/interfaces and in non-centrosymmetric crystalline

material 211, 213, 215. The chapter reported here is the first application of HR-BB-SFG-VS to study

crystalline materials, such as crystalline cellulose.

60

3.3.3 Polarization Null angle measurement with picosecond scanning SFG-VS

Polarization dependent scanning SFG-VS measurement was also performed with the

picosecond scanning SFG-VS system (EKSPLA, Inc.), pumped by an Nd:YAG laser with a 50

Hz repetition rate and ~30 ps pulse width. This system is similar to the system used in previous

cellulose studies 85. The resolution of the system was ~6 cm-1. The picosecond SFG was carried

out in the scanning mode. IR (energy 180µJ) and visible (energy 100µJ) beams overlapped on

the surface of the sample with IR incident angle of 56° and visible incident angle of 45°. The

SFG signal was detected by a monochromator and a photomultiplier controlled by a PC

computer.

In the polarization null angle measurement, the SFG signal polarization was kept at -45°

with respect to the p-polarization direction and IR polarization was fixed at 0° (p-polarized). IR

wavelength was kept at ~2956 cm-1. The polarization angle of the visible beam was changed with

a rotating half-wave plate from -45○ to 360○ 213. As shown below, there was no apparent

polarization dependence for the SFG-VS signal from all cellulose samples, indicating that the

SFG-VS was indeed from the bulk of the crystalline material instead of from the surface of these

materials.

3.3.4 Atomic force microscopy (AFM)

AFM was performed in air using the method reported previously 219. A multimode scanning

probe microscope (SPM) with NanoScope V controller (Veeco, Santa Barbara, CA. USA) was

utilized for all AFM measurements. To ensure stability, the AFM was situated in a specially

designed laboratory with acoustic and vibration isolation. A customized Nikon optical

microscope with deep focus (maximum 800 x magnification) was used to aid the positioning of

61

the AFM tip to the desired face and location on the cell wall. A standard 15-μm scanner was used

with the ScanAsyst™ imaging mode and probes SCANASYST-AIR was used for imaging in air,

and SCANASYST-FLUID+ for imaging under fluid (Bruker, Camarillo, CA USA). The software

Nanoscope Analysis v1.4 was used for AFM operation and later image processing. The AFM

imaging recorded many parameters, while all images presented in this study were height images,

which were flattened at 3rd order and filtered with low pass filter.

3.4 Results and discussion

3.4.1 Traditional Raman in cellulose characterization

Raman spectra were taken in NREL. Raman spectrometer was equipped with a video camera,

CW Nd: YAG diode laser, LabRamHR UV spectrometer, and CCD detector. Samples were

gently placed on the glass slides and focused with the video camera. The laser excitation from

diode laser was 2-20mW of 514.0 nm. The confocal hole was kept at 100 µm. Raman spectra

were analyzed using GRAMS/32or LabSpec data acquisition and analysis software 220.

Raman spectra of the (Figure 3.1a) cellulose Iα from alga Valonia ventricosa (Glaucocystis

(nostochinearum)) and (Figure 3.1b) cellulose Iβ from Halocynthiaroretzi tunicate. It is clear that

the Raman spectral features of the two crystalline cellulose forms are very similar to each other,

and do not show significant difference. This suggests the advantage of using HR-BB-SFG-VS as

the spectroscopic tool for cellulose structure characterization.

62

Figure 3.1 (a) Raman spectra of cellulose Iα from alga Valonia ventricosa (Glaucocystis

(nostochinearum)) and (b) cellulose Iβ from Halocynthiaroretzi tunicate in the frequency regions

of 300 to 1600 cm-1 and 2500 to 3700 cm-1

3.4.2 Polarization Null Angle (PNA) testing

PNA method in SFG-VS is a molecule orientation analysis to determine the relative phase

and amplitude ratio between the ssp and ppp polarization combination of SFG response 213.

Figure 3.2 shows the SFG polarization null angle measurements of cellulose Iα, cellulose Iβ, and

Avicel. The SFG intensity at 2956 cm-1 showed consistent flat response under different visible

beam polarization angles in Avicel, cellulose Iα and cellulose Iβ. The lack of null signal at all the

polarization angles of all the samples indicated that the SFG signals of Avicel, cellulose Iα and

63

cellulose Iβ materials were polarization-independent, e.g. not sensitive to the polarizations of the

incident laser beams. Such results suggested that the SFG signals detected from these samples

indeed originated from the bulk crystalline structure other than from the amorphous domain

surface 213. Another indication that these signals were from crystalline bulk is that the SFG signal

was more than two orders of magnitude stronger than the surface SFG signal with full monolayer

coverage, such as the air/DMSO interface. Since the SFG signal is not sensitive to polarization

angles and with bulk crystalline origin, polarization dependent measurement usually conducted

on molecular surfaces is not necessary, and the SFG measurements in this chapter were carried

out in the same ssp polarization consistently throughout this work.

Figure 3.2 PNA for different visible polarization angles of cellulose Iα from alga Valonia

ventricosa (Glaucocystis (nostochinearum)), cellulose Iβ from Halocynthiaroretzi tunicate, and

Avicel using scanning SFG system at 2956 cm-1

64

3.4.3 HR-BB-SFG-VS spectra of cellulose Iα, Iβ and Avicel samples

SFG-VS was applied to study cellulose microfibril assembling 212 and can serve as an

important tool in cellulose structure characterization. Uniaxial-aligned cellulose Iα and Iβ were

studied by resolution of 6 cm-1 212. It is critical to well understand two natural crystals Iα and Iβ

from different sources using high resolution SFG-VS with resolution of 0.6 cm-1. HR-BB-SFG-

VS was applied in the chapter to probe Avicel, cellulose Iβ from Halocynthiaroretzi tunicate,

cellulose Iβ from red tunicate, and cellulose Iα from alga Valonia ventricosa (Glaucocystis

(nostochinearum) in the frequency range of 2700 cm-1 to 3450 cm-1. The entire HR-BB-SFG-VS

spectra gave a comprehensive SFG profile of cellulose from different sources (Figure 3.3a).

Since SFG spectral features require a vibrational mode to be both IR and Raman active, not all

peaks seen in IR and Raman spectra were necessarily observed in SFG-VS 212. Spectral

differences in CH and OH stretch regions are evident, including peak positions, intensities, and

spectral shapes among different cellulose samples. These significant spectral differences enable

the identification of different types of cellulose as discussed below. Spectral fittings were

performed to reveal the accurate real peak positions, amplitude, and bandwidth (Fitting results

are listed in Table 3.1). Theoretically, SFG peaks could be fitted into Lorentz models convoluted

with Gaussian intensity distribution (Equation 3.3) 206, 211, which determines amplitude and other

peak shape information 221. However, in most cases Lorentzian lineshape is used in SFG-VS

fitting. Figure 3.3(b) and 3.3(c) display the fitting results of both CH and OH stretch regions of

Iβ, Avicel and Iα using commonly used Lorentzian lineshape function, respectively. Figure 3.3

showed significant difference as the Raman data (Figure 3.1) in the CH and OH stretching

frequency region.

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Figure 3.3 (a) Experimental HR-BB-SFG-VS spectra of Avicel, cellulose Iβ from

Halocynthiaroretzi tunicate, cellulose Iβ from red tunicate, and cellulose Iα from alga Valonia

ventricosa (Glaucocystis (nostochinearum)) at wavelengths of 2700 cm-1 to 3450 cm-1. All

spectra intensities were normalized and presented on the same intensity scale. (b) Spectra for two

cellulose Iβs after peak fitting via Lorentz profile convoluted with a Gaussian intensity

distribution method; (c) Spectra for cellulose Iα (left axis) and Avicel (right axis)

Previous SFG-VS studies characterizing the polymorphism of crystalline cellulose mostly

focused on the CH stretching vibration frequency region. In the literature, it was shown that the

spectral features in the surface SFG-VS did not simply follow the IR and Raman spectral

assignments, while the assignments in the IR and Raman studies in these spectral regions were

not always correct, and the SFG-VS spectral assignment can be achieved with a set of

polarization selection rules 206, 222, 223. However, the SFG-VS peak assignments are challenging

for crystalline materials, because the polarization selection rules 206 developed for the surface

SFG-VS cannot be directly applied to the SFG-VS spectra from crystalline materials, as the SFG

67

signal from randomly oriented crystalline domains lacks polarization dependence. In previous

study, based on IR or Raman literatures, cellulose peak at 2945 cm-1 was assigned as CH2

asymmetric vibration of exocyclic 6CH2OH and peaks in the range of 3100-3500 cm-1 were

assigned to OH vibrational region 85. Therefore, some of these assignments may not be correct,

and the proper assignment of the cellulose SFG-VS spectral peaks is an issue that warrants

further studies.

Apparently, Figure 3.3 indicates more peaks are detected by HR-BB-SFG-VS spectra

compared to previously reported in previous studies 199, such as the 2913, 2930, 3007 and 3377

cm-1. This phenomenon is attributed to the higher SFG resolution in HR-BB-SFG-VS

measurement which allows detection of intrinsic spectral lineshape without instrument

broadening. As the result, more detailed vibrational stretch peaks can be identified. Curve fitting

results (See Table 3.1(a, b, c, d) also indicated that some fitted peaks are not shown obviously in

SFG spectrum. These data warrant future detailed studies in understanding the detailed

crystalline structure and interaction. In SFG-VS fitting, usually the Gaussian width is neglected.

One can see that not all the peaks in the fitting have the same certainty, and it can be seen from

the error bar of the fitting parameters. Usually the small peaks would have significantly larger

error bar. It needs to be very careful when discussing such detailed spectral features. We also

know that without the high-resolution spectra, one cannot identify as many peaks and the fitting

would be even less reliable. Nevertheless, one has to realize that so far the HR-BB-SFG-VS

represents the best effort one can have with the SFG-VS spectral measurement and analysis.

Table 3.1 Peak position and relative peak intensity parameters from curve fitting

using Lorentzian lineshape profiles (as in Equation 3.3 in the main text) of (a) Avicel, (b)

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cellulose Iα from alga Valonia ventricosa (Glaucocystis (nostochinearum)), and (c) cellulose Iβs

from red reef tunicate and (d) Halocynthiaroretzi tunicate within wavelength of 2700 to 3050 cm-

1 and 3200 cm-1 to 3450 cm-1

Table 3.1a

Avicel

q /(cm-1) q /(a.u) q /(cm-1)

2846.4±1.5 2.1±0.2 32.49±2.6

2892.7±0.9 0.6±0.1 12.81±1.9

2945.6±0.2 1.9±0.3 9.17±0.5

2956.3±0.5 3.2±0.3 13.37±0.4

3200 cm-1 to 3450 cm-1

3332.3±0.5 7.7±0.3 33.67±0.9

3377.9±0.6 0.8±0.1 13.61±1.9

3432.4±2.6 5.9±1.1 47.76±6.9

Table 3.1b

Cellulose Iα from alga Valonia ventricosa

q /(cm-1) q /(a.u) q /(cm-1)

2830.8 ± 5.8 0.4±0.9 10.9±8.8

2849.2 ± 1.4 0.07±0.06 4.4±3.2

2884.6±0.8 0.08 ± 0.1 3.8±1.7

2902.3±0.5 0.12±0.1 4.2±1.5

2925.4±0.3 0.7±0.08 8.7±1.0

2947.9±0.1 3.2±0.06 6.9 ± 1.2

2972.3±0.1 1.4±0.05 8.1±0.2

3048.4±1.5 3.0±0.3 40.4±3.7

3232.8±0.5 1.0±0.1 15.4±1.5

3248±0.6 -0.35±0.1 10.0±1.0

3290±0.5 0.11±0.1 4.2±1.0

3304.2±0.9 0.20±0.1 8.7±1.8

3332.8±0.3 -1.5±0.3 16.3±0.6

3395.5±0.5 -2.5±0.1 26.3±1.3

Table 3.1c

Cellulose Iβ from red reef tunicate

q /(cm-1) q /(a.u) q /(cm-1)

2863.9 ± 1.3 4.7 ± 0.5 26.4 ± 2.0

2912.1 ± 0.5 -0.04 ± 0.1 6.3 ± 0.8

2929.8 ± 0.2 0.8 ± 0.1 5.6 ± 0.6

2949.3 ± 0.1 10.2± 0.7 6.0 ± 0.1

2973.3 ± 0.4 2.5 ± 0.1 15.5± 0.4

2990.3 ± 0.4 0.8 ± 0.2 6.9 ± 1.2

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3200 cm-1 to 3450 cm-1

3280.8 ±1.6 10.6±0.5 18.8±1.4

3320.5±9.1 76.9±1.1 3.6±0.7

3368.9±6.6 4.4±0.7 24.8±0.8

Table 3.1d

Cellulose Iβ from Halocynthiaroretzi tunicate

q /(cm-1) q /(a.u) q /(cm-1)

2863.4±0.4 0.4±0.0 34.1± 6.8

2916.3 ± 0.3 -0.6 ± 0.1 3.1± 0.6

2920.8±0.3 0.5±0.02 2.9±0.8

2945.2 ± 8.9 8.5±0.3 7.9±0.7

2975.7±0.1 16.2 ± 3.7 7.8±0.1

2991.5±0.2 0.7±0.1 4.0±0.5

3007.7 ± 1.0 3.6 ± 1.8 19.8 ± 4.6

3276.8 ±1.5 10.1±1.8 19.5±1.7

3321.7±17.3 79.0±1.5 3.6±1.5

3369.4±6.6 6.6±0.4 23.6±1.7

3.4.4 Morphology of cellulose Iα and cellulose Iβs from AFM image

Atomic force microscopy (AFM) can obtain high-resolution images to reveal lateral packing

of the chains. AFM images of four cellulose samples were taken to provide detailed

morphological structure and arrangement of the microfibrils measured with HR-BB-SFG-VS.

Figure 3.4 compares AFM images of (a) Avicel, (b) cellulose Iα, and (c, d) two cellulose Iβs

taken in the same nano scale resolution. AFM images show similar morphological structures of

cellulose microfibrils from Valonia and tunicates (Figure 3.4 b-d), which are large crystals (20-40

nm in dimensions) that differ from Avicel with small microfibrils (3-5 nm). Cellulose sample

from red reef tunicate (C) appears to contain some amorphous structures. Also, Figure 3.4

presents different microfibril lateral packing information of Avicel, cellulose Iα and cellulose Iβs,

which showed randomly oriented cellulose crystals, different densities of microfibrils, and

microfibrils packing forms. The two cellulose Iβ samples showed similar microfibrils stacking

but different in microfibrils density. All those phenomena reveal the complexity of cellulose

microfibrils arrangements including fibrils overlapping, twisting and packing forms among four

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samples. The packing of microfibrils provides information on the nature of the enzyme

complexes responsible for oriented cellulose synthesis during plants growth 177, 224, cellulose

species and cellulose extraction methods.

Figure 3.4 Comparison of AFM images of (a) Avicel, (b) cellulose Iα from alga Valonia

ventricosa (Glaucocystis (nostochinearum), (c) cellulose Iβ from red tunicate and (d) cellulose Iβ

from Halocynthiaroretzi tunicate. The scale of all images is 2 x 2µm

The differences in the microfibrils packing of the four celluloses are also reflected in the

HR-BB-SFG-VS spectra. As shown in Figure 3.3a, the SFG intensity of the cellulose Iβ from

Halocynthiaroretzi tunicate sample has the highest SFG intensity, followed by the cellulose Iβ

71

from red tunicate, the cellulose Iα from alga Valonia ventricosa, and Avicel. Since the SFG signal

should be roughly proportional to the density of the same microfibrils in the unit volume, it is

easy to understand why the cellulose Iβ from Halocynthiaroretzi tunicate has highest SFG

intensity and why the cellulose Iβ from red tunicate and the cellulose Iα from alga Valonia

ventricosa have smaller SFG intensity as their microfibril densities in the AFM images in Figure

3.4 followed the same decreasing order, i.e. from Figure 3.4d, Figure 3.4c, to Figure 3.4b. It is

interesting to see that even though the Avicel sample appears to have high density from its AFM

image (Figure 3.4a), its SFG intensity is the smallest and comparable to the cellulose Iα from

alga Valonia ventricosa. This fact can only suggest that certain amount of the materials in the

Avicel did not contribute to the SFG intensity. In another word, a certain part of the material in

the Avicel sample is most likely not in the crystalline cellulose form, or is in an amorphous form

that is SFG insensitive. These results suggested that SFG intensity and cellulose material density

correlation might be used to classify the SFG-sensitive crystalline and SFG-insensitive

amorphous cellulose materials, and it might also be used to quantify the amount of SFG-sensitive

crystalline material in the cellulose material.

3.4.5 Unique O-H signatures for cellulose Iα and cellulose Iβ

The unique spectral signatures for the different forms of crystalline cellulose materials were

shown in Figure 3.5. Results showed that the O-H spectra of two Iβ samples were almost

identical in their spectral lineshape even though they had significantly different spectral

intensities. In Figure 3.6, the Iα and Iβ samples clearly showed significantly different spectral

lineshapes. Figure 3.7 showed that the C-H spectra of the two Iβ samples had quite different

spectral lineshape even though the frequency positions of most of their C-H peaks were the

72

same. These results clearly indicated that the O-H spectra features were the unique signatures

that could be used to differentiate the Iα and Iβ forms of crystalline cellulose.

Figure 3.5 HR-BB-SFG-VS spectra of cellulose Iβs from Halocynthiaroretzi tunicate and red

reef tunicate between wavenumber of 3100 cm-1 to 3500 cm-1

Previous literature reported that the characteristic peaks of cellulose Iα in IR spectra located

at 3240 cm-1 79 while cellulose Iβ in IR spectra showed characteristic peak at 3270 cm-1 78. In

Figure 3.5, two cellulose Iβs from different sources presented almost identical peak positions and

shapes. It indicated characteristic peaks of cellulose Iβs locating in wavenumber range from 3100

to 3500 cm-1. From peak fitting results, the two cellulose Iβs showed peak positions at 3277,

3321, and 3369 cm-1.

73

Figure 3.6 HR-BB-SFG-VS spectra of cellulose Iβ from Halocynthiaroretzi tunicate and

cellulose Iα from alga Valonia ventricosa (Glaucocystis (nostochinearum)) between wavenumber

of 3200 cm-1 to 3450 cm-1

Figure 3.6 showed different spectral signatures of the cellulose Iα and cellulose Iβ in terms

of both the peak positions and lineshapes. For example, peaks of cellulose Iα were at 3232, 3248,

3290 3304, 3332, and 3395 cm-1, while peaks of cellulose Iβ presented at 3277, 3321, and 3369

cm-1. It appeared that 3232, 3248 and 3440 cm-1 peaks were unique to the cellulose Iα form,

while the main 3322 cm-1 peak of Iβ overlapped mostly with the 3332 cm-1 peak of the Iα form

although it is uncertain that how much Iβ or Iα form was present or dominant. Nevertheless,

these spectral signature differences of the OH vibrational peaks allowed clear identification of

cellulose Iβ and cellulose Iα and also indicated basic structural differences of cellulose Iβ and

cellulose Iα in terms of the OH groups structure and interactions in their unit cells. It was

reported that there were different OH bonds stretch in cellulose Iβ and cellulose Iα allomorphs,

74

which determined the different packing of cellulose chains 201, 225. The high-resolution SFG

spectral differences of the cellulose Iβ and cellulose Iα samples in this chapter also revealed

different cellulose microfibrils stacking and arrangements. The detailed connection and

quantitative correlation of these SFG spectral signatures to the specific crystalline structures of

the different forms of crystalline forms warrant further studies in the future.

3.4.6 CH Spectral features for cellulose Iβ samples from different sources

Figure 3.7 Difference of cellulose Iβs from red reef tunicate and Halocynthiaroretzi tunicate by

HR-BB-SFG-VS

SFG-VS with resolution of 0.6 cm-1 was applied to probe laterally packed and aligned films

of cellulose Iα nanowhiskers and cellulose Iβ Nanowhiskers which showed obvious peaks at

2850, 2944 cm-1, and slightly visible peaks at 2886, 2920, and 2988 cm-1. However, the HR-BB-

SFG-VS spectra results showed that even though the two cellulose Iβs had almost identical O-H

spectral features, their C-H stretching regions were significantly different (Figure 3.7). Dramatic

75

differences of the relative spectral intensities for the two Iβ forms could be identified. In order to

compare the relative intensity of different spectral features, the intensities of all peaks in the

cellulose Iβs were normalized by the intensity of peak at 2973 cm-1 that was the highest peak in

the CH region. Detailed peak positions, peak amplitudes, and peak widths are shown in Table

3.1. Cellulose Iβs from red reef tunicate and Halocynthiaroretzi tunicate showed similar peak

positions at 2863, 2913, 2925, 2946, 2973, and 2991. Several relative peak intensity of the two

cellulose Iβs was prominently similar, but some of them were quite different. For example, the

2946 cm-1/2973 cm-1 amplitude ratio of cellulose Iβ from Halocynthiaroretzi tunicate was 0.52

while ratio of cellulose Iβ from red reef tunicate was 4.08. Such differences indicated that the C-

H groups in the two cellulose Iβ samples were different. Such differences might be attributed to

different microfibrils stacking. However, the detailed structural differences need to be further

examined in future works.

The two cellulose Iβs were obtained from different species of tunicates and through different

cellulose isolation and purification methods, thus resulting in different modification on cellulose

microfibrils orientation. Meanwhile, during tunicate growth, microfibrils are also stacked

differently. It was demonstrated that the process of cell expansion and/or elongation that

occurred during plant development influenced the arrangement of the cellulose elementary fibril

in different plant cell wall 224. Therefore, microfibrils are responsible for oriented cellulose

synthesis and directional cell expansion during plants growth 177. Therefore, CH stretching

vibrational region might provide critical information in identification of cellulose Iβs from

different sources. However, such stacking differences should not alter the OH interaction in the

unit cell of the cellulose Iβ. Otherwise, the OH spectra of the two forms of Iβ samples should

76

also be quite different in the peak position or relative intensity.

It is clear that different microfibril stacking structures as well as glucose unit cell structure

can result in different vibrational environment for different CH groups in the cellulose. It is well

known that cellulose microfibrils aggregation significantly influences cellulose crystalline

structures. Microfibril is a cellulose morphological unit which contains a single cellulose

elementary fibril 224. In the primary plant cell wall, the cellulose elementary fibril is the nascent

fibril formed by cellulose synthase rosettes. It was reported that there are six C-H stretching

modes in glucose ring that is detectable with vibrational circular dichroism (VCD) in the

wavelength range of 2800 to 3000 cm-1 226, 227. Therefore, it is not surprising to identify the same

or even more number of peaks in the HR-BB-SFG-VS spectra of cellulose samples even though

the detailed assignment and understanding of their relative intensities can be a challenging task.

SFG signals would be canceled under the symmetry inversion, which leads to weaker or stronger

peak intensity. Carbons in glucose ring have different molecular vibration environment that can

result in different peak shape in SFG spectra. Even with high resolution SFG and more peaks are

able to be detected, detailed peak assignments and understandings on the six C-H stretchings are

still not quite clear.

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3.4.7 Characterization of Avicel probed by HR-BB-SFG-VS

Figure 3.8 HR-BB-SFG-VS spectra of Avicel and cellulose Iα from alga Valonia ventricosa

(Glaucocystis (nostochinearum)) (a) and cellulose Iβ from Halocynthiaroretzi tunicate (b),

respectively, between wavelengths of 3200 cm-1 to 3450 cm-1

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Avicel is considered mainly composed of Iβ and a small portion of Iα 85, 196. Figure 3.8

shows the Avicel OH stretching region SFG spectrum together with the spectra of both the

cellulose Iα and the cellulose Iβ. The peaks parameters of the three samples after curve fitting are

listed in Table 3.1. These results showed that Avicel appeared mostly composed of cellulose Iβ

peaks and a small portion of cellulose Iα peaks although it presented some different peak

positions and peak shapes compared with pure cellulose Iβ and cellulose Iα. In the early section

we discussed that OH stretch peaks were characteristic spectral signatures to distinguish

cellulose Iα and cellulose Iβ. While Avicel simply had peaks at 3332, 3378, and 3432 cm-1,

cellulose Iα had peaks at 3232, 3248, 3290, 3304, 3332, and 3395 cm-1, and cellulose Iβ had

peaks at 3277, 3322, and 3369 cm-1. The CH vibrational region (from 2700 to 3050 cm-1) of

cellulose Iα and cellulose Iβs both showed a peak at ~2973 cm-1 (Figure 3.3a, 3.3b, and 3.3c).

Peak fitting results of C-H region showed that four peaks in the Avicel CH stretch region at

~2846, ~2895, ~2946 and ~2956 cm-1 which present a more detailed structure than the previous

report. Since there were no Avicel spectral peaks in the 3232 and 3248 cm-1 region, it is unlikely

that there is much Iα form present in Avicel. It is therefore reasonable to conclude that Avicel

consists of mostly the Iβ form in addition to certain amount of SFG-insensitive amorphous

material. Thus, it appears that, as advantages of high resolution SFG-VS, O-H stretch region is a

signature to identify Avicel, cellulose Iα and cellulose Iβ, while C-H vibrational peaks reflect the

complex microfibril packing and microfibril composed sheets packing in cellulose structure

without manual alignment 200, 228-230.

Although both peak intensity and integrated peak ratios have been used to estimate

crystalline cellulose and cellulose lateral packing orientation 194, 196, 228, 231. However, intensity

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ratio might not be accurate enough since some peaks were overlapped thus resulting in miscount

issues. For example, ~2913 and ~2925 cm-1 of cellulose Iβ are presented shoulders against main

peak at 2946 cm-1. Instead, the ratio (R) of the total C-H stretching vibrational peak areas over

the total integrated O-H peak areas seems to be useful to evaluate spectra difference of Avicel,

cellulose Iα and cellulose Iβs. Results showed that that the R values differed among Avicel,

cellulose Iα and cellulose Iβs, while they are similar among two cellulose Iβs (Table 3.2). For

example, cellulose Iβ from Halocynthiaroretzi tunicate showed R of 0.54 while cellulose Iβ from

red reef tunicate showed R of 0.52 via R of cellulose Iα is 1.1. The R of Avicel (0.67) was more

similar to that of cellulose Iβ. It is agreeable to previous reports that Avicel is composed of most

Iβ form 196. It indicates that integrated area ratio might be useful in characterizing cellulose

structures.

Table 3.2 Comparison of C-H, O-H peaks areas and their ratios of Avicel, cellulose Iβ (different

sources) and cellulose Iα

Samples C-H peaks area O-H peak area R

cellulose Iβ from Halocynthiaroretzi tunicate 147.0 273.0 0.54

cellulose Iβ from red reef tunicate 36.6 69.3 0.52

cellulose Iα from alga Valonia ventricosa

(Glaucocystis (nostochinearum))

12.5 11.4 1.10

Avicel 7.0 10.5 0.67

3.5 Conclusion

This is the first HR-BB-SFG-VS study on cellulose Iα and cellulose Iβ, and Avicel in order

to identify the spectral signatures of different cellulose microfibrils. The findings suggested that

the O-H stretching region was unique spectral signatures which can be used to identify cellulose

Iα and cellulose Iβ, while the C-H spectral features do not exhibit such explicit correlation to

different types of crystalline structures of the cellulose. We also found that the ratios of the total

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C-H stretching vibrational peak areas over the total integrated O-H peak areas of Avicel,

cellulose Iα and cellulose Iβs correlated well to different types of cellulose microfibrils. In

addition, many more peaks were revealed in the C-H stretching region by the HR-BB-SFG-VS

than by the conventional SFG-VS. For example, the peak at ~2945 cm-1 was assigned to the

C6H2 asymmetric stretching peaks, however, HR-BB-SFG-VS detected another obvious peak at

~2973 cm-1 nearby, which is usually not present in the conversional SFG-VS. Such new insights

can help us define crystalline cellulose structures in more detail and can provide confirmation in

measuring spectral lineshape, resolving different spectral peaks of the high-resolution approach.

With the detailed spectral measurement with the HR-BB-SFG-VS, more future work should be

expected to focus on peak assignment, and on illustrating the cellulose structure changes in the

cellulose treatment or transformation processes as their detailed SFG-VS spectral features

change. These developments shall make SFG-VS a useful in-situ spectroscopic tool not only in

cellulose structure and kinetics studies, but also in other crystalline biopolymer material studies.

The significant differences of cellulose Iα, Iβ, and Avicel in peak positions, intensities and peak

ratios indicates that the traditional understandings of these celluloses are not enough and

accurate. Avicel, cellulose Iα, and cellulose Iβ might be completely different from each other and

the statement of Avicel is composed of major Iβ might not be true. More future work is still

needed to provide evidences and it will surely be a breakthrough in cellulose structures.

3.6 Acknowledgements

This work was made possible through the support of the DARPA Young Faculty Award

contract # N66001-11-1-414. The author acknowledges the Bioproducts, Sciences and

Engineering Laboratory, Department of Biosystems Engineering at Washington State University

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and the BioEnergy Science Center, a DOE Bioenergy Research Center, and the Genomic Science

Program (ER65258), both supported by the Office of Biological and Environmental Research in

the DOE Office of Science. Part of this work was conducted at the William R. Wiley

Environmental Molecular Sciences Laboratory (EMSL), a national scientific user facility located

at the Pacific Northwest National Laboratory (PNNL) and sponsored by the Department of

Energy’s Office of Biological and Environmental Research (BER). The theis author was partially

supported by the grant from the Chinese Scholarship Council (CSC). The author would like to

express special appreciation to Drs. Hongfei Wang and Bin Yang for their encouragements and

providing opportunities to do this research. The author of this thesis would like to thank Drs.

Hong-Fei Wang, Bin Yang, Zhou Lu, Luis Velarde, Li Fu, Yunqiao Pu, Shi-You Ding, Arthur J.

Ragauskas, and Dr. Seong Kim (Penn State University) for their contribution to this work and

publish it in the Cellulose journal.

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CHAPTER FOUR

TOWARD DYNAMICAL UNDERSTANDING OF FUNDAMENTALS OF

PRETREATMENT AND ENZYMATIC HYDROLYSIS

OF CELLULOSIC BIOMASS VIA A SFG-VS

4.1 Abstract

In this chapter, the SFG-VS was applied to dynamically characterize the structural changes

of cellulose during biomass aqueous pretreatment and enzymatic hydrolysis. To mimic the

aqueous pretreatment system, a SFG-VS with a fluid reactor/cell system was developed to study

the effects of thermal heating and water media from aqueous pretreatment on the disruption of

cellulose crystals. To selectively characterize the molecular structures in surface layers of Avicel

and cellulose Iβ crystals, this work developed Total Internal Reflection Sum Frequency

Generation Vibrational Spectroscopy (TIR-SFG-VS) for the first time. Combined with the

conventional non TIR-SFG-VS, our study demonstrated the significant difference in molecular

orientations of surface layers and bulks of these two types of cellulose. The spectral difference of

cellulose surface layers and bulks suggested that the peaks at ~2948 cm-1 and ~2857 cm-1 were

from C-H vibrations on cellulose carbon rings and side chains -6CH2OH, respectively. Also, the

surface layers of Avicel and Iβ were much less crystallized than their bulks while Iβ surface

layers and bulks were more uniform than that in Avicel. The SFG signals in O-H stretching

region showed the different intermolecular, intramolecular, or free hydrogen bonding

distributions in surface layers and bulks of these two types of celluloses. This work demonstrated

the capacity of TIR-SFG-VS and conventional Non TIR-SFG-VS in selectively studying the

surface layers and bulk structures for cellulose crystalline materials. Therefore, the molecular

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characterization of the surface layers of cellulose crystals is performed for the first time and an

important step to fundamental understanding the cellulose surface layers in biomass

pretreatment.

By the capacities of studying cellulose surface layers by SFG-VS, the molecular structural

changes of the surface layers and bulks of Avicel in aqueous pretreatment were studied under

dynamic heating program from room temperature to 280C for the first time. The high

temperature fluid cell was used to simulate the aqueous pretreatment up to 280°C with/without

pretreatment solvents (e.g. DI water). It was found that the bulk crystalline structures of dry and

wet Avicel were dramatically disrupted with the increasing of temperatures, and wet Avicel was

indicated faster. The possibilities of the gradual conversion of cellulose Iα and Iβ in Avicel to

disordered metastable intermediates with the increasing of high temperatures were proposed.

Significantly, a possible recrystallization process of disrupted Avicel bulks was hypothesized for

the first time to explaining the cellulose structural transformation in the process of and after

cooling down to room temperature. The thermal stability of surface layers was studied and the

recrystallization was found not occurred. Additionally, the surface layers needed 240-280°C to be

completely disrupted which was higher than the disruption of bulks required. Thus, the

temperature dependences of bulks and surface layers of cellulose as well as the recrystallization

of disrupted bulk crystals might be correlated with the requirements of elevated temperatures to

achieve high pretreatment effectiveness. These initial findings are important for not only in the

thermal design of pretreatment but also other thermal processing of cellulose. Based on our

results and analysis, a cellulose recrystallization hypothesis was proposed that heating-disrupted

cellulose bulk microfibrils are recrystallized during and after cooling down to room temperature

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which is driven by the rearrangements of the disrupted cellulose microfibrils to a stable

crystalline state. Although cellulose presents highly ordered crystalline structure, the

recrystallization is not a complete recovery process. Thus, the recrystallization results in new

crystals which are differed from original crystals but show similarities. However, the surface

layers of cellulose were not recrystallized during and after cooling off due to their less

crystallized structures. The hypothesis still needs more experimental work to validate besides

this chapter indicated.

SFG-VS was also found useful in studying enzymatic hydrolysis system. Interestingly, with

the proceeding of enzymatic hydrolysis, Avicel molecular structure was changing during

enzymatic hydrolysis according to the changes of the peak area ratios. The C-H vibrational

region was indicated a crucial region to study the enzymatic hydrolysis. SFG-VS was also

proposed to study the interactions of enzymes and cellulose without peak overlaps and separating

enzymes and cellulose. Thus, SFG-VS demonstrated as an important tool in enzymatic

hydrolysis study but more work is still needed. These aspects of work will greatly contribute to

the fundamental understandings of aqueous pretreatment and enzymatic hydrolysis processes.

Keywords: Cellulose surface layers • Cellulose bulk crystals • Sum frequency generation •

vibrational spectroscopy • Total internal reflectance • High temperature • Cellulose

recrystallization • Enzymatic hydrolysis

4.2 Introduction

The interests of seeking a renewable energy are growing in recent years. Lignocellulosic

biomass serves as a sustainable source for biofuel production, which can be a promising option

for the renewable energy. The efficient utilization of cellulose is crucial in biorefinery

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economics, which relies on the fundamental understandings of cellulose structural changes

during biorefinery processes. Cellulose is the major component in biomass plant cell wall. The

cellulose structural information is very important to cellulose accessibility to various treatments

such as enzymatic hydrolysis 232, chemical treatments 233, 234, and other industrial applications

(e.g. nanocellulose materials production) 235. Cellulose is the only crystalline structure source of

wood and believed to be thermally stable. It is commonly believed that cellulose microfibrils

form both crystalline and amorphous structure although their assembling is not clear 236-238.

Nowadays, the fundamental studies of cellulose structural changes during aqueous pretreatent

enzymatic hydrolysis are crucial to a successful biorefinery.

The cellulose behaviors in high temperatures have been studied. It was reported that

cellulose Iα and Iβ can be converted to a high temperature phase, and after cooling to room

temperature produces cellulose Iβ 239-243. This was explained by two proposed elucidations

including the relative free energy of cellulose Iα and Iβ predicted by molecular simulation which

cannot take into accounts of all influencing factors 244 and high temperature phase structure (I-

HT) intermediated pathways 245. However, experimental evidences to clarify these proposals are

still required. The two models of structural transformation mechanisms from cellulose Iα and Iβ

were a transformation/break-slip model by moving these chains up or down by one glucose

residue 246, and a rotation model with 180 degrees rotation along cellulose chain axis due to the

different crystal arrangements and hydrogen bonding of cellulose Iα and Iβ 240. The

computational molecular dynamics (MD) simulations with two force fields were applied to

elucidate the high temperature behaviors of cellulose Iβ suggesting the changes in layer spacing

and hydrogen bond patterns of I-HT.

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Experimental work on cellulose thermal behaviors as a function of temperatures is not

widely reported. The spectroscopic tools have been used in the characterization of cellulose

thermal behaviors. For example, the infrared spectroscopy with a perturbation-correlation

moving-window two-dimensional correlation spectroscopy was used in studying the

temperature-dependent changes of hydrogen bonding in cellulose Iα and Iβ from 30-260C, and

proposed that the cellulose Iα and Iβ phase transformation was induced by the changes of

hydrogen bonding networks (e.g. 220C as hydrogen bond transition temperature) 247, 248. The

(CP/MAS) 13C NMR spectroscopy has been used to studying the structural transformation of

cellulose I at 230 to 280C for 30min suggesting that both cellulose Iα and Iβ were transformed

into a new crystal cellulose Iα similar to cellulose Iβ 243, 249. It was reported the thermal

expansion of cellulose crystal occurred during cellulose heating from room temperature to 250C

studied by XRD analysis, and found a high temperature cellulose phase at 220-230C 239.

However, the molecular structural transformation of cellulose Iα and Iβ during a dynamic heating

process, the molecular structure of I-HT, and the transformation of cellulose during cooling

process were not illustrated. More efforts are still needed to determine the molecular level details

of the high-temperature phase. Besides, the crystallinity changes of different wood cellulose was

reported with oven heating dry/wet cellulose at 220 ºC for 0-240min suggesting the different

crystallinity changes of heated wet/dry cellulose 250. Similarly, the effects of moisture in cellulose

were investigated on cellulose crystallinity changes under 180, 200, and 220 ºC, however, the

molecular changes were not well studied 251. Our SFG-VS system is able to characterize both the

surface layers and the interior transformations of cellulose Iα to Iβ, and study the molecular

structural transformations of cellulose I polymorphs during and after cooling process.

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The enhancement of enzymatic hydrolysis efficiency can be achived by biotechnology and

improving cellulose substrates accessibility. The biotechnology is the long term goal while the

cellulose substrates study is a short term goal. The understanding of cellulose structure and its

interaction with enzymes is the key for the short term goal. In this chapter, the enzymes and pure

cellulose involving in the regular enzymatic hydrolysis system will be studied, and this was a

start of conducting the interactions of enzymes with cellulose surface layers and bulks.

The cellulose in pretreatment and enzymatic hydrolysis system mainly involves in surface

reactions. To assist in studying the cellulose surface layers, a new tool is urgently needed.

Recently, the research progress of cellulose bulk structure has been advanced with the

development of analytical methods. For exmple, the difference of Iα and Iβ was distinguished by

hydrogen bondings and cellulose chain arrangements, e.g monoclinic and triclinic, as well as the

difference in side chain -6CH2OH orientations 78, 79, 171, 252. However, the understanding of

cellulose surface layers (e.g. Iα and Iβ surface layers) at molecular level is limited. This

limitation might be due to the lack of quantitative in situ surface-selective tools for analyzing the

complex cellulose structures.

The crysalline surface/surface layers are still not well defined yet. Some literature mentions

cellulose surface studies but not clearly defined. For example, X-ray diffraction has been used to

simulate wide and narrow surfaces and surface hydrogen bonding of cellulose Iβ 253 and the

homogeneity of the cellulose crystalline surface with bulk 254-256. Also the 13C NMR was applied

to suggest a splitting of broad C4 and C6 signals of different celulose samples to be the

anhydroglucoses on the surface of cellulose elementary fibrils 257 or the surface molecules of

crystalline domains (indicating by the broad C-4 peak) 258, 259. Some studies assigned the peaks to

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the mixture of Iα and Iβ forms 254. These controversia peak assignments were the basics of most

cellulose surface studies. It was reported that the celluose surface has a different conformation in

C6 with the bulk by solid state NMR and meawhile showed the reduction of intramolecular

hydrogen bonding on surface 260. Although the study of cellulose surface by solid state 13C NMR

was well recognized, the peak assignments were still doubtful 254. Atomic Force Microscopy

(AFM) was another tool to directly oberserve the morphology of cellulose. The surface

roughness and corrugations on the surface were found 254. Besides, the ultra-high resolution

AFM combining with computer modeling was applied to image the surface of native cellulose

and was proposed to study triclinic and monoclinic crystals on surface 256. Electron

microdiffraction study indicated the surface might only exist Iα form 261, while it required more

evidences 255. Additionally, a new imaging apporach of simulated Raman scattering (SRS)

microscopy was developed to image lignin and cellulose in vasular bundle with subcellular

resolution at 1600 and 1100cm-1 262. However, the AFM is limited in observing surface

morphological information and not applicable for molecular level study. Some studies even

demonstrated the distribution of glucan chains 260 or other β(1,4)-glycan polymers onto the

surface of cellulose263. However, these statements lack strong proofs. Unitil now, the cellulose

surface is not well understood, especially at molecular scale and how microfibrils on surface are

assembled. The interaction of cellulose crystalline surface with water or other solvents was

unknown as well, which impeded the research progress of aqueous pretreatment and enzymatic

hydrolysis.

The study of cellulose crystalline surface was greatly influcenced by characterization

methods. The traditional characterization methods of cellulose surface at the molecular level

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were interefered by the bulk crystalline signals. The cellulose surface spectra were reported to

obtain from subtraction of crystalline spectra by cellulose whole spectra, which was not in-situ

study for cellulose surface. For example, the celery collenchyma cellulose was believed as

cellulose surface standard 264, consisting of 60% of surface chains which was twice amount of

surface chains found in textile celluloses like flax. Also, an another challenge of the study of

cellulose surface is that the majority of spectroscopic assignments were not clear yet. For

example, 87-91 ppm was assigned to crystalline structure C4 while 82-86 ppm (a broader peak)

was assigned to surface structure C4. However, 82-86ppm was also proposed from amorphous

cellulose instead of surface structure 265. Whether the amorhpous cellulose has similarities with

the cellulose surface is unknown.

In our study, the SFG-VS is a second-order nonlinear spectroscopy, which has been

exploited as a significant tool in monitoring surfaces or interfacial systems, such as solid–liquid,

liquid–liquid, solid–gas, and solid–solid interfaces 266, 267. The SFG-VS studies of cellulose

structures are popular recently 85, 171, 268 but all focusing on measuring the bulk crystalline

structures of cellulose. It was reported that SFG-VS can selective characterize cellulose

crystalline molecular structures without the influence of amorphous structures or polymers from

biomass 85. Furthermore, our previously developed high resolution broad band (HR-BB) SFG-

VS was first time applied to observe structural signatures in different cellulose sources and

polymorphs, which obtained more structural information than conventional SFG-VS settings 268.

The objectives of this chapter were firstly to design laser input angles and geometries of

SFG-VS to selectively characterize cellulose surface layers. We integrate total internal reflection

geometry in SFG-VS (TIR-SFG-VS) for the purpose of selectively measuring the cellulose

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surface molecular structure and conformation, while suppresses the SFG contribution from the

crystalline bulk. This surface information is more from surface layers (length depends on the

geometry and laser wavelength) and may or maynot the same with previously reported surface

due to the undefined cellulose surface previously. In traditional characterization, the total internal

reflection (TIR) geometry in spectroscopic techniques was reported that can significantly

enhance the spectroscopic sensitivity on sample surface layers, thus, greatly increased signals

with several orders of magnitude 269. In TIR configuration, the evanescent wave from TIR beam

can be utilized to address the interface molecular structures 270-272. For example, the TIR

geometry has been widely used in IR and SFG investigation of various material surfaces or

interfaces, such as metal catalysts surface characterization, surface adsorption phenomena, and

nanoparticle surfaces or interfacial characterization 273-276. These studies concentrate on the

characterization of sample surface information of non cellulose crystalline materials because

these samples usually don’t have signals from bulk besides the surface. The goal of TIR setting

in our study is to use SFG-VS in the characterization of the surface layers of cellulose crystalline

material.

Additionally, the chapter investigated the dynamic thermal stabilities of Avicel surface layers

and bulk with/without water up to 280ºC which is a temperature limit used in biomass aqueous

pretreatment. At last, SFG-VS was used to study the fundamentals of enzymatic hydrolysis

system including molecular and crystal structural changes of cellulose with/without enzymes free

during enzymatic hydrolysis.

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4.3.1 SFG-VS set-up

The picosecond scanning SFG-VS system was a commercial EKSPLA SFG-VS

spectrometer utilized a 50 Hz and 29 ps Nd:YAG laser (EKSPLA PL2251A-50) which was

reported before 28. The visible beam at 532 nm though a KD*P crystal was produced by a portion

of the fundamental output (1064 nm). And the rest of the Nd:YAG output pumped an optical

parametric generation/amplification and difference frequency generation system (EKSPLA

PG401/DFG), generating an infrared (IR) beam tunable between 650 and 4300 cm−1. The

spectral resolution is ∼6 cm−1 277. The details of HR-BB-SFG-VS spectrometer system were

reported in previous work 268, 278. Briefly, HR-BB-SFG-VS can achieve sub-wavenumber

resolution to 0.6 cm−1, made possible by the precise synchronization of two sets of Ti:sapphire

oscillators/amplifiers that can achieve precisely timed 1 kHz pulse sequences of (nominally) 40

fs and 100 ps pulses at the fundamental (∼800 nm). The two mode locked oscillators are actively

synchronized in a master-slave configuration by a Synchrolock-AP (Coherent Inc.) system. The

master radio frequency (r.f.) pulse train originates from a ∼100 ps oscillator (TIGER, Time-

Bandwidth, Inc.) and its repetition rate (76 MHz) is used as the frequency reference for the

stabilization of a sub-40 fs oscillator (Micra-5, Coherent, Inc.). A convenient phase delay can be

applied within the Synchrolock-AP controls providing ∼12 ns of continuous relative time delay

between the slave and the master pulse trains while maintaining a fixed phase/frequency

relationship between them. This continuous delay adjustment is maintained in the amplified

pulses and becomes extremely useful when finding the temporal overlap between the two

amplified pulses and changing SFG experimental geometries 278.

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4.3.2 Design of SFG-VS geometries to observe cellulose surface layers

In particular, the Avicel or cellulose Iβ particles are placed under a equilateral CaF2 triangle-

prism to achieve the total internal reflection for both the IR and Visible laser lights. The

evanescent waves at the surface can only penetrate the depth of λ/2 of the incident light, e.g.

roughly <250nm, and thus the TIR-SFG-VS only probes the very top surface layers of the

cellulose materials, without being overwhelmed by the SFG signal from the crystalline bulk.

This is completely different from previous TIR studies for the surface materials (non cellulose

crystalline materials) and SFG characterization for the bulk crystalline cellulose. Instead, our non

TIR and TIR-SFG-VS are built to characterize the surface and bulk cellulose separately for the

first time. The TIR-SFG-VS study of cellulose surface is investigated by examining both C-H

and O-H stretching regions of cellulose vibrations. The study is the first time to observe cellulose

surface molecular structure, which facilitates understandings of critical cellulose surface

molecular information that could not be achieved by conventional characterization methods.

Figure 4.1 a) TIR-SFG-VS design on cellulose surface observation and b) non TIR-SFG-VS on

93

cellulose bulk characterization. IR and VIS refers to infrared and visible 532nm beams,

respectively. c) total internal reflectance concept; d) simulation of SFG intensity with the change

of TIR and non TIR SFG-VS geometry, in which b) refers to non TIR SFG-VS while TIR

represents TIR-SFG-VS

Two SFG designs and their mechanisms were displayed in Figure 4.1a,b,c,d. To observe

cellulose surface layers, TIR-SFG-VS is equipped with a CaF2 material based prism (equilateral

triangular prism) to specifically monitor the cellulose surface information without bulk cellulose

interference (Figure 4.1a). To achieve TIR geometry, the incident angles should be above the

critical angle on prism surface which resulted in none transmission lights through the sample.

Meanwhile, evanescent wave was formed which allowed the characterization of the sample

surface layers up to λ/2 thickness (<250 nm in our study) 270-272. The thickness of the cellulose

samples being probed by TIR-SFG-VS can be approximately estimated by comparing the

intensity difference between bulk and surface spectra if the linear correspondence of sample

thickness with SFG-VS signal intensity is assumed regardless of detailed structural variance and

the increasing magnitude order of TIR on surface signals.

The direct contact of visible and IR beams was widely used and showed great advantages of

SFG-VS in detecting cellulose crystalline structure selectively 85, 171, 196, 268, 279 similar to Figure

4.1b where the bulk cellulose crystalline is prominent in the SFG signals. The bulk cellulose

crystalline structural information can be monitored due to both reflection and transmission lights.

Also, the SFG signal from the surface structure of cellulose is overwhelmed by the bulk signals,

and thus the structural difference between the surface and bulk cellulose remains largely unclear.

According to TIR-SFG-VS, the TIR set-up should enhance surface SFG signals to more than 100

94

times by SFG-VS intensity simulation (100 vs 0.8; Figure 4.1d). Detailed simulation method is

described in supplemental materials (Calculation of the Fresnel factor in the SFG measurement).

Therefore, if SFG-VS signals of samples from prism (Figure 4.1a,c) are below or more than 0-

100 times of the signals from flat window (Figure 4.1b), the surface and bulk are different.

4.3.3 The fluid heating cell system development

The fluid heating cell system was applied in the study of cellulose thermal behaviors and

interactions with solvents in aqueous pretreatment. Several other reactor systems were reported

to study cellulose heating process such as 50ml micro reactor using oven and oil bath as heating

source 251. In our study, we used the reactor designed with an accurate temperature monitoring

and short heating time (Figure 4.2). The cell was connected to three thermal heaters, a thermal

couple (connected to the little hole above three heaters) and a temperature controller (OMEGA).

Besides, the cell is connected to a nitrogen purging device and a pressure regulation system with

a back pressure regulator. The temperature applied to the cell is limited within ambient-300C

while the pressure performed should be ambient-500 psi. The flow rate limite for gas is 0-6

mL/min and liquid is 0.001-5 mL/min.

Figure 4.2 Cellulose heating high temperature fluid cell systems

The dry/wetted samples were filled up the cell sample holder under the prism or CaF2

95

window. SFG-VS system was well warmed up and sample signals were well normalized to the

same voltage (1000 PMT) otherwise indicated at the similar visible (120 µJ) and infrared (200

µJ) beam energy inputs. The incident angles of visible and infrared lights applied were 65° and

55° respectively otherwise notified. The sample covers were cylindrical CaF2 window (Red

Optronics; WIN-3104; Diameter: 25.4 mm; Thickness: 3.0mm) and CaF2 based prism (60°

triangular).

4.3.4 Cellulose sample preparation and heating program

The cellulose samples used in the study were Avicel and cellulose Iβ. Avicel (11365

SIGMA-ALDRICH Avicel PH-101) with ~50 μm particle size was purchased from Sigma

Aldrich. The cellulose Iβ was provided by National Renewable Energy Laboratory. It was

prepared according to the reported method (Tunicate (Halocynthiaroretzi) cellulose 217) and the

same sample used in our previous study 28. Avicel PH-101 (11365) was purchased from Fluka

BioChemika (Ireland). The wetted Avicel paste was prepared by adding 200ul DI water with

80mg Avicel powder. The dry or wetted samples were filled out in the cell sample holder and

covered with the prism or CaF2 window. SFG-VS system was well warmed up and sample

signals were well normalized to the same voltage (1000 PMT) at the similar visible (120 µJ) and

infrared (200 µJ) beam energy inputs. The incident angles of visible and infrared lights applied

were 65° and 55° respectively. All spectra were normalized to the same voltages and laser

intensities displayed in the same spectrum. The spectra were fitted by Lorentz profile convoluted

with a Gaussian intensity distribution method.

The thermal heating of Avicel is programed to higher temperatures continuously and

monitored by SFG-VS in the fixed time intervals at each temperature. The cellulose heating

96

process was programed with heating rate 1°C per 5s and stayed at 65, 100, 130, 175, 200, 220,

240, 260 and 280°C for 2min before taking SFG-VS spectra. The cool-down of heating cellulose

was performed under room temperature with the heating device off and temperature monitor on.

4.3.5 Molecular observation of cellulose structural changes in enzymatic hydrolysis using

SFG-VS

The restart samples used in our study were referring to the interrupted samples from

enzymatic hydrolysis followed by well cleaning and drying of cellulose to remove enzymes in

each sample 280. After withdrawing some amount clean cellulose sample for analysis, the left

well-washed samples were proceeding to enzymatic hydrolysis by adding in new enzymes and

others. The restart samples were used in the study to study the cellulose structural changes in

enzymatic hydrolysis. The other materials used in the uninterrupted batch hydrolysis were Avicel

PH101 and cellulase (Ctec 2 from Novozymes). The uninterrupted batch hydrolysis operation

was carried out at 2% w/v cellulose and enzyme loadings 60 FPU/g cellulose (65 mg protein

Ctec/g Avicel) with 50 mL of 50 mM acetate buffer (pH 4.8) in 50 °C incubator with a rotation

speed of 160 rpm. The pre-incubation of the slurries to the desired temperature was performed

for 15 min before adding enzymes. 10µl aliquots was withdrawn to a flat window after

enzymatic hydrolysis for 0-120 h. SFG-VS was used to probe each aliquot along with the

measurement of the Avicel conversion yields.

97


4.4.1 Structural characterization of cellulose surface

Figure 4.3 SFG-VS spectra and peak fittings via Lorentz profile convoluted with a Gaussian

intensity distribution method of Avicel surface layers (red color) and bulk crystals (blue color)

within wavelength of (a) 2800 to 3000 cm-1 and (b) 3000 to 3750 cm-1; Dots represented

experimental data while lines represented curve fittings. All intensities were calibrated at the

same voltage (1000 v) for the SFG-VS measurements

Conventionally as discussed, TIR-SFG-VS was used to enhance the SFG signal (up to >100

times) in non-crystalline materials 269, 276. However, the signal of Avicel using TIR-SFG-VS was

98

not increased at all compared to that in non-TIR-SFG-VS, and in contrast, the signal was reduced

more than 10 times (Figure 4.3). The whole spectra comparison presenting a scope of Avicel

surface layers and bulks were shown in Figure S4.1. Interestingly, the spectra of Avicel surface

layers varied from different batches of Avicel since the surface was non-uniformly arranged.

Thus, the spectrum of surface layers above is a representative of many spectra of Avicel surface

layers which can present common peak characteristics of Avicel layers (other spectra shown in

Figure S4.2). Figure 4.3 showed our TIR-SFG-VS signals of Avicel were different from non-

TIR-SFG-VS signals. This was because the traditional TIR-SFG-VS in other non-crystalline

materials characterization only focus on the surface signal regardless of the bulk, and the purpose

of TIR setting was to enhance the surface signal of the non-crystalline materials. However, the

particle size (~50µm diameter) of Avicel (a crystalline material) in our study was much bigger in

diameter than the wavelength. Thus, TIR-SFG-VS signals of Avicel was prominent from Avicel

surface layers. If 100 times of intensity increasing of surface signals was assumed by TIR setting

like in conventionals non-crystalline material characterization, it was estimated that only ~50nm

thickness of the surface layers of Avicel were characterized in our study according to 10 times

intensity difference (Figure 4.3a,b). Beyond doubt, the TIR-SFG-VS provided an approach to

selectively probe the surface layers versus the crystalline bulk. The SFG signal of Avicel from

the TIR-SFG-VS setting was greatly dominant by Avicel surface structure while the non TIR-

SFG-VS could dominantly detect the bulk crystalline structures. Non TIR and TIR-SFG-VS

provide crucial tools to systematically study Avicel structures and its changes in various

processes, such as cellulose surface changes and bulk crystalline disruptions.

The TIR-SFG-VS spectrum of Avicel was significantly different from bulk crystalline

99

spectrum, which indicated the structural difference between Avicel surface and bulk crystals

(Figure 4.3). The reduction of SFG signal intensity of Avicel surface compared to bulk

crystalline structure signal by 10 times suggested that Avicel surface was not as crystalline as

bulk Avicel but the surface was still crystalline since it has signals in SFG-VS. Thus, the results

demonstrated layers of less ordered or less crystalline cellulose distributed on Avicel surface

which surrounded cellulose core crystalline structure. The cellulose microfibrils assembling is

still controversial and several models were proposed. For example, microfibrils were reported to

be consist of bands of crystalline cellulose which was surrounded by amorphous cellulose 281.

But the fringed micellar hypothesis suggests that both crystalline and amorphous areas appeared

at any one cross section of the microfibrils alternatively 237, 282. In addition, the folded-chain

structure demonstrated that microfibrils were helical along the fibril axis, and the model

indicated the distortion or twisted of cellulose chain exists as less crystalline areas without

amorphous areas 282, 283. Thus, our results tended to support 'the surrounding hypothesis'.

The TIR-SFG-VS spectra of Avicel surface under two different polarization settings

indicated slightly but noticeable polarization dependence (Figure S4.3a). Bulk Avicel crystalline

structure was reported to be complete polarization independence 268. The difference in

polarization dependence of Avicel surface and bulk crystalline region also demonstrated that

Avicel surface was not as crystalline as bulk Avicel. Avicel surface was composed of less

crystalline cellulose which surrounded around bulk crystalline structure. Since Avicel surface

showed same peak distribution with the bulk in SFG-VS spectra, those less crystalline cellulose

on surface was probably because of the disordered arrangement of cellulose chain at surfaces.

The disordered arrangement of crystalline cellulose surface referred to the less organized or

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packed cellulose microfibrils than the bulk orderly and linearly arranged crystalline structures,

which caused different molecular orientations (C-H and O-H in Figure S4.4). This disorder might

be because of the reported twisted cellulose fibrils, paracrystalline celluose or simply randomly

distributed cellulose chains.

In addition, the comparison of non TIR and TIR-SFG-VS spectra of Avicel can help resolve

some mysteries of peak assignments. The curve fitting via Lorentz profile convoluted with a

Gaussian intensity distribution method provides theoretical peak position, amplitude and width

(Figure 4.3; Table S4.1a,b). Avicel surface spectra of TIR-SFG-VS showed peaks at 2872, 2937,

2968, 3494, and 3696 cm-1. Avicel surface and bulk crystalline spectra showed similar peak

positions at ~2857, ~2948, and ~2964 cm-1 but completely different at ~3328, ~3467, ~3508 and

~3700 cm-1. Although Avicel surface and bulk crystalline spectra shared some similar peaks in C-

H vibirational region, the peak intensities and width were differed greatly. All those difference

revealed significant structural difference between Avicel surface and bulk crystalline. The peak

assignments of C-H region are challenging because of Fermi resonance effects. The peaks at

~2857 and ~2948 were assigned to be -CH2 symmetric stretching by Fermi resonance effects 284,

285. Also, the peaks at ~2857 and ~2948 cm-1 in previous Avicel crystalline structure study were

assigned to the -CH2 symmetric and asymmetric vibrations, of the exocyclic -6CH2OH group,

respectively 85. Our results suggested these peak assignments should be revisited. If both 2857

and 2948 cm-1 were from same C-H stretching of -6CH2OH group, these two peak relative ratios

in Avicel surface and bulk crystalline spectra should be the same. However, our results showed

significant difference of peak relative intensity at 2857 and 2948 cm-1 of Avicel surface and bulk

crystal spectra, which firmly suggested that these peaks were from different C-H vibrational

101

sources. Since in glucose unit, C-H vibration from carbon ring is more abundant than in side

chain -6CH2OH. The carbon ring was usually believed in ordered and linear distribution 286.

Given in bulk crystalline spectra, peak at 2948 cm-1 was a lot higher than 2857 cm-1, 2948 cm-1

was more likely to originate from C-H stretching of carbon rings while 2857 cm-1 was from C-H

vibration of the exocyclic -6CH2OH group (Figure S4.4). Our peak assignment at ~2948 cm-1

also agreed with some IR or VCD studies of glucose indicated that ~2948 cm-1 was likely to be

C-H vibration mode from glucose carbon ring 227, 287. Additionally, due to the higher SFG peak

intensity of 2857 cm-1 compared to 2948 cm-1 on Avicel surface, -6CH2OH groups showed more

crystalline signals than glucose carbon ring on surface layers. This indicated that glucose carbon

ring might not well oriented on the surface. Thus, the arrangement of glucose units on Avicel

surface was less crystalline or less ordered distribution than the bulk, which was consistent with

previous discussion. In O-H vibrational region (Figure 4.3b), the peak at 3328 cm-1 could not be

seen in the TIR-SFG-VS spectra of Avicel surface, however, it was a major peak in Avicel bulk

crystalline structure. Peak at 3328 cm-1 was usually assigned to intermolecular O-H vibration

such as O(6)H…O(3) 81, 288. Thus, Avicel bulk contained strong intermolecular hydrogen bonding

while Avicel surface was not, which also agreed with previously less crystalline on surface

discussed. The intramolecular hydrogen bonding and free O-H appeared more likely in high

frequency of wavenumbers 81, 288-290. Figure 4.3b showed the prevalence of intramolecular

hydrogen bonding in low intensity and free O-H on Avicel surface. This also endorsed that Avicel

surface layers were less crystalline than the bulk crystals.

Avicel is mainly composed of Iβ. To test our concept of characterizing cellulose surface

layers and bulks separately, Iβ from tunicate (Halocynthiaroretzi species) was probed by TIR-

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SFG-VS and Non TIR-SFG-VS. Detailed peak fitting parameters were shown in Table S4.1c,d.

The non TIR-SFG-VS spectra of Iβ showed common peaks with TIR-SFG-VS spectra at ~2919,

~2947, ~2970, ~3263, and ~3372 cm-1, which indicated the similarities of Iβ surface and bulk

structures. However, the intensities of these peaks in Iβ surface were about two times more than

Iβ bulk, which indicated that Iβ surface glucan chains presented more ordered orientation than

Avicel surface versus Avicel bulk. Although the peak intensities of Iβ surface were two times

more than Iβ bulk, Iβ surface glucan chains were still less ordered in orientation or crystalline

than Iβ bulk since TIR was supposed to enhance the signals for more than 100 times in surface if

the surface and bulk were uniformed or crystalline at the same level (Figure 4.1d). The results

were consistent with previous conclusion of Avicel on the significant difference of cellulose

surface layers and bulks characterized by Non TIR and TIR-SFG-VS. The difference of relative

peak ratios of these common peaks in Iβ surface and bulk can predict different molecular

orientations on surface and bulk. The peak ratios of ~2947 cm-1 with other peaks were greatly

differed in Iβ surface and bulk. The peak at ~2947 cm-1 was assigned to C-H vibrations from

carbon ring as discussed. The peak ratios difference in the surface and bulk indicated

significantly different C-H orientations in Iβ surface and bulk. These different C-H orientations

on the cellulose surface probably were caused by disordered arrangements of glucan chain,

which confirmed previous discussion. The peak of Iβ surface at 2864 cm-1 was right-shifted

compared with the peak in Iβ bulk. This peak was assigned to C-H vibrations from -6CH2OH

group. The difference of Iβ surface and bulk in peak from -6CH2OH groups indicated the

different –CH2 orientations in Iβ surface and bulk. The peaks at ~2919 and ~2970 cm-1 were

proposed to be C-H vibrations from different carbons on carbon ring of glucose units in

103

cellulose, which was difficult to be distinguished exact carbon locations from each other. Their

different peak ratios with peak at ~2947 cm-1 in Avicel bulk and surface were attributed to the

complex disordered arrangement of carbon ring, which cause peak cancellation. Compared to

Avicel C-H vibrational region, Iβ surface and bulk demonstrated more peaks in C-H region. The

difference of Avicel and Iβ in these peaks might be because of peak cancellation of the

composition of Iα in Avicel. Instead of polarization independence of Iβ bulk crystalline as

reported 268, Iβ showed obvious polarization dependence (Figure S4.3b), which also confirmed

the difference of Iβ surface and bulk.

Figure 4.4 SFG-VS spectra and peak fittings via Lorentz profile convoluted with a Gaussian

intensity distribution method of Iβ surface (red color) and bulk (blue color) within wavelength of

104

(a) 2800 to 3000 cm-1 and (b) 3000 to 3750 cm-1; Dots represented experimental data while lines

represented curve fittings. All intensities were calibrated at the same voltage (700 v) for the SFG-

VS measurements

In O-H vibration region, Iβ bulk showed peak at 3322cm-1 while Iβ surface shifted right to

3337cm-1. 3322cm-1 was assigned to intermolecular hydrogen bonding while 3337cm-1 was

attributed to intramolecular hydrogen bonding such as O(3)H…(O)5 81, 288. This shift was

consistent with previous Avicel surface hydrogen bonding that surface was distributed

intramolecular hydrogen bonding while lack of intermolecular bonding. 3322cm-1 was assigned

to intra-chain/molecular hydrogen bonding while peaks at less than 3300 cm-1 were more likely

to be intermolecular hydrogen bonding 291. The difference implied the difference of hydrogen

bonding network in surface and bulk of cellulose Iβ.

In summary, the surface layers of Avicel and Iβ are selectively probed by our developed

TIR-SFG-VS. The significant spectra difference between the surface and bulk domains of these

two cellulose particles indicated that Avicel and Iβ surfaces were less crystalline in comparison

to their bulk crystals. However, Iβ was more uniformed than Avicel on surface and bulk and thus

the surface of Iβ showed more crystalline or orderly oriented than Avicel surface layers. The

peak assignments of the surface layers and the crystalline cores of the two cellulose suggested

that the main peak around 2948cm-1 was likely to originate from C-H vibration on the carbon

ring of the glucose units instead of reported from side chain –6CH2OH mode while peak at

~2860cm-1 was from C-H vibration from side chain –6CH2OH. The difference of C-H vibrational

regions in Avicel surface layers and bulks suggested the different –CH and –CH2 molecular

orientations which might be attributed to the disordered arrangments of cellulose surface

105

microfibrils. Also, the O-H vibrational region observation indicated cellulose crystalline surface

layers mainly distribted intramolecular hydrogen bonding and free OH, while the bulk contained

mainly intermolecular hydrogen bonding. These valuable information helps the understanding of

cellulose bulk and surface, which contributes to future cellulose transformation studies. Non TIR

and TIR-SFG-VS can allow performing in situ observation of chemical and enzyme degradation

experiments on the cellulose surface and bulk, separately.

4.4.2 The characterization of thermal behaviors of dry cellulose by SFG-VS

The effects of high temperature on cellulose stability during biomass aqueous

pretreatment are important to help improve sugar yields. In our study, SFG-VS was used to

observe the molecular structural changes of cellulose crystalline bulks and surface layers in

dynamic heating program from room temperature to 280°C.

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Room temperature (RT) fit_RT 65°C fit_65°C 100°C fit_100°C 130°C fit_130°C 175°C fit_175°C 200°C 220°C

106

Samples R (C-H peaks area/ O-H peak area)

Original Avicel 0.73

65C 0.69

100C 0.60

130C 0.53

175C 0.55

200C N/A

Figure 4.5 Cellulose crystalline structural changes under different temperatures during heating

process

The molecular structural characterization of the dynamic heating process of cellulose

(Avicel) is for the first time reported in this study. As the increasing of temperature, all peak

intensities at ~2850, ~2945cm-1 and ~3310 cm-1 decreased suggesting the disruption of cellulose

crystalline structures (Figure 4.5). In the previous discussion, peaks at ~2945 cm-1 and ~2850 cm-

1 were from C-H vibration on cellulose carbon rings and side chain -6CH2OH, respectively. At

temperature >175°C, all peaks disappeared indicating the complete disruption of cellulose

crystals. It was reported that cellulose Iα and Iβ can be converted to a I-HI 240, 241. However, the

molecular structure of this I-HI is unknown. Our results showed that with the dynamic increasing

of heating temperatures, this metastable intermediate was less and less crystalline suggested by

the peak decreasing in Figure 4.5. When reaching 280C, the crystalline features of this high

temperature intermediates were gone. Besides, the mild hydrolysis and dehydration of cellulose

might occurred in this thermal disruption on dry cellulose 292. Interestingly, peak at ~3310 cm-1

was shifted to a higher wavenumber along with the increasing of heating temperatures. This

change of peak intensity and position at ~3310 cm-1 indicated the gradually break-down of

hydrogen bonding with the thermal effects as well as transformation of hydrogen bond. Thus, we

hypothesize that the heating temperatures induced the dislocation, random-arrangement and

disordering of cellulose Iα and Iβ microfibrils resulting in the gradually loss of crystalline

107

structure to form a complete amorphous high temperature metastable intermediates eventually.

More future work is still needed to confirm this hypothesis.

Additionally, the peak area ratios of C-H and O-H vibrations decreased with the increasing

of heating temperatures till close to the previously reported ratio signature of cellulose Iβ 28

(Figure 4.5). Thus, the resulting new cellulose intermediates at heating temperatures have some

similarities with cellulose Iβ before turning to amorphous. The Iα may be converted to Iβ besides

to the high temperature intermediates during the dynamic heating process to cause these changes

of the peak area ratios. It was proposed that the conversion of cellulose Iα to Iβ was through

converting cellulose Iα to the high temperature phase and then followed by the conversion of the

intermediates to cellulose Iβ 240. Our results indicated that the cellulose Iβ was aggressively

transformed to the metastable intermediates at > 175C. However, a more comprehensive

conclusion still needs more work, such as the test of cellulose Iα and Iβ individually in dynamic

heating process.

The heated Avicel samples were cooled down to room temperature and characterized by

SFG-VS (Figure 4.6). Surprisingly, Figure 4.6 presented the regaining of crystalline structures

after cellulose was cooled down to room temperature. When cellulose was heated to 220°C, all

peaks disappeared compared to the original spectra (not heated sample).

108

Figure 4.6 The recrystallization of dry cellulose polymer after cooling down from heating

process

After the sample was cooled down to room temperature for one hour, the peaks’ intensity

was recovered 2/3 in comparison with the original peak intensity. The observation of the sample

after cooling overnight at room temperature showed almost the same intensity levels with the

original spectrum. When observing the different spots of the sample, the spectra showed different

peak intensities and peak ratios between ~2945 and 3310cm-1 among them, suggesting that the

regained crystalline structure was heterogeneous. When compared the spectra of newly regained

crystals to the original Avicel, the crystals were not exactly back to the original state of

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Original sample at RT (before heating process) Heated to 220°C RT (cooling from 220°C for 1 hour) RT after cooling overnight RT after cooling overnight different sample spot



RT after cooling overnight 0.63

RT after cooling overnight 2nd spot 0.57

109

crystalline structure, for example, the peak area ratios of new crystals were 0.63 and 0.57

compared to the original 0.73. Also, the O-H region showed slightly shift to higher wavelength.

Significantly, the peak area ratios of new crystals were 0.63 and 0.57 which suggested that the

recrystallized crystals were showing more similarities to cellulose Iβ which indicated that the

transformation of cellulose Iα was irreversible. It was reported that after heated samples cooling

down to room temperature, the heated cellulose was transformed to cellulose Iβ 249. Our results

indicated that the new formed crystals were not exactly the same with Iβ. We hypothesize that

the cooling process of heated cellulose samples undergoes a cellulose recrystallization process

which refers to the rearrangement of dislocated/disordered cellulose microfibrils during heating.

The recrystallization process during cooling might be triggered by the tendency of cellulose

microfibrils to reach stable structure/microfibrils orientation. Cellulose crystals are high ordered

crystalline structure, which makes the recrystallization of disrupted microfibrils back

substantially but still presenting differences. This hypothesis should still be validated via many

experimental results and observations. More work is needed to study the differences of the

recrystallized crystals with the original cellulose crystals.

The recrystallization and the related annealing are the thermomechanical processing of

materials which are known to occur in all types of crystalline materials. The recrystallization has

been widely studied in crystalline metals or metallic materials 293. The recrystallization of most

crystals (grains) has been widely studied. It was reported that the recrystallization may occur

during or after deformation (during cooling or a subsequent heat treatment) driven by a

thermodynamic driving force to reduce the internal energy of the system 293. It is noteworthy to

mention that most of recrystallization of metal crystals is random on deformed crystals but the

110

cellulose crystalline materials were mostly recovered although they are still different from

originals (Figure 4.6). However, the disrupted cellulose microfibrils were still not a complete

recovery process which may be because of the irreversible cellulose Iα. The cellulose crystalline

structure is the highly ordered structure but its recrystallization of disrupted crystals is not well

defined and understood before, especially at the molecular structure level. The molecular

recrystallization phenomenon of cellulose is not seen reported, although some studies might be

trying to describe the phenomena, such as the existence of cellulose Iα or Iβ after cooling down

heated cellulose Iα and Iβ 249, and the returning back of XRD reflections of cooled cellulose to

the originals 239. Our SFG-VS can study detailed molecular structural changes during the

recrystallization process of crystalline bulks and surface layers. However, more future work

should be carried out to confirm the hypothesis and provide more details.

4.4.3 Structural changes of wet cellulose during heating process

Water plays a significant role in biomass hydrolysis in aqueous pretreatment. However,

the fundamental studies of the impact of moist on cellulose crystalline changes during dynamic

heating are little, probably, due to the limitation of analytical tools. The wet cellulose crystalline

structural change during dynamic heating process was studied by SFG-VS (Figure 4.7). As the

increase of heating temperatures, similar to the dry cellulose dynamic heating process, the peak

intensities at ~2850, and 2950 cm-1 decreased. All peaks disappeared when the temperature

reached 170°C which was lower than the transformation temperature of dry cellulose. This might

be because of the assistance of water in the disruption of cellulose crystalline structures by

hydrolysis. Also, this was consistent that a previous report that high moist cellulose was easier to

decrystalline than oven-dry cellulose 239.

111

Figure 4.7 Cellulose crystalline structural changes of wet Avicel heating process

Similar to the recrystallization of heat-disrupted dry cellulose, when the sample was

cooled down to room temperature, the peaks 2850 and 2950 cm-1 went back to the similar

lineshapes of original cellulose peaks (Figure 4.7). However, the water has great impact on the

SFG characterization which will be discussed in next few sections. But the peak intensity at

~3310cm-1 increased more than three times. Thus, this recrystallized cellulose structure was

different from the original crystalline structure. This indicated that the recrystallization process

of wet cellulose occurred at room temperature after it was cooled down from high heating similar

to dry cellulose recrystallization process discussed above. In the study of the transformation of

native cellulose crystals with saturated steam at high temperatures, it was reported that both

cellulose Iα and Iβ were transformed into a cellulose Iα' or Iβ after cooling down 249. This Iα' or

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Room temperature (RT) fit_RT 65°C fit_65°C 100°C fit_100°C 135°C 170°C Cool down to RT

112

Iβ might be originated from the recrystallization of disrupted cellulose crystals indicated in our

study. Our study provides an alternative explanation of the formation of crystalline structure Iα'

or Iβ in the cooling process of heat-disrupted cellulose crystals. Nevertheless, the

recrystallization of disrupted cellulose crystals is a significant consideration in designing

effective aqueous pretreatment techniques.

Figure 4.8 Recrystallization process during cooling process of wet Avicel

The recrystallization of heated wet cellulose observed was similar to the recrystallization of

heated dry cellulose. Figure 4.8 dynamically monitored recrystallization process during the

cooling process from high heating temperature of 280°C to room temperature. As the

temperature slowly decreased, the peak intensities increased back to almost same level as

original cellulose peak intensities. The O-H vibrational peaks were returning back fast suggesting

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Original sample at RT (Before heating process) At 280°C 175 to125°C 125 to 90°C 90 to 70°C 57 to 45°C 32 to 30°C RT after cooling

113

that the hydrogen bonding was easier to be rearranged with water presence. These new crystals

by recrystallization during cooling process showed differed peak intensity ratios with originals

which was probably attributed to different molecular structure and orientation.

4.4.4 The different SFG-VS signals of dry and wet Avicel bulk crystals

Figure 4.9 Impact of water on SFG-VS characterization of Avicel

The peak intensity of wet cellulose by SFG-VS was 140 times less than dry cellulose

peak intensity (Figure 4.9). This was because the water can absorb IR and VIS lasers which

reduced the intensity of peaks. Also the peak intensity ratio between peak ~2945 and ~3310cm-1

was different in dry and wet Avicel bulks. The peak ratio/area ratio of peak 2945cm-1 and

3310cm-1 (-OH) in dry Avicel spectrum was smaller than in wet spectrum. This also might be

because the impact of water on the hydrogen bonding of cellulose, which can be an interesting

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Dry Wet

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story to study in the future.

4.4.5 Study of the temperature dependence of dry Avicel surface layers

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room temperature 65°C 100°C 130°C 175°C 200°C 220°C 240°C 280°C

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Original untreated At 280°C 280°C-121°C 120°C-70°C Room temperature Room temperature overnight

115

Figure 4.10 Temperature dependence of Avicel surface layers characterized by SFG-VS (up:

structural changes of dry Avicel surface layers during heating process; down: heated dry Avicel

surface layers cooling down to room temperature)

The molecular structural characterization of cellulose surface layers in a dynamic heating

process has not been reported before. With the increasing of temperatures, the peak intensities of

Avicel surface layers (at 2872, 2940cm-1) slightly increased until 175-200°C, which might be

because some cellulose amorphous gained more crystalline structure at elevated temperature 294.

When temperatures were higher than 200C, the cellulose surface layers were disrupted.

However, at 280C, the signals of the surface layers were still weakly present. Thus, the cellulose

surface layers need a higher temperature (280C) to completely disrupt surface layers of

cellulose than cellulose crystalline bulks (Figure 4.10). Unlike Avicel bulk crystals, the heat-

disrupted surface layers did not recrystallize during/after cooling down from high heat (Figure

4.10). This probably because the cellulose bulks possessed high stored energy of recrystallization

while not the amorphous surface layers although the surface layers present some ordered crystals

293. Since the interference of DI water on SFG-VS signals of cellulose surface layers, more future

work is still needed to analyze the results and will not be discussed in this thesis.

In aqueous pretreatment, the complete disruption of some ordered microfibrils in cellulose

surface layers requires temperature higher than 240C according to our observations. Our results

might explain some publications including our previous publications that the optimal

temperatures were higher than 240C in dissolving cellulose completely 59, 60, 243. Even the bulk

crystals can be disrupted under less than 200C, the recrystallization of these disrupted to

crystalline structures again may lead to the difficulties of aqueous pretreatment. More

116

experimental results or observation should be conducted to validate this hypothesis.

4.4.6 Molecular structural changes of cellulose in enzymatic hydrolysis using SFG-VS

Cellulose enzymatic hydrolysis is a solid-liquid interface surface reaction to hydrolyze

carbohydrate biopolymers, which is slower than direct enzyme and bulk cellulose reactions 295,

296. Thus, the rate of cellulose hydrolysis is controlled by several factors, including the cellulose

surface and structure after aqueous pretreatment, the assay cellulosic enzymes and the surface

interaction between them 296. Enzymatic hydrolysis technology requires a better understanding of

the fundamental mechanisms underlying the bio-interactions of enzymatic cellulose hydrolysis,

which includes the tracking of cellulose structural change after biomass aqueous pretreatment

and potential affinities with enzymes to facilitate disrupting crystallizing cellulose during

enzymatic hydrolysis. Further efforts should focus on the interaction of cellulase with biomass

substrates at molecular-level hydrolysis with the enhancement of cellulose decrystallization.

These goals rely on a reliable characterization tool that is highly sensitive to cellulose crystalline.

This tool will be helpful in characterizing aqueous pretreated cellulose structure and its

interaction with various enzymes.

The SFG-VS peak decay of the samples withdrawn from enzymatic hydrolysis was explored

in this study to correlate with cellulose enzymatic hydrolysis efficiency (Figure 4.11). The life

decay of O-H was about two times of life decay of C-H, which indicated that C-H vibrational

regions are important in studying enzymatic hydrolysis system. Figure A1 showed the SFG-VS

can study enzymatic hydrolysis without the concerns of peak interferences between enzymes and

cellulose. Here, the feasibility of the application of SFG-VS in characterizing enzymes and

cellulose together was proposed for the first time. This is important to study the molecular

117

structural changes of enzymes and cellulose and their interaction in enzymatic hydrolysis.

Figure 4.11 SFG-VS spectra of the decay of enzymatic hydrolysis to study enzymatic hydrolysis

kinetics

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1 h 0.77

2 h 0.83

3 h 0.94

4 h 0.92

13 h 0.93

20 h 1.07

Figure 4.12 SFG-VS characterization of Restart samples from interrupted enzymatic hydrolysis

The cellulose restart samples (preparation method explained in material section) from

enzymatic hydrolysis slurries were characerized by SFG-VS to determine the crystalline

structural changes. With the increasing of enzymatic hydrolysis time, the peak area ratios of

vibrational C-H and O-H regions increased from 0.70 to 1.07 (Figure 4.12). Compared to the R

SF

G inte

nsity (

a.u

.)

40003800360034003200300028002600

Wavenumbers (cm-1

)

Avicel restart samples (Flat Window)850 V; SSPVIS 45°; IR 56°

Avicel fit_Avicel 1 h fit_1 h 2 h fit_2 h 3 h fit_3 h 4 h fit_4 h 13 h fit_ 13 h 20 h fit_ 20 h

119

values of Avicel, cellulose Iα and cellulose Iβ our previous study reported 28, the cellulose

crystals were closer and closer to cellulose Iα with the time goes. Thus, cellulose Iβ might be

more reactive in enzymatic hydrolysis and left cellulose Iα behind. However, the enzymatic

hydrolysis rate of cellulose Ia was found higher than cellulose Ib due to the differences in crystal

structures 297, 298. This indicated that the statement that cellulose Iβ may have better reactivity

than cellulose Iα during enzymatic hydrolysis might be wrong. More experimental work is

needed to clarify this standpoint.

4.5 Conclusion

The selective characterization of the surface layers of Avicel and Iβ is achieved by our

developed TIR-SFG-VS for the first time. The significant spectra difference between the surface

layers and bulk domains of Avicel and Iβ particles indicated that the surface layers of these two

celluloses were less crystalline compared to their bulk crystals. However, Iβ was more uniformed

than Avicel on surface layers and bulks and thus the surface layers of Iβ showed more crystalline

or orderly oriented than Avicel surface layers. The peak assignments of the surface layers and the

crystalline bulks of the two celluloses suggested that the main peak around 2948cm-1 was likely

to originate from C-H vibration on the carbon ring of the glucose units instead of reported from

side chain –6CH2OH mode while peak at ~2860cm-1 was from C-H vibration from side chain –

6CH2OH. The difference of C-H vibrational regions in Avicel surface and bulk suggested the

different –CH and –CH2 molecular orientations which might be attributed to disordered

arrangments of cellulose surface microfibrils. Also, the O-H vibrational region observation

indicated cellulose crystalline surface mainly distributed intramolecular hydrogen bonding and

free O-H, while the bulks contained mainly intermolecular hydrogen bonding. These valuable

120

information helps the understanding of cellulose bulk and surface, which contributes to future

cellulose transformation studies. Non TIR and TIR-SFG-VS can allow performing in situ

chemical and enzyme degradation experiments on the cellulose surface layers and bulks,

separately.

The TIR-SFG-VS enabled the molecular structural characterization of cellulose surface

layers for the first time, which is important for the study of cellulose behaviors in aqueous

pretreatment and enzymatic hydrolysis. In this chapter, a fluid cell was applied to mimic aqueous

pretreatment so that it allows the study of the molecular structural changes of the surface layers

and bulks of cellulose in a dynamic heating program with/without water. Both wet and dry

cellulose crystalline bulks were disrupted with the increasing of temperatures but wet cellulose

bulks were degraded faster. It was proposed that the microfibrils of cellulose Iα and Iβ were

possibly dislocated/disrupted/random-oriented by heating to form an microfibril-disordered

metastable intermediate during dynamic heating process and cellulose Iα may be converted to Iβ

besides converted to the intermediate which is not reversible. Based on the results of thermal

stability of heat-disrupted cellulose crystals during and after cool-down to room temperature, a

hypothesis of a possible cellulose recrystallization was proposed that the rearrangments of

dislocated/disrupted cellulose microfibrils possibly occured and were possibly drived by the

tendancy of disrupted cellulose microfibrils to a stable structure. The recrystallization was not a

complete recovery process which resulted in new rearranged crystals. These crystals were similar

to the orignal crystals but different in the orientation of microfibrils, probably, due to the

orignally highly ordered crystalline structure of cellulose. Due to the amorphous of cellulose

surface layers, some ordered crystals in surfaca layers were not recrystallined during/after

121

cooling to room temperature. This hypothesis still needs more future work to get stronger

evidences besides this chapter indicated, such as the structural characterization of newly

recrystallized crystals by HR-BB-SFG-VS and other tools, weight and wet chemistry analysis of

heated cellulose, dynamic heating test on cellulose Iα and Iβ, and applying other analytical tools

to test the hypothesis. This work is helpful in designing an effective pretreatment technology and

other thermal processing of cellulose. At last, our SFG-VS study on enzymatic hydrolysis system

suggested that SFG-VS can be possibly developed to study cellulose crystalline structure

degradation during enzymatic hydrolysis, such as the interaction of enzymes and cellulose

without separating them. Results indicated that C-H structural changes are important in

enzymatic hydrolysis since the decay of C-H was two times slower than O-H vibrational peaks.

Thus, the application of SFG-VS in the study of fundamental issues of aqueous pretreatment and

enzymatic hydrolysis is important to gain new insights of cellulose transformation and properties

as a disordered crystalline material.


The author acknowledges the support of Bioproducts, Sciences and Engineering Laboratory,

Department of Biosystems Engineering at Washington State University. The thesis author was

partially supported by the grant from the Chinese Scholarship Council (CSC). The author thanks

Dr. Shi-you Ding for insightful discussions and providing one of the celluose samples at the

BioEnergy Science Center, a DOE Bioenergy Research Center, and the Genomic Science

Program (ER65258), both supported by the Office of Biological and Environmental Research in

the DOE Office of Science. Part of this work was conducted at the William R. Wiley


122


Energy’s Office of Biological and Environmental Research (BER). The author acknowleges

Zizwe Chase from Washington State University for his help in completing some of the

experiments. The author would like to present appreciations to Drs. Hongfei Wang and Bin Yang

for providing great platforms and have insightful discussions with the author. The author of the

thesis would like to thank Drs. Li Fu, Shunli Chen, HongFei Wang, and Bin Yang for

contributing to this work and helping in preparing this work for submission to a journal.

4.7 Supplementary materials

Table S4.1 Peak positions, amplitudes, and widths after curve fittings of SFG-VS spectra via

Lorentz profile convoluted with a Gaussian intensity distribution method in (a) TIR-SFG-VS

spectra of Avicel surface layers, (b) SFG-VS sepctra of Avicel bulks, (c) TIR-SFG-VS spectra of

cellulose Iβ surface layers, and (d) SFG-VS spectra of cellulose Iβ bulks within the wavelength

of 2800 to 3750 cm-1

(a)

position/(cm-1) amplitude/(a.u) width/(cm)

2872 ± 2.9 3.3 ± 0.2 49 ± 2

2940 ± 1.3 0.3 ± 0.1 16 ± 3

2965 ± 0.8 -0.1± 0.03 5.6 ± 1.4

3508 ± 3.7 0.3 ± 0.06 22 ± 2

3700 ± 3.4 0.2 ± 0.05 17 ± 2

(b)


2857 ± 2 1.8 ± 0.2 38 ± 3

2948 ± 0.2 2.8 ± 0.1 13.4 ± 0.4

2964 ± 0.5 0.49 ± 0.1 9.0 ± 1.2

3328 ± 1.3 3.4 ± 0.3 41.0 ± 2

3467±7 7.3±1.1 143 ±19

3686 ± 3 4.2 ± 0.0.5 76 ± 6

(c)


2864 ± 1.2 0.75 ± 0.06 23.2 ± 2

2919±1.2 0.19±0.05 10.9±3

123

2947 ± 0.3 1.2 ± 0.07 15.2 ± 0.7

2968 ± 0.2 0.23± 0.02 6 ± 0.4

3263±1.6 -2.55±0.1 37±1.7

3337±0.6 3.75±0.2 34±1.1

3375 ± 2 0.13 ± 0.07 14 ± 5

(d)


2851 ± 1 1.0 ± 0.04 34.1 ± 1.5

2919±1 0.27±0.04 14.4±1.9

2947 ± 0.5 0.28 ± 0.04 11.9 ± 1.3

2970 ± 0.1 0.37± 0.01 7.3 ± 0.2

3260 ± 1.6 -1.1 ± 0.06 26.7 ± 1.5

3322±0.8 1.63±0.05 30.4±0.8

3372 ± 1.1 0.04 ± 0.01 6.0 ± 1.9

124

Figure S4.1 Whole spectra of Avicel and Iβ bulks and surface layers a) Avicel; b) Iβ (black:

blank control; blue: bulks; red: surface layers)

125

Figure S4.2 Demonstrations of various molecular structures of Avicel surface layers and

relatively stable Iβ structure

126

Figure S4.3 TIR-SFG-VS spectra of (a) Avicel and (b) cellulose Iβ under two polarizations, (red

color) SSP and (Blue color) PPP; s-, s-, p- polarization corresponding to the polarization of the

optical fields of the SFG signal, visible and IR beams, respectively. p polarization is defined

when electric field vector parallel to the plane of incidence within the incident plane formed by

the incident beam direction and the surface normal, and s polarization is perpendicular to the

incident plane

127

Figure S4.4 Cellulose structure (two glucose units) and different molecular vibrations (cellulose

adpated from 286 )

Figure S4.5 Heat disruption and recrystallization hypothesis (Rollett, A., et al. (2004).

Recrystallization and related annealing phenomena, Elsevier.)

128

Calculation of the Fresnel factor in the SFG measurement

In our experiment, we used ssp polarization setting (s-polarized SFG, s-polarized visible and

p-polarized IR) for SFG measurement, with the effective second order susceptibility as

(2)

ssp ( ) ( ) ( )sinyy SFG yy Vis yy IR IR yyzL L L ,

where ( ) ( ) and ( )yy SFG yy Vis yy IRL L L ，，

are Fresnel factors, and IR is the incident anlge of IR

beam. The Fresnel factors can be calculated as follows:

1

1 2

2 ( )cos( )

( )cos ( )cos

SFG SFGyy SFG

SFG SFG SFG SFG

nL

n n

1

1 2

2 ( )cos( )

( )cos ( )cos

Vis Visyy Vis

Vis Vis Vis Vis

nL

n n

2

2 1

1 2

2 ( )cos ( )( )

( ) cos ( )cos '( )

IR IR IRyy IR

IR IR IR IR IR

n nL

n n n

in which i is the refractive angle from medium 1 into medium 2 defined by

1 2( )sin ( )sini i i in n . '( )IRn is the effective refractive index parameter of the interface layer,

whose definition and physical meaning was elucidated by Shen et al.

All the angles has been calculated and incorporated into the Figure 4.1, with the simulated

SFG intensity plotted in Figure 4.1d. In particular, the relative ratio of the SFG intensity of the

two geometries used in our experiments can be determined. Under the TIR reflection geometry

using the prism (Figure 4.1a), the SFG intensity from the interface should be ~100 times of that

from the geometry used in Figure 4.1b.

129

CHAPTER FIVE

CHARACTERIZATION OF FLOWTHROUGH SOFTWOOD AQUEOUS

PRETREATMENT UNDER NEUTRAL AND ALKALINE PH AT

ELEVATED TEMPERATURES

5.1 Abstract

To well understand the fundamentals of aqueous pretreatment of softwood, the flowthrough

reactor was used and run pretreatment at 200-270 °C and 6.4-12.0 pH for 2-10 min with the flow

rate of 25 mL/min. Significantly, the starting pHs of pretreatment media have great impact on

pretreatment kinetics and chemistry. It was found that the initial pretreatment pHs decreased

from 6.4-9.0 to about 4.0 after pretreatment, which led to the acid hydrolysis kinetics of biomass.

However, high initial pHs (around 11.0) showed not significant changes of pHs after the

pretreatment, which resulted in the moderate alkaline pretreatment kinetics. When the starting

pH was ~12, most of the carbohydrates underwent severe alkaline degradation reactions resulting

in the formation of organic acids with the substantial loss of sugars. This makes the analysis of

the mixtures of products difficult. Moreover, 2-D 1H-13C NMR analysis of pretreatment

recovered insoluble lignin revealed that the cleavage of C-O-C linkages in lignin was the main

alkaline depolymerization mechanism. Thus, the hot water flowthrough pretreatment kinetics and

chemistry by controlling initial pretreatment pHs can add more fundamental understandings of

aqueous pretreatment and help make wise pretreatment designs.

Keywords: Softwood • Flowthrough pretreatment • Neutral and Alkaline • pH • Lignin

5.2 Introduction

Cellulosic biomass pretreatment is the key step to overcome the natural recalcitrance of

130

biomass in biofuel production. Softwood is one of the main types of woody biomass for biofuel

production and is well distributed in western United States. However, several leading

pretreatment methods were proposed and intensively studied on biomass such as poplar, corn

stover and switchgrass but not softwood 31. Softwood is more challenging to pretreat using the

leading pretreatment methods than hardwood and other herbaceous species 299, 300. This is

attributed to softwood lignin contents, compositions, and structures by most literatures 302, 304.

For example, in softwood lignin, 90-95% of Guaiacyl (G unit) is present while softwood lignin

contained less than 1% and 4% of Syringyl (S unit) and hydroxyphenyl (H unit), respectively 301,

302. Thus, it was proposed that softwood lignin possessed more available reactive C5 and more

condensed β-5 and 5-5 sub-units than hardwood lignin 302, 303. These characteristics add the

difficulties of softwood lignin removal in pretreatment. Besides, softwood was reported having

dense, strong and lacking pores 304, which makes less attention on application of softwood in

pretreatment.

Due to the lack of sufficient efforts on softwood pretreatment, the aqueous pretreatment of

softwood was poorly understood. Currently, there are very few effective pretreatment options

available for softwood 300. Some most commonly reported pretreatment methods of softwood

include dilute acid 305, post-alkaline (peroxide) after steam explosion 306 and acid/SO2 steam

explosion pretreatment 307, 308. In softwood dilute acid pretreatment, the recovery yield of

hemicellulose is achieved by more than 90% of maximum compared to the hemicellulose sugar

yield of ~65% of the maximum possible from autohydrolysis. Also, it was reported that in 0.4%

sulfuric acid pretreatment, 90-95% hemicellulose of the Douglas fir and 20% of the cellulose

were removed to liquid phase at 200-230°C for 1-5 min and 90% of C6 sugars was recovered in

131

enzymatic hydrolysis process 305. Besides, 90% of glucose yield was achieved by enzymatic

hydrolysis of pretreated Douglas-Fir at 200°C with 4% (w/w) SO2 for 5min 308. However, these

studies were facing the difficulties in lignin removal and generating inhibitions on enzymatic

hydrolysis, which impedes an efficient pretreatment technique on softwood. To utilize softwood

for sugar production, the effective softwood pretreatment is necessary.

The alkaline pretreatment was reported to be effective in lignin removal and the potential to

achieve pretreatment economic viability 309. The alkaline pretreatment is using high dosage of

alkalis (0.5-30 wt%) at low temperature (e.g. room temperature or < 200 °C) for a long residence

time. For example, the NaOH pretreatment was operated at room temperature to 180 °C, 0.5-

10% (w/v) loading, and reaction time of 5 min to several hours 310. The pine wood lignin was

extracted by alkali 1–8% NaOH, for 30–120 minutes at temperature 130–150°C and the

produced lignins were characterized regarding lignin removal efficiency and molecular weight of

derived lignin 311. However, it was reported sodium hydroxide is more effective with non-woody

biomass species indicated by calculating the activation energy and the Arrhenius constant of

pretreatment kinetics 312. It was also reported that the alkaline pretreatment methods could

remove lignin effectively, swollen cellulose fibers, release acetyl groups, reduce sugar

degradation loss and improve sugar yields in enzymatic hydrolysis 313, 314. Thus, the alkaline

pretreatment of softwood is still not well performed and understood yet.

The objective of this study is to improve fundamental understandings of neutral and alkaline

pretreatment of softwood by implementing the aqueous flowthrough pretreatment with starting

pH of neutral to 12. The flowthrough reactor system can serve as a significant tool to provide

great mass and heat transfer with a short residence time which warranties our accurate studies of

132

the patterns, kinetics, and mechanisms of biomass hydrolysis 27. It was reported a nearly

complete hydrolysis of poplar wood was achieved in aqueous and dilute acid flowthrough system

at elevated temperatures 59. Therefore, the study postulated that high lignin removal and yields of

sugars can be achieved when dilute alkalis and elevated temperatures (>200ºC) were applied.

The flowthrough pretreatment of softwood has not been reported yet. A detailed

understanding of the softwood flowthrough pretreatment at several neutral and alkaline

conditions is significant. The dilute alkali pretreatment is also not well seen in softwood 300. In

this study, aqueous flowthrough pretreatment of softwood at neutral-pH 12 at temperature of

200-270C was carried out to evaluate the hydrolysis kinetics of cellulose, hemicellulose, and

lignin. The study gains a fundamental knowledge of softwood aqueous pretreatment which can

be applied to improve pretreatment technology on softwood.


5.3.1 Feedstock

Beetle-killed Lodge pole pine was provided by Forest Concepts, LLC, and milled to 40-60

mesh. The standing dead pine was harvested after death for 2-4 years in watershed creek

drainage near Walden, CO (no needles included). The pine wood chips contain 37.89±0.59% of

glucan, 5.25±0.047% of xylan, 29.95±0.78% of lignin, 3.62±0.093% of galactan, 2.25±0.018%

of arabinan and 9.89±0.021% of mannan, according to NREL composition analysis protocol 128.

5.3.2 Flowthrough pretreatment system

The same flowthrough pretreatment system was reported previously 59, 60. Briefly, the

flowthrough pretreatment system contains a 4kW fluidized sand bath, a high-pressure pump with

a flow rate of 0-100 ml/min, a cooling tank, solvent flowing tubing and connection fittings, and a

133

tubular reactor (1.3cm i.d. × 15.2cm length with an internal volume of 20.2 mL). Other

accessories include a pressure gauge, a thermal monitor, a stainless steel thermocouple, and a

back-pressure regulator.

5.3.3 Flowthrough pretreatment of pine wood chips

Pretreatment solutions were prepared using H2SO4 and NaOH for different pH solutions (6.4

(DI water), 8, 9, 11, and 12). The pH meter (pH 510 series, Oakton) was used for pH

measurement before and after pretreatment at room temperature. 0.5 g biomass was loaded into

the tubular reactor. The pretreatment solvents with various pHs were pumped by 25 ml/min

through the reactor. The reactor was placed in the fluidized sand bath which was heated to the

target temperatures (200-270 °C). The sand bath was set 10 ºC higher than targeted temperatures

and monitored by a thermal meter. After 2-10 min completion of experiments, the pretreated

hydrolysates were collected and 2ml of them was injected to HPLC for monomeric sugar

analysis. In addition, 10 ml of the hydrolysates were subjected to the posthydrolysis analysis at

121°C autoclave for 1 h with 4% sulfuric acid for total sugar recovery in pretreatment

(monomeric plus oligomeric sugars). The solid residues in tubular reactor were frozen dried for

K-lignin analysis which determined the compositions of residues after pretreatment 128. For the

recovered insoluble lignin collection (RISL), the pretreatment hydrolysates precipitated

overnight and followed by DI water washing and freeze drying. 2-D 1H-13C HSQC NMR of the

RISL structure was carried out following the same method reported 60. Pretreatment severity

(equation 5.1) was used to evaluate pretreatment performance.

LogR0 = Log[t × exp(T−100

14.75)] (5.1)

T: the temperatures applied in the pretreatment; t: the pretreatment time.

134

Besides, several parameters were used to evaluate the effectiveness of softwood flowthrough

pretreatment. The parameters were divided into two categories which were the removal and

recovery. Biomass, hemicellulose, cellulose and lignin removal results represented the removal

of biomass, hemicellulose, cellulose and lignin out of solid biomass loaded in the reactor

respectively, which was obtained by determination of dry mass weight change of biomass, K-

lignin analysis of the solid residues followed by a dry mass weighing after acid hydrolysis and

HPLC measurement of sugars in the solid residues. The water HPLC system (model 2695) is

equipped with a 410 refractive detector and a Waters 2695 auto-sampler using Waters Empower

Build 1154 software; Waters Co., Milford, MA, USA).The recovery result was from the direct

measurements of pretreatment or enzymatic hydrolysis liquid through HPLC and UV on sugar

contents in the liquids. In summary, the parameters included hemicellulose removal (%),

hemicellulose recovery (%), cellulose removal (%), lignin removal (%), and recovered soluble

lignin (RSL): (Equation details in previous study 60). Hemi-sugars recovery used in this study

refers to the recovery of C6 and C5 sugars from hemicellulose including xylose, mannose,

galactose, and arabinose. Other parameters included the following:

Cellulose recovery in stage 1 as C6 sugars%=mCRecovery

mC× 100 (5.2)

mCRecovery: HPLC detected glucose mass weight recovered in liquid phase of pretreatment after

posthydrolysis (Stage 1); mC: mass weight of glucose in the original loaded biomass.

Total cellulose recovery as C6 sugars%=mCRecovery

mC× 100 (5.3)

mCRecovery: glucose mass weight recovered in the liquid phases of pretreatment (Stage 1) and

enzymatic hydrolysis (Stage 2); mC: mass weight of glucose in the original loaded biomass.

135

5.3.4 GC/MS analysis

The hydrolysates after pretreatment were extracted with ethyl acetate, following vigorous

mixing and the subsequent suspension. 1 ml solvent phase was injected into an Agilent gas

chromatography mass spectrometer (GC/MS; GC, Agilent 7890A; MS, Agilent 5975C) equipped

with a fused silica capillary column (DB-5MS column:30 m × 320 µm × 0.25 µm). The carrier

gas was Helium at a flow rate of 1.3 ml/min. The splitter/injector was kept at 300oC with a

splitless mode. The oven temperature was programmed from 45°C to 270°C at ramping rate of

5°C /min. The solvent delay was held for 5 minutes.

5.3.5 Enzymatic hydrolysis on pretreated whole slurries

The whole slurries of pretreated samples enzymatically hydrolyzed in 0.05 M citric acid

buffer at pH 4.8 supplemented with 10mg/mL sodium azide, in which Novozymes Cellic®

CTec1 (220 mg protein/mL, preserve 200 mg glucose/mL, 205 FPU/mL) and Novozymes

Cellic® HTec2 (230 mg protein/mL, preserve 180 mg xylose/mL) at a ratio of 5:1. The

experiment was controlled at 50 °C for 72 h. The cellulase loading was 100 mg protein/ g

(glucan and xylan) to get maximum theoretical sugar recovery. In addition, 1% (w/v) bovine

serum albumin (BSA) was used to block lignin on enzymes.


RISL

50 mg recovered insoluble lignin (RISL) was dissolved in 600 μL deuterated DMSO

(Cambridge Isotope Laboratories). The resulting liquid sample was placed in 5 mm Wilmad 535-

PP NMR tubes. NMR spectra were collected at 25° C on 500 and 600 MHz Agilent (Varian)

Inova NMR spectrometers equipped with z-axis pulsed-field triple-resonance HNCP probes.

136

Samples contained 0.05% (v/v) TMS for chemical shift referencing. Two-dimensional 1H-13C

HSQC spectra of the aliphatic and aromatic regions were collected separately using the BioPack

gchsqc pulse sequence, with 1H spectral width of 17 ppm and 13C spectral widths of 100 or 60

ppm for the aliphatic or aromatic regions, respectively. Spectra were collected with 1024 points

(Varian parameter np) and 61 ms acquisition time with 128 or 256 transients and 128 or 96

complex points (Varian parameter ni in States-TPPI mode) in the indirect dimension, for

aliphatic and aromatic spectra, respectively. Adiabatic WURST decoupling was applied during

acquisition. Delayed times tCH and lambda for 1/4*JCH, were 1.8 ms and 1.6 ms for aliphatic

spectra, and 1.45 ms and 1.3 ms for aromatic spectra, respectively. Reference one-dimensional

1H spectra were collected with 32k points and 128 transients. HSQC spectra were processed and

analyzed with Felix 2007 (FelixNMR, Inc) or MestReNova 6.0.4 (Mestrelab Research), with

matched cosine-bell apodization in both dimensions, 2X zero filling in both dimensions, and

forward linear prediction of 30% more points in the indirect dimension. One-dimensional 1H

spectra were processed with no apodization or linear prediction and 2X zero filling. Relative

peak integrals were measured in MestReNova.


5.4.1 pH changes of pretreatment solvent after pretreatment

The pH of pretreatment media is an important factor in pretreatment, which were reported to

influence biomass pretreatment sugar yields 315. Some efforts have been made on investigating

the effects of pH on biomass pretreatment, such as in the previous reviews on acidic, neutral and

alkaline conditions 316. For example, the fundamentals of pretreatment at higher and lower pHs

were reviewed 317, 318. The continuous pH monitoring during pretreatment was attempted in

137

pressure cooking water pretreatment of yellow poplar wood sawdust 38. Additionally, the

pretreatment of corn bran for enhanced enzymatic arabinoxylan degradation has been performed

at various pHs (1.7-9.8). Also, the catalyst types (different acids and alkalis) at various pHs in

mild thermal pretreatment were employed to pretreat wheat straw to achieve a high yield of

monomeric sugars and a high lignin removal 315. However, the comprehensive studies of

pretreatment at pHs ranged from specific 6 to 12 in elevated temperature (> 200 °C) was not

carried out, especially in flowthrough pretreatment of softwood. In specific, the detailed kinetic,

chemistry and products distribution were not well discussed in flowthrough pretreatment of

softwood at neutral and alkaline conditions.

138

Figure 5.1 pH of the pretreatment hydrolysates at different severities after the hydrolysates were

cooled down to room temperature, pretreatment conditions: pH 6.4-12, 200-270°C, and 0-10 min

In the flowthrough reactor system, the pretreatment solvent was pumped and passed through

the tubular reactor continuously. The pH of pretreatment media could be affected by the complex

biomass pretreatment reactions and different water dissociations at various heating temperatures.

However, there was no pH measuring method during pretreatment right at high temperatures.

The final pH values were measured after the pretreatment hydrolysates cooling down to room

temperature. It was found that the final pHs of pretreatment hydrolysates decreased to ~4.0 when

the initial pHs ranged from 6.4 to 9.0 regardless of the operating severities (Figure 5.1). This

pretreatment system acted like a buffering system which buffered pH to 4.0 when the initial pHs

were not too high (≤ 9.0). This can be caused by the following factors, such as the water self-

ionization at elevated temperatures (200-270 °C) 319, the biomass self-buffering system 320,

and/or a releasing of organic acids during pretreatment. Surprisingly, we found the initial pHs

were related to pretreatment chemical reactions. For example, the pretreatment reaction

pathways followed acid hydrolysis instead of alkaline degradation when initial pHs were ≤ 9.0.

At the initial pH of 11.0, the pHs slightly decreased but remained alkali conditions (pH > 10.0)

after pretreatment. When the initial pH was 12.0, the final pHs of hydrolysates presented no

significant changes at the different pretreatment severities. Probably, the high concentration of

NaOH might break the buffering system which led to strong alkaline conditions.

139

5.4.2 Hemicellulose removal and hemi-sugars recovery

Figure 5.2 Hemicellulose removal and hemi-sugars recovery in stage 1 (pretreatment only, pH

6.4-12, 200-270°C, and 0-10 min)

Hemicellulose is usually easier to be degraded than cellulose and lignin. In Figure 5.2a & b,

the hemicellulose removal could reach 100% when the initial pHs of pretreatment solutions were

≤ 9.0 in all tested pretreatment severities (4.0-6.2). This is probably because of the acid

hydrolysis of hemicellulose. Hemi-sugars can be recovered more than 90%. Only a portion of

hemi-sugars (<10%) was degraded when pretreatment severities were larger than 4.8. However,

when the pretreatment pHs were higher than 11.0 at the severities less than 5.2, hemicellulose

could not be completely removed (Figure 5.2a). Up to 50% of hemi-sugars could be recovered in

pH 11.0 but none was recovered in pH of 12 under tested pretreatment severities (Figure 5.2b).

Thus, at pHs 11.0 (severities>4.5) and 12.0 (all tested severities), hemi-sugars underwent severe

degradation reactions resulting little hemi-sugar yields. The low hemi-sugars recovery yield in

pH of 11.0 was because of the degradation of hemi-sugars 71. At pH 12.0, no hemi-sugar was

observed, which might due to the direct and fast degradation of hemicellulose to small fractions.

140

5.4.3 Cellulose removal and glucose recovery

Figure 5.3 Cellulose removal and glucose recovery (Stage 1: pretreatment; Stage 2: enzymatic

hydrolysis), pretreatment conditions: pH 6.4-12, 200-270°C, and 0-10 min

In Figure 5.3a, when the initial pHs were less than 9.0, glucan removal decreased slightly

from pH 6.4 to 9.0. When the initial pHs were higher than 11.0, However, the glucan removal

increased with pH increasing. This was maybe attributed to pretreatment reactions under initial

dilute alkali (pH less than 9.0); the reactions were H+ dominant acid hydrolysis indicating by the

final pHs (Figure 5.1). The slight difference of final pHs (initial 6.4-9.0) resulted in slightly

difference of glucan removal. However, high pH (≥ 11.0) broke the buffering range which led to

141

the alkaline hydrolysis of biomass. It was reported that under alkaline conditions, carbohydrates

underwent both hydrolysis and peeling off reactions 48. The peeling off reactions of

carbohydrates increased with the increase of NaOH concentration, which might be the main

reason to resulting in high glucan removal. When pretreatment severities were higher than 5.2 at

pH of 12.0, glucan was abruptly removed at 100% which was because of the severe peeling off at

very high temperature and relatively strong alkalis.

Total glucose recovery (stage 1 + stage 2) included glucose yield obtained from both the

pretreatment and enzymatic hydrolysis processes. In Figure 5.3, the glucose recovery in stage 1

and stage (1+2) was similar when initial pHs were less than 9.0 (e.g. 6.4, 8, and 9), and the total

glucose yield can be higher than 80%. It was indicated that the similar glucose yield was because

of the similarities of the final pH of pretreatment hydrolysates at room temperature. The total

glucose yields at the initial pHs of 11.0 and 12.0 were comparable with others under severities

less than 4.8. However, at severities larger than 4.8, the glucose yield was much lower than

others presenting less than 50% (Figure 5.3b). The difference between the high cellulose removal

and low glucose recovery at pH of 11.0 and 12.0 might be the degradation of cellulose or glucose

during pretreatment. Moreover, these alkaline degradation products might inhibit enzymatic

hydrolysis sugar yields. In the pretreatment stage only (Figure 5.3c), cellulose was recovered as

monomeric and oligomeric sugars with similar and high yields when the initial pHs were less

than 9.0. However, at pH 11.0, less than 10% of glucose was observed, and no glucose was

obtained at pH 12.0 in pretreatment stage. Thus, these results showed the possibilities of severe

degradation of carbohydrates in pH 11.0-12.0. The initial pHs of pretreatment hydrolysates were

proposed to be correlated with these tremendously different pathways.

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5.4.4 Lignin removal and recovered soluble lignin yield

Figure 5.4 Lignin removal and recovered soluble lignin yield, pretreatment conditions: pH 6.4-

12, of 200-270°C, and 0-10 min

Lignin can be recovered from pretreatment hydrolysates as recovered soluble and insoluble

phases. Lignin structural changes in acids and alkalis were different. In this study, lignin removal

showed no significant increase when the initial pHs increased from 6.4 to 11.0 at the same

severities (Figure 5.4a). When the initial pH reached 12.0 at pretreatment severities lower than

4.5, lignin removal was close to that at pH 11.0 under the same pretreatment severities. Probably,

the alkali stable lignin structure in softwood lignin limited its solubilization in alkaline

conditions. However, when the pretreatment severities were increased to higher than 4.5, lignin

was removed significantly until 100% of lignin removal. The initial pHs presented the similar

influence on changes of recovered soluble lignin recovery (Figure 5.4b). The increasing of initial

pHs and/or pretreatment severities improved lignin solubilization. It was reported that lignin

depolymerization mechanism under acidic conditions was mainly the cleavage of β-O-4 linkages

68. Nevertheless, lignin degradation under alkaline conditions first induced the generation of -

Quinone methide intermediates, which led to the subsequent cleavage of C-O-C linkages and

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even ring open reactions 321, 322. The different mechanisms of lignin acidic and alkaline

degradations might cause the different lignin solubilization and removal in pretreatments at pHs

ranged from 6.4 to 12.0. It was observed in Figure 5.4a & b, the alkaline degradation of lignin

removed more lignin than in the acidic depolymerization of lignin. Additionally, the alkaline

degradation of lignin might lead to ring open reactions 321, 322. Thus, the different initial pHs of

pretreatment media can result in various lignin degradation mechanisms.

In summary, the results showed at initial pHs 6.4-9.0, the pretreatment reactions followed

the acid hydrolysis kinetics while presented alkaline degradation mechanisms at pH 11.0 and

12.0. These results were consistent with the final pHs of pretreatment hydrolysates which have a

close relation with pretreatment reactions mechanism (Figure 5.1).

5.5 Determination of the degradation products from carbohydrates and lignin by GC/MS and

HPLC

The determination of the degradation products in pretreatment hydrolysates derived from pH

11.0 and 12.0 was really difficult. It was because the carbohydrates were degraded to a mixture

of small fragments of organics. The characterization method of this mixture has not been

reported yet and the challenges were reported that alkaline pretreated liquor is a complicated

mixture of acids (derived from carbohydrate degradation reactions that occur in alkaline

conditions), partially depolymerized polysaccharides, monosaccharides, aromatic monomers

(derived from lignin), high molecular weight lignin, and acetate. At present, no single method

exists to completely identify and quantify all components present in the liquor 310, 323. In our

study, we also faced the same challenges so that the study could not provide a comprehensive

analysis of products from alkaline degradation of carbohydrates.

144

Firstly, the HPLC was applied to detect possible degradation products from carbohydrates at

pH 11.0-12.0. Only glycolic acid, acetic acid, and formic acid were detected (Figure S5.1), which

might be from alkaline degradation of carbohydrates. Besides, a trace furfural was detected when

the initial pHs ranged 6.4 to 9.0 at high pretreatment severities. Figure 5.5 showed some

monomers derived from lignin alkaline degradation in some selected hydrolysates. These

compounds were recovered in soluble lignin phase. The vanillin was found in lignin degradation

products under alkaline conditions, which was originated from lignin oxidations. Also, the

compounds similar to vanillin structure were detected (compound 8-12). The yield of recovered

soluble lignin can be achieved more than 50% of maximum lignin recovery (Figure 5.4b). These

monomeric lignin aromatics can play a significant role in producing value added aromatics.

Figure 5.5 GC/MS results of the pretreatment hydrolysates collected at pH of 12 and

pretreatment severity of 5.2

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RISL

Note: the assignments and NMR detected lignin linkages were shown in supplementary materials

(Table S5.1 and Figure S5.2 60).

Figure 5.6 2-D 1H-13C HSQC NMR spectra of ball milled pine lignin and pretreatment obtained

RISLs at 11.0 and 12.0. (a,b) ball milled pine wood lignin, (c,d) RISL derived from pH of 11.0

and pretreatment severity of 6.1, (e,f) RISL derived from the pH of 12.0 and pretreatment

severity of 5.8

The cleavage of lignin C-O-C linkages is important in lignin solubilization under alkaline

solutions. The quantification of the basic linkages referred to the reported method 157, 324. In this

study, lignin linkages such as β-O-4, β-β and β-5 were cleaved at pH 11.0-12.0 (Figure 5.6a, c, &

d; Table 5.1). The table 5.1 showed that the cleavage of β-5 was slower than the cleavages of β-

O-4 and β-β. The cleavages of these linkages were increased from pH of 11.0 to 12.0 resulting in

high lignin solubility in liquid phase. At pH 12.0, the β-β and β-5 was not presented (Figure 5.6e,

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Table 5.1). The aromatic regions of the two RISL showed the majority aromatic rings remaining

even in this high tested pretreatment severity (5.8) at pH of 12.0 (Figure 5.6b, d & f). The

majority of lignin ether and C-C bonds were cleaved as the increasing of strong alkalis. The

quantification of each linkage is from the volume-integration of cross-peak contours in HSQC

spectra. The oxidized G units were completely disrupted during pretreatment and not seen in 2-D

NMR spectra of RISLs. The peak integrals of cinnamaldehyde and p-hydroxybenzoate found

decreasing.

Table 5.1 Distribution of the detected linkages of ball milled pine lignin and RISLs. BML: ball

milled wood lignin; A: RISL collected at pH of 11.0 and pretreatment severity of 6.1; B: RISL

collected at pH of 12 and pretreatment severity of 5.8. The percentages of inter linkages of BML

were based on all detected peaks

Inter linkages (%) β-O-4 Resinol

(β-β)

Phenylcoumarane

(β-5, α-O-4)

BML 77.5 5.4 17.1

A 58.36 2.74 38.90

B 100.00 Gone Gone

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5.4.6 Pretreatment reactions kinetics at pH 6.4-12.0

149

Figure 5.7 Proposed biomass degradation mechanisms of softwood in aqueous flowthrough

pretreatment at initial pH 6.4-12, (a) hemicellulose; (b) cellulose; (c) lignin 60, 67, 68, 71-74, 321, 322

The mechanisms of aqueous flowthrough pretreatment at neutral-12 pH on softwood were

proposed in Figure 5.7 to suggest the impact of initial pHs. The proposed chemical products

include some of our detected compounds in this study. The hemicellulose and cellulose was

proposed to experience an acid hydrolysis and dehydration in the aqueous pretreatment at pH

6.4-9.0 which produced sugars and subsequent limited sugars degradation if high severities were

applied. At the initial pH of 11.0, the final pH decreased to the alkali (pH > 10.0) after

pretreatment. Thus, the degradation of these conditions was proposed to follow a moderate alkali

kinetics which most of biomass solid was preserved while alkaline degradation of sugars

occurred at high severities (>5.2). Additionally, it was reported that the carbohydrates

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isomerization, dehydration, fragmentation, and condensation can occur in forming a mixture of

sugars, aldehydes, acids and humins in the water media at elevated temperature 66. However,

when pH increased to 12.0, the carbohydrates were degraded in fast alkaline reaction pathways

and none sugars were detected in products. The carbohydrates in cellulosic biomass can undergo

severe degradation reactions such as the peeling off reactions to a mixture of carboxylic acids as

well as the isomerization reactions to a mixture of different sugars 67. These mechanisms make

biomass chemistry complex and attractive in producing an abundant of bio-chemicals. However,

the study encountered difficulties in the characterization of the alkali pretreated liquor. Thus, the

comprehensive determination of products in the hydrolysates derived from aqueous pretreatment

at pH 12 is still not resolved, which needs more future work. Furthermore, the alkaline

delignification chemistry has been discussed and reviewed before 71. In the study, when pH was

6.4-9.0, the lignin was proposed to be depolymerized through the cleavage of C-O-C bonds. At

pH of 11.0 and 12.0, our 2D HSQC 1H-13C NMR results indicated that the main mechanism of

lignin depolymerization was still the cleavage of β-O-4. Under pH 12.0, all β-β and β-5/α-O-4

were disappeared while some β-O-4 remained although β-O-4 showed the fastest degradation

rate. Besides, although the ring open reactions were reported to occur, our results suggested the

substantial aromatic rings remained and no significant changes were observed. In summary, the

proposed pathways of the aqueous pretreatment of softwood were greatly affected by the initial

pHs applied. However, more future efforts still need to be made to investigate the comprehensive

distribution of aromatic chemicals in alkali pretreated liquor. Our study gained more

understandings of the application of pretreatment pHs and help in design wise production of

aromatic chemicals.

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5.5 Conclusion

The study demonstrated several highlights include a) the aqueous flowthrough pretreatment

of softwood was studied for the first time; b) the detailed pretreatment mechanisms was proposed

in neutral and alkaline pHs; c) the biomass solubilization was achieved at pH 12 with a mixture

of major carboxylic acids and aromatic chemicals. However, more work is needed to

characterize this mixture of compounds. To be specific, the initial pretreatment pH has

significant effects on sugar yields, lignin solubilization, and pretreatment kinetics. When the

initial pHs were 6.4-9.0, the biomass degradation was indicated to follow the acid hydrolysis

suggested by the final pH of ~4.0 at room temperature after pretreatment terminated if the initial

pHs were 6.4-9.0. This buffering capacity of aqueous flowthrough pretreatment system was

proposed to originate from the water self-ionization at elevated temperatures, the biomass self-

buffering system and/or a releasing of organic acids during pretreatment. However, when the

initial pHs were 11.0 and 12.0, the biomass degradation pathways proceeded with an moderate

and strong alkaline hydrolysis under applied pretreatment severities (3.8-6.0), respectively.

Besides, the 2-D 13C-1H HSQC NMR analysis showed the majority of C-O-C cleaved during the

alkaline degradation of lignin, resulting the increasing of lignin solubilization, while most of

aromatic rings still remained but the β-O-4, resinol, and phenylcoumarane linkages were

completely degraded at pH of 12.


The author acknowledges the Sun Grant-DOT Award, # T0013G-A- Task 8 for supporting

this work. Also, the author thanks Department of Biological Systems Engineering at Washington

State University. The author thanks Mr. Gildardo Soto for his assistance in conducting some

152

experiments. The thesis author appreciated Drs. Yucai He, John R. Cort, and Bin Yang for their

insightful discussions.


`

Figure S5.1 The HPLC analysis of degradation products in pretreatment hydrolysates at pH of

12.0 and pretreatment severity of 6.1

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Table S5.1 Main lignin 2-D 1H -13C Cross-peaks assignments in the HSQC Spectra

Lignin linkages and monolignols Chemical shift

-OCH3 55.47(C) 3.70(H)

A: β-O-4 71.70(S-Cα) 4.84(S-Hα) 59.50-59.70(Cγ) 3.40-3.63(Hγ) 85.90(S- Cβ)

4.09(S-Hβ) 83.49(G/H-Cβ) 4.28(G/H-Hβ)

B: Resinol 84.85(Cα) 4.62(Hα) 53.30(Cβ) 3.46(Hβ) 70.85(Cγ) 4.14/3.78(Hγ)

C: Phenylcoumaran 86.79(Cα) 5.41(Hα) 53.3(Cβ) 3.05(Hβ) 62.52(Cγ) 3.68(Hγ)

D: Spirodienone 59.67(Cβ) 3.19(Hβ)

G: Guaiacyl 111.02(C2) 6.95(H2) 115.05(G5) 6.74(H5) 119.01(G6) 6.78(H6)

G: Oxidized (Cα=O) guaiacyl 111.56(C2) 7.50(H2) 123.55(C6) 7.54(H6)

H: p-hydroxyphenyl 128.0(C2/6) 7.2 (H2/6)

E: p-hydroxybenzoate 131.33(C2/6) 7.62(H2/6)

K: Cinnamaldehyde 126.23(Cβ) 6.74(Hβ)

Note: G, S, H-C or G, S, H-H refers to C and H in the lignin sub-units, guaiacyl, syringyl and p-

hydroxyphenyl

154

Figure S5.2 Main detected lignin linkages

155

CHAPTER SIX

CHARACTERIZATION OF LIGNIN DERIVED FROM WATER-ONLY

AND DILUTE ACID FLOWTHROUGH PRETREATMENT OF

POPLAR WOOD AT ELEVATED TEMPERATURES

6.1 Abstract

Flowthrough pretreatment of biomass is a critical step in lignin valorization via conversion

of lignin derivatives to high-value products, a function vital to the economic efficiency of

biorefinery plants. Comprehensive understanding of lignin behaviors and solubilization

chemistry in aqueous pretreatment such as water-only and dilute acid flowthrough pretreatment

is of fundamental importance to achieve the goal of providing flexible platform for lignin

utilization. In this study, the effects of flowthrough pretreatment conditions on lignin separation

from poplar wood were reported as well as the characteristics of three sub-sets of lignin

produced from the pretreatment, including residual lignin in pretreated solid residues (ReL),

recovered insoluble lignin in pretreated liquid (RISL), and recovered soluble lignin in

pretreatment liquid (RSL). Both the water-only and 0.05 % (w/w) sulfuric acid pretreatments

were performed at temperatures from 160 to 270 °C on poplar wood in a flowthrough reactor

system for 2–10 min. Results showed that water-only flowthrough pretreatment primarily

removed syringyl (S units). Increased temperature and/or the addition of sulfuric acid enhanced

the removal of guaiacyl (G units) compared to water-only pretreatments at lower temperatures,

resulting in nearly complete removal of lignin from the biomass. Results also suggested that

more RISL was recovered than ReL and RSL in both dilute acid and water-only flowthrough

pretreatments at elevated temperatures. NMR spectra of the RISL revealed significant β-O-4

156

cleavage, α-β deoxygenation to form cinnamyl-like end groups, and slight β-5 repolymerization

in both water-only and dilute acid flowthrough pretreatments. Elevated temperature and/or dilute

acid greatly enhanced lignin removal to almost 100 % by improving G unit removal besides S

unit removal in flowthrough system. Only mild lignin structural modification was caused by

flowthrough pretreatment. A lignin transformation pathway was proposed to explain the

complexity of the lignin structural changes during hot water and dilute acid flowthrough

pretreatment.

Keywords: Aqueous • Dilute acid • Flowthrough pretreatment • Poplar • Lignin •

Characterization

6.2 Introduction

Lignin is a main constituent of lignocellulosic biomass (15-30% by weight, up to 40% by

energy) and the second most abundant biopolymer on Earth. Recently, lignin has received broad

attention as its intermediates have found wide use in various carbon products, such as electrodes,

carbon fibers, jet fuels, biochemicals, plastics, and antioxidants 87, 325-330. Thus, the utilization of

waste lignin in pretreatment as a feedstock for conversion to fuels and chemicals offers a

promising opportunity for enhancing the overall operational efficiency, carbon conversion,

economic viability, and sustainability of biorefineries.

Lignin is an amorphous, cross-linked biopolymer that consists of three phenylpropanoid

units, namely, p-hydroxyphenyl (H units), guaiacyl (G units), and syringyl (S units). These units

are derived from the three monolignol building blocks: p-coumaryl, coniferyl, and sinapyl

alcohols. The major inter-unit linkages in lignin are β-O-4, α-O-4, β-5, β-β, 5-5, β-1 and 4-O-5

331. Selective depolymerization of lignin is important to lignin utilization and also considered to

157

be very challenging due to the broad distribution of bond energies in the various C-O and C-C

linkages within the structure of lignin and the tendency for uncontrollable thermal and catalytic

fragmentation when trying to cleave them 87. Most lignin extraction processes usually cause

many lignin structural changes and modifications. As removing lignin is favorable to the process

of enzymatic hydrolysis for sugar conversion, great efforts have been made to boost lignin

extraction from the solid biomass to the liquid phase during the aqueous pretreatment 332. Most

water-only and dilute acid batch pretreatments were reported to achieve limited lignin removal

333, 334 dictated by the lignin depolymerization and condensation chemistry 68, 335, 336. Under acidic

pretreatment conditions, the predominant reactions associated with lignin are fragmentation by

acidolysis of aryl ether linkages and acid catalyzed recondensation, while linkages like resinol

and phenylcoumaran subunits are fairly stable. The β-O-4 linkages in lignin are susceptible to

acidic hydrolysis and the pretreatments generally result in their lower relative content in the

pretreated biomass 337. The dilute acid pretreatment led to an increase of phenolic OH groups in

lignin apparently resulting from cleavage of aryl ether linkages 100, 182, 338. In addition, lignin in

different biomass species has various characteristics, which influence pretreatment sugar yield

and enzymatic hydrolysis effectiveness. It was reported that sugar yield was related to lignin

content and structural characteristics, especially S/G ratio in the biomass substrate 339. Also,

changes in cellulose structure were influenced by lignin content 340. Biomass genetic

modification and manipulation were performed to reduce lignin recalcitrance, modify S/G ratio

and improve sugar yields 341-344. However, biomass pretreatment is still challenging for high

sugar recovery with maximum usable lignin recovery since sugars are easily degraded under

high pretreatment severities. High yields of sugars and lignin are critical to achieve favorable

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pretreatment economics 345.

Sugar yields and removal of lignin by pretreatment can be greatly improved by flowing hot

water through the biomass 345, 346. Use of a flowthrough reactor system was reported to increase

lignin removal as much as 98% 345, and the addition of dilute acid further increased the removal

extent 53, 56, 345, 347. The main advantage of a flowthrough reactor system is its ability to restrict

condensation reactions by constantly removing lignin into the aqueous phase and reducing the

chance of pseudo-lignin formation 348, 349 and lignin deposition 350. In addition, pretreatment was

shown to mitigate lignin droplets on the cellulose surface 107 thus it led to effective lignin

deconstruction and hemicellulose recovery 59, 351. In pretreatment, lignin is believed to

depolymerize via both homolytic and acidolytic cleavage into low molecular weight lignin

globules 337, 352. Thus, solubilized lignin derivatives continuously exit the flowthrough system,

thus further thermal or chemical treatments on depolymerized lignin fractions can be avoided. In

this case, flowthrough-derived lignin is believed to only exhibit mild structural change compared

to the native original lignin. Effective recovery of sugars and lignin with whole biomass

solubilization by flowthrough pretreatment at elevated temperatures under tested pretreatment

conditions was previously reported 345. However, application of lignin derivatives to produce

added value products cannot be realized unless the corresponding lignin recovery yield and

chemistry under these pretreatment conditions are well understood. In this study, the three lignin

sub-sets, including residual lignin in pretreated solid residues (ReL), the purity of recovered

insoluble lignin in pretreated liquid (RISL) and recovered soluble lignin in pretreatment liquid

(RSL) from flowthrough pretreatment were measured following acetyl bromide treatment, and

their structure was analyzed using Fourier transform infrared (FTIR) spectroscopy, GC/MS,

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pyrolysis GC/MS, and two-dimensional (2-D) 1H-13C heteronuclear single-quantum coherence

(HSQC) NMR spectroscopy. Structural characteristics of three lignin isolates were investigated

in order to reveal intrinsic structural modification of lignin under the tested pretreatment

conditions, which will benefit the utilization of these lignin sources in the context of a

biorefinery dependent on high yields of sugars.


6.3.1 Materials

Hybrid poplar wood chips were kindly provided by the Forest Concepts Corporation, located

in Auburn, Washington. The chips were air-dried and then milled to 0.4-0.8 mm. The resulting

poplar particles were stored at -20 ℃ for experimental use with their moisture content at about

5%. Compositions of poplar wood were determined using the NREL Laboratory Analytical

Procedure (LAP; NREL, 2008) 353. Poplar wood contains 48.5±0.6% glucan, 17.2±0.5% xylan,

and 23.4±0.4% lignin. Ball-milled poplar lignin was kindly provided by Georgia Institute of

Technology, Atlanta, Georgia.

6.3.2 Flowthrough pretreatment of poplar wood

Poplar chips (0.5 g dry weight) were loaded into a tubular reactor (1.3 cm i.d. ×15.2 cm

length with an internal volume of 20.5 mL). Two silver-plated snubber gaskets with 5 µm were

applied in both ends of the tubular reactor. The reactor was then connected to our flowthrough

system. The flowthrough system was equipped with a 4-kW fluidized sand bath (model SBL-2D,

Omega Engineering, Inc., Stamford, CT). Distilled water or 0.05% (w/w) sulfuric acid at room

temperature was pumped through a preheated coil in sand bath and then passed through the

reactor continuously. The pretreatment hydrolysates were quenched by submerging the coil in ice

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water. The pretreatment was performed at 160 °C to 280 °C and the temperature was monitored

by a thermometer (Omega Engineering Co., Stamford, CT) located in the reactor outlet. The

back pressure was regulated by a pressure gauge in the range of 10-70 bar, which corresponded

to the saturated steam pressures. The sand bath temperature was controlled at 5-10°C above the

reaction temperature using a temperature controller (Omega, HH501AJK), with consideration of

heat loss and transfer. The flow rate was set at 25 mL/min through the high-pressure pump

(Acuflow Series III Pumps, Fisher, and Pittsburgh, PA, USA). After the pretreatment was

completed, the reactor was immediately transferred to ice water. The undissolved poplar solid

residues were collected and freeze-dried for analysis. In this study, the pretreatment severity

factor was applied to evaluate pretreatment conditions (equation 6.1).

Log R0= Log t*exp (TH−TR

14.75) (6.1)

Where 𝑡 is the reaction time in minutes, 𝑇𝐻 is the reaction temperature in °C, 𝑇𝑅 is the reference

temperature in °C (100°C) 354.

6.3.3 Composition analysis of the solid residues and determination of lignin removal

Composition analysis of solid residues was carried out by the NREL procedure 353. 0.03g of

solid residues underwent 72% sulfuric acid hydrolysis in a 30°C water bath for one hour,

followed by 4% sulfuric acid in autoclave with pressure at 121°C for one hour. The resulting

slurries were filtrated and dried in the 105°C oven. According to this method, the remaining

oven-dried solids were ReL, known as acid insoluble lignin. Lignin removal refers to the

percentage of original lignin released to aqueous phase (equation 6.2). This parameter presents

the effectiveness of delignification by pretreatment.

Lignin removal % = 100 −mRe

mL × 10 (6.2)

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Where mRe refers to the dry weight mass of remaining lignin after composition analysis of solid

residues; mL represents the mass weight of lignin in the original loaded biomass (mL= the loaded

biomass mass weight×the lignin content obtained from section 6.3.1).

6.3.4 Ultraviolet visible spectroscopy (UV-Vis) quantification of RSL

The RSL content (%) was determined by the UV absorbance measurement of the pretreated

hydrolysates at 320 nm according to the NREL procedure 128. RSL was calculated according to

the following equations (equation 6.3, 6.4).

RSL% =UVabs×Volumepretreatment liquor×Dilution

ε×ODWsample×Pathlength× 100 (6.3)

Where 𝑈𝑉𝑎𝑏𝑠 is the average UV-Vis absorbance at 320 nm; Volumepretreatment liquor refers to

the volume of pretreatment hydrolysate; ε represents molar absorptivity of biomass at 320 nm

(30 L/g cm); ODWsample is the weight of sample in milligrams; Pathlength is the pathlength of

UV-Vis cell in cm.

Dilution =Volumesample+Volumediluting solvent

Volumesample (6.4)

6.3.5 Recovered insoluble lignin (RISL) isolation and purity measurement

The pH of some collected pretreatment hydrolysate samples that were not at pH 2-3, was

adjusted to pH 2-3 to precipitate the RISL. The resulting precipitates were freeze-dried to obtain

dry, solid RISL 325. The RISL yield was calculated according to equation 6.5.

RISL%=mp

mL × 100 (6.5)

Where, mp is the mass weight of precipitated lignin; 𝑚𝐿 represents the mass weight of the lignin

in the original loaded biomass (mL = the loaded biomass mass weight × the lignin content

calculated in section 6.3.1).

162

The RISL purity analysis was performed by determining the ultraviolet absorbance at 280

nm after subjecting the lignin sample to acetyl bromide (AcBr method), followed by acetic acid

treatments 355. The purity was calculated by using the equation of Morrison (equation 6.6).

AcBr lignin = (3.37 × Uabs

CL− 1.05) × 100 (6.6)

Uabs refers to the UV absorbance at 280 nm; CL is the lignin concentration before sending to UV

measurement.

6.3.6 Gel permeation chromatography analysis of molecular weight of flowthrough lignin

The molecular weight analysis of the flowthrough derived lignin was carried out using gel

permeation chromatography (GPC). Lignin samples were individually acetylated by stirring with

2 ml of acetic anhydride– pyridine (1/1, v/v) at room temperature for 24 hours. After acetylation,

the acetylated lignin samples were dissolved in THF for GPC analysis using an Agilent 1200 LC

equipped with an ultraviolet (UV) detector. The sample was filtered through a 0.45 μm

membrane filter prior to injection of a 20 μl sample. GPC analyses were carried out using a UV

detector (280 nm) on a 4-column sequence of WatersTM Styragel columns (HR0.5, HR2, HR4

and HR6) at 1.00 ml min−1 flow rate. Polystyrene standards were used for calibration. WinGPC

Unity software (Version 7.2.1, Polymer Standards Service USA, Inc.) was used to collect data

and determine molecular weight profiles 325.

6.3.7 Py-GC/MS analysis of ReL

Py-GC/MS was performed with a CDS 5000 pyrolysis autosampler (Oxford, PA, USA)

attached to a Thermo Trace GC (6890N/MSD, 5975B) gas chromatography/mass spectrometry

system (Bellevue, WA, USA). Approximately 0.5 mg of the solid residues was loaded into a

quartz tube that was filled with a thin layer of quartz wool in advance. The initial temperature

163

performed in the process was 250°C for 6 seconds and then the sample was pyrolyzed up to a

temperature of 610°C within 1 min. The generated vapor was separated by a 30 m 0.25 μm inner

diameter (5% phenyl)-methylpolysiloxane column. The pyrolyzed gas was held in the pyrolysis

oven for 56 min and then sent to the mass spectrometer that was operated in EI mode (70 ev) for

analysis.

6.3.8 Fourier transformed infrared (FTIR) spectroscopic analysis of ReL

The spectra (4500-800 cm-1) were obtained with a Bruker IFS 66v/s spectrometer equipped

with an IR-microscope using about 2mg of each sample. Spectra were obtained using the

triangular apodization with a resolution of 4 cm-1 and an interval of 1 cm-1. 64 scans were

conducted for each background and the sample spectra.

6.3.9 Characterization of RSL after flowthrough pretreatment by GC/MS

10 mL of diethyl ether was used to extract RSL derivatives in 5 mL of hydrolysates,

following vigorous mixing and subsequent suspension and separation. A 1mL aliquot of the

solvent phase was injected into an Agilent gas chromatography mass spectrometer (GC/MS; GC,

Agilent 7890A; MS, Agilent 5975C) equipped with a fused silica capillary column (DB-5MS

column: 30m × 320µm × 0.25µm). The carrier gas was helium at a flow rate of 1.3 mL/min. The

splitter/injector was kept at 300°̊C with a split ratio of 5:1. The oven temperature was

programmed from 100 °C to 270 °C at a ramping rate of 5 °C /min. Both the initial and final

temperatures were held for 5 minutes.

6.3.10 RISL structural characterization by 2-D 1H -1C NMR HSQC NMR

50 mg recovered insoluble lignin (RISL) was dissolved in 600 μL deuterated DMSO

(Cambridge Isotope Laboratories). The resulting liquid sample was placed in 5 mm Wilmad 535-

164

PP NMR tubes. NMR spectra were collected at 25° C on 500 and 600 MHz Agilent (Varian)

Inova NMR spectrometers equipped with z-axis pulsed-field triple-resonance HNCP probes.

Samples contained 0.05% (v/v) TMS for chemical shift referencing. Two-dimensional 1H-13C

HSQC spectra of the aliphatic and aromatic regions were collected separately using the BioPack

gchsqc pulse sequence, with 1H spectral width of 17 ppm and 13C spectral widths of 100 or 60

ppm for the aliphatic or aromatic regions, respectively. Spectra were collected with 1024 points

(Varian parameter np) and 61 ms acquisition time with 128 or 256 transients and 128 or 96

complex points (Varian parameter ni in States-TPPI mode) in the indirect dimension, for

aliphatic and aromatic spectra, respectively. Adiabatic WURST decoupling was applied during

acquisition. Delayed times tCH and lambda for 1/4*JCH, were 1.8 ms and 1.6 ms for aliphatic

spectra, and 1.45 ms and 1.3 ms for aromatic spectra, respectively. Reference one-dimensional

1H spectra were collected with 32k points and 128 transients. HSQC spectra were processed and

analyzed with Felix 2007 (FelixNMR, Inc) or MestReNova 6.0.4 (Mestrelab Research), with

matched cosine-bell apodization in both dimensions, 2X zero filling in both dimensions, and

forward linear prediction of 30% more points in the indirect dimension. One-dimensional 1H

spectra were processed with no apodization or linear prediction and 2X zero filling. Relative

peak integrals were measured in MestReNova.


In this study, poplar carbohydrates were mostly recovered as sugars under tested

pretreatment conditions 48, 59. Results showed that flowthrough pretreatment at elevated

temperatures enhanced sugar recovery to over 90% and limited degradation loss. This chapter

focuses on understanding lignin recovery and structural characteristics are the recovered lignin,

165

which are crucial for conversion to high-value products. To characterize the recovery of the three

lignin sub-sets, a comprehensive process was followed as summarized in Figure 6.1. Recovered

insoluble lignin was collected by settling pretreatment liquid at pH 2-3 overnight to precipitate

solid lignin at the bottom followed by fast filtration to collect lignin solids, avoiding filter paper

clogging.

Figure 6.1 Scheme for lignin recovery and analytical analysis in this study

6.4.1 Lignin removal and recovery in aqueous phase

Lignin in its pretreated aqueous phase was recovered as the RSL and RISL. Lignin recovery

by flowthrough pretreatment increased as the pretreatment severity increased (LogR0) under both

water-only and dilute acid conditions (Figure 6.2). The pretreatment severity (LogR0) was used

to evaluate the severity of the pretreatment process by combining two variables, including the

166

temperature and reaction time (Equation 6.1 in the method section). Nearly 100% of lignin was

released into the aqueous phase at around LogR0=6.0 in water-only pretreatment or at LogR0=5.0

in dilute acid pretreatment. Under such conditions, it was previously reported that nearly whole

biomass was solubilized into the liquid phase and carbohydrates were almost completely

recovered as monomeric and oligomeric sugars 345.

Figure 6.2 Lignin removal and recovery as RSL and RISL. (a) water-only and (b) 0.05% (w/w)

H2SO4 flowthrough pretreatment (temperature: 160-270 °C, time: 0-12min, flow rate: 25mL/min)

Dilute acid was found to be more effective for lignin recovery than water-only pretreatment

at the same severities (Figure 6.2). Addition of 0.05% (w/w) sulfuric acid enhanced

delignification and solubilization into the aqueous phase. Considering that the pH of the water at

220 °C was 5.5, and that it dropped to 3.2-4.0 at temperatures higher than 220°C 48, 59, water-only

pretreatment at increased temperatures is similar to acid-catalyzed pretreatments. Different RSL

and RISL yields were observed in water-only and dilute acid pretreatments (Figure 6.2a, b). The

RSL yields were less than 20% and 5% in water-only pretreatment and dilute acid pretreatment,

respectively. Higher yields of RSL were found in water-only than in dilute acid pretreatment at

the same LogR0. In addition, the dilute acid-derived RSL was observed to gradually increase

(a) (b)

167

with increasing LogR0, followed by a slight decline at about LogR0=4.6, which differed from the

climbing trend of RSL in water-only pretreatment. These suggested that reactions shifted from

lignin depolymerization to counterproductive RSL condensation or repolymerization in dilute

acid pretreatment, thereby limiting the removal of depolymerized lignin derivatives. Since

flowthrough pretreatment possibly limited the number of condensation reactions, little decrease

in RSL yield was observed (Figure 6.2b). More than 60% of the recovered RISL was found in

flowthrough pretreatment at LogR0>4.0, and dilute acid addition further improved RISL

formation. The pretreatment severities at ~6.0 and ~5.0 appeared to be the most promising

conditions tested to produce high yield of RISL as well as high sugar yields by water-only and

dilute acid pretreatments, respectively 345. The lignin structural analysis in this study mainly

focused on these two conditions.

168

6.4.2 Lignin purity determination of the isolated RISL

Figure 6.3 The RISL purity determined by AcBr method. (a) water-only; (b) 0.05% (w/w)

H2SO4 at the flow rate of 25 ml/min

The flowthrough pretreatment hydrolysates contained a mixture of degradation products

from carbohydrates and lignin derivatives. The RISL was isolated by precipitating hydrolysates

at pH 2-3, followed by filtration, washing, and freeze-drying. The RISL purity was found to be

higher than 50% in both water-only and dilute acid pretreatment hydrolysates under the tested

conditions (Figure 6.3). The highest purity of RISL was achieved at about 80% to 90% in water-

only and dilute acid pretreatments, respectively. RISL pretreated using dilute acid was found to

have higher purity than that in water-only pretreatment. This was because most carbohydrates

(a)

(b)

169

were hydrolyzed into monomeric sugars and washed away in dilute acid flowthrough

pretreatment 345.

RISL recovered at high temperatures (240°C and 270°C for 0.05% wt sulfuric acid and hot

water pretreatment, respectively) were treated with pyridine/acetic anhydride. THF was used as

eluent. Individual lignin samples were dissolved in THF. GPC analysis was carried out in Agilent

GPC system with UV detector. The set flow rate is 1 ml/min. Polystyrene was used for

calibration curve. The number average molecular weight (Mn) and the weight average molecular

weight (Mw) for lignin obtained under 240°C, residence time of 10mins, 0.05% sulfuric acid and

flow rate of 25ml/min were 1083 and 1955 respectively. However, Mn and Mw for lignin

obtained under 270°C, residence time of 10mins, water-only and flow rate of 25ml/min were

1197 and 2661, respectively (Figure S6.2). The polydispersity (D; M̅w/M̅n) of lignin derived

from dilute acid condition (240°C, 10mins, 0.05% sulfuric acid and flow rate of 25ml/min,) and

hot water condition (270°C, 10mins, water-only and flow rate of 25ml/min) was 1.81 and 2.22,

respectively. The molecular weight distribution provides evidence of low molecular weight

nature of our lignins, which offers great opportunities in lignin utilization.

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6.4.3 Py-GC/MS spectroscopic analysis of untreated poplar wood and pretreated solid

residues

Figure 6.4 Py-GC/MS results of untreated poplar wood (control) and solid residues after

flowthrough pretreatments. (a) Untreated poplar wood and solid residues pretreated at 240 °C,

0.05% (w/w) H2SO4 for 10 min (pretreatment severity ~5.0); (b) Untreated poplar wood and

solid residues pretreated at 270 °C, water-only for 10 min (pretreatment severity ~6.0)

Py-GC/MS analysis of ReL revealed structural information in the form of pyrolytic products.

Selected py-GC/MS results of residual solids pretreated at 240 °C with 0.05% (w/w) H2SO4 for

10 min (LogR0 ~5.0) and at 270 °C by water-only for 10 min (LogR0 ~6.0) are shown in Figure

6.4. The pyrolysis products of residual lignin can be found in Table A1. As seen from the

differences among peak intensities in Figure 6.4, solid residues after pretreatment showed much

less lignin remaining than that in the untreated poplar wood due to the enhanced lignin

depolymerization.

(a) (b)

171

Figure 6.5 Effects of pretreatment severity on S, G and H unit removal by water-only and 0.05%

(w/w) dilute acid pretreatment (poplar wood control sample: G:S:H= 7:11:2)

The ratios of the S, G, and H units contain structural information about lignin. The S/G ratio

was used as a criterion for evaluating delignification352, 356. Py-GC/MS was applied to investigate

S/G ratio change during flowthrough pretreatment. The preferential release of sub-units (G and

S) of lignin into the aqueous phase was reported in flowthrough pretreatment at temperatures up

to 200°C 357. In this study, nearly complete lignin removal was achieved under severities of

higher than ~5.0 and ~6.0 in dilute acid and water-only pretreatments, respectively. The dynamic

changes of the S, G and H units during flowthrough pretreatment are shown in Figure 6.5. The

spectra in Py-GC/MS results were normalized and the relative percentages of the S, G, and H

172

units or their reduction based on untreated poplar lignin are shown.

Figure 6.5 shows the high extent of release of S, G, and H units into the aqueous phase at

severities higher than 4.2 and 4.5 in dilute acid and water-only pretreatments, respectively. The

removal rate of the S unit was higher than that of the G units in flowthrough pretreatment,

indicating the preferential removal of S units versus G units. Such preference was consistent with

previous results of water-only pretreatment at temperatures lower than 200°C 62, 358. The H unit,

constituting about 10% of lignin in untreated poplar wood, showed the fastest removal, which

might due to greater reactivity at the C3 and C5 positions. The elevated temperatures and

addition of 0.05% (w/w) H2SO4 enhanced lignin depolymerization, thus, it not only led to

removal of the S units but also promoted solubilization of the G unit.

6.4.4 FTIR spectroscopic analysis of ReL

FTIR was carried out to evaluate ReL structural modification in functional groups by

flowthrough pretreatment. The assignments of the functional groups of lignin in this study were

shown in Figure 6.6 and Table 6.1. Peaks at 1260 cm-1-1760 cm-1 were assigned to lignin

structural spectral vibration regions 62. In the selected spectra of solid residues obtained from

both flowthrough pretreatments, ~80% reduction of peak intensity at 1594, 1455, and 1423 cm-1

was observed, indicating removal of the aromatic rings to liquid phase (Figure 6.6). The shape

and intensity changes in the peak at 1115 cm-1 were also observed to correspond with the

modification of the aromatic ring and cleavage of the ester linkages, which was especially

obvious in sample C (solid residues by 0.05% sulfuric acid pretreatment at 240℃ for 5 min). The

peak at 1735 cm-1 disappeared in all samples, which indicated the removal of oxidized side

chains. In addition, the peak intensity at 1655 cm-1 increased, indicating the formation of

173

conjugated carbonyl groups, particularly under acidic conditions.

Figure 6.6 Characterization of ReL in pretreated solid residues by FTIR (800-2000 cm-1).

Control: Untreated Poplar wood; a) Solid residues from 0.05% (w/w) sulfuric acid pretreatment

at 240 °C for 2.6 min; b) Solid residues from water-only pretreatment at 230℃ for 3.8 min; c)

Solid residues pretreated with 0.05% sulfuric acid at 240℃ for 5 min

Changes of the S and G units were also observed in the IR spectra. Peaks at 1325, 1244, and

1268-1270 cm-1 decreased in solid residues, demonstrating the release of the syringyl and

guaiacyl derivatives into the liquid phase. These results were consistent with the observation by

Py-GC/MS analysis in which addition of dilute acid enhanced releasing of S and G units to

aqueous phase. The FTIR results showed that more than 90% of S and G units from biomass

solids could be removed.

(a)

(b)

(c)

174

Peaks at 1375, 1120 and 1030-1086 cm-1 was assigned to bond stretching vibrations in

cellulose and hemicellulose. The decrease of these peak intensities was attributed to the

solubilization of cellulose and hemicellulose into the aqueous phase during pretreatment.

Particularly, sample C presented almost a 90% peak decrease in the carbohydrates region.

Table 6.1 FTIR spectra band assignments 124, 359-364

No. Wavenumber(cm-1) Assignment

1 1708-1738 Stretching of C=O unconjugated to aromatic rings (oxidized side-chains,

non-conjugated carbonyl)

2 1655 Stretching of C=O conjugated to aromatic rings (conjugated carbonyl)

3 1590-1609 Aromatic ring vibrations and C=O stretching

4 1500-1515 Aromatic ring vibrations

5 1455/1425 C-H deformation and aromatic ring vibration

6 1420-1424 Aromatic ring vibrations

7 1375 C-H stretching in cellulose and hemicellulose

8 1330-1325 Syringyl nuclei (C-O stretching)

9 1270-1268/1244 Guaiacyl nuclei (C-O stretching)

10 1221 C-C, C-O, C=O stretching in G ring

11 1160 Deformation vibrations of C-H bonds on benzene rings

12 1120 Carbon ring stretching of cellulose

13 1115 Vibrations of ester linkage

14 1086 C-O stretch of secondary alcohols and aliphatic ethers

15 1048 C-O stretch in cellulose and hemicellulose

16 1060/1040/1030 C-O stretch (primary alcohols)

6.4.5 Characterization of the RSL by GC/MS

GC/MS can identify lignin derivatives in soluble phase but it is less sensitive to most

oligomeric lignin polymers (>1000) because of their low volatility. As discussed above, RSL was

shown only less than 20% and 5% of the total yields of original lignin in water-only and dilute

acid flowthrough pretreatments, respectively. However, profiles of lignin derivatives in the RSL

phase can provide evidence of the lignin depolymerization mechanism.

In this study, the identified derivatives from water-only and 0.05% (w/w) sulfuric acid

pretreatments were mainly vanillin, benzaldehyde, hydroxybenzaldehyde, 2-methoxy-4-

vinylphenol, 2,6-dimethoxy-phenol, 4-hydroxy-3,5-dimethoxy-benzaldehyde, and benzoic acids.

175

These compounds indicated possible Cα oxidation and cleavage of β-O-4 linkages 357. In

addition, these products suggested that dehydroxylation or demethoxylation reactions were

present in both flowthrough pretreatments, which is consistent with FTIR results. Other

identified derivatives included, interestingly, the compounds 2,2'-methylenebis[6-(1,1-

dimethylethyl)-4-methyl-phenol-], 4-(1,1,3,3-tetramethylbutyl)-phenol, and 2,6-bis(1,1-

dimethylethyl)-4-methyphenol (butylated hydroxytoluene) (Table S6.1).

6.4.6 2-D NMR of Ball-milled poplar lignin and RISL

Two-dimensional 13C- 1H NMR (2-D NMR) provided evidence of lignin alteration by

flowthrough pretreatments. Both the aliphatic (δC/δH 50-90/2.5-6.0 ppm) and aromatic (δC/δH

100-135/5.5-8.5 ppm) regions’ HSQC spectra were collected (Figure 6.7). The assignments of

the NMR signals are shown in Figure 6.7 and summarized in detail in the supplemental material

(Table S6.2; Figure S6.1). Relative peak area integrations for ball milled lignin from the same

poplar wood sample and RISLs were measured to determine each lignin linkage and monolignol

change (Table 6.2). The quantification of each linkage is from the volume-integration of cross-

peak contours in HSQC spectra according to previous publication 62. Monolignol quantification

was performed by peak integrations of peak C2/6 from S and H units and peak C2 from G units

(doubled). The quantification of the basic linkages, Aα, Bα, and Cα contours were integrated to

represent A, B, and C, respectively, among which Cα and Cβ contours in C represented α-O-4´

and β-5´ linkages 62, 365.

176

Figure 6.7 2-D 13C - 1H HSQC correlation NMR spectra of aliphatic regions (left column) and

aromatic regions (right column), (a, b) Ball milled poplar wood lignin; (c, d) 240 °C, 0.05%

(w/w) H2SO4 for 10 min (pretreatment severity ~5.0); (e, f) 270 °C, water-only for 10 min

(pretreatment severity ~6.0); Label A: β-O-4 aryl ether linkages; Label B: resinol substructures

(β-β, α-O-γ, and γ-O-α linkages); Label C: phenylcoumaran substructures (β-5’and α-O-

4linkages); Label D: spirodienone substructures ( β-1 and α-O-α linkages); Label G: guaiacyl

(a) (b)

(d) (c)

(f) (e)

177

units; Label G: oxidized guaiacyl units with an Cα-ketone; Label S: syringyl units; Label S:

oxidized syringyl units with a Cα ketone; Label E: p-hydroxybenzoate substructures; Label F:

cinnamyl alcohol end groups; Label K: cinnamaldehyde end groups. Their structures can be

found in supplementary material in Figure S6.1

Table 6.2 Relative proportions of major structural linkages and S, G, H percentages in ball

milled lignin and flowthrough derived RISL; A: RISL collected at 240 °C, 25 ml/min, 10 min,

with 0.05% (w/w) H2SO4; B: 270 °C, 25 ml/min, 10 min, with water-only

Samples β-O-4 Resinol

(β-β´)

Phenylcoumaran Spirodienone

( β-1´, α-O-α´)

S/G

β-5´ α-O-4´

Ball milled 73.07 10.71 3.81 12.01 0.40 0.602

A 46.57 8.18 5.72 8.88 0.00 0.674

B 49.38 8.19 5.94 8.49 0.00 0.669

The NMR spectra of pretreatment derived lignins showed the basic linkages of lignin as in

ball milled wood lignin. Comparing pretreatment-derived lignin and ball milled wood lignin

indicates that flowthrough pretreatment only caused mild modification on original lignin. These

structural changes include, first in the aliphatic region (in Figure 6.7a, c, e), loss of structure D

(spirodienone) which was not seen in both RISLs pretreated by water-only and dilute acid,

indicating that flowthrough pretreatment decomposed this structure. Second, in the aromatic

region, the RISL showed formation of propenyl end group structures F (cinnamyl alcohol and its

O4 ether and 3- and/or 5-methoxy analogues) and K (like cinnamaldehyde and analogues), which

were not found in ball milled poplar lignin. A plausible explanation for these propenyl end

groups is β-O-4´ cleavage by a mechanism involving eliminative dehydration to remove the α-

OH and produce an enol ether, which could then be cleaved by protonation of the enol, addition

178

of water, and formation of a carbonyl at the α or β position upon cleavage of the aliphatic ester

bond. Carbonyls at Cβ were not observed, while Cα carbonyls were observed, although either of

these could be reduced and eliminated (dehydration) to yield the alkene. Oxidation of the γ-OH

to the aldehyde or carboxylic acid could be a source of electrons for reduction of these carbonyls

(Figure 6.8).

Quantification of lignin linkages and substructures was obtained from 1-D 13C NMR

spectra and by measuring contour volume integrals of 2-D HSQC peaks 157, 366. The

aromatic region provided S and G ratios in RISL while the aliphatic region indicated an

abundance of C-O-C and C-C linkages (Table 6.2). Ball-milled poplar lignin was mainly

composed of β-O-4 ether, resinol, and phenylcoumaran structures with a small portion of

spirodienone structure, p-hydroxybenzoate, and the p-hydroxycinnamyl alcohol. After

pretreatment, the RISL showed different ratios of these linkages. However, the

flowthrough pretreatment only caused a modest change in the ratio, especially for peak

volumes in the aromatic region. First, the decline in percentages of β-O-4´ linkage, α-O-

4´, and resinol structures indicated the depolymerization of these structures in the original

lignin by pretreatment. Secondly, β-5´ structure slightly increased compared to ball milled

wood lignin indicating the occurrence of lignin β-5´ condensation reactions. The flowthrough

system prevented severe condensation reactions that occur in batch reactors65, 367. Finally,

cinnamyl-like structures appear in the NMR spectra, suggesting a β-O-4´ cleavage mechanism

linked to dehydration at the α-position and oxidation at γ-OH. A possible mechanism is

illustrated in Figure 6.8.

179

Figure 6.8 Plausible lignin β-O-4 cleavage and condensation reaction pathways in hydrolysates

6.5 Conclusion

Our previous work has shown great advantages in recovering sugars for our flowthrough

pretreatment system operating at elevated temperature 345. Understanding the fundamental

chemistry of lignin during pretreatment is crucial to the development of advanced biorefinery

strategies. In the flowthrough system, elevated temperature and addition of dilute acid was found

to lead to cleavage of primarily C-O-C as well as C-C linkages in lignin, and effective

180

solubilization of lignin derivatives into the aqueous phase with mild modification of lignin

aromatic structures. Results indicated that 0.05% (w/w) dilute sulfuric acid improved RISL yield

as well as its purity. Together with elevated temperatures, 0.05% (w/w) dilute sulfuric acid

enhanced removal of not only S units but also most of G units, thus resulting in almost 100%

removal of lignin. Modest structural changes in side chains were observed, with formation of

benzylic carbonyl groups at the α-position, as well as α-β unsaturation and oxidation of the γ-OH

to the aldehyde. A new plausible explanation for these propenyl end groups during the

pretreatment is β-O-4´aryl ether cleaved by dehydration at the α-position and oxidation at γ-

OH. However, besides basic lignin linkages in RISL, such as resinol, β-O-4´, and the

phenylcoumaran structures observed, slight repolymerization occurred in the form of new Cβ-

C5´ linkages in RISL. Thus, flowthrough pretreatment of biomass has the potential to enable

high-yield recovery of lignin derivatives for further upgrading, a potentially vital factor in the

economics of biorefinery operations 325, 330, 337.The lignin derived in this chapter has been tested

on the conductive electrode materials application 328.


The author is grateful to the DARPA Young Faculty Award # N66001-11-1-414, DOE-EERE

Award # DE-EE0006112, The Sun Grant-DOT Award # T0013G-A- Task 8, and the National

Science Foundation Award # 1258504 for funding this research. Part of this work was conducted

at the William R. Wiley Environmental Molecular Sciences Laboratory (EMSL), a national

scientific user facility located at the Pacific Northwest National Laboratory (PNNL) and

sponsored by the Department of Energy’s Office of Biological and Environmental Research

(BER). Thank Drs. Yunqiao Pu, Hongliang Wang, and Hongfei Wang for insightful discussions.

181

The thesis author would like to thank Drs. Lishi Yan, Zheming Wang, Dhrubojyoti D. Laskar,

Marie S. Swita, John R. Cort, and Bin Yang for their support to this work and publish in

Biotechnology for Biofuels journal.

182


Table S6.1 Major GC/MS detected aromatic compounds in hydrolysates

a:0.05%(w/w) H2SO4, 62.5mL/min; b: Water-only, 25mL/min; c: 0.05%(w/w) H2SO4, 25mL/min; d: pretreatment severity

factor. + 0－10%; ++ 10%－20%; +++ 20%－50%; - not available.

Major lignin derivatives Relative abundance Structure MW

4.2d

ACIDa

4.4d

ACIDa

4.7d

ACIDa

4.5d

HWb

6.0d

HWb

4.8d

ACIDc

5.1 d

ACIDc

Vanillin

+

-

-

-

-

+

+

152

Butylated Hydroxytoluene (BHT)

++

++

++

++

+++

++

+

220

Phenol, 4-(1,1,3,3-tetramethylbutyl)-

+

+

+++

+

++

+++

+++

206

Coniferyl alcohol

-

-

-

-

-

+

-

180

Phenol, 2,2'-methylenebis[6-(1,1-

dimethylethyl)-4-

+++

+++

+++

++

++

+++

+++

340

183

Figure S6.1 Assigned interunit linkages of lignin, including different side-chain linkages, and

aromatic units: (A) β-O-4 aryl ether linkages; (B) resinol substructures (β-β′, α-O-γ′, and γ-O-α′

linkages); (C) phenylcoumarane substructures (β-5′ and α-O-4′ linkages); (D) spirodienone

substructures (β-1′ and α-O-α′ linkages); (G) guaiacyl units; (G′) oxidized guaiacyl units with an

Cα-ketone; (S) syringyl units; (S′) oxidized syringyl units with a Cα ketone; (E) p-

hydroxybenzoate substructures; (F) cinnamyl alcohol end groups; (K) cinnamaldehyde end

groups

184

Table S6.2 Assignments of main lignin 1H- 13C cross-peaks in the HSQC Spectra of the RISLs

144, 157, 368-371


-OCH3 55.47(C) 3.70(H)

A: β-O-4 71.70(S-Cα) 4.84(S-Hα) 59.50-59.70(Cγ) 3.40-3.63(Hγ) 85.90(S-

Cβ) 4.09(S-Hβ) 83.49(G/H-Cβ) 4.28(G/H-Hβ)

B: resinol 84.85(Cα) 4.62(Hα) 53.30(Cβ) 3.05(Hβ) 70.85(Cγ) 4.14/3.78(Hγ)

C: phenylcoumaran 86.79(Cα) 5.41(Hα) 53.3(Cβ) 3.46(Hβ) 62.52(Cγ) 3.68(Hγ)

D: spirodienone 59.67(Cβ) 3.19(Hβ)

G: guaiacyl 111.02(C2) 6.95(H2) 115.05(G5) 6.74(H5) 119.01(G6) 6.78(H6)

G: oxidized (Cα=O) guaiacyl 111.56(C2) 7.50(H2) 123.55(C6) 7.54(H6)

S: syringyl 103.95(C2/6) 6.67(H2/6)

S: oxidized (Cα=O) syringyl 106.52(C2/6) 7.29(H2/6)


F: cinnamyl alcohol 128.39(Cβ) 6.20(Hβ) 128.59(Cα) 6.42(Hα)

K: cinnamaldehyde 126.23(Cβ) 6.74(Hβ)

a Note: G, S, H-C or G, S-H refers to C and H in the lignin sub-units, guaiacyl, syringyl and p-

hydroxyphenyl

185

CHAPTER SEVEN

REVEALING THE MOLECULAR STRUCTURAL TRANSFORMATION

OF HARDWOOD AND SOFTWOOD IN DILUTE ACID

FLOWTHROUGH PRETREATMENT

7.1 Abstract

To better understand the intrinsic recalcitrance of lignocellulosic biomass, the main hurdle to

its efficient deconstruction, the effects of dilute acid flowthrough pretreatment on the dissolution

chemistry of hemicellulose, cellulose, and lignin for both hardwood (e.g. poplar wood) and

softwood (e.g. lodgepole pine wood) were investigated at temperatures of 200 °C to 270 °C and

a flow rate of 25 mL/minute with 0.05% (w/w) H2SO4. Results suggested that the softwood

cellulose was more readily to be degraded into monomeric sugars than that of hardwood under

same pretreatment conditions. However, while the hardwood lignin was completely removed into

hydrolysate, ~30% of the softwood lignin remained as solid residues under identical conditions,

which was plausibly caused by vigorous C5-active recondensation reactions (C-C5). Unique

molecular structural features that pronounced the specific recalcitrance of hardwood and

softwood to dilute acid pretreatment were identified for the first time in this study, providing

important insights to establish the effective biomass pretreatment.

Keywords: Softwood and Hardwood • Lignin • Structural transformation • Recondensation •

Solid State NMR • 2-D HSQC NMR • Dilute acid flowthrough pretreatment

7.2 Introduction

Cellulosic biomass has a rigid plant cell wall mainly composed of cellulose, hemicellulose,

and lignin. Biomass is recalcitrant to chemical or microbiological conversions and a pretreatment

186

process is usually needed to disrupt the cell wall matrix and reduce the recalcitrance of biomass

to improve sugar yields for fermentation. The characterization of lignin in pretreatment is

important for understanding biomass recalcitrance and developing effective pretreatment

methods. Among various biomass species, hardwood and softwood vary greatly in lignin content,

monolignol types, and interunit linkages abundance 89, 372, 373. For example, softwoods generally

contain more lignin than hardwoods, and softwood lignin is composed of mostly G units while

hardwood lignin contains both S and G units. In addition, softwood presents more C-C linkages,

and fewer C-O-C linkages (Table 7.1). Unfortunately, pretreatment of softwood is more

challenging for reducing the recalcitrance and improving sugar recovery than hardwood, and the

mechanism still remains to be fully understood.

Table 7.1 General lignin difference in hardwood and softwood

Biomass Linkages

Abundance (%) 89

Monolignol types

(%)

Molecular weight

(Bjorkman lignin) 372, 373

Amount of

lignin (%)

Softwood β-O-4: ~50

α-O-4: ~8

5-O-5: ~5

β-5: ~11

5-5: ~18

β-1: ~7

β-β: ~2

G: 90-95

S: 0-1

H: 0.5-3.5

e.g. spruce

Mn: ~3000

Mw: ~10000

PD: ~3.33

~28

Hardwood β-O-4: ~60

α-O-4: ~8

5-O-5: ~5

β-5: ~6

5-5: ~10

β-1: ~7

β-β: ~2

G: 25-50

S: 50-75

H: Trace

e.g. Eucalyptus

globulus

Mn: ~2600

Mw: ~6700

PD: ~2.6

~20

Generally, softwood pretreatment achieved less sugar yield and lignin removal than

hardwood pretreatment. Softwood pretreatment methods previously reported include biological,

187

steam explosion with or without acid/SO2, alkaline, hot water, dilute acid, and organosolv

pretreatment 300, 374-384. The dilute acid pretreatment is known for achieving more than 80%

recovery yield of hemicellulose sugars. However, most dilute acid pretreatments ranging up to

200°C can only achieve a limited improvement of cellulose sugar yield (i.e, glucose yield) from

softwood. For example, the dilute acid pretreatment of various body parts of pine wood achieved

a glucose yield of 8.7-42.3% of original glucan in the pretreatment stage at 180°C with 2-4 %

(w/w) sulfuric acid for 6-12 min and a glucose yield of 13.6-32.6% in the a 72 h enzymatic

hydrolysis of pretreated solid residues 385. Also, lodgepole pine was pretreated at 180°C with

2.2 % (w/w) sulfuric acid for 30min and achieved 18.3% yield of original glucose on pretreated

solid residues after a 72 h of enzymatic hydrolysis 386. In addition, diluted acid pretreatment was

also observed to have a limitation for lignin removal in softwood. Li et al. reported a 2.2% lignin

removal in lodgepole pine wood at 180 °C. At higher temperatures of 200-230°C, with 90 – 95%

of the hemicellulose and ~ 20% of the cellulose solubilized, residual Klason lignin remained in

pretreated solid residues was reported at levels of more than 129% of original lignin content,

which was plausibly due to the formation of pseudo lignin from carbohydrates 305, 387. When

compared to pretreatment of softwood, dilute acid pretreatment of hardwood showed different

results in sugar yield, enzymatic digestibility and lignin removal. For example, when poplar

wood was pretreated with steam heated to 210°C for 5 min, ~68% of the original glucose was

recovered while 10-18% lignin was removed 388. In the Consortium for Applied Fundamentals

and Innovation (CAFI) study, polar wood was subjected to dilute acid pretreatment at 190°C

with 2.0% (w/w) sulfuric acid for 1.1 min, resulting in a 23.9% of the original glucose recovery

in the pretreatment stage, 62.8% of the original glucose in enzymatic hydrolysis stage (with

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enzyme loading of 15 FPU/g glucan), and lignin removal of ~28% 389.

The diverse pretreatment methods and conditions on hardwood and softwood pose

challenges to resolving the recalcitrance of softwood and hardwood in pretreatment. Most of

pretreatment approaches have been carried out in batch systems, which have limitations in

selectively characterizing pretreatment derived biomass components. Under batch process, the

lignin chemistry during dilute acid or hot water pretreatment was reported to be predominantly

centered about depolymerization reactions via rupture of the β-O-4 linkages forming Hibbert

ketones and repolymerization by acid catalyzed condensation between the aromatic C6 and a

carbonium ion at Cα 68, 69. In addition, literature reports have documented that during batch dilute

acid pretreatments a) lignin droplet formation and redeposit occurs, b) pseudo lignin formation,

c) lignin coalesce and migration within and out of cell wall 103, 104, 106, 390. In contrast, a

flowthrough system can reduce the above mentioned phenomena by exiting fractionated products

out of the reactor in a very brief time and providing effective mass and heat transfer 27. This

continuous pretreatment concept was pioneered by Bobleter et al. and reported in 1983 44-47. The

hot water flowthrough systems can carry soluble materials away from the reaction zone and limit

the opportunity for further reaction or degradation. These result in high distributions of

oligomers and low prevalence of degradation products 50, 391. Furthermore, a sizable portion of

the hemicellulosic and lignin sheath surrounding cellulose macrofibrils is solubilized, and the

solubilized components are rapidly removed from the reaction zone. Similar results to

flowthrough pretreatments with just hot water are obtained if very dilute sulfuric acid is used as

the flowing solvent, with the exception that more cellulose is solubilized. Increasing the

temperatures of hot water flowthrough pretreatments to 225-270°C within or above saturated

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steam pressure also solubilizes more cellulose 60, 345, 392. Thus, the flowthrough pretreatment

system is the promising tool to study the unique molecular structural features that pronounce the

specific impacts on hardwood and softwood during dilute acid pretreatment.

A recent study in our group reported almost 100% whole poplar biomass solubilization in

the dilute acid flowthrough system 60. The flowthrough system allowed separation of lignin from

biomass to study the lignin characteristics, and the results indicated the predominant β-O-4

cleavage and slight β-5 recondensation mechanisms in dilute acid pretreatment, which improves

our understanding of poplar lignin chemistry and structural transformation in dilute acid

pretreatment on a molecular structural basis. In this study, dilute acid pretreatment of softwood

loblolly pine was carried out in the flowthrough system for the first time to investigate the

behaviours of its major components as compared to those of hardwood poplar under same

conditions 59, 60. The effectiveness of the flowthrough pretreatment on softwood was assessed by

sugar yields after pretreatment and enzymatic hydrolysis. The softwood lignin structural

transformation was investigated through two-dimensional heteronuclear single quantum

coherence solution nuclear magnetic resonance spectroscopy (2-D 1H-13C HSQC NMR) and

solid state CP/MAS 13C NMR.


7.3.1 Materials

Beetle-killed Lodge pole pine was provided by Forest Concepts, LLC., and milled to a

particle size of 40-60 mesh. The feedstock compositions were analyzed according to the K-lignin

analysis protocol 128. The pine wood chips contained 37.89±0.59% of glucan, 5.25±0.047% of

xylan, 29.95 ±0.78% of lignin, 3.62±0.093% of galactan, 12.14 ±0.018% of arabinan and

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mannan, and 0.62±0.015% of ash.

7.3.2 Dilute acid flowthrough pretreatment of pine wood

The flowthrough pretreatment of Beetle-killed Lodge pole pine was conducted with a 0.05%

(w/w) sulfuric acid solution at 200-270°C for 2-10 min (3.7-6.0 pretreatment severities) 59, 60.

The pretreatment severity refers to the variable incorporating temperature and time (equation

7.1) 354.

LogR0 = Log[t × exp (T−100

14.75)] (7.1)

T refers to the temperatures applied in the pretreatment and t represents the pretreatment time.

The flow rate was set to 25ml/min. 0.5 g biomass was loaded into the tubular reactor (0.5′′ O.D.

× 6′′ length; 0.035′′thickness; 20.2 ml), which was attached to the flowthrough system. The

0.05% (w/w) sulfuric acid solution was pumped through the reactor continuously while the

reactor was placed in a fluidized sand bath with a thermocouple (Omega Engineering Co.,

Stamford, CT) to monitor the actual temperature. The flowthrough system was set up as

previously reported 59, 60. After pretreatment, the solid residues remaining in the reactor were

collected, and their composition was analyzed according to the K-lignin protocol 128. Monomeric

sugar in pretreated hydrolysate was analyzed by HPLC (Biorad Aminex HPX-87P column). In

addition, the pretreated hydrolysate was posthydrolysed with 4% (w/w) sulfuric acid and

autoclaved at 121 °C for 1 h to determine total sugar recovery yield and oligomeric sugars yield

in pretreatment. For pretreatment recovered insoluble lignin collection, the pretreatment at the

selected condition was repeated 10 times to accumulate hydrolysates to precipitate lignin at pH

2-3 followed by washing, filtration, and freeze drying for analysis 325.

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7.3.3 Structural characterization of pretreatment recovered insoluble lignin by 2-D 1H-13C

HSQC NMR

The pretreatment recovered insoluble lignin (50 mg) was dissolved in 600 μL deuterated

DMSO (Cambridge Isotope Laboratories). The resulting pretreatment recovered insoluble lignin

was placed in 5 mm Wilmad 535-PP NMR tubes. NMR spectra were collected at 25° C on 500

and 600 MHz Agilent (Varian) Inova NMR spectrometers equipped with z-axis pulsed-field

triple-resonance HNCP probes. Samples contained 0.05% (v/v) TMS for chemical shift

referencing. Two-dimensional 1H-13C HSQC spectra of the aliphatic and aromatic regions were

collected separately using the BioPack gchsqc pulse sequence, with 1H spectral width of 17 ppm

and 13C spectral widths of 100 or 60 ppm for the aliphatic or aromatic regions, respectively.

Spectra were collected with 1024 points (Varian parameter np) and 61 ms acquisition time in 1H

dimension and with 128 or 256 transients and 128 or 96 complex points (Varian parameter ni in

States-TPPI mode) in the indirect 13C dimension, for aliphatic and aromatic spectra, respectively.

Adiabatic WURST decoupling was applied during acquisition. Delay times tCH and lambda for

1/4*JCH, were 1.8 ms and 1.6 ms for aliphatic spectra, and 1.45 ms and 1.3 ms for aromatic

spectra. Reference one-dimensional 1H spectra were collected with 32k points and 128 transients.

HSQC spectra were processed and analyzed with Felix 2007 (FelixNMR, Inc) or MestReNova

6.0.4 (Mestrelab Research), with matched cosine-bell apodization in both dimensions, 2X zero

filling in both dimensions, and forward linear prediction of 30% more points in the indirect

dimension. One-dimensional 1H spectra were processed with no apodization or linear prediction

and 2X zero filling. Relative peak integrals were measured in MestReNova 60. The same method

has been reported in our previous study 60.

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7.3.4 Solid state CP/MAS 13C NMR characterization of dilute acid flowthrough pretreated

poplar and pine lignin

The solid-state CP/MAS 13C NMR experiments were performed on a Bruker Avance III 400

MHz spectrometer operating at frequencies of 100.59 MHz for 13C using a Bruker double-

resonance 4-mm MAS probe head at ambient temperature. The samples were packed in a 4 mm

ZrO rotor fitted with a Kel-F cap and spun at 8,000 Hz. CP/MAS 13C data were acquired with a

Bruker CP pulse sequence under the following acquisitions: pulse delay 4s, contact pulse

2000ms, and 2k to 4k numbers of scans.

7.3.5 Gel permeation chromatography molecular weight analysis of the flowthrough

derived pine lignin

The lignin molecular weight distribution was analyzed with gel permeation chromatography

(GPC) after acetylation. The lignin samples were dissolved in a mixture of acetic anhydride/

pyridine (1:1 v/v) and stirred at room temperature for 24 h. After acetylation, ethanol was added

to the reaction mixture and then removed with a rotary evaporator. The addition and removal of

ethanol was repeated until until all traces of acetic acid and pyridine were removed from the

sample. The resulted acetylated lignin samples were analysed on a PSS-Polymer Standards

Service (Warwick, RI, USA) GPC Security 1200 system featuring Agilent HPLC 1200

components equipped with four Waters Styragel columns (HR0.5, HR1, HR4 and HR6) and an

UV detector (270 nm). Tetrahydrofuran was used as the mobile phase, and flow rate was 1.0

mL/min. Standard narrow polystyrene samples were used for calibration 325.

7.3.6 Enzymatic hydrolysis

Enzymatic hydrolysis of pretreated whole slurries was carried out by adding Novozymes

193

Cellic® CTec1 (220 mg protein/mL, preserve 200 mg glucose/mL, 93 FPU/mL) and Novozymes

Cellic® HTec2 (230 mg protein/mL, preserve 180 mg xylose/mL) at a ratio of 5:1. The enzyme

loadings were 20 or 100 mg protein/ g (glucan+xylan), in which the higher enzyme loading can

evaluate the theoretical glucose yield while the lower enzyme loading was designed to compare

the digestibility of substrates from different pretreatment conditions. The pretreated whole slurry

was adjusted to pH 4.8-5.0 with 0.1 N NaOH. 1% (w/w) bovine serum albumin (BSA) was

added and supplemented with 10mg/mL sodium azide. The resulting mixture was placed in a

shaker at a temperature of 50°C with shaking speed of 180 mph for 72 h.


7.4.1 Delignification and cellulose removal of poplar and pine wood with dilute acid

flowthrough pretreatment

Figure 7.1 Lignin and cellulose contents in pretreated solid residues of poplar and pine wood

after the flowthrough pretreatment (200-270°C, reaction time of 2-10min, and 0.05% (w/w)

H2SO4) (Poplar pretreatment data was adapted from previous publications 59, 60)

The dilute acid flowthrough pretreatment was studied for the effectiveness in delignification

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and carbohydrates solubilization in loblolly pine during pretreatment. The lignin content in

pretreated solid residues was 0-25% and 25-65% for poplar and pine under tested pretreatment

conditions, respectively (Figure 7.1a). Under the same tested pretreatment conditions, 20-40%

more poplar lignin was removed than pine lignin, based strictly on mass corrected for different

levels of starting lignin, indicating a higher resistance to removal of pine lignin than poplar

lignin. The difficulty in softwood lignin removal during dilute acid pretreatment has been well

documented, probably because of the unique structural features and complexity of softwood

lignin behaviors in dilute acid pretreatment. For example, more than 100% of the original pine

lignin reportedly remained in pretreated solid residues during batch dilute acid pretreatment,

partially attributed to the generation of pseudo lignin 305. The flowthrough system can reduce the

formation of pseudo lignin by exiting solubilized products in a short time, resulting in a higher

lignin removal than batch system. Only less than 5% of original poplar lignin reportedly

remained in solid residues with flowthrough pretreatment 59. Thus, flowthrough pretreatment

provides a unique approach to characterize lignin behaviors during pretreatment on a molecular

structural basis.

Comparing lignin and cellulose contents in pine solid residues under pretreatment conditions

(Figure 7.1a, b), pine lignin removal was 20-40% lower than pine cellulose removal. Under

pretreatment severities higher than 5.2, pine cellulose was completely solubilized (0% in solid

residues), but nearly 30% lignin was still present in solid residues even at the highest tested

pretreatment severities. This 30% remaining lignin was collected for structural analysis in order

to understand the pine lignin chemistry and structural transformation in pretreatment. In

comparison, poplar lignin was nearly completely removed when the pretreatment severity was

195

higher than 5.3 while complete removal of poplar cellulose required higher severity over 5.8. At

the tested range of pretreatment severity (3.7-6.0), ~5% less poplar cellulose was solubilized in

general than pine cellulose while 25-40% more poplar lignin removal than pine lignin was

observed under identical conditions (Figure 7.1b). The difference between poplar and pine lignin

solubilization was bigger than the difference between their cellulose solubilization. Therefore,

poplar and pine showed different solubilization behaviors during dilute acid pretreatment.

7.4.2 Sugar recovery in dilute acid pretreatment of pine wood

Figure 7.2 Sugar yields of pine wood at 0.05% (w/w) sulfuric acid flowthrough pretreatment

under different severity log R0 (a) Cellulose recovery; (b) Hemi-sugars recovery

The sugar recovery pattern of pine wood was different than reported hardwood poplar sugar

recovery under the identical flowthrough pretreatment conditions 59. Pine wood sugar recovery

was higher than poplar wood under the same pretreatment conditions (see Figure 7.2). For

example, when the pretreatment severity was 5.2, the total sugar recovery (monomeric and

oligomeric sugars) from pine was ~90% of glucose and 90% of hemi-sugars. However, under the

same pretreatment conditions, the poplar glucose was recovered ~75% with a xylose recovery of

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80% 59. Pine wood carbohydrates were mostly recovered as monomeric sugars and much less

oligomers. Conversely, poplar carbohydrates were recovered mostly as oligomers and less as

monomeric sugars under the same dilute acid flowthrough pretreatment conditions 59. It appeared

that hardwood cellulose showed more resistance to hydrolysis compared to softwood cellulose

during pretreatment.

The hemi-sugars in pine wood were recovered as high as 95% of original hemi-sugars.

When the pretreatment severity was higher than 4.7, the degradation of hemi-sugars to furfural

and formic acid occurred (Figure 7.2b). The results also showed that the dilute acid flowthrough

pretreatment of pine wood achieved high total sugar yield. For example, under pretreatment

severities higher than 4.8, the glucose recovery yield (monomeric sugar yield) was higher than

60% of original glucose (Figure 7.2a) while the hemi-sugars (monomeric sugars) was as high as

90% but declined when the pretreatment severities were higher than 5.2. Besides, under the same

conditions, our previous results reported 40-50% of monomeric glucose recovery yield in poplar

59, which was 10-30% less than those of pine wood in this study. This might have some relation

with the chemical compositions of hardwood and softwood. Hardwood has higher glucan

content, more xylan, and much less arabinan and mannan than softwood. Therefore, results

indicated that a higher yield of monomeric sugars could be obtained from softwood through

dilute acid pretreatment than that from hardwood under same conditions, which showed better

feasibility of softwood in producing monomeric sugars.

Results showed that softwood pine wood cellulose was hydrolyzed to a high yield of glucose

in the pretreatment stage regardless of its retarded delignification (Figure 7.1a). For example,

under the pretreatment severities higher than 4.8, the monomeric glucose yield from pine was as

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high as 70% even though still 30% of original lignin was left in the solid residues. Thus, pine

cellulose is easier to be hydrolyzed than pine lignin by dilute acid flowthrough pretreatment.

7.4.3 Characterization of pretreatment recovered insoluble lignin by 2-D 1H-13C HSQC

NMR

Figure 7.3 Characterization of (a,b) ball milled pine lignin; (c,d) flowthrough pretreatment

recovered insoluble pine lignin from liquid phase by 2-D 1H-13C HSQC NMR. (pretreatment

conditions: 240 °C, 0.05% (w/w) sulfuric acid, and 10min; poplar wood data by 2-D 1H-13C

HSQC NMR under the identical analytical conditions has been published 60

The pretreatment recovered insoluble lignin refers to the removed lignin fragments in the

pretreatment liquid, which were collected through the precipitation of pretreatment liquid at pH

2-3 followed by water washing and freeze-drying. In this study, pretreatment recovered insoluble

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pine lignin was collected for structural analysis along with ball milled pine lignin, a

representative of native lignin. To study the difference of poplar and pine pretreatment recovered

insoluble lignin, pretreatment recovered insoluble pine lignin was collected under the identical

pretreatment conditions performed on poplar previously 60, which was 240 °C with 0.05% (w/w)

sulfuric acid for 10min. The characterization of poplar wood pretreatment recovered insoluble

lignin along with ball milled poplar lignin was investigated by 2-D 1H-13C HSQC NMR in

previous publication 60, suggesting the modest modification of lignin structure. However, in this

study, results on pretreatment recovered insoluble pine lignin characterized under the same NMR

characterization conditions (Figure 7.3) showed distinct pine wood lignin spectra compared with

that of poplar wood as reported 60. The lignin linkages distribution of poplar 60 and pine before

and after pretreatment are displayed in Table 7.2. The quantification of each lignin linkage is

from the volume-integration of cross-peak contours in HSQC spectra according to previous

publications 60, 65, 365. The quantification of the β-O-4 ether, resinol group, and phenylcoumarane

was based on Aα, Bα, and Cα contours integration, respectively (peak assignments see Table

S7.2 and Figure S7.1).

The ratios of the three linkages of poplar wood and pine wood ball milled lignin were

81:11:9 60 and 78:5:17 in the order of β-O-4 ether, resinol group, and phenylcoumarane (Table

7.2), respectively. Compared to pine and poplar ball milled lignin, the β-O-4 ether in pine and

poplar flowthrough recovered insoluble lignin decreased ~52% and ~46%, respectively. This

indicates the cleavage of β-O-4 ether in pine wood lignin similar to poplar wood lignin under

acidic conditions. The slight difference of β-O-4 cleavage might be because S units were

reported to be less difficult to removal than G and hydroxyphenyl-like units 393. Similar to

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hardwood poplar lignin, the β-O-4 cleavage was also the main depolymerization mechanism of

pine lignin. Additionally, the resinol groups in recovered insoluble pine wood lignin showed an

almost three times increasing in percentage more than in poplar lignin, suggesting their different

changes of resinol groups, and resinol groups were degraded slower than β-O-4. Significantly,

the phenylcoumarane groups in poplar pretreatment recovered insoluble lignin increased, which

was proposed due to the recondensation at β-5 in our previous study 60, while the results of pine

pretreatment recovered insoluble lignin also showed that β-5 condensation might occur because

phenylcoumarane groups in pine wood increased despite of the percentage of β-O-4 decreasing.

Thus, the results indicated the main β-O-4 depolymerization mechanism and possible β-5

condensation mechanism of pine pretreatment recovered insoluble lignin.

Table 7.2 The linkage distribution of ball-milled and flowthrough-derived poplar wood and pine

wood lignin (flowthrough pretreatment conditions: 240 °C, 0.05% (w/w) H2SO4, 10min)

Detected linkages β-O-4 ether Resinol group Phenylcoumarane

Poplar wood Ball milled 80.6 10.5 8.9

Flowthrough derived 38.9 17.5 43.6

Pine wood Ball milled 77.5 5.4 17.1

Flowthrough derived 41.8 16.3 41.8

7.4.4 Solid state CP/MAS 13C NMR characterization of residual pine lignin in pretreated

solid residues

In the dilute acid flowthrough pretreatment of this study, the pine carbohydrates were

completely removed at 240°C with 0.05% (w/w) H2SO4 for 10min as demonstrated previously,

which isolated the residual lignin for structural characterization. This undissolved portion of pine

residual lignin remained as solids in the reactor labeled as pine residual lignin. This residual

200

lignin was collected and freezing dried for solid-state CP/MAS 13C NMR analysis (Figure 7.4),

providing opportunities to study the unique structural transformation of pine lignin towards

dilute acid pretreatment. Instead, poplar lignin was completely solubilized, thus no residual

poplar lignin was available for comparison under the identical pretreatment conditions.

Additionally, both pine and poplar pretreatment recovered insoluble lignins in the pretreatment

liquid were analyzed by solid state CP/MAS 13C NMR for comparison to comprehensively

understand the pine wood lignin pretreatment mechanisms (Figure 7.4).

Figure 7.4 Solid state CP/MAS 13C NMR spectra of a) pine residual lignin; b) pine pretreatment

recovered insoluble lignin; c) poplar pretreatment recovered insoluble lignin under 240°C with

0.05% (w/w) H2SO4 for 10min

Solid state CP/MAS 13C NMR can determine the abundance of condensed structures in

lignin. The characterization of pine residual lignin and two pretreatment recovered insoluble

lignins from pine and poplar are displayed in Figure 7.4. The peak assignments of the spectra in

Figure 7.4 are shown in Table 7.3. Peaks at 146-147 ppm and within 100-120 ppm are assigned

to chemical shifts of carbons in G units. Results indicated the high abundance of G units in pine

lignin while less G units in poplar lignin (Figure 7.4a,b,c), which was consistent with high G

units content in original softwood lignin. The poplar pretreatment recovered insoluble lignin

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showed a sharp peak at 143-144ppm, indicating a possible β-5 recondensed structure, consistent

with our previously reported poplar lignin recondensation mechanism 60.

The pine wood pretreatment recovered lignin in pretreated liquid and residual lignin in solid

residues showed different spectra. For example, the pine residual lignin contained a large amount

of saturated aliphatic chain (15-40ppm) and rare ether linkages (60-90ppm), suggesting that the

pine residual lignin was pure C-C linked interunit. However, the pine pretreatment insoluble

lignin demonstrated none saturated aliphatic chain while high abundance of ether linkages. Thus,

the pine residual lignin presented complete condensed C-C linkages in the structure, which

explained peak absence in testing of residual lignin in 2-D 1H-13C HSQC NMR using DMSO as

a solvent. Additionally, the residual lignin was consisted of a considerable amount of aliphatic R-

OR or R-OH linkage/groups, while these groups were almost depleted in pine pretreatment

recovered insoluble lignin. Instead, the pretreatment recovered insoluble lignin contained much

higher alkane groups in its aliphatic region. Therefore, the structures of pine residual lignin were

dramatically different from pine pretreatment recovered insoluble lignin.

The above-mentioned complete C-C condensed structures of pine residual lignin can be

observed in 120-140ppm. To be more specific, pine residual lignin presented a great amount of

C-C5 recondensed structures indicated by the broad peak at 120-140ppm. The peaks at 131-132

and 125-126ppm were possible from C5-C5. The C5-C5 in residual lignin was from

recondensation of solubilized lignin fractions since the majority of native 5-5 structure exited the

reactor along with dilute acid stream during the lignin depolymerization by the cleavage of β-O-

4, staying inside corresponded pretreatment recovered lignin. In addition, the most native C5-C5

linkages in lignin were reported to be etherified by the α and β carbons of another phenyl

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propane unit to give the existing format of the eight-membered dibenzodioxocin ring instead of

simply C5-C5 156. Thus, this raised intriguing conclusion that C5-C5 recondensation is one of

mechanisms of forming pine residual lignin.

The chemical shift of C5 in pine residual lignin (115ppm) was abundant, indicating the

possibly intensive C-C5 recondensation reactions to form new C-C5 linkages. The broad peak

between 120-140ppm can be originated other condensed structures related to C5 position

(Labeled as C-C5) 144. The C-C5 linkages could possibly be Cα-C5, Cβ-C5, C6-C5 or C4-C5

structures besides C5-C5. The Cα and Cβ are relatively reactive under acidic conditions.

Therefore, the C-C recondensation active sites were proposed to be possibly located at stable Cα-

C5 and Cβ-C5 besides other possible C-C5 recondensation. Because the model compounds of

those new C-C5 linkages were not available, the study is unable to get direct proof through NMR

analysis of structural transformation of the model compounds. Cα-C5 recondensation

mechanisms were reported in Kraft lignin structure derived from the Kraft pulping and from

cellulolytic enzyme lignin repolymerization in 0.1 M H2SO4 dilute acid pretreatment at 160°C

for 0−20 min 70, 71, 322, 394, 395. Also, Cα-C5 recondensation pathways were mentioned possible in

the steam explosion of aspen wood but not proposed due to the lack of straightforward evidences

68, 396. Thus, the possible recondensation mechanisms of Cα-C5 and Cβ-C5 were proposed in

Figure 7.5. The chemical pathways of C5-C5, C6-C5 or C4-C5 recondensation under acidic

conditions are still not clear and need further research.

Table 7.3 Selected chemical shifts and signal assignments in a 13C NMR spectrum 144, 397-399

δ (ppm) Assignment

146-147 C-3/C-4 in (non-)etherified G units (β-O-4

type)

143-144 C-4 in β-5 units

131-132 C-1/C-5/C-5′ in (non-) etherified 5-5 units

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134.6 C-1 in etherified G units

125-126 C-5/C-5′ in non-etherified 5-5 units

100-120 C-1,2,5,6 in G units

115 C-2, 5, 6 in G units

86-87 Cα in G type β-5 units

60-85 Cα,β,γ in G type (β-O-4)

15-40 CH3 and CH2 in saturated aliphatic chain

56 -OCH3

Figure 7.5 Possible recondensation mechanisms of pine lignin during dilute acid pretreatment

60,70

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7.4.5 GPC analysis of the flowthrough derived lignin

The molecular weight of pine and poplar pretreatment recovered insoluble lignin and pine

residual lignin were measured by GPC (Figure S7.2). The results showed that the pretreatment

recovered insoluble and residual pine lignin in this study possessed relatively small molecular

weight lignin with limited polydispersity (Table 7.4). In addition, the molecular weight of

pretreated pine wood residual lignin sub-unit (Mn) in this study was smaller than pine wood

recovered lignin in hydrolysate, poplar wood recovered lignin, and other reported pretreated

lignin 400.

The molecular weight of pine residual lignin was lower than pine and poplar pretreatment

recovered insoluble lignin (Table 7.4). Also, the polydispersity of pine residual lignin was

much higher than both pine and poplar pretreatment recovered insoluble lignin. These results

indicated that the condensed C-C5 in pine residual lignin originated from recondensed structures

of small lignin fractions regardless of the C-C5 in the original lignin. This fact confirmed our

proposed recondensation mechanisms (Figure 7.5) and eliminated the possibility of condensation

structures deriving from the original lignin structure. The molecular weight of poplar

pretreatment recovered insoluble lignin was higher than pine pretreatment recovered

insoluble lignin. It might be attributed to the more β-5 recondensation reactions that occurred as

reported in our previous study 60. This also indicated that poplar lignin is relatively easy to

remove compared to pine lignin.

Table 7.4 The molecular weight of pine lignin (flowthrough pretreatment at 240 °C, 0.05%

(w/w) sulfuric acid, and 10 min)

Sample name Mn Mw Polydispersity (D;

M̅w/M̅n)

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Pine liquid phase lignin 542.0 1556.0 2.87

Pine residual lignin 224.5 1205.5 5.37

Poplar liquid phase lignin 60 1083 1955 1.81

7.5 Conclusion

The dilute acid flowthrough pretreatment effectively removes cellulose and hemicellulose

fractions from both hardwood and softwood. For the hardwood lignin, the structure’s sight

condensation in Cβ-C5 and the loss of the γ–methyl group in the completely removed lignin

fraction occurred 60. However, 30% of the softwood lignin with relatively low molecular weight

remained in the pretreated solid residues due to C-C recondensation in reactive C-C5 sites (e.g.

Cα-C5, Cβ-C5, and 5-5) even when carbohydrates were completely hydrolyzed. These unique C-

C condensation mechanisms were reported for the first time with straightforward proofs in

causing the significant retention of undissolved softwood lignin in the reactor as solid residues

and the difficulties of removing softwood lignin by dilute acid pretreatment. However, under

identical conditions, the poplar lignin was completely solubilized. Interestingly, pretreated

softwood hydrolysate contained more monomeric sugars and less oligomeric sugars than that of

hardwood under the same reaction conditions. The pretreated softwood whole slurries showed

decent enzymatic digestibility. The insights in different chemistry of softwood and hardwood

during dilute acid flowthrough pretreatment contribute to the fundamental understanding of

biomass pretreatment technology.


We are grateful to the Sun Grant-DOT Award # T0013G-A- Task 8, DOE-EERE Award #

DE-EE0006112 for funding this research. We acknowledge the Bioproducts, Sciences and

Engineering Laboratory, Department of Biosystems Engineering at Washington State University.

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L. Zhang was partially supported by the Chinese Scholarship Council (CSC). Part of this work

was conducted at the William R. Wiley Environmental Molecular Sciences Laboratory (EMSL),

a national scientific user facility located at the Pacific Northwest National Laboratory (PNNL)

and sponsored by the Department of Energy’s Office of Biological and Environmental Research

(BER). We also thank Dr. Haisheng Pei and Ms. Marie S. Swita for insightful discussions.

We acknowledge the Bioproducts, Sciences and Engineering Laboratory, Department of

Biosystems Engineering at Washington State University. L. Zhang was partially supported by the

Chinese Scholarship Council (CSC). Part of this work was conducted at the William R. Wiley



Energy’s Office of Biological and Environmental Research (BER).


Table S7.1 Enzymatic hydrolysis of flowthrough pretreated pine wood whole slurries

Pretreatment conditions Enzyme loading

(mg/g biomass solid residues)

Glucose yield (% of

original glucosea)

Temp. t H2SO4 LogR0 72h

220 2 0.05 3.83 20 70.9

100 73.6

220 4 0.05 4.14 20 75.5

100 80.1

220 6 0.05 4.31 20 85.7

100 91.7

220 8 0.05 4.44 20 87.7

100 92.6

220 10 0.05 4.53 20 85.9

100 94.6

240 2 0.05 4.61 20 85.8

100 93.7

240 4 0.05 4.72 20 87.4

100 90.8

240 6 0.05 4.90 20 90.4

100 92.7

240 8 0.05 5.03 20 90.9

207

100 96.7

240 10 0.05 5.12 20 92.1

100 97.2

270 2 0.05 5.20 20 82.4

100 90.7

270 4 0.05 5.61 20 80.7

100 89.5

270 6 0.05 5.78 20 79.4

100 88.9

270 8 0.05 5.91 20 80.4

100 86.7

270 10 0.05 6.01 20 75.8

100 82.1

a based on original glucose content in loaded biomass; Temp. refers to the pretreatment

temperature (°C), t refers to the pretreatment time (min), and H2SO4 refers to the concentration

of dilute acid, % (w/w). 20 mg protein Ctec2 (12 FPU) with 4 mg protein Htec2/g glucan +

xylan; 100 mg protein Ctec 2 (60 FPU) with 20mg Htec2/g glucan + xylan.

Pretreated pine whole slurry, which contained solid residues and pretreatment liquid

fractions was hydrolyzed by enzymes to test their digestibility for glucose generation. The

enzyme loading employed in this study was 100 mg protein CTec2 with 20 mg HTec2/g

glucan + xylan and 20 mg protein Ctec2 with 4 mg protein Htec2/g glucan + xylan,

respectively. Table S7.1 shows enzymatic glucose yields from pretreated pine reached in

the range of 70% to 97% (LogR0 from 3.83 to 6.01, 220oC-270oC, 2-10mins, 0.05%

(w/w) acid) after 72h enzymatic hydrolysis. This elevated efficiency of glucose

generation from the dilute sulfuric acid flowthough pretreatment could be attributed to the

high yield of monomeric carbohydrates and low inhibitors in the pretreated whole slurry

401. The total glucose yield increased along with the increasing of pretreatment severities

until reaching more than 92% with only 12FPU enzymes at the pretreatment severity of

5.12 (240oC, 10mins, 0.05% (w/w) acid). However, when pretreatment severities were

208

higher than 5.12, the glucose yields started decreasing which might be attributed to the

generation of more inhibitors to enzymatic hydrolysis under higher severities.

209

Table S7.2 Assignments of main lignin 1H- 13C cross-peaks in the HSQC Spectra of the

pretreatment recovered insoluble lignin 144, 157, 368-371


-OCH3 55.47(C) 3.70(H)

A: β-O-4 71.70(S-Cα) 4.84(S-Hα) 59.50-59.70(Cγ) 3.40-3.63(Hγ)

85.90(S-Cβ) 4.09(S-Hβ) 83.49(G/H-Cβ) 4.28(G/H-Hβ)

B: resinol 84.85(Cα) 4.62(Hα) 53.30(Cβ) 3.05(Hβ) 70.85(Cγ)

4.14/3.78(Hγ)

C: phenylcoumaran 86.79(Cα) 5.41(Hα) 53.3(Cβ) 3.46(Hβ) 62.52(Cγ) 3.68(Hγ)

D: spirodienone 59.67(Cβ) 3.19(Hβ)

G: guaiacyl 111.02(C2) 6.95(H2) 115.05(G5) 6.74(H5) 119.01(G6)

6.78(H6)

G: oxidized (Cα=O) guaiacyl 111.56(C2) 7.50(H2) 123.55(C6) 7.54(H6)

S: syringyl 103.95(C2/6) 6.67(H2/6)

S: oxidized (Cα=O) syringyl 106.52(C2/6) 7.29(H2/6)


F: cinnamyl alcohol 128.39(Cβ) 6.20(Hβ) 128.59(Cα) 6.42(Hα)

K: cinnamaldehyde 126.23(Cβ) 6.74(Hβ)

a Note: G, S, H-C or G, S, H-H refers to C and H in the lignin sub-units, guaiacyl, syringyl and p-

hydroxyphenyl.

210

Figure S7.1 Assigned interunit linkages of lignin, including different side-chain linkages, and

aromatic units: (A) β-O-4 aryl ether linkages; (B) resinol substructures (β-β′, α-O-γ′, and γ-O-α′

linkages); (C) phenylcoumarane substructures (β-5′and α-O-4′ linkages); (D) spirodienone

substructures (β-1′ and α-O-α′ linkages); (G) guaiacyl units; (G′) oxidized guaiacyl units with an

Cα-ketone; (S) syringyl units; (S′) oxidized syringyl units with a Cα ketone; (H) hydroxyphenyl

groups; (E) p-hydroxybenzoate substructures; (K) cinnamaldehyde end groups

211

Figure S7.2 GPC analysis of lignin molecular weight of a) poplar flowthrough pretreatment

recovered insoluble lignin, b) pine wood flowthrough pretreatment recovered insoluble

lignin, c) pine wood flowthrough derived residual lignin under 240°C with 0.05% (w/w) H2SO4

for 10min

0.0

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Vial 14: Pu3'-240A-HW-FL - 1Thursday 11/12/15 21:04:43 27.323 mlThursday 11/12/15 21:18:42 41.309 mlYoo_09232015.CAL THF 0.000E+0 1.000E+0 ml/g 50.000 ml ------- mlPSS SECcurity 1.000 ml/min 1.000 g/l 25.000 ulWaters Styragel HR6 30.000 °CWaters Styragel HR4 30.000 °CWaters Styragel HR1 30.000 °CWaters Styragel HR0.5 30.000 °CPSS SECcurity UV 0.000 mlPSS SECcurity RI 0.006 mlAdministrator 1.000 sec

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Mn :

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Project : Account :Date : Sign :

C:\ORNL GPC\Data\Mi Li\Mi Li.LDXTuesday 11/17/15 15:10:25

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Mn :

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5.4193e2

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0.0

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Vial 15: Pu4-SWSR-240A - 1Thursday 11/12/15 21:58:42 29.681 mlThursday 11/12/15 22:10:35 41.553 mlYoo_09232015.CAL THF 0.000E+0 1.000E+0 ml/g 50.000 ml ------- mlPSS SECcurity 1.000 ml/min 1.000 g/l 25.000 ulWaters Styragel HR6 30.000 °CWaters Styragel HR4 30.000 °CWaters Styragel HR1 30.000 °CWaters Styragel HR0.5 30.000 °CPSS SECcurity UV 0.000 mlPSS SECcurity RI 0.006 mlAdministrator 1.000 sec

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a) b)

c)

212

CHAPTER EIGHT

HOT WATER AND DILUTE ACID PRETREATMENT OF SUB-

MILLIMETER BIOMASS PARTICLES

8.1 Abstract

The hot water and dilute acid pretreatment studies of a variety of feedstock with different

shapes and sizes are significant towards the optimal sub-millimeter biomass particles for sugars

production at low cost. Physical modifications on biomass, such as milling and cutting, result in

different biomass shapes and sizes, thus have significant impact on pretreatment performances

and sequential enzymatic hydrolysis sugar yields. The kinetics of hot water-only and dilute acid

pretreatments considering mass transfer effects and the following enzymatic hydrolysis process

were evaluated using hardwood (e.g. poplar wood) and softwood (e.g. Douglas fir) substrates

with four different particle sizes by, two different cutting approaches, hammer milling and

crumbling (e.g. Shards, Cubic prismatic). Results showed that different biomass substrates,

particle sizes, cutting approaches and pretreatment methods (e.g. hot water, and dilute acid) led

to different pretreatment performances. Interestingly, Biomass particles with 12-30/16-40 mesh

size instead of finer particle size led to a better total sugar release and pretreatment lignin

removal. Also the crumbled biomass chips showed comparable pretreatment and enzymatic

hydrolysis sugar yields but consumed three times less energy in comparison with the traditional

hammer milling method. To further explore the mass transfer effects of particle sizes and cut

methods on pretreatment effectiveness, the measurements of the mass transfer coefficients were

proposed using reported and designed devices. It was proposed that various particle sizes and

cutting methods on biomass resulted in their different mass transfer capacities (coefficients) and

213

the pretreatment performances were corresponded to the mass transfer diffusion capacities of

biomass. However, more future work is needed in order to provide more conclusions and

evidences. This work is significant to the preprocessing of feedstock for aqueous pretreatment.

Keywords: Biomass particles • Size • Biomass cutting • Pretreatment • Mass transfer • Diffusion

coefficient

8.2 Introduction

Cellulosic biomass is low in cost, with biomass at $60/dry ton about the same cost as

petroleum at $20/barrel on the basis of equivalent energy content 169, 402. Furthermore,

technologies are well established and low in cost for fermenting sugars that can be derived from

cellulose and hemicellulose in biomass into ethanol and other products 403. The challenge to

lower costs has been to achieve high yields of sugars from biomass with cost competitive

technologies. In other words, we must find ways to overcome the biomass recalcitrance as a

barrier to economic production of fuels from biomass 404, 405. The costs of pretreatment and

enzymes are the primary barriers to release sugars with low cost, and it is vital to apply low cost

pretreatments to open up the structure of biomass to enzymes 32, 169, 405. In particular,

pretreatments are needed to achieve high sugar yields from cellulose and hemicellulose through

the coupled operations of pretreatment and enzymatic hydrolysis while requiring little if any

chemical inputs, minimal need for inhibitor removal, low sugar losses in

pretreatment/conditioning, low energy demands, low operating pressures, short reaction times,

inexpensive materials of construction, and simple operation to avoid expenses for chemical

recovery and recycle 32, 406, 407. In light of these demands, only pretreatments that require none or

minimal chemical additions can provide the lowest cost route provided yields from pretreatment

214

and enzymatic hydrolysis are high. Thus, aqueous pretreatments that only use steam or hot water

are preferred, backed up with dilute sulfuric acid approaches if aqueous yields are too low to be

cost competitive. High sugar yields must be achieved with low enzyme loadings for these or any

other pretreatment options to be economically viable 32.

Mechanical treatments, such as chipping, milling and grinding, were commonly used to

reduce the size of the particles, essentially increasing the surface area of the lignocellulose before

aqueous pretreatment 408. The size reduction can influence the pretreatment efficiency by

producing different biomass particle sizes and shapes via various cutting methods, and affect

pretreatment and total cost. The size reduction can also influence enzymatic hydrolysis

efficiency. For example, the cellulose crystalline structure and its degree of polymerization (DP)

might be reduced during mechanical treatment. Hammer mills and ball mills were commonly

used to reduce particle size and have been demonstrated to enhance the digestibility of the

lignocellulose in enzymatic hydrolysis 409. However, high energy consumption of mechanical

pretreatment hindered its large scale application 410. Thus, developing low energy mechanical

processes for biomass size reduction is essential for economic biorefinery production 411.

The impact of biomass mechanical prehandling on biorefinery processes was not widely

studied although it has been reported before. Some studies focus on the effects of sub-millimeter

substrates on the thermal conversion. For example, the effects of sub-millimeter scale and

macroscopic wood particles on the pyrolysis under different conditions were determined 412-414.

Also, some studies showed the effects of biomass particle characteristics on enzymatic

hydrolysis efficiency 161, 415. However, the impact of particle size reduction on pretreatment

performance is not well understood yet 416. Some studies have been carried out to study effects of

215

physical treatments or impregnations of lignocellulosic biomass on wood structure alteration and

solvent mass transfer, which influenced subsequent pretreatment performance 168, 417, 418. For

example, a diffusivity data of different biomass from batch pretreatment was fitted into the

Arrhenius equation for extrapolation to higher temperatures, which was subsequently

incorporated into a theoretical model to determine acid concentration profiles within biomass

matrix 160. Additionally, a study developed a model based on the diffusion of liquid into biomass

by considering the effects of the particle size to reduce energy consumption 419. Biomass

pretreatment was typically based on particle size of 10 mm or less 420, 421. However, the effects of

sub-millimeter scale biomass particle size on pretreatment diffusion have not been reported 422,

not mentioning the effects of biomass cutting approaches such as crumbling (Figure S8.1) and

hammer-milling.

The study of effects of biomass particles with sub-millimeter scales on mass transfer

diffusion in aqueous based pretreatment is critical to achieve the large scale production of

biomass pretreatment. Bench-top batch tubular reactors were widely applied in many

pretreatment studies for a long time 423. The gaps between bench-top batch reactors and pilot-

scale reactors are mass and heat transfer issues 424. However, few pretreatment studies focused

on resolving mass and heat transfer in literature. Also, most pretreatment studies apply relatively

lower solid loadings than industrial scale, which is an issue directly related to mass transfer

effects. The high solid loading with excellent mass transfer is usually preferred in industrial

application. First of all, high solid loading offers many advantages, such as reducing production

and capital costs and increased sugar and ethanol concentrations for downstream processes.

Secondly, a consideration of high solid loading are particle shapes and sizes which have been

216

reported to have a significant impact on the viscosity of pretreatment slurry because particle

properties influence the particle networking and packing in bulk slurry 425-427. However, high

viscosity of pretreated slurry will cause mixing difficulties as well as the problems of solvent

pumping and flowing, which leads to poor mass transfer. At last, biphasic reaction rate behaviors

of biomass dissolution might be also attributed to mass transfer limitation as reported 428.

Therefore, the motivations of understanding impacts of particle sizes and cutting shapes of

substrates in pretreatment mass transfer and diffusion are critical for a successful large-scale

production.

The objective of this chapter is to identify the most promising substrate particle sizes and

cutting approaches (e.g. Shards, Cubic prismatic) through comparative study of water only and

dilute acid pretreatment as well as the digestibility of pretreated biomass particles to generate

sugars. Also, investigate mass transfer differences during pretreatment among biomass substrates

with different particle sizes and by different cutting approaches.


8.3.1 Materials

All samples used in this study were provided by the Forest Concepts Corporation. Poplar

chips, Douglas fir and corn stover were cut through two approaches, namely, crumbling and

hammer milling. Specifically, poplar and corn stover underwent 0.8 mm hammer milling and

passed 16 mesh but not 40 mesh, which formed 16-40 mesh shards shape of poplar chips and

corn stover. Besides, polar wood and corn stover went through 0.8 mm crumbling and were

orbital screened by passing 16 mesh but not 40 mesh, which formed 16-40 mesh cubic prismatic

poplar chips and corn sotver. In addition, poplar was crumbled by 1.6 mm crumble and passed

217

1/8 inch sieving and no pass 16 mesh, which formed 1/8′′-16 mesh cubic prismatic poplar chips

and corn stover. Thus, the hammer milling and crumbling resulted in different particle sizes and

shapes. Table 8.1 listed the details of feedstock used in the study. The composition analysis was

carried out to determine the contents of glucan, xylan and lignin in the feedstocks according to

NREL procedure -Determination of Structural Carbohydrates and Lignin in Biomass (NREL/TP-

510-42618). Poplar contained 43.25±0.32% of glucan, 17.25±0.52% of xylan, 26.12±0.21% of

lignin and 1.16±0.04% of ash. Corn stover contained 33.60±0.31% of glucan, 20.71±0.24% of

xylan, 18.44±0.38% of lignin and 5.50±0.03% of ash. Douglas fir presented 45.55±1.10% of

glucan, 5.14±0.14% of xylan, 31.16±0.12 of lignin, 7.81±0.45% of galactan, 8.66±0.24% of

arabinan and mannan, and 0.44±0.02% of ash.

Table 8.1 Characteristics of feedstock used in the study

Biomass Size Cutting approaches Sieve size Particle geometry

Poplar wood 16-40 mesh 0.8mm Hammer milled Pass 16 mesh

No Pass 40 mesh

Shards

Poplar wood 16-40 mesh 0.8mm Crumbled Pass 16 mesh

No Pass 40 mesh

Cubic prismatic

Poplar wood 1/8′′-16 mesh 1.6mm Crumbled Pass 1/8 inch

No Pass 16 mesh

Cubic prismatic

Corn Stover 16-40 mesh 0.8mm Hammer Milled Pass 16 mesh

No Pass 40 mesh

Shards

Corn Stover 16-40 mesh 0.8mm Crumbled Pass 16 mesh

No Pass 40 mesh

Cubic prismatic

Corn Stover 1/8′′-16 mesh 1.6mm Crumbled Pass 1/8 inch

No Pass 16 mesh

Cubic prismatic

Biomass Size Cutting approaches Sieve size Particle geometry

Green poplar 30-60 mesh Hammer milled N/A Shards

Green poplar 12-30 mesh Hammer milled N/A Shards

Green poplar 1/8′′-16 mesh Hammer milled N/A Shards

218

Green poplar 30-60 mesh Crumbled N/A Cubic prismatic

Green poplar 12-30 mesh Crumbled N/A Cubic prismatic

Green poplar 1/8′′-16 mesh Crumbled N/A Cubic prismatic

Douglas fir 30-60 mesh Hammer milled N/A Shards

Douglas fir 12-30 mesh Hammer milled N/A Shards

Douglas fir 1/8′′-16 mesh Hammer milled N/A Shards

Douglas fir 30-60 mesh Crumbled N/A Cubic prismatic

Douglas fir 12-30 mesh Crumbled N/A Cubic prismatic

Douglas fir 1/8′′-16 mesh Crumbled N/A Cubic prismatic

8.3.2 Batch tubular pretreatment

Batch pretreatment conditions were selected based on water only and dilute acid conditions

that were reported to achieve a better enzymatic hydrolysis yield as presented in Table 8.2.

Pretreatment was carried out in a fluidized sand bath system (model SBL-2D, Omega

engineering, Inc., CT) equipped with a temperature controller. Tubular reactors were made as 1.3

cm diameter ×15.2 cm long with volume of 13.4 ml. 0.5 g feedstock was loaded into the batch

tubular reactor, which was followed by an addition of 10 ml water or 1% sulfuric acid. Both

reactor ends were sealed tightly with Teflon plugs and stainless cups. Reactors were gently

placed in the heated sand bath with a temperature controller setting of desired temperature

monitored by a thermometer. The pretreated solid residues and liquors were separated through

filtration, and 2 ml liquid fraction was analyzed using HPLC to determine mono sugars in

hydrolysates. The Waters HPLC system (model 2695) is equipped with a 410 refractive detector

and an auto-sampler (Waters 2695) using a Waters Empower Build 1154 software (Waters Co.,

Milford, MA, USA). A Bio-Rad Aminex HPX-87H column (Bio-Rad Laboratories, Hercules,

CA, USA) was operated under 60°C. The liquid fraction was further post hydrolyzed by the 4%

219

of sulfuric acid in 121ºC autoclave to determining the amount of oligomeric sugars in

hydrolysates after pretreatment following by a NREL procedure 429. The solid residues were

freeze dried and experienced a solid residue composition analysis following by NREL procedure

353.

Table 8.2 Selected pretreatment conditions performed in the study

Feedstock Pretreatment

methods T (℃) Residence time

(min)

Chemical

(%)

Solid loading

(w/w)

Log R0 Reference

Poplar/corn stover Dilute acid 140 10,20,30,40,50 1% H2SO4 5% 2.1-2.9 423

poplar/corn stover Water only 190 5,10,15,25,30 N/A 5% 3.3-4.0 430

Douglas fir Dilute acid 140-

200

3-70 1% H2SO4 5% 2.6-3.7 N/A

Douglas fir Water only 190-

220

1-70 N/A 5% 3.6-4.5 N/A

Poplar wood was pretreated at 140°C with ~1% wt sulfuric acid for 40min and 190°C with

DI water for 15min, yielding nearly 100% of xylose recovery 431, 432. Therefore, in our study,

dilute acid pretreatment of poplar wood was carried out with 1% (w/w) H2SO4 for 10, 20, 30, 40,

and 50 min at temperature of 140°C (Log R0=2.1-2.9), and hot water pretreatment was

performed with DI water for 5, 10, 15, 20, and 25min at temperature of 190°C (Log R0=3.3-4.0).

However, no comparable data could be found in softwood acid/hot water pretreatment under the

same pretreatment conditions. In this study, we applied conditions similar to these for poplar

wood on Douglas fir and evaluate the softwood pretreatment sugar yields. The selection of the

pretreatment conditions for Douglas fir was based on literature reviews and the criteria of more

than 95% of hemicellulose recovery in most of Biomass Refining Consortium for Applied

Fundamentals and Innovation (CAFI) pretreatment methods. Because the softwood pretreatment

is reported challenging, especially in delignification, this study applied reaction times from 30 to

70 mins (pretreatment severities at 2.6-3.7). In addition to 140°C, 180°C was applied to pretreat

220

Douglas fir for 3-20min with controlled severities at the same range 2.6-3.7 in order to determine

the impact of temperatures. Besides, the current softwood pretreatment is mainly performed by

dilute acid or acid/SO2 catalyzed steam explosion at temperature of about 200°C 300, 380, 433. Thus,

190 and 200°C dilute acid were selected to pretreat Douglas fir with 0.5% wt sulfuric acid for 2-

10min (severities at 3.6-4.5). In summary, dilute acid pretreatment of Douglas fir was performed

with 0.5, or 1% (w/w) H2SO4, for 3-70 min at 140-200°C (Log R0= 2.6-3.7). Similarly, hot water

pretreatment conditions were selected using DI water for 1-70min at 190-220°C (Log R0=3.6-

4.5).

8.3.3 Digestibility evaluation of pretreated whole slurries by enzymatic hydrolysis

The whole slurries of pretreated samples including hydrolysates and solid residues were

hydrolyzed with CCTEC cellulase (Genencor, Palo Alto, CA, USA) and HTEC2 Novozyme 188

β-glucosidase (Novozyme, Franklinton, NC, USA)- Novozymes Cellic® CTec1 (220mg

protein/mL, preserve 200mg glucose/mL, 93 FPU/mL) and Novozymes Cellic® HTec2 (230mg

protein/mL, preserve 180mg xylose/mL). All enzymatic hydrolysis experiments were conducted

in duplicates under the standard conditions (50 °C, 0.05 M citric acid buffer at pH 4.8). The pH

of the pretreated whole slurries from batch reactors was adjusted to pH4.8 with 0.1N NaOH. A

mixture of CCTEC2 and HTEC2 enzymes at a ratio of 5:1 based on protein weight was added

with enzyme loading of 20, 100, or 200 mg protein Ctec2 (93 FPU) with 4, 20, and 40 mg

Htec2/g (glucan + xylan). In the study, 10 mg/L sodium azide and 1% (w/v) boving serum

albumin (BSA) were added. The resulting liquid samples were withdrawn at 72 hours and

injected to HPLC for monomeric sugars analysis. Yields of enzymatic hydrolysis can be

evaluated through the following equations (see equation 8.1, 8.2).

221

Enzymatic glucose yield% =WG2−WG1

WTG× 100% (8.1)

Enzymatic xylose yield% =WX2−WX1

WTX× 100% (8.2)

In these equations, WG1 and WX1 are the glucose and xylose released in the pretreatment (stage

1); WG2 and WX2 are the glucose and xylose released in enzymatic hydrolysis (stage 2); WTG and

WTX are the total theoretical glucose and xylose released (glucose and xylose in untreated

biomass). The unit of W consistently refers to g/100g DW raw biomass.

8.3.4 Kinetic modeling of biomass degradation

A leaching model was reported to depict the degradation behaviors of xylan, glucan and

lignin during the pretreatment process.

(8.3)

where As is the unit surface area of biomass particles (cm2/g), Vc is the unit volume of the solid

(cm3/g), Xn is dissolved xylan (g), kM is the mass transfer coefficient (cm/min).

(8.4)

where As is the unit surface area of the particles (cm2/g), Vc is the unit volume of the solid

(cm3/g), C is dissolved glucan (g), kM is the mass transfer coefficient (cm/min).

(8.5)

MkXylan Dissolved Xylan

( )sM

c

AdXnk Xn

dt V

MkCellulose Dissolved Cellulose

( )sM

c

AdCk C

dt V

MkLignin Dissolved Lignin

( )sM

c

AdLk L

dt V

222

where As is the unit surface area of the particles (cm2/g), Vc is the unit volume of the solid

(cm3/g), L is dissolved lignin (g), kM is the mass transfer coefficient (cm/min) 434. After

integration, equations applied in our study can be presented as following (See equation 8.6, 7, 8).

G = 100e(−KM

AsVc

)t (8.6)

H = 100e(−KM

AsVc

)t (8.7)

L = 100e(−KM

AsVc

)t (8.8)

here G, H, and L refer to the percentage of solid residues after pretreatment in untreated biomass

(unit: %).

The surface area of feedstocks in this study was measured by exposing biomass particles to

nitrogen gas after drying samples under 200ºC for 1 hour. The absorption of nitrogen was

determined. The surface area was calculated by applying Brunauer, Emmett and Teller-BET

hypothesis equations 435. The volume of solids is measured by packing 5g biomass in a graduated

cylinder. The volume occupied by loaded samples was read and divided by 5g to get unit volume

of biomass solids (m3/g) 436. The volume of solids refers to the biomass density which the ratio of

the mass of a quantity of a substance to its volume and was expressed in terms of weight per unit

volume. Table 8.3 showed that crumbled particles and smaller sizes have smaller density than

hammer milled particles and large sizes, respectively.

Table 8.3 Some unit volume of biomass solids (biomass density)

Sample information Volume of solids, cm3/g

30-60 mesh, Cubic prismatic, poplar 6.2

16-40 mesh Shards, Poplar 7.2

16-40 mesh Cubic prismatic, Poplar 6.8

12-30 mesh, Cubic prismatic, poplar 6.8

12-30 mesh, Shards, poplar 7

223


8.4.1 Mass transfer modeling of hot water and dilute acid pretreatment of corn stover and

poplar wood

The surface area of biomass particles is important in chemical kinetics. The surface area of

corn stover was 2.8m2/g and poplar had 0.7 m2/g surface area (BET). The surface area of a solid

object is a measure of the total area that the surface of an object occupies. Here surface area

refers to internal surface area of particles accessible for chemical interactions. Increasing the

surface area of a substance generally increases the rate of a chemical reaction. Results showed

that corn stover has a higher surface area than poplar, which may be attributed to a denser wood

structure of polar than corn stover structure. Corn stover particles are more loosen and shows

more accessible surface than that of poplar wood chips. Volume of solids was calculated as

shown in Table 8.3. Particle sizes and cutting approaches both affect solid volume. The solid

volume is a factor that impacts the mass transfer capacities. Smaller and Cubic prismatic -shaped

particles can occupy space efficiently which gave smaller solid volumes. Both biomass surface

area and density factors are significant factors in kinetic modeling considering mass transfer (See

equation 8.6, 8.7, 8.8) 434. The different mass transfer capacities of biomass particles with various

cutting methods and particle sizes might be caused by the different volume of solids.

1/8′′-16 mesh, Shards, Poplar 8.4

1/8′′-16 mesh Cubic prismatic, Poplar 7.2

16-40 mesh Shards, Corn stover 7.2

16-40 mesh Cubic prismatic, Corn stover 6.8

1/8′′-16 mesh Cubic prismatic, Corn stover 7.2

1/8′′-16 mesh, Cubic prismatic, Douglas fir 6.4

12-30 mesh, Shards, Douglas fir 7.2

http://en.wikipedia.org/wiki/Chemical_kinetics

http://en.wikipedia.org/wiki/Reaction_rate

http://en.wikipedia.org/wiki/Chemical_reaction

224

(a) (b)

(c) (d)

225

Figure 8.1 Kinetic diffusion modeling (equation 8.6, 7, 8) of pretreatment behaviors of major

components in six feedstock 1% (w/w) H2SO4 pretreatment at 140ºC with residence time of 0-50

min including (a) xylan dissolution, (b) glucan dissolution, and (c) lignin dissolution, and hot

water pretreatment at temperature of 190ºC with residence time of 0-30 min including (d) xylan

dissolution, (e) glucan dissolution, and (f) lignin dissolution

Experimental results in Figure 8.1 (plotted with different markers) showed the effects of

particle sizes and cutting approaches on major biomass components removal. Results showed

that different particle sizes and cutting approaches resulted in different removal pattern of

biomass components (i.e. cellulose, hemicellulose, and lignin) during pretreatment. Since

biomass structure is porous and heterogeneities 161, solvent diffusion during pretreatment was

influenced by the size of particles and cutting approaches of biomass substrates. Meanwhile, our

experimental results were consistent with reported statements that different physical

modifications of the biomass (e.g. size reductions and particle cutting methods) which have

different levels of compression and shear forces affected pretreatment performance 416. Results of

(e) (f)

226

hot water and dilute acid pretreatment of Poplar and corn stover indicated that the release of

glucan, xylan and lignin follows the leaching model with consideration of mass transfer effects

(See equation 8.6, 7, 8) 434. Experimental data for glucan, xylan and lignin recovery and removal

percentages in hot water and dilute acid pretreatment were fitted into equation 8.6, 8.7, and 8.8,

which is shown in Figure 8.1. Figure 8.1 shows the experimental and modeling fitting results of

xylan, glucan, and lignin removal for some of the samples by hot water and dilute acid

pretreatment. The accuracy of modeling results in this study fitting to experiments is acceptable

with average chi square of about 50. The differences of fitting results with the experimental data

points were maybe corresponded to the complicated biomass dissolution mechanisms which

cannot be simply simulated by a kinetic model that solely aims at mass transfer impacts.

However, for this study, mass transfer is the key factor to evaluate the effects of particle size and

cutting approaches. Therefore, such kinetic modeling simulation would reveal insights for the

purpose of this study.

8.4.2 Effects of particle sizes and cutting approaches on mass transfer coefficients

The effects of particle sizes and cutting approaches on mass transfer of poplar and corn

stover during hot water and dilute acid pretreatment were investigated by integration of

experimental data into the mass transfer leaching model described in the method section. In

equations 8.6, 8.7, and 8.8, KM was calculated by integrating measured AS, and VC into fitting

equations. Table 8.4 showed mass transfer coefficients for the selected six biomass samples

performed with two different pretreatment methods under different pretreatment conditions.

Table 8.4 Kinetic modeling parameters (physical mass transfer coefficients) of xylan, glucan,

and lignin removal with consideration of mass transfer (a) 1% sulfuric acid pretreatment with

227

residence time of 0 to50 min at 140ºC and (b) hot water pretreatment with residence time of 0 to

30 min at 190ºC

(a)

Sample information Mass transfer coefficients (KM

)

Xylan removal Glucan removal Lignin removal

16-40 mesh Shards, Poplar 0.0391 0.0050 0.0062

16-40 mesh Cubic prismatic, Poplar 0.0298 0.0035 0.0044

1/8′′-16 mesh Cubic prismatic, Poplar 0.0286 0.0029 0.0047

16-40 mesh Shards, Corn stover 0.3394 0.0484 0.0505

16-40 mesh Cubic prismatic, Corn stover 0.2474 0.0305 0.0412

1/8′′-16 mesh Cubic prismatic, Corn stover 0.2229 0.0293 0.0376

(b)

Sample information Mass transfer coefficients (KM

)

Xylan removal Glucan removal Lignin removal

16-40 mesh Shards, Poplar 0.0593 0.0179 0.0182

16-40 mesh Cubic prismatic, Poplar 0.0436 0.0089 0.0116

1/8′′-16 mesh Cubic prismatic, Poplar 0.0431 0.0084 0.0100

16-40 mesh Shards, Corn stover 0.4221 0.1269 0.1389

16-40 mesh Cubic prismatic, Corn stover 0.2863 0.0840 0.0827

1/8′′-16 mesh Cubic prismatic, Corn stover 0.2856 0.0825 0.0823

For dilute acid pretreatment at 140ºC, results showed that xylan was dissolved with the

highest mass transfer coefficients for all six samples than glucan and lignin (Table 8.4a, b). It

was consistent with previous reports that xylan was released in the initial phase of pretreatment

56. For all samples, mass transfer coefficients of lignin degradation appeared slightly to moderate

higher than those of glucan degradation. The explanation was amorphous structure was easy for

solvents to diffuse compared to dense crystalline cellulose structure 420. Meanwhile, another

reason for better lignin dissolution than glucan dissolution was probably attributed to the existing

of LCC (lignin carbohydrates complex) that deconstructed some lignin together with

hemicellulose dissolution. On the other hand, it was consistently found that in biomass

dissolution including glucan, xylan, and lignin degradation, medium particle sizes (16-40mesh)

228

showed better mass transfer during pretreatment process. Also the shard shape particles

presented some higher mass transfer coefficients. It might be because smaller biomass particle

has more accessible surface for solvents and shards cutting has a sharp easy ending for solvent

attack. However, the crumbling method can achieve comparable results with hammer milling

because of crumbled particles possessed smaller volume of solids. Results also indicated that

different cutting approaches have more significant effects on mass transfer coefficient than

particle size. Some studies reported the effects of particle size on pretreatment behaviors 411, 421,

434, 437, however, no study has reported effects of biomass cutting and resulted biomass shapes on

solvent diffusion and pretreatment. Our study showed that the cutting approaches were important

since they resulted in different shapes of particles which maybe determine the packing of particle

when loaded in tubular reactors. The packing influenced interaction between solvents and

biomass. For example, 16-40 mesh Shards Corn stover showed best mass transfer among other

samples with the value of 0.3394 cm/min. As different cutting approaches were applied, 16-40

mesh Shards poplar and corn stover showed higher mass transfer coefficients for all three

biomass main components than same particle size. The medium particles (16-40 mesh Shards) of

poplar and corn stover with same cutting approach (i.e. Crumbler) only resulted in slightly higher

mass transfer coefficients than larger particle size of 1/8-16 mesh. Interestingly, 16-40 mesh

Shards and Cubic prismatic poplar presented higher mass transfer coefficients than that of 1/8′′-

16 mesh Cubic prismatic Poplar which indicated the advantage of smaller particle size of 16-40

mesh (For example, 0.391 Versus 0.0298 and 0.0286 cm/min in xylan dissolution). Although the

mass transfer coefficients of hammer milled particiles were larger than crumbled particles, the

volume of solids of hammer milled was ~1.2 times of crumbled particles. Thus, according to

229

equations (8.6, 7, 8), the kinetic coefficients of crumbled particles were comparable to hammer

milled particles. These results indicated that both Cubic prismatic and Shards shaped particles

achieved comparable biomass dissolution for 1% sulfuric acid pretreatment at 140ºC.

For hot water pretreatment at 190ºC, mass transfer coefficients of glucan, xylan and lignin

degradation showed similar pattern as those for 1% sulfuric acid pretreatment at 140ºC. Xylan

was first dissolved with high mass transfer and degradation rate. Lignin deconstruction rate of

most samples had better mass transfer than glucan dissolution. Results suggested that particle

sizes play less significant role in hot water pretreatment at 190ºC than roles of biomass cutting

methods. For example, comparing 16-40 mesh Cubic prismatic poplar and corn stover with 1/8′′-

16 mesh Cubic prismatic poplar and corn stover, mass transfer coefficients of biomass

dissolution (i.e. glucan, xylan, and lignin)showed slight difference (For example, 0.2856 Versus

0.2863 cm/min in xylan dissolution). However, the cutting approaches appeared a more

determined factor as the mass coefficient increased from 0.2856 for 16-40 mesh Cubic prismatic

corn stover to 0.4221cm/min for the same particle size of 16-40 mesh corn stover but using

Shards approach. Biomass cutting methods were usually ignored in pretreatment. The study

sheds lights on understanding of significance of biomass cutting, not only for energy

consumption but also for pretreatment and enzymatic hydrolysis yields.

For all tested particle sizes and cutting approaches, corn stover showed much higher mass

transfer coefficients than poplar under both hot water and dilute sulfuric acid. Results indicated

better solvent diffusion into corn stover. For example, 1/8′′-16 mesh Cubic prismatic corn stover

showed 0.2229 cm/min coefficient for 1% sulfuric acid pretreatment at 140ºC, while similar

particle size poplar prepared by the same cutting approach only has mass transfer coefficient of

230

0.0286% cm/min. It can be corresponded to the less dense structure of corn stover compared to

poplar.

Comparing different pretreatment methods, all samples in hot water pretreatment (e.g.

190ºC) indicated better mass transfer coefficients for all three biomass main components (i.e.

cellulose, hemicellulose, and lignin) than those in dilute acid pretreatment at 140ºC. This

revealed advantageous solvent diffusion in 190ºC hot water pretreatment over dilute acid

pretreatment at 140ºC, especially for lignin deconstruction by hot water pretreatment at 190ºC,

mass transfer coefficients of all six samples were consistently higher than those by 1% sulfuric

acid pretreatment at 140ºC. For example, corn stover sample (16-40 mesh Shards, Corn stover)

presented 0.1389 cm/min mass transfer coefficient for hot water pretreatment at190ºC. However,

the mass transfer during lignin dissolution of the same corn stover sample only showed 0.0412

cm/min mass transfer coefficient by 1% sulfuric acid pretreatment at 140ºC. The phenomenon

was attributed to recondensation and repolymerization of dissolved lignin occurred at 140ºC for

1% sulfuric acid pretreatment while 190ºC hot water pretreatment showed less lignin

recondensation than that for 1% sulfuric acid pretreatmentat140ºC 438, 439.

8.4.3 Experimental results of hot water and dilute acid pretreatment of corn stover and

poplar wood

For dilute acid pretreatment at 140ºC (See Figure 8.1 (a,b,c)), xylan removal was the slowest

for the poplar sample with larger particles (1/8′′-16 mesh) and prepared by Cubic prismatic

cutting while it is the fastest for the corn stover sample with finer particle size (16-40 mesh) and

prepared by Shards cutting (Figure 8.1a). Smaller particle size has higher accessible surface area

which led to high accessibility of chemicals to biomass. For both poplar and corn stover samples,

231

when cutting approaches changed from Cubic prismatic to Shards, xylan removal was improved

5-10%. Shards cutting might result in sharp edges of biomass particles which were easy for

chemicals to interact. When the particle size decreased for Cubic prismatic cut samples, xylan

removal was increased to the slightly lower degree than changing cutting methods. Results

indicated that xylan removal was enhanced with smaller particle size. It was consistent with the

mass transfer coefficient’s increasing trend (Table 8.4). The sub-millimeter biomass pretreatment

behaviors were different from biomass pretreatment performance with conventional particle size.

It was reported larger steam-exploded particle (8-12 mm) resulted in higher cellulose and

enzymatic digestibility because biomass particle was broken down during pretreatment process

421, 440. Interestingly, our results showed the biomass removal and mass transfer coefficient

changed obviously with cutting approaches and only slightly with particle size. This indicated

the significant effects of cutting methods on pretreatment performance. Xylan removal was

altered slightly as these two factors changed. The explanations were that xylan was easy to be

degraded due to amorphous structure of xylan 56. On the other hand, xylan removal of corn

stover samples was insignificantly higher than that of poplar samples with similar particle size

and by similar cutting methods. This was correlated to the mass transfer coefficient differences

between poplar and corn stover. This was because corn stover was easier to be hydrolyzed than

poplar under dilute acid and hot water conditions 428, 437, 441. For glucan hydrolysis, glucan

removal was significantly improved 10-20% when cutting methods was changed from Cubic

prismatic to Shards, with more obvious effects with poplar than with corn stover. It might be

because wood is difficult to be pretreated and slightly positive change of mass transfer can

enhance obvious pretreatment performance. As particle size became finer, both poplar and corn

232

stover showed about 5% higher glucan removal. Similar to xylan removal, corn stover samples

resulted in higher glucan removal than poplar did. Glucan removal results correlated strongly

with the mass transfer coefficients (Table 8.4) which indicated that mass transfer could be a key

factor to glucan removal. Similar to glucan, lignin removal was more significantly affected by

cutting methods than particle size with 10-20% improvement of lignin removal with Shards and

about 5% improvement with finer particles at 30 min pretreatment. In summary, faster mass

transfer was effective force to promote efficient pretreatment. For all three biomass components,

corn stover showed higher removal than poplar and reached the highest removal of 42.6%

glucan, 90.9% xylan and 44.7% lignin (16-40 mesh Shards) at 30 min while the highest removal

of 35% glucan, 80% xylan and 30% lignin for poplar (16-40 mesh Shards) at 30 min. Compared

to best pretreatment performance under similar conditions reported 441, 442, sub-millimeter

particles and modified cutting approaches showed advantageous performance.

For hot water pretreatment at 190ºC, hydrolysis of three biomass main components followed

the same pattern as dilute acid pretreatment: the particle sizes and cutting approaches showed

less effect on xylan removal but cutting approach of Shards improved lignin and glucan removal

at a degree of 5-10%. The removal extent and rate of three biomass components for corn stover

were higher than poplar. The highest removal of xylan, glucan and lignin was about 90%, 54%

and 56%, respectively; using 16-40 mesh Shards corn stover. However, the highest removal for

poplar was about 13% glucan, 70% xylan and 15% lignin at 30 min using 16-40 mesh Shards

poplar. The results confirmed our finding that different pretreatment mass transfer caused by

various particle sizes and cutting approaches influenced pretreatment performance significantly.

These results provided evidences that mass transfer during pretreatment affected

233

carbohydrates recovery and lignin removal. Higher mass transfer coefficients lead to better

biomass removal. It turned out that biomass with particle size 16-40 mesh in the study has better

performance, such as resulting high carbohydrates recovery and high amount of lignin removal.

Meanwhile, shards cutting methods showed better pretreatment performance than Cubic

prismatic cutting method. In dilute acid pretreatment, xylan, glucan and lignin removal could

reach as high as 90.86%, 42.64% and 44.66% of original xylan, glucan and lignin in raw biomass

(16-40 mesh mm Shards, corn stover), respectively. In hot water pretreatment, xylan, glucan and

lignin can achieve 90.73%, 54.4% and 56.6%, respectively. In summary, particle size of 16-40

mesh and Shards cutting method showed great advantages in carbohydrates recovery and lignin

removal during pretreatment.

8.4.4 Optimization of the Douglas fir hot water and dilute acid pretreatment conditions

The softwood pretreatment is found challenging. The detailed hot water and dilute acid

pretreatment of Douglas fir is rarely studied. Our study optimized the pretreatment conditions for

Douglas fir on the substrate with particle size of 40-60 mesh cut by hammer mill. The optimized

conditions were optimized and selected to be 1% wt sulfuric acid pretreatment at 140°C for

50min and hot water pretreatment at 200°C for 15min, which was assessed by lignin removal,

sugar yield and pretreated substrates digestibility (Figure 8.2; Figure 8.3; Figure S8.2).

234

(a)

(b)

235

Figure 8.2 Biomass removal trend of Douglas fir in hot water and dilute acid pretreatment (a)

Biomass removal; (b) lignin removal; (c) cellulose removal, pretreatment severities 2.1-4.5

Biomass removal is a parameter that can allow the evaluation of pretreatment effectiveness

directly. To achieve the same biomass removal, the less pretreatment time was needed when the

higher temperatures and concentrations of sulfuric acids applied (Figure 8.2a). Figure 8.2a

suggested that one can achieve similar biomass removal when the same range of biomass

pretreatment severities was applied. The biomass removal under all tested conditions was 10-

45% of original loaded biomass. In Figure 8.2b, lignin removal was less than 10% at all tested

conditions. Even in some conditions, lignin content in solid residues was increasing with the

increasing of the reaction time under the same pretreatment temperatures, such as the hot water

pretreatment at 190 and 220°C. This was probably attributed to pseudo-lignin formation 348, 349

form carbohydrates and/or lignin deposition 350 on solid residues under hot water and dilute acid

0 10 20 30 40 50 60 70

0

5

10

15

20

25

30

35

40

45

50

Original cellulose weight

in 100g biomass

140oC-1% (w/w) H

2SO

4

180oC-1% (w/w) H

2SO

4

160oC-0.5% (w/w) H

2SO

4

190oC-0.5% (w/w) H

2SO

4

200oC-0.5% (w/w) H

2SO

4

190oC-hot water

200oC-hot water

220oC-hot water

Ce

llulo

se

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mo

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in

10

0g

ori

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iom

ass)

Pretreatment time (min)

(c)

236

conditions. The dilute acid pretreatment at 140C with 1% (w/w) sulfuric acid achieved the least

lignin removal among all tested conditions, while the hot water pretreatment at 190 and 200C

can obtain comparable lignin removal with the other conditions. The cellulose removal was in

the range of 7-20% of original cellulose under the tested conditions (Figure 8.2c). All tested

pretreatment conditions under the same pretreatment severities can achieve the similar cellulose

removal. Thus, the optimal pretreatment conditions for Douglas fir depend on the sugar recovery

under these conditions.

237

Figure 8.3 Total sugar yields from Douglas fir (1% H2SO4), left axis is Absolute (hemi-

sugars+glucan)/hemi-sugars/glucan recovery (from 100g dry biomass) while right axis is

Relative (hemi-sugars +glucan) /hemi-sugars/glucan recovery (% of maximum), Stage 1: sugar

release during pretreatment (measured after posthydrolysis); Stage 2: sugar release during

238

enzymatic hydrolysis; Stage 1+2: sugar release during pretreatment and enzymatic hydrolysis,

100 mg protein Ctec2 (93 FPU) with 20 mg Htec2/g (glucan + xylan)

The sugar recovery in stage 1 (pretreatment) and stage 2 (enzymatic hydrolysis) was shown

in Figure 8.3. In stage 1, cellulose derived sugars increased with pretreatment time except the

conditions under 1% (w/w) H2SO4 at 140C after 60min. However, in dilute acid pretreatment

with 1% (w/w) H2SO4, hemi-sugars underwent degradation at 180C for all tested reaction time.

The highest sugar recovery yield was at 140C for 50min (more than 90% of original

hemisugars) until hemisugars degraded afterwards. Thus, dilute acid with 1% (w/w) H2SO4 at

140C can achieve higher sugar yields than at 180C. The condition using 1% (w/w) H2SO4 at

140C for 50min in Douglas fir was applying 10mins more than the poplar’s optimal conditions

(1% (w/w) H2SO4 at 140C for 40min) in CAFI projects. Other sugar recovery results were

shown in Figure S8.2. The 0.05% sulfuric acid pretreatment achieved about a 90% sugar yield at

160C for 30min which was comparable to the yield at 1% (w/w) sulfuric acid at 140C for

50min (See Figure S8.2a). In the hot water pretreatment, the optimal conditions were 200C for

15min and 190C for 30min, which obtained up to 95% of sugar yields (See Figure S8.2b). In

comparison with the reported and optimized conditions for CAFI poplar wood discussed in

Section 3.2, Douglas fir needed higher temperature (200 vs 190C) to achieve high sugar yields.

In our study, the pretreatment conditions of 140C for 50min with 1% (w/w) sulfuric acid and

hot water at 200C for 150min were applied to evaluate the effects of biomass particle sizes and

cutting methods (particle shapes) on dilute acid and hot water pretreatment, respectively.

The dilute acid and hot water pretreatment we conducted is comparable to the target of

NREL in sugar yields and concentrations. The final maximum sugar concentration of Douglas fir

239

pretreatment was ~1.5% wt water (5% loading) and NREL Process Design and Economics (30%

loading) presented ~11.86% wt water in NREL process design 443. In Stage 2- enzymatic

hydrolysis, the final maximum sugar yield was ~60% (w/w) C5+C6 sugar Douglas fir while corn

stover in NREL Process Design and Economics demonstrated ~63.44% (w/w) 443. Thus, our

pretreatment technique on Douglas fir achieved comparable theoretical final sugar concentration

with the reported leading processes.

8.4.5 Pretreatment results of Douglas fir with various particle sizes and cuttings approaches

The appearance of poplar wood chips (particle size of 1/8′′-16 mesh and cut by crumbler)

under hot water pretreatment for 5,10,15,25, and 30 min were presented in Figure S8.3. The

colors of the chips were darker along with the increasing of pretreatment reaction times. In dilute

acid pretreatment, the hemicellulose was completely removed (~20% of original biomass) in all

tested substrates. The Douglas fir feedstock with different combinations of two cut methods (e.g.

crumbling and hammer mill) and three particle sizes (e.g. 1/8’’-16mesh; 12-30mesh; 30-60

0

20

40

60

80

100

CB 30-60

CB 12-30

CB 1/8-16

HM 30-60

HM 12-30

HM1/8-16

Control

Bio

mas

s re

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%

of

ori

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al b

iom

ass)

Douglas fir feedstock

0

20

40

60

80

100

CB 30-60

CB 12-30

CB 1/8-16

HM 30-60

HM 12-30

HM 1/8-16

ControlCe

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o

f o

rigi

nal

bio

mas

s)

Douglas Fir feedstock

0

20

40

60

80

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CB 30-60

CB 12-30

CB 1/8-16

HM 30-60

HM 12-30

HM 1/8-16

Control

Lign

in r

em

ova

l (%

of

ori

gin

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ass)

Douglas fir feedstock

(a) (b)

(c) Figure 8.4 Biomass and major

components removal of Douglas Fir

(140°C, 1% (w/w) sulfuric acid,

50min), Absolute biomass removal

(from 100g dry biomass), crumbler

(CB) and hammer cut (HM)

240

mesh) showed similar biomass (~30% of original biomass), cellulose (~15% of original

cellulose) and lignin removal (<5% of original lignin) (Figure 8.4). Thus, in Douglas fir

pretreatment, the lignin removal is much less than cellulose removal in the same substrates.

However, in the Consortium for Applied Fundamentals and Innovation (CAFI) study, when polar

wood was subjected to dilute acid pretreatment at 190°C with 2.0% (w/w) sulfuric acid for 1.1

min, the lignin removal of ~28% was higher than its cellulose removal (<25%) 389. Due to this

difficulty of removing lignin in Douglas fir, it is significant to study the effects of biomass

particle sizes and cutting methods on lignin removal in dilute acid pretreatment.

241

Figure 8.5 Recovery of Douglas fir sugars through dilute acid pretreatment (1% (w/w) sulfuric

acid), Stage 1: sugar release during pretreatment (measured after posthydrolysis); Stage 2: sugar

release during enzymatic hydrolysis; Stage 1+2: sugar release during pretreatment and enzymatic

hydrolysis, 20 and 200 mg protein Ctec2 with 4 and 40 mg Htec2/g (glucan + xylan)

The majority of sugars were recovered in monomeric forms under acidic conditions. The

242

recoveries of hemi-sugars of all tested substrates were similar in dilute acid pretreatment (Figure

8.5). In the pretreatment stage, the cellulose recovery was comparable on Douglas fir with all

combinations of three sizes and two cutting methods. However, the pretreated substrates with

various cutting methods and particle sizes showed larger difference of sugar yields in the

subsequent enzymatic hydrolysis than in pretreatment stage. Thus, the biomass particle sizes and

cutting methods have both effects on pretreatment and enzymatic hydrolysis. In our study, we

achieved very compatible sugar recovery yields in crumbler (CB; Cubic prismatic shape of

particles) and hammer cut (HM, shards shape of particles) (Figure 8.5). However, crumbler

consumed three times less energy than hammer cut, which showed potential biomass

preprocessing in the future large scale biorefinery.

0

20

40

60

80

100

CB30-60 CB12-30 CB1/8''-16 HM30-60 HM12-30 HM1/8''-16 Control

Bio

mas

s/m

ajo

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on

en

ts

rem

ova

l (%

of

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Dougals fir feedstock

Biomass removal Lignin removal(a)

243

Figure 8.6 Recovery of Douglas fir sugars through hot water and enzymatic hydrolysis (a)

biomass and lignin removal; (b) sugar recovery; Stage 1: sugar release during pretreatment

(measured after posthydrolysis); Stage 2: sugar release during enzymatic hydrolysis; Stage 1+2:

sugar release during pretreatment and enzymatic hydrolysis, 20 and 200 mg protein Ctec2 with 4

and 40 mg Htec2/g (glucan + xylan)

(b)

244

Nearly complete glucose recovery and over 90% hemi-sugars recovery from medium (12-

30) and finer (30-60) particle size substrates cut by both approaches were achieved through acid

pretreatment at milder temperature followed by sequential enzymatic hydrolysis (Figure 8.5). All

tested substrates had similar hemi-sugars recovery through both pretreatments while crumbler

cutting showed slight benefit over hammer milling, especially with bigger particle size

substrates. When pretreated at higher temperature with hot water only, to achieve near complete

combined glucose recovery (pretreatment+enzymatic hydrolysis), finer particle size (less than

12-30 size) was required for crumbler cutting than for hammer milling (Figure 8.6). In enzymatic

hydrolysis, Douglas fir particles with crumbler cut (&12-30 mesh size) showed compatible sugar

yields with other cut and size combination particles.

8.4.6 Hot water and dilute acid pretreatment of poplar wood

Effects of particle sizes and cutting approaches on poplar wood were shown in Figure 8.7

and 8.8. All hemicellulose was completely removed (~20.62% of original biomass). Compatible

pretreatment performance with different particle sizes and cutting, e.g. crumbler (CB) and

hammer cut (HM), was found. The final maximum sugar concentration (pretreatment) of the

sugar stream of poplar wood hot water or dilute acid pretreatment was ~1.01% wt (5% loading)

water in comparison with ~11.86% wt water in NREL Process Design and Economics (30%

loading) 443.

245

Figure 8.7 (a) Biomass removal, (b) Cellulose removal, (c) Lignin removal, (d) Manan and

xylan removal, and (e) sugar yields of poplar in dilute acid pretreatment, 140°C, 1% (w/w)

sulfuric acid, 40min, and enzymatic hydrolysis (72 hours), 100 mg protein Ctec2 with 20 mg

Htec2/g (glucan + xylan)

Biomass, cellulose, hemicellulose and lignin removal were comparable in dilute acid

pretreatment of poplar wood (Figure 8.7a,b,c,d). The sugar recovery was also consistently similar

0

20

40

60

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40

60

80

100

CB 30-60

CB 12-30

CB 1/8-16

HM 30-60

HM 12-30

HM1/8-16

Control

Lign

in r

em

ova

l (%

of

ori

gin

al

bio

mas

s)

Poplar feedstock

0

20

40

60

80

100

Bio

mas

s/m

ajo

r co

mp

on

en

ts

rem

ova

l (%

of

ori

gin

al

bio

mas

s)

Poplar feedstock

Mannan Xylan

(a) (b)

(c) (d)

(e)

246

among tested substrates. In hot water pretreatment, although hammer milling (HM) showed

slightly higher biomass and cellulose removal than crumbler (CB), their lignin removal was

comparable. The medium size (12-30mesh) poplar achieved slightly higher lignin removal than

other substrates (Figure 8.8a). Additionally, medium size (12-30mesh) presented ~10% higher

sugar recovery than others.

Figure 8.8 Biomass removal, sugar yields of green poplar in hot water pretreatment, 190°C, hot

water, 15min (a) biomass/major components removal, (b) combined sugar yields after

pretreatment and enzymatic hydrolysis, 100 mg protein Ctec2 with 20 mg Htec2/g (glucan +

xylan)

In summary, the crumbler (CB) and medium size (12-30mesh) achieved 5-15% higher

sugars than other biomass feedstock. The biomass removal and sugar recovery yield of hammer

0

10

20

30

40

Biomass Removal Lignin Removal Cellulose Removal

Bio

mas

s/m

ajo

r co

mp

on

en

ts

rem

ova

l (%

of

ori

gin

al

bio

mas

s)

Poplar feedstock

CB 30-60 CB 12-30 CB 1/8-16 HM 30-60 HM 12-30 HM 1/8-16(a)

(b)

247

milled and crumbler cut particles were compatible in dilute acid and hot water pretreatment and

particles with 12-30 mesh gained 5% more sugars than others. The crumbler cut poplar particles

with 12-30 mesh size showed better sugar yields than other cut and sizes particles in enzymatic

hydrolysis.

8.4.7 Sugar yields from hot water and dilute acid pretreatment of poplar and corn stover

and sequential enzymatic hydrolysis

Table 8.5 Sugar yields from pretreatment and enzymatic hydrolysis

Size and biomass particle shape

Pretreatment sugar yield

Stage 1

Enzymatic hydrolysis sugar

yield Stage 2 Total sugar yield

Glucose Xylose Glucose Xylose Glucose Xylose

16-40 mesh Shards, Poplar 16.86 71.14 70.79 5.42 87.65 76.56

16-40 mesh Cubic prismatic,

Poplar 12.03 67.81 69.83 3.61 81.86 71.42

1/8′′-16 mesh Cubic prismatic,

Poplar 9.71 67.04 68.88 2.21 78.59 69.25

16-40 mesh Shards, Corn

stover 33.66 90.78 61.30 3.77 94.97 94.55


Corn stover 28.79 87.58 55.00 3.57 83.79 91.15


Corn stover 24.81 82.02 54.92 2.98 79.73 85.00

Notes

1.With 100 mg protein Ctec2 (93 FPU) with 20mg Htec2/g (glucan+xylan)

2. Pretreated whole slurry was obtained from 1% dilute acid pretreatment at140ºC with

retention time of 40 min

Size and biomass particle shape

Pretreatment sugar yield

Stage 1

Enzymatic hydrolysis sugar

yield Stage 2 Total sugar yield

Glucose Xylose Glucose Xylose Glucose Xylose

16-40 mesh Shards, Poplar 15.75 72.27 61.86 10.00 77.61 82.27


Poplar 18.52 69.77 50.70 7.51 69.22 77.28


Poplar 15.39 63.19 37.52 3.60 52.91 66.79

16-40 mesh Shards, Corn

stover 37.43 79.82 56.94 19.80 94.36 99.62


Corn stover 49.24 90.04 40.80 5.08 90.04 95.12


Corn stover 40.21 86.25 40.22 4.53 80.43 90.78

Notes

1.With 100 mg protein Ctec2 (93 FPU) with 20mg Htec2/g (glucan+xylan)

2. Pretreated slurry was obtained from hot water pretreatment at 190ºC with retention

time of 25 min

248

Enzymatic hydrolysis of pretreated biomass whole slurries was carried out with enzyme

loading of 100 mg protein Ctec2 (93 FPU) plus 20 mg protein Htec2/ g (glucan+xylan). Table

8.5 showed sugar yields of pretreatment and enzymatic hydrolysis of pretreated biomass by hot

water pretreatment at 190ºC for 25 min and 1% dilute acid pretreatment at 140ºC for 40 min. All

sugar yields were calculated based on the original content of glucan or xylan in untreated

biomass. The total sugar yields in stage 1 pretreatment included yields of both monomeric and

oligomeric sugars. Total xylose and glucose were monomeric sugar yield because oligomeric

sugars were hydrolyzed into monomeric sugars during stage 2 enzymatic hydrolysis. It was

found that the highest sugar yields from pretreatment could be achieved at 25 min for hot water

pretreatment at 190ºC and at 1% dilute acid pretreatment for 40 min at 140ºC, respectively.

Results of sugars’ yields from both pretreatments showed that higher glucose and xylose

yields were observed from corn stover samples than those of poplar samples. 82-90% xylose

yield from corn stover samples and 67-71% xylose yield from poplar samples were obtained

through 1% dilute acid pretreatment. Although similar xylose yields are found for corn stover

and poplar samples using hot water pretreatment, higher portion of xylan degradation products in

hydrolysate was xylan oligomers with hot water pretreatment. The pretreatment glucose yields

are strongly correlated to the mass transfer coefficient of the samples (Table 8.4).

Effective pretreatment is able to disrupt biomass plant cell wall and results in high enzymatic

digestibility. Enzymatic yields can be criteria to reveal effectiveness of pretreatment methods.

For pretreated biomass by 1% sulfuric acid pretreatment at140℃, the enzymatic hydrolysis yield

appeared highly dependent on particle sizes, biomass types and cutting approaches of pretreated

biomass. 16-40 mesh Shard scorn Stover showed the highest enzymatic digestibility of 94.97%

249

of glucose yield and 96.81% of xylan yield based on glucan and xylan content of pretreated

biomass. Enzymatic hydrolysis of 16-40 mesh Shard scorn stover pretreatment by hot water

pretreatment at 190ºC attained 94.36% of glucose and 99.62% of xylose yield. This indicated an

effective pretreatment on 16-40 mesh shards corn stover. Hot water pretreatment showed relative

better enzymatic hydrolysis yield, which might be because of more lignin was removed in hot

water pretreatment. Lignin remained in pretreated slurries after dilute acid pretreatment at 140ºC

inhibited enzymatic hydrolysis 164. Results from both hot water and dilute acid pretreatment also

indicated that samples with high mass transfer coefficients obtained higher pretreatment sugar

yields and achieved higher enzymatic hydrolysis yield too (Table 8.4, 8.5).

The total sugar yield, which was the combined yield of stage 1 (pretreatment) and stage 2

(enzymatic hydrolysis) showed strong correlation to the mass transfer coefficients of substrates.

16-40 mesh particle size and Shards cutting resulted in the highest total sugar yield. It was

reported increased solvent diffusion during pretreatment by changing particle size can increase

pretreatment severity which led to a good pretreatment yield 419. Among three poplar samples,

16-40 mesh Shards poplar showed the highest total glucose yield of 87.65% and total xylose

yield of 76.56% for dilute acid pretreatment, 77.61% total glucose yield and 82.27% total xylose

yield for hot water pretreatment. 16-40 mesh Shards corn stover also resulted in the highest total

glucose yield of 94-95% and total xylose yield of 94.55-99.62% for both pretreatments. Results

showed particle size and biomass cutting approaches (i.e. Shards and Cubic prismatic) have

significant impact on the enzymatic hydrolysis yield. One reason was their positive effects on

pretreatment process which disrupted plant cell wall, increased porosity and removed lignin 161,

169, 444. Those chemical and physical change of biomass affected enzymatic hydrolysis yield.

250

Second reason was the effects of particle sizes and shapes on biomass accessibility for enzymes.

Our results showed medium particle size (16-40/ 12-30 mesh) shards and Cubic prismatic cutting

were comparable in enzymatic hydrolysis and higher compared to other combinations of particle

sizes and cuttings.

8.5 Conclusion

The hot water and dilute acid pretreatment of corn stover, poplar and Douglas fir with four

particle sizes and two cutting methods was carried out to determine their different degradations

of xylan, glucan and lignin. Results showed that biomass species and cutting methods have more

impact on pretreatment mass transfer and yields than particle sizes. Also, results of enzymatic

hydrolysis on hot water pretreatment and 1% (w/w) dilute acid pretreatment indicated the

significant roles of particle sizes and cutting approaches in enzymatic hydrolysis yield. It was

found that the sugar yields of biomass particles with two cutting methods and four particle sizes

were compatible. Although shards shaped (hammer milling) biomass particles showed a bit

higher sugar yields, the crumbling cut still achieved comparable sugar yields with hammer

milling. Interestingly, the crumbler cutting showed 5-15% advantages in getting better sugar

yield in enzymatic hydrolysis. Addtionally, the energy consumption of crumbling was three times

less than traditional hammer milling. Biomass particles with 12-30/16-40 mesh size could

achieve similar combined sugar yields as finer particle size substrates. The mass transfer

modeling was proposed in Appendix (proposed mass transfer modeling). The preliminary work

showed that larger particle sizes and crumbling cutting have better mass transfer diffusion

capacities. Thus, the crumbling cut biomass has advantages in mass diffusion. However, more

future work is needed such as the improvements of measurements of mass transfer coefficients

251

and integrating these values into the proposed models, and study of the effects of particle sizes

and cutting shapes on mass diffusion. This study is significant for choosing wise preprocessing

methods and pretreatment methods for biomass in the future.


I would like to thank Forestconcepts, LCC for the funding support for this work. I would

like to thank Dr. Jim Dooley and Leading Mechanical Engineer David Lanning from

Forestconcepts for their support. Also, I want to acknowledge Dr. Jie Xu and Dmitry Gritsenko

from University of Illinois at Chicago for their help in mass transfer modeling development.

Additionally, I thank Mr. Pei-yu Leu and Sohrab H. Mood for his assistance in completing

experiments.


Figure S8.1 Left) Crumbler M24 research prototype machine used with 4.8 and 1.6 mm cutters,

Right) Research scale Crumbler machine with 0.8 mm cutters (by ForestConcept, LCC)

252

(a)

254

(b)

Figure S8.2 Total sugar yields from Douglas fir of (a) 0.05% wt sulfuric acid (b) Water only, left

axis is Absolute (hemi-sugars+glucan)/hemi-sugars/glucan recovery (from 100g dry biomass)

while right axis is Relative (hemi-sugars +glucan) /hemi-sugars/glucan recovery (% of

maximum), Stage 1: sugar release during pretreatment (measured after posthydrolysis); Stage 2:

sugar release during enzymatic hydrolysis; Stage 1+2: sugar release during pretreatment and

enzymatic hydrolysis

255

Figure S8.3 Hot water pretreatment of poplar wood chips for 5,10,15,25, and 30 min (left to

right), particle size of 1/8′′-16 mesh and cut by Crumbler

256

CHAPTER NINE

CONCLUSIONS AND FUTURE PERSPECTIVES

Lignocellulosic biomass is a renewable source for the production of liquid transportation

fuels which potentially can cause a major impact on energy use architecture. The recalcitrant

nature of biomass is the main challenge to overcome. The goal of pretreatment is to reduce the

biomass recalcitrance and improve yields of sugars and ethanol. Recent aqueous pretreatment

fractionation of biomass in producing sugar and lignin intermediates facilitates the utilization of

the whole biomass to achieve cost-competitive biorefinery.

The topics of this thesis focus on the study of key fundamental issues in biomass aqueous

pretreatment. In chapter 3, the HR-BB-SFG-VS was developed to assisting the characterization

of crystalline cellulose in aqueous pretreatment for the first time. New structural information

(e.g. more peaks in C–H and O–H regions) of cellulose was found, and this information has not

been achieved by traditional methods, which demonstrated a significant role of SFG-VS in

cellulose structure study. It was found that O–H vibrational regions were the signatures of

various cellulose polymorphs while C–H vibrations distinguished celluloses from various

sources. Additionally, the peak integrated area ratio of C–H and O–H was developed as a factor

to distinguish different crystals, such as cellulose Iα, Iβ, and Avicel. Furthermore, the chapter 4

investigated the structural changes of cellulose bulks and surface layers of crystals in a dynamic

heating process with/without water in aqueous pretreatment using the SFG-VS equipped with a

fluid reactor. In order to characterize the significant surface layers, an innovative design by

changing the laser angles and geometries was proposed and successfully selectively characterize

the cellulose surface layers for the first time. The results showed the distinguished structural

257

differences between crystals’ surface layers and bulks of Avicel and Iβ, and the SFG assignments

of cellulose C-H and O-H peaks were revisited. In aqueous pretreatment, the high temperature is

a crucial factor in influencing pretreatment efficiency. The chapter 4 showed the cellulose bulk

crystalline structures were completely disrupted at elevated temperatures (170-220C).

Interestingly, it was proposed that both cellulose Iα and Iβ in Avicel were converted gradually to

an intermediate which possessed dislocated/disordered/random-oriented microfibrils with the

dynamic increasing of heating temperatures, and the transformation of cellulose Iα to the

intermediates were not reversible and Iα may or may not be converted to cellulose Iβ. In

comparison, the surface layers of cellulose were not recrystallized to gain crystalline structures.

The possible hypothesis was proposed that the disrupted cellulose microfibrils recrystallize

during and after cooling down to room temperature. This was possibly triggered by the

rearrangements of dislocated crystals to reach a stable phase. However, this recrystallization is

not a recovery process so that the newly formed crystals were differed from the original

structures but showing similarities. Because cellulose bulks are highly disordered crystals, the

new crystals showed considerate similarities with the original cellulose structures and molecular

orientations. However, due to the amorphous of the surface layers of crystalline cellulose, the

recrystallization of the surface layers is not occurred. This hypothesis still needs more

experimental work to validate, such as a detailed characterization of the newly formed crystals

by HR-BB-SFG-VS, the application of other analytical tools to assist analysis, the weight and

wet chemistry analysis of the heated cellulose and the tests of cellulose Iα and Iβ in dynamic

heating process. Besides, the enzymatic hydrolysis of cellulose was also characterized by the

SFG-VS which suggested the potential of SFG-VS in characterizing the interaction of celluloses

258

and enzymes without separating them. Also, the C-H vibrational region with slow peak decay is

important for the study of enzymatic hydrolysis in SFG characterization. Surprisingly, with the

undergoing of enzymatic hydrolysis, it was proposed that cellulose crystals were changing

suggested by the characterization of peak area ratios of enzymatic restart samples. However, this

conclusion remains open. The thesis provided some preliminary work on the cellulose structure

characterization and application of SFG-VS in characterization of cellulose structural changes.

We found significant spectra differences between cellulose Iα, Iβ, and Avicel such as peak

position, intensity and ratios. The traditional understandings of cellulose structure are not enough

and some assumptions might not be accurate. More research work is needed to give the final

conclusions but our preliminary results are significant and point out the research direction. The

SFG-VS system has evidently demonstrated its significant roles in studying cellulose crystalline

structural changes in biorefinery.

The flowthrough reactor system provides a useful tool to study the fundamental lignin

behavior and chemistry in aqueous pretreatment. The flowthrough aqueous pretreatment of

softwood and hardwood in chapter 5, 6, and 7 introduced the effectiveness of flowthrough

reactor in fractionating biomass to produce high yields of fermentable sugars and usable forms of

lignin by exiting products in time to avoid sugar degradation and lignin condensation. The liquid

phase lignin derived from flowthrough aqueous pretreatment after acidifying, precipitation, and

purification was provided for the hydrodeoxygenation to produce high yield of hydrocarbons

(Table A2) which is not the focus on this thesis although some work has been published in Green

Chemistry journal 445. The aqueous flowthrough pretreatment has helped understand several

fundamental questions. In chapter 5, the softwood flowthrough pretreatment is studied at pH

259

neutral-12 for the first time. The results showed the great impact of pretreatment initial pHs on

products distribution, kinetics and biomass chemistry. The solubilization of the whole biomass

was found possible with proper controlling of temperature and pHs. The value-added

biochemical was produced (pH≥11.0) while presented difficulties in analysis. However, the

potential utilization of the mixture of aromatics or other compounds is an interesting topic in the

future. The pretreatment mechanisms with impact from initial pHs of pretreatment media were

proposed. The chapter 6 comprehensively studied the lignin characteristics derived from

hardwood flowthrough pretreatment with main lignin depolymerization mechanism by the

cleavage of β-O-4, producing the proper properties of lignin with low molecular weight, very

mild C-C condensation (Cβ-C5), and a high purity and yield. The lignin was provided in

carbonation conversion for the production of carbon Nanoporous materials which were found

promising as electrodes in supercapacitor. Some of the work has been published in

ChemSusChem journal 328. The lignin characterization of aqueous flowthrough pretreatment in

the thesis provides significant information on the properties of aqueous pretreated lignins, which

are the basics of utilizing them. The results showed that the flowthrough system and optimized

pretreatment conditions can separate and isolate lignin in aqueous phase for more than 95% yield

and 85% of purity while still ensured high recovery of sugars. Lignin was produced through

three subsets which includes little residual lignin, substantial acid precipitated insoluble solid

lignin and less than 20% of soluble lignin. The structural analyses of the flowthrough derived

lignin showed the cleavage of β-O-4 and lignin condensation reactions, which contributes the

lignin reaction chemistry of aqueous pretreatment. The flowthrough moved depolymerized lignin

fragments out of reactor in a short time to preventing from further reactions, which produced

260

lignin that possessed similarities to the original native lignin. Due to the high purity and yield of

the lignin as well as the modest modification of lignin structure in flowthrough with low

molecular weight, the valorization or utilization of flowthrough aqueous pretreatment derived

lignin is attractive in the future.

In chapter 7, the softwood and hardwood lignin structural transformation at molecular level

was revealed for the first time starting with their magnificent difference of lignin removal under

the same flowthrough pretreatment conditions. The advanced aqueous and solid phase NMR

analytical methods supported the severe recondensation mechanisms (proposed at Cα-C5, C5-

C5, Cβ-C5 and C5-C4) of softwood lignin resulting in the remaining of 30% softwood lignin as

residual lignin in solid residues even when all softwood carbohydrates were completely

dissolved. The GPC analysis of the softwood residual lignin revealed the low molecular weight

distribution of residual lignin which confirmed the recondensed mechanisms in forming the

softwood residual lignin. This study is the first time to find this opportunity (complete poplar

solubilization while 30% softwood lignin remained as solids) to study the different structural

transformations between softwood and hardwood, and the mechanisms of difficult softwood

lignin removal have remained for a long time in the leading pretreatment.

The pretreatment process can have a great impact on the preprocessing methods and

costs. Conversely, the preprocessing of feedstock can affect the pretreatment efficiency and total

biorefinery cost. In chapter 8, the pretreatment performances of new preprocessed feedstock

substrates (e.g. new cutting approach crumbling and particle sizes) with three times lower energy

consumption than traditional methods, were assessed in aqueous pretreatment. It was found that

the aqueous pretreatment of the feedstock cut by crumbling, the new approach, achieved the

261

comparable pretreatment performance with traditional hammer milling. More importantly, it was

found that the cutting methods which have rarely received attentions were more important than

particle sizes in mass diffusion. Also the biomass species and cutting approaches are more

influencing in pretreatment and enzymatic hydrolysis performances than the effects from particle

sizes. Surprisingly, our results suggested that finer particle sizes did not always lead to high

pretreatment and enzymatic hydrolysis sugar yields. The mass transfer modeling and the

measurements of diffusion capacities (acid/alkali diffusivity) of various feedstocks with different

cutting methods, biomass species, and particles were proposed by reported and designed

apparatus. The preliminary results indicated the particle sizes and cutting methods were

correlated with mass diffusion capacities. However, more efforts are still needed to improve the

mass transfer model. All these results are important for preprocessing of feedstocks at low cost

and energy, especially in large scale biorefinery in the future.

Future work

The SFG-VS is demonstrating a significant role in helping study the structural changes of

cellulose crystals in biomass aqueous pretreatment. However, this thesis only focuses on the

fundamental study of cellulose structural changes with/without the effects of water. More work

can be done on the understandings of the effects from other chemicals such ad acids and alkalis.

Also, the SFG-VS system can be developed to study the enzymatic hydrolysis system, such as

the interactions of cellulose with enzymes in enzymatic hydrolysis. Additionally, the

recrystallization hypothesis of cellulose crystalline materials has been proposed in the thesis.

More experimental work is needed to provide solid proofs to enhance the hypothesis, such as

applying HR-BB-SFG-VS in the characterization of the newly formed crystals, using other

262

analytical tools to assist in studying the recrystallization process, and studying thermal behaviors

of other cellulose crystalline materials.

The flowthrough reactor system is indicating the advantages in fundamental studies of

pretreatment processes by separating the three major components for characterization in this

thesis. However, the thesis did not cover the utilization of the recovered sugar streams into

applications. More efforts in the future can be devoted to the application of the sugar streams to

produce chemicals, materials or fuels. Besides, the commercialization study of flowthrough

reactor is necessary considering its strong capacities in producing a high yield and purity of

intermediates. Additionally, the flowthrough reactor can be expanded to producing more

intermediates from biomass besides sugars and lignins. The alkaline pretreated liquor in the

thesis was indicated hard to analyze but it contains a valuable mixture of chemicals. Thus, the

future work also can focus on the design of the analysis methods and utilize these compounds in

producing biochemical. In chapter eight, the mass transfer modeling and the measurements of

mass transfer efficient were proposed but more future work is still needed to improve the

modeling.

The thesis highlighted the process characterization of aqueous pretreatment, focusing on the

characterization of cellulose and lignin. In the future, most of research related to lignin and

cellulose can be implemented by the capacities and skills demonstrated in the thesis. The author

is interested in the following aspects of work. Firstly, continue the fundamental studies of

aqueous pretreatment and enzymatic hydrolysis. For example, study the interaction between

enzymes with cellulose can be studied by various spectroscopic tools and molecular dynamic

simulation. Study mass transfer issues in aqueous pretreatment. Continue research on alkaline

263

pretreatment triggering by the preliminary results of excellent mass diffusion capacities at

<100C. Second, the author is interested in applying lignin and cellulose as carbon sources to

produce value-added products. Apply intermediates, such as cellulose, lignin, chemicals, and

aromatic compounds, indicated in this thesis to produce various chemicals, materials, and fuels.

For example, the conversion of sugars and lignin to biolipids, the conversion of cellulose and

lignin to hydrocarbons, the carbonization of cellulose and lignin to Nanoporous carbon materials,

carbon fibers or other materials, and the conversion of lignin to valuable aromatic compounds.

Third, develop new pretreatment techniques to tailor cellulose and lignin properties for different

applications. At last, learn commercialization knowledge to facilitate our processes to

commercialization.

Achievements and perspectives

This research has revealed new structural information of cellulose by HR-BB-SFG-VS for

the first time. The in-situ characterization of the molecular structure of cellulose surface layers

was also achieved for the first time. The fundamental study of cellulose structure helps reduce

the cost of pretreatment and enzymatic hydrolysis by fine-tuning the loading of chemicals and

enzymes results in needing fewer of these chemicals for the purpose of increasing the cellulose

accessibility. Our research found that the traditional understanding of cellulose structure should

be should be adjusted and our study pointed out the future research direction to do this. An

accurate understanding of the recrystallization phenomena of cellulose materials strongly affects

the design of thermal heating in pretreatment, and ensures that the most cost-effective amount of

heat in the large-scale pretreatment of cellulose. Our research also uncovered new insights into

the thermal modification of cellulose in industry. The poplar lignin was comprehensively

264

characterized in water-only and dilute acid flowthrough pretreatment and a mechanism of side

chain transformation and Cβ-C5 condensation was proposed based on the evidence. The recent

lignin valorization studies insufficiently studied the connection between lignin characteristics

and the effectiveness and efficiency of lignin conversion to value-added products. The thorough

characterization of pretreatment produced lignin facilitates the future conversion of lignin

substrates to useful commercial products and controls their qualities. The 30% pure undissolved

residual softwood lignin was isolated for the first time and strong evidence was found that this

residual pure C-C5 condensed structure was caused by severe recondensation mechanisms. This

can be used to study softwood lignin removal mechanisms which have been historically

challenging. This is significant to the application of mix-feedstock or the use of multiple biomass

types in biorefinery. The mix-feedstock is a solution to solve the shortage of biomass since

different types of biomass can be distributed differently by seasons and locations. Since

undissolved C-C lignin is less reactive to conversion it can instead be used to heat the factories

while converting the more reactive portions to value-added products. The impact on pretreatment

mechanisms by different levels of the initial pHs of pretreatment media was studied and the

impact was found to be very significant. This will help alkaline and dilute acid pretreatment to

better produce sugars and guide the selection of methods for producing sugars and biochemicals.

The significance of cutting methods of biomass was researched and it was found that finer

particle sizes did not always lead to high pretreatment and enzymatic hydrolysis sugar yields. A

new research direction of the study of cost-effective cutting methods and the biorefinery

performances of cut biomass is needed because of this finding. Using optimal particle sizes can

reduce the cost of expensive preprocessing to finer sizes.

265

APPENDIX

Figure A1 SFG-VS characterization of (a) Avicel 2600-3600cm-1; (b) Avicel 1000-1600cm-1; (c)

Cellulase and BSA 2600-3800 cm-1; (d) Cellulase and BSA 1500-1800 cm-1, Note: (A)-Avicel

without a flat CaF2 window on top of the sample; (B)-Avicel with a flat CaF2 window on top of

the sample

The application of a flat CaF2 window showed similar signals of cellulose samples with

the one without a flat CaF2 on top. The wavelength of 1000-1600 cm-1 is the fingerprint region of

cellulose crystalline structures. Thus, the current SFG-VS characterization of cellulose only

focuses on the wavelength of 2600-4000cm-1due the complexity of the fingerprint region.

In order to study the complex enzymatic hydrolysis system, the functionality of various

cellulase components should be closely inspected firstly. To assist the study of the interaction of

cellulose with enzymes in enzymatic hydrolysis system, various proteins (i.e. cellulase, lignin

(a) (b)

(d) (c)

266

blocking agent-BSA) were characterized by SFG-VS. The cellulase and BSA were characterized

in solution with 1.2g/cm-3 and 40mg/mL, respectively, which are their maximum solubility to

guarantee the molecules locating on the surface of the solution so that SFG-VS can detect their

signals. As seen in Figure A1, the SFG-VS characterization of enzymatic hydrolysis system can

avoid the overlaps of peaks of cellulose and cellulase. Thus, the SFG-VS presented a significant

tool to study the enzymatic hydrolysis without separating cellulase and cellulose for

characterization.

In the SFG-VS characterization of proteins, the region of wavelength of 1500-1800 cm-1

is usually a signature of proteins, thus, these peak characteristics located in this region can be

developed to study the enzymes in the enzymatic hydrolysis system. However, the cellulase and

BSA presented the same peak positions in ~2870 and ~2940 cm-1, however, their peak ratios

were differed. Additionally, both cellulase and BSA showed strong SFG polarization

dependence. In the same enzymatic hydrolysis system, when cellulase and BSA are both applied,

their peaks might interfere with each other. Even though, this problem can still be avoided by

good design in experiments. The SFG-VS is a valuable tool to characterize molecular structural

changes in enzymatic system.

267

Table A1 Pyrolysis products of residual lignin derived from aqueous flowthrough pretreatment

by Py-GC/MS

Number Retention time (min) Compounds MW Sources

1 8.74 Phenol 94 H

2 10.65 Phenol, 2-methyl- 108 H


4 11.38 Phenol, 2-methoxy-

(Guaiacol)

124 G

5 11.81 Phenol, 2,6-dimethyl- (2,6-

Xylenol)

122 H

6 12.68 Phenol, 2-ethyl- 122 H


Xylenol)

122 H

8 13.41 2,3-Dihydroxybenzaldehyde 138 G


Xylenol)

122 H

10 13.89 Phenol, 2-methoxy-4-methyl-

(4-methylguaiacol)

138 G

11 13.98 Benzoic acid 122 H

12 14.14 Phenol, 2,4,6-trimethyl- 136 H

13 15.01 Phenol, 4-ethyl-3-methyl- 121 H

14 15.10 1,2-Benzenediol 110 G

15 15.30 Phenol, 2-(1-methylethoxy)-,

methylcarbamate

G

16 15.52 Phenol, 2-propoxy- 152 G

17 15.81 Benzaldehyde, 2-hydroxy-5-

methoxy-

152 G

18 15.87 Phenol, 4-ethyl-2-methoxy-

(4-ethylguaiacol)

152 G

19 15.91 1,2-Benzenediol, 3-methoxy- 140 S

20 15.97 1,4-Benzenediol, 2-methoxy- 140 S

21 16.19 Phenol, 2-ethyl-4,5-dimethyl- 150 H

22 16.73 2-Methoxy-4-vinylphenol 150 G

23 16.83 1,2-Benzenediol, 3-methyl- 124 G



26 17.46 Phenol, 4-(2-propenyl)- 134 H

27 17.64 Phenol, 2,6-dimethoxy-

(Syringol)

154 S

28 18.50 Phenol, 3,4-dimethoxy- 154 S

29 17.84 Phenol, 2-methoxy-4-(1-

propenyl)- (Isoeugenol)

164 G

30 18.84 Vanillin 152 G

31 18.97 Phenol, 2-methyl-6-(2-

propenyl)-

148 H

32 19.24 1,4-Benzenediol, 2,6-

dimethyl-

138 G

268

33 19.31 1,3-Benzenediol, 4,5-

dimethyl-

138 G

34 20.02 Phenol, 2-methoxy-4-propyl- 166 G

35 22.63 Phenol, 2,6-dimethoxy-4-(2-

propenyl)-

194 S

36 23.97 Benzaldehyde, 4-hydroxy-3,5-

dimethoxy-

182 S

37 26.35 Phenol, 4-(ethoxymethyl)-2-

methoxy

182 G

38 23.69 Benzoic acid, 4-ethoxy- 166 G


40 11.72 Phenol, 2,3-dimethyl- 122 H

41 12.77 Phenol, 2-propyl- 136 H

42 13.38 Phenol, 4-ethyl- 122 H

43 13.43 Phenol, 3-ethyl- 122 H

44 17.58 Eugenol 164 H

45 18.67 Phenol, 2-methoxy-6-(2-

propenyl)-

164 H

46 23.11 Benzene, 1,4-dimethoxy-2-

methyl-

152 S

47 23.48 Benzoic acid, 3-hydroxy- 138 H

48 25.19 Benzaldehyde, 3,4,5-

trimethoxy-

196 S

49 25.42 4-((1E)-3-Hydroxy-1-

propenyl)-2-methoxyphenol

(coniferol)

180 G

50 25.61 Benzaldehyde,-2-propoxy,-5-

methoxy

194 G

51 28.68 Benzoic acid, 3,4-dihydroxy- 154 G

52 20.34 2,3-Dimethoxytoluene 152 G

53 28.99 Phenol, 3,5-dimethoxy- 154 S

54 8.04 Benzaldehyde 106 H

55 16.94 3-Methoxy-5-methylphenol 138 G

56 17.77 Ethyl Vanillin 166 G

57 17.90 2,4-Dimethoxyphenol 154 S

58 18.15 Resorcinol 110 G

59 18.21 Phenol, 3-methoxy- 124 G

60 19.54 Benzoic acid, 4-hydroxy-3-

methoxy- (Vanillic acid)

168 G

61 20.81 4-Hydroxy-2-

methoxybenaldehyde

152 G

62 22.63 2,5-Dimethoxy-4-

ethylbenzaldehyde

182 S

63 24.15 Benzoic acid, 3-ethoxy- 166 G

64 24.38 Benzoic acid, 4-hydroxy- 138 S

65 9.21 Benzene, ethoxy- 122 G

66 10.70 Benzene, 1,2,3-trimethyl- 120 H



69 20.23 Benzaldehyde, 2-ethoxy- 150 H

70 20.68 3,5-Dimethoxybenzaldehyde 166 S

71 20.86 4-Hydroxy-2- 152 G

269

methoxybenaldehyde

72 22.75 4-Propyl-1,1'-diphenyl 196 H

73 24.59 Phenol, 3-methoxy-2-methyl- 138 G

74 25.19 Ethanone, 1-(4-hydroxy-3,5-

dimetho

196 S

75 27.97 1,1'-Biphenyl, 2-methyl- 168 H

76 29.80 Benzene, 1-phenoxy-2-(2-

propenyl)-

210 H

77 10.13 Benzeneacetaldehyde 120 H

78 15.12 Phenol, 4-ethoxy- 138 G

79 17.02 Benzene, 1,3-dimethoxy- 138 G

80 18.11 3,5-Dihydroxytoluene

(Orcinol)

124 G

81 23.30 Benzaldehyde, 2,4-

dimethoxy-

166 G

82 9.49 Benzene, 1-methoxy-4-

methyl-

122 H

83 10.12 Benzaldehyde, 4-hydroxy- 122 H

84 13.45 2-Methoxy-6-methylphenol 138 G

85 14.95 Benzene, 1-ethyl-4-methoxy- 136 H

86 15.01 Phenol, 2-ethoxy- 138 G

87 18.02 1,2,3-Trimethoxybenzene 168 S


89 19.42 Benzoic acid, 4-methoxy- 152 H

90 20.57 Benzoic acid, 5-hydroxy-2-

methoxy-

168 G

91 21.13 Benzene, 1,2,3-trimethoxy-5-

methyl

180 S

92 25.35 Phenol, 4-(3-hydroxy-1-

propenyl)-2-methoxy-

180 G

270

Figure A2 Isolation processes of the native and pretreatment derived lignin

271

Table A2 The distribution of hydrodeoxygenation (HDO) products of flowthrough derived lignin

and the selectivity of HDO conversion with noble metal catalyst matrix

272

NO. Retention time (min) Compounds

2 13.049 Vanillin

4 15.606 Butylated Hydroxytoluene (BHT)

6 18.055 Phenol, 4-(1,1,3,3-tetramethylbutyl)- (antioxidants)

13 38.079 Phenol, 2,2'-methylenebis[6-(1,1-dimethylethyl)-4-methyl-

(antioxidants)

18 14.516 Phenol, 2-methoxy-4-propyl-

19 16.771 Phenol, 4-(1,1-dimethylpropyl)-

20 19.004 Phenol, 2-methyl-4-(1,1,3,3-tetramethylbutyl)-

26 9.856 Phenol, 4-tert-butyl-

27 10.503 Phenol, 3-methyl-5-(1-methylethyl)-

28 17.138 Phenol, 2,6-bis(1,1-dimethylethyl)-4-methyl-

29 22.155 Phenol, 2,5-bis(1-methylpropyl)-

Figure A3 GC/MS determination of water/dilute acid soluble lignin fragments from 240°C with

25 ml/min flow of 0.05% sulfuric acid; the compounds 4 and 13 are the potential high value

intermediates for biodiesel production

273

Figure A4 Determination of cellulose crystallinity before and after flowthrough pretreatment by

XRD

274

Figure A5 Our flowthrough system

275

Figure A6 Application of SFG-VS in observing pretreated biomass substrate and in comparison

with XRD results

276

Proposed mass transfer modeling

The most common kinetic modeling is proposed by Saeman in 1945 446.

𝐻𝑒𝑚𝑖𝑐𝑒𝑙𝑙𝑢𝑙𝑜𝑠𝑒𝑘1→ 𝑋𝑦𝑙𝑜𝑠𝑒

𝑘2→ 𝐷𝑒𝑔𝑟𝑎𝑑𝑎𝑡𝑖𝑜𝑛 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑠 (A1)

where 𝑘 is Arrhenius temperature dependence and can be calculated based on equation (A2).

𝑘𝑖 = 𝑘𝑖0 × 𝐴𝑚𝑖 × exp (−𝐸𝑖/𝑅𝑇) (A2)

where 𝑘𝑖0 is the preexponential factor; 𝐴 is the concentration of acid (wt %); 𝑚𝑖 is the power,

and 𝐸𝑖 is the activation energy.

𝐶𝑒𝑙𝑙𝑢𝑙𝑜𝑠𝑒 𝑘1→ 𝐺𝑙𝑢𝑐𝑜𝑠𝑒

𝑘2→ 𝐷𝑒𝑔𝑟𝑎𝑑𝑎𝑡𝑖𝑜𝑛 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑠 (A3)

𝑑𝐶

𝑑𝑡= −𝑘1 × 𝐶 (A4)

𝑑𝐺

𝑑𝑡= 𝑘2 × 𝐺 − 𝑘1 × 𝐶 (A5)

Considering the dependence of Arrhenius temperature,

𝑘𝑖 = 𝑘𝑖0 × 𝐴𝑚𝑖 × exp(−𝐸𝑖/𝑅𝑇) (A6)

Thus, the glucose yield followed equation (A7).

𝐺 = 𝐶0 [𝑘1

𝑘1−𝑘2] × (𝑒−𝑘2𝑡 − 𝑒−𝑘1𝑡) + 𝐺0 × 𝑒−𝑘2𝑡 (A7)

in which G is the fraction of the total potential glucose; 𝐶0 𝑎𝑛𝑑 𝐺0 are the initial fractions of

cellulose and glucose, respectively. However, these modeling equations are assumed first order

and no mass transfer effects considered. Thus, the development of mass transfer modeling in

aqueous pretreatment is important since mass diffusion is closely related to pretreatment

performances.

Gas mass transfer coefficients are more accurate by experimental measurements than using

empirical equations. Liquid mass transfer coefficients were calculated mostly by empirical

277

equations summarized by experiences involving several factors, such as the viscosity of the

solution, molar mass, the chemicals, and solvent dissociation factor (Equation A8).

𝐷𝐴𝐵 =7.4×10−8(𝑎𝑀𝐵)0.5𝑇

𝜇𝑉𝐴0.6 (A8)

DAB-the mass transfer coefficient of liquid compound A in B, cm2/s; T-absolute temperature, K;

µ-the viscosity of the solution, mPa·s; MB-molar mass of B, kg/kmol; VA-component A molar

volume under the boiling point, cm3/mol. VA(water)=75.6, α-the solvent association factor,

α(water)=2.6.

This study proposed to measure liquid mass transfer coefficients experimentally.

Conductivity is a measurement of ability of electrolyte solution to conduct electricity (current).

Its unit is S/m (1 S/m=1 mho/m) and usually refers to the value at 25°C. The measurement of

solution conductivity is a fast, cheap and reliable way of measuring the ionic contents in a

solution. Conductivity is in direct proportion to total dissolved solids (T.D.S). It is well known

that 0.54-0.96 1µS/cm equals 0.64 mg of NaCl per kg of water. The measuring of solution

conductivity is through AC resistance of the solution between two electrodes. Usually, dilute

solution meets Kohlrausch’s laws of concentration dependence and additivity of ionic

contributions. If electrolytes dissociate completely in solution, Kohlrausch’s laws can be shown

as following 447.

𝛬𝑚 = 𝛬𝑚0 − 𝐾√𝑐

𝛬𝑚0 -limiting molar conductivity; K-empirical constant; c-electrolyte concentration

𝛬𝑚0 = 𝜐+𝜆+

0 +𝜐−𝜆−0

𝜐+ 𝜐− -the number of moles of cations and anions, respectively (1 mole of the dissolved

electrolyte); 𝜆+0 𝜆−

0 -limiting molar conductivities of the individual ions (For H+: 34.96; OH-:

278

19.91; Na+:5.011; SO42-:15.96).

Nowadays, the cell for measuring conductivity is built as electrical conductivity meter. The

cell includes a solution of an electrolyte, two flat or cylindrical electrodes, a fixed distance, an

alternating voltage. The conductivity is revealed by the resistance of the solution in cell which

can be expressed as followed.

𝑅 =𝑙

𝑆𝜌

𝜌-resistivity (material property); l and s are distance and surface area of two electrode poles.

Calibration of cell constant can be performed to avoid knowing l and s.

𝑅∗ = 𝐶𝜌∗

C-cell constant; 𝑅∗ and 𝜌∗ -known resistance and resistivity of standard compound. Specific

conductance, k (kappa):

𝜅 =1

𝜌=

𝐶

𝑅; 𝛬𝑚 =

𝜅

𝑐

c-solution concentration; 𝛬𝑚-molar conductivity. Therefore, the conductivity has a linear relation

with the concentration of the solution or the ionic content in the solution. The study will use

conductivity meter to determine the acid/alkali concentration change during their diffusion into

biomass particles. The solution concentration can’t be calculated simply by the above equations

since K is unknown. Thus, a diffusion model is needed to simulate solution concentration with t

to calculate De.

279

Figure A7 Designed measuring cell for mass transfer coefficients, (a) reported device (b)

designed device

The measurements of mass transfer coefficients were reported to use the reported device

(Figure 8.1a) 160. The unsteady state acid diffusion into a solid sphere in a well agitated tank was

used in this device and is a classical problem that the solution has already been reported shown

in equation (A9).

280

𝐹(𝑡) = 1 − ∑6𝛼(1+𝛼)

9+9𝛼+𝑞𝑛2𝛼2

exp (−𝑞𝑛

2𝐷𝑒𝑡

𝑅2)∞

𝑛=1 (A9)

Where 𝑞𝑛 are the non-zero roots of tan 𝑞𝑛 = 3𝑞𝑛/(3 + 𝛼𝑞𝑛2) , 𝛼 = 3𝑉/(4𝜋𝑅3) is the ratio of the

volumes of liquid and solid spheres, 𝐹 = (𝐶0 − 𝐶(𝑡))/(𝐶0 − 𝐶∞) is the fractional uptake, 𝐶(𝑡) is

the concentration at time 𝑡 and 𝐶∞ is the concentration at infinite time (Crank, 1983). Our study

applied C0/2 as the 𝐶∞ while the concentration at 30min was measured as 𝐶∞ in one study 168.

The designed system can avoid the reported apparatuses which biomass chips were directly

merged in bulk solution. In the designed device (Figure 8.1b), the cell was equipped with two

individual 50ml tubes held by two tube holders. The PDMS molds were prepared by mixing

elastomer and curing agents at a 10 parts to 1 part ratio (10:1) and dried at 105°C. The two

chambers were filled with 50ml acid (1% wt)/alkali (0.41% wt NaOH (0.0102 mol/L)) and DI

water, respectively, and well mixed. The conductivity meter (EXTECH instruments; Model

number: DO700) was used to measure the change of DI conductivity caused by acid or alkali

mass transfer from left chamber to right chamber. At room temperature, 4mm poplar wood chip

was molded in a PDMS plate and dried in 105°C.

The conductivity calibration curve was made by measuring conductivities of different

acid/alkali concentrations (Figure A9). The concentration change of right chamber during

measurements was calculated by referring to calibration curve on detected conductivities.

281

Figure A8 Calibration of conductivity (a) Sulfuric acid (b) NaOH

Different mass transfer models were selected to describe the two devices of measuring mass

transfer coefficients. In the designed device, a non-steady-state and one-dimensional mass

transfer equation can be expressed and used as followed:

𝜕𝐶

𝜕𝑡= 𝐷(𝑡)

𝜕2𝐶

𝜕𝑥2 (A10)

where C is the concentration of sulfuric acid or sodium hydroxide, D is the diffusion coefficient

of sulfuric acid or sodium hydroxide, and the initial and boundary conditions to solve the

equation include the following:

𝐶 = 𝐶0 = 0 when t=0

𝐶0 = 𝐶 at the edge of biomass particle when x=0.

The final solution is:

𝐶(𝑥, 𝑡) =1

2𝑐0 + ∑ 𝑐𝑛

∞𝑛=1 cos(

𝑛𝜋𝑥

𝐿)𝑒−𝐷𝑒(𝑛𝜋/𝐿)2𝑡 (A11)

Where 𝑐𝑛 =2

𝐿∫ cos (

𝑚𝜋𝑥

𝐿) 𝐷(𝑥, 0)𝑑𝑥

𝐿

0, C is the chemical concentration in wood but we measure

chemical concentration in water chamber. D is the coefficient. L is the particle length in

diffusion direction. X is the diffusion distance to the edge of biomass particle. (Source: Weisstein,

http://mathworld.wolfram.com/about/author.html


282

Eric W. "Heat Conduction Equation." From MathWorld--A Wolfram Web Resource.

http://mathworld.wolfram.com/HeatConductionEquation.html). C1,2,3 are the concentrations of

left chamber, wood sample and right chamber, respectively. What we measured is dC3/dt. The

concentrations C1(t) and C3(t) are constant while C2(t) decreased linearly. A refers to the tube

cross area (28.26cm2). L refers to the length of 10 cm. Then the mass rate was:

𝑑𝐶1

𝑑𝑡= −𝑘1𝐶1 + 𝑘2𝐶20

𝜕𝐶2

𝜕𝑡= 𝐷

𝑑2𝐶2

𝑑𝑥2

𝑑𝐶3

𝑑𝑡= 𝑘3𝐶3 − 𝑘2𝐶21

𝐶1 = 𝐶1(𝑡), 𝐶2 = 𝐶2(𝑥, 𝑡), 𝐶3 = 𝐶3(𝑡)

𝐶1(0) = 𝐶0, 𝐶2(𝑥, 0) = 0, 𝐶2(0, 𝑡) = 𝐶1, 𝐶3(0) = 0

𝐶20 = 𝐶2(0, 𝑡), 𝐶21 = 𝐶2(𝐿, 𝑡)

To simplify the model, we assumed that the volume of biomass was small in comparison with the

volume of chambers. Thus,

𝐷 ∗ 𝐴 ∗∆ 𝐶

𝐿= −𝑉 ∗

𝑑𝐶1

𝑑𝑡= 𝑉 ∗

𝑑𝐶3

𝑑𝑡

∆𝐶 = 𝐶1 − 𝐶3

𝐶1 + 𝐶3 = 𝐶0 = 1

𝑑𝐶3

𝑑𝑡=

𝐷∗𝐴

𝐿∗𝑉(𝐶0 − 2𝐶3)

𝐶3(0) = 0

𝐶3(𝑡) =𝐶0

2(1 − 𝑒−2𝑎𝑡), a=

𝐷∗𝐴

𝐿∗𝑉

Plot: 2𝐷𝐴

𝐿𝑉𝑡 = ln(

𝐶0

𝐶0−2𝐶3)


http://mathworld.wolfram.com/

http://mathworld.wolfram.com/HeatConductionEquation.html

283

𝑑𝐶3

𝑑𝑡=

𝐷∗𝐴

𝐿∗𝑉(𝐶0 − 2𝐶3)

Bring known values in, we can get 0.113D=ln(1

1−2𝐶3). However, the designed device has a

limitation on the biomass particle sizes. The applicable particle size is larger than 4cm to avoid

the difficulties of imbedding particles with PDMS. Thus, the sub-millimeter particles applied in

our study applied the reported device to measure both the acid and alkali concentration changes

during their diffusion into the sub-millimeter particles.

The diffusion model in equation A9 was used to describe the diffusion process in the

reported device. The temperature dependence of diffusion coefficients can be expressed by the

simplest and commonly used Arrhenius model.

D=D0 exp (-Eact/kT) (A.12)

where Eact is activation energy, k is Boltzmann constant, T is temperature. The different values of

D at different T were measured. Polynomial expansion is ebT=1-bT+0.5b2T2+…. Thus, when

T=140, 160, 180, 190, and 200°C in our pretreatment conditions, D can be obtained to represent

the true mass transfer coefficient during pretreatment.

Cellulose and hemicellulose followed the similar kinetics. For hemicellulose (H) hydrolysis

to hemicellulose oligomers (HO) and then to hemicellulose monomers (HM), the kinetic

modeling equation can be expressed as following:

𝐻𝑘1→𝐻𝑂

𝑘2→𝐻𝑀

𝑘3→ 𝐻𝐷

𝑑[𝐻]

𝑑𝑡= −𝑘1[𝐻]

𝑑[𝐻𝑂]

𝑑𝑡= 𝑘1[𝐻] − 𝑘2[𝐻𝑂]

284

𝑑[𝐻𝑀]

𝑑𝑡= 𝑘2[𝐻𝑀] − 𝑘3[𝐻𝐷]

𝑘1 = 𝐴1[𝐶�̅�(𝑡)]𝑛1𝑒−𝐸1𝑅𝑇

𝑘2 = 𝐴2[𝐶�̅�(𝑡)]𝑛2𝑒−𝐸2𝑅𝑇

𝑘3 = 𝐴3[𝐶�̅�(𝑡)]𝑛3𝑒−𝐸3𝑅𝑇

𝜕𝐶𝑠(𝑟, 𝑡)

𝜕𝑡= 𝐷

𝑑2𝐶𝑠(𝑟, 𝑡)

𝑑𝑟2

Solutions:

𝐶𝐻(𝑡) = 𝐶𝐻0𝑒−𝑘1𝑡

𝐶𝐻𝑂(𝑡) = 𝐶𝐻0

𝑘1

𝑘2−𝑘1[𝑒−𝑘1𝑡 − 𝑒−𝑘2𝑡]

𝐶𝐻𝑀(𝑡) = 𝐶𝐻0

𝑘1𝑘2

(𝑘2−𝑘1)(𝑘3−𝑘1)[𝑒−𝑘1𝑡 − 𝑒−𝑘3𝑡] − 𝐶𝐻0

𝑘1𝑘2

(𝑘2−𝑘1)(𝑘3−𝑘2)[𝑒−𝑘2𝑡 − 𝑒−𝑘3𝑡]

𝐶𝐻(𝑡) = 𝐶𝐻0𝑒−𝑘1𝑡

𝐶𝐻𝑂(𝑡) = 𝐶𝐻0

𝑘1

𝑘2−𝑘1[𝑒−𝑘1𝑡 − 𝑒−𝑘2𝑡]

𝐶𝐻𝑀(𝑡) = 𝐶𝐻0

𝑘1𝑘2

(𝑘2−𝑘1)(𝑘3−𝑘1)[𝑒−𝑘1𝑡 − 𝑒−𝑘3𝑡] − 𝐶𝐻0

𝑘1𝑘2

(𝑘2−𝑘1)(𝑘3−𝑘2)[𝑒−𝑘2𝑡 − 𝑒−𝑘3𝑡]

𝐶𝑠(𝑟, 𝑡) = 𝐶0erf (𝑟

2√𝐷𝑡), where 𝐶0 =

2𝐶0√𝜋𝐷

𝐴; erf (r) =

2

√𝜋∫ 𝑒−𝑡2

𝑑𝑡𝑡

0

Notes:

H-hemicellulose; can get it from experiment

HO-hemi-sugars in oligomeric form; can get it from experiment

HM-hemi-sugars in monomeric form; can get it from experiment

HD-degradation of hemi-sugars; can get it from experiment

k1, k2, k3-kinetic reaction rate constants; unknown we should get from simulation

285

E1, E2, E3-activation energy for kinetics; unknown we should get from simulation

A1, A2, A3-pre-exponential factors for kinetic equations; unknown we should get from

simulation

n1, n2, n3-chemical concentration factor

R, T-gas constant (8.314) and temperature (T=413.15 K))

t-reaction time, 30,40,50,60,70 min

D-mass transfer coefficients, 0.25

r-biomass particle diameter

A-surface area of biomass particle

𝐶𝑠(𝑟, 𝑡) –concentration of pretreatment solvent inside the biomass particles, such as acid and

alkalis

Some preliminary results

Figure A9 Fractional uptake of NaOH, Douglas fir 1/8’’-16mesh, CB substrates

The conductivity changes in the solution were measured by the reported design (Figure A8a).

Thus, the fractional uptake was calculated to simulate with time to calculate mass transfer

diffusion coefficients according to equation A9. It was found that NaOH was much easier to

diffuse into biomass particles than sulfuric acid under tested temperatures (room temperature, 35,

1.0

0.8

0.6

0.4

0.2

0.0

Fra

ctio

na

l U

pta

ke

F(t

)

6005004003002001000

Time (s)

(b)

22°C

35°C

50°C

286

and 50; Figure A10). This was indicated by the high fractional uptakes of NaOH regardless of

time increasing. This conclusion can be used to explain the usual NaOH pretreatment that is

carried out under low temperatures. Additionally, the diffusion capacities of NaOH were not

showing significant changes with the increasing of tested temperatures. Thus, the NaOH

diffusion phenomenon was not obeying the uptake modeling (equation A9) and Arrhenius

temperature dependence of mass transfer coefficients. More detailed mass transfer study of

NaOH in pretreatment is still needed in the future. The acid diffusion coefficients using the same

equation were reported 1-20× 10−6 in the study of 0.1N sulfuric acid pretreatment of Bagasse,

Corn stover, Rice straw and Yellow poplar with particle sizes of 14-20 and 20-24 mesh at 25, 50,

and 75C 168. The fitting results of some feedstocks in our study under 1% (w/w) sulfuric acid

were shown in Table A3. The difference of our coefficients and the reported number might be

because the selection of concentration on solid surface at infinite time. Table A3 showed

crumbled particles possessed about four times better diffusion capacities than hammer milled

ones. The larger sizes showed better diffusion than finer particles. For biomass species, poplar

presented around two times higher diffusivities than Douglas fir. However, this work still need

more future work such as verification of the modeling feasibilities, improving data collection

method, and integrating mass transfer coefficients with kinetic modeling in simulating

experimental data of pretreatment.

Table A3 Some acid diffusivity of various sized and cut biomass feedstock

Biomass Cuttings Size Diffusivity (cm2/s) × 10−7

22C 35C 50C 75C

Douglas fir Crumbler 12-30mesh 0.53±0.055 0.90±0.076 9.96±1.24 19.65±1.73

287

Douglas fir Crumbler 30-60mesh 0.068±0.00081 0.11±0.011

Poplar Crumbler 12-30mesh 0.62±0.042 1.62±0.040 1.466±0.55 2.765±0.73

Poplar Crumbler 30-60mesh 0.13±0.013 0.28±0.013 0.377±0.20 0.598±0.38

Poplar Hammer 12-30mesh 0.37±0.020 1.73±0.075 3.00±0.11

288

REFERENCES

1. B. Yang, Y. Lu and C. E. Wyman, Biofuels from Agricultural Wastes and Byproducts. HP Blaschek, TC

Ezeji and J. Scheffran, Eds., Wiley-Blackwell: USA, 2010, 175-200.

2. R. D. Perlack, L. M. Eaton, A. F. Turhollow Jr, M. H. Langholtz, C. C. Brandt, M. E. Downing, R. L.

Graham, L. L. Wright, J. M. Kavkewitz and A. M. Shamey, 2011.

3. R. D. Perlack, L. L. Wright, A. F. Turhollow, R. L. Graham, B. J. Stokes and D. C. Erbach, Biomass as

feedstock for a bioenergy and bioproducts industry: the technical feasibility of a billion-ton annual supply,

DTIC Document, 2005.

4. L. R. Lynd, M. S. Laser, D. Bransby, B. E. Dale, B. Davison, R. Hamilton, M. Himmel, M. Keller, J. D.

McMillan and J. Sheehan, Nature biotechnology, 2008, 26, 169-172.

5. B. Yang and C. E. Wyman, APBN, 2007, 11(9), 555-563.

6. C. E. Wyman, Annual Review of Energy and the Environment, 1999, 24, 189-226.

7. C. Wyman, An overview of ethanol production for transportation fuels, National Renewable Energy Lab.,

Golden, CO (United States), 1993.

8. US. DOE, Office of Energy Efficiency and Renewable Energy, Washington, DC, 2012.

9. R. Wooley, M. Ruth, J. Sheehan, K. Ibsen, H. Majdeski and A. Galvez, Lignocellulosic biomass to ethanol

process design and economics utilizing co-current dilute acid prehydrolysis and enzymatic hydrolysis

current and futuristic scenarios, DTIC Document, 1999.

10. R. Davis, L. Tao, E. Tan, M. Biddy, G. Beckham, C. Scarlata, J. Jacobson, K. Cafferty, J. Ross and J. Lukas,

Process design and economics for the conversion of lignocellulosic biomass to hydrocarbons: Dilute-acid

and enzymatic Deconstruction of biomass to sugars and biological conversion of Sugars to Hydrocarbons,

National Renewable Energy Laboratory (NREL), Golden, CO., 2013.

11. L. Montague, A. Slayton and J. Lukas, Lignocellulosic biomass to ethanol process design and economics

utilizing co-current dilute acid prehydrolysis and enzymatic hydrolysis for corn stover, Citeseer, 2002.

12. R. Davis, A. Aden and P. T. Pienkos, Applied Energy, 2011, 88, 3524-3531.

13. L. Tao, A. Aden, R. T. Elander, V. R. Pallapolu, Y. Y. Lee, R. J. Garlock, V. Balan, B. E. Dale, Y. Kim, N. S.

Mosier, M. R. Ladisch, M. Falls, M. T. Holtzapple, R. Sierra, J. Shi, M. A. Ebrik, T. Redmond, B. Yang, C.

E. Wyman, B. Hames, S. Thomas and R. E. Warner, Bioresource Technology, 2011, 102, 11105-11114.

14. B. Yang and C. E. Wyman, Biofuels Bioproducts & Biorefining-Biofpr, 2008, 2, 26-40.

15. T. Hsu, Applied Energy Technology Series, Taylor & Francis, Washington DC, 1996, 179.

16. P. Harmsen, W. Huijgen, L. Bermudez and R. Bakker, 2010.

17. C. E. Wyman, Aqueous pretreatment of plant biomass for biological and chemical conversion to fuels and

chemicals, John Wiley & Sons, 2013.

18. X. Zhao, K. Cheng and D. Liu, Appl Microbiol Biotechnol, 2009, 82, 815-827.

289

19. R. S. Ramirez, M. Holtzapple and N. Piamonte, Aqueous Pretreatment of Plant Biomass for Biological and

Chemical Conversion to Fuels and Chemicals, 2013, 145-167.

20. H. L. Trajano and C. E. Wyman, Aqueous Pretreatment of Plant Biomass for Biological and Chemical

Conversion to Fuels and Chemicals, 2013, 103-128.

21. B. Yang and C. E. Wyman, Biofuels, Bioproducts and Biorefining, 2008, 2, 26-40.

22. N. Mosier, C. Wyman, B. Dale, R. Elander, Y. Y. Lee, M. Holtzapple and M. Ladisch, Bioresource

Technology, 2005, 96, 673-686.

23. M. Foston and A. J. Ragauskas, Industrial Biotechnology, 2012, 8, 191-208.

24. M. E. Himmel, S.-Y. Ding, D. K. Johnson, W. S. Adney, M. R. Nimlos, J. W. Brady and T. D. Foust,

science, 2007, 315, 804-807.

25. J. Zheng and L. Rehmann, International journal of molecular sciences, 2014, 15, 18967-18984.

26. Y. Lee, P. Iyer and R. W. Torget, in Recent progress in bioconversion of lignocellulosics, Springer, 1999,

pp. 93-115.

27. B. Yang and M. Tucker, Aqueous Pretreatment of Plant Biomass for Biological and Chemical Conversion

to Fuels and Chemicals, 2013, 489-521.

28. L. Zhang, Z. Lu, L. Velarde, L. Fu, Y. Pu, S.-Y. Ding, A. Ragauskas, H.-F. Wang and B. Yang, Cellulose,

2015, DOI: 10.1007/s10570-015-0588-0, 1-16.

29. J. D. McMillan, 1994.

30. N. Mosier, C. Wyman, B. Dale, R. Elander, Y. Lee, M. Holtzapple and M. Ladisch, Bioresource technology,

2005, 96, 673-686.

31. C. E. Wyman, B. E. Dale, R. T. Elander, M. Holtzapple, M. R. Ladisch and Y. Lee, Bioresource technology,

2005, 96, 1959-1966.

32. B. Yang and C. E. Wyman, Biofuels, Bioproducts and Biorefining, 2008, 2, 26-40.

33. B. Yang and C. E. Wyman, in Biofuels, Springer, 2009, pp. 103-114.

34. Y. Kim, N. S. Mosier, M. R. Ladisch, V. R. Pallapolu, Y. Y. Lee, R. Garlock, V. Balan, B. E. Dale, B. S.

Donohoe, T. B. Vinzant, R. T. Elander, M. Falls, R. Sierra, M. T. Holtzapple, J. Shi, M. A. Ebrik, T.

Redmond, B. Yang, C. E. Wyman and R. E. Warner, Bioresource Technology, 2011, 102, 11089-11096.

35. C. E. Wyman, B. E. Dale, R. T. Elander, M. Holtzapple, M. R. Ladisch and Y. Y. Lee, Bioresource

Technology, 2005, 96, 1959-1966.

36. R. T. Elander, B. E. Dale, M. Holtzapple, M. R. Ladisch, Y. Lee, C. Mitchinson, J. N. Saddler and C. E.

Wyman, Cellulose, 2009, 16, 649-659.

37. J. Weil, A. Sarikaya, S.-L. Rau, J. Goetz, C. Ladisch, M. Brewer, R. Hendrickson and M. Ladisch, Applied

Biochemistry and Biotechnology, 1997, 68, 21-40.

38. J. Weil, M. Brewer, R. Hendrickson, A. Sarikaya and M. Ladisch, Applied Biochemistry and Biotechnology,

290

1998, 70-72, 99-111.

39. J. Weil, A. Sarikaya, S.-L. Rau, J. Goetz, C. Ladisch, M. Brewer, R. Hendrickson and M. Ladisch, Applied

Biochemistry and Biotechnology, 1998, 73, 1-17.

40. M. Holtzapple, J.-H. Jun, G. Ashok, S. Patibandla and B. Dale, Applied Biochemistry and Biotechnology,

1991, 28-29, 59-74.

41. X. Chen, J. Shekiro, M. A. Franden, W. Wang, M. Zhang, E. Kuhn, D. K. Johnson and M. P. Tucker,

Biotechnology for Biofuels, 2012, 5, 1.

42. K. Kobayashi, S. Kimura, U.-J. Kim, K. Tokuyasu and M. Wada, Cellulose, 2012, 19, 967-974.

43. M. Wada, M. Ike and K. Tokuyasu, Polymer Degradation and Stability, 2010, 95, 543-548.

44. R. W. Torget, Patent US6228177, 2001.

45. R. W. Torget, K. L. Kadam, T.-A. Hsu, G. P. Philippidis and C. E. Wyman, Patent US5424417, 1996.

46. O. Bobleter, G. Bonn and R. Concin, Alternative Energy Sources, 1980, 3, 323-332.

47. H. Hörmeyer, W. Schwald, G. Bonn and O. Bobleter, Holzforschung-International Journal of the Biology,

Chemistry, Physics and Technology of Wood, 1988, 42, 95-98.

48. O. Bobleter, Progress in polymer science, 1994, 19, 797-841.

49. V. Archambault‐Léger, Z. Losordo and L. R. Lynd, Biofuels, Bioproducts and Biorefining, 2015, 9, 95-108.

50. B. Yang and C. E. Wyman, Bioresource Technology, 2008, 99, 5756-5762.

51. T. Sakaki, M. Shibata, T. Sumi and S. Yasuda, Industrial & Engineering Chemistry Research, 2002, 41,

661-665.

52. H. Ando, T. Sakaki, T. Kokusho, M. Shibata, Y. Uemura and Y. Hatate, Industrial & Engineering Chemistry

Research, 2000, 39, 3688-3693.

53. C. Liu and C. E. Wyman, Industrial & Engineering Chemistry Research, 2003, 42, 5409-5416.

54. C. G. Liu and C. E. Wyman, Applied Biochemistry and Biotechnology, 2004, 113, 977-987.

55. C. Liu and C. E. Wyman, Industrial & Engineering Chemistry Research, 2004, 43, 2781-2788.

56. B. Yang and C. E. Wyman, Biotechnology and bioengineering, 2004, 86, 88-98.

57. W. S. L. Mok and M. J. Antal, Industrial & Engineering Chemistry Research, 1992, 31, 1157-1161.

58. S. G. Allen, L. C. Kam, A. J. Zemann and M. J. Antal, Industrial & Engineering Chemistry Research, 1996,

35, 2709-2715.

59. L. Yan, L. Zhang and B. Yang, Biotechnology for Biofuels, 2014, 7, 76.

60. L. Zhang, L. Yan, Z. Wang, D. D. Laskar, M. S. Swita, J. R. Cort and B. Yang, Biotechnology for Biofuels,

291

2015, 8, 1-14.

61. V. Archambault-Leger, X. Shao and L. R. Lynd, Biotechnol. Biofuels, 2012, 5, 49.

62. D. D. Laskar, J. Zeng, L. Yan, S. Chen and B. Yang, Industrial Crops and Products, 2013, 50, 391-399.

63. M. Foston, H. L. Trajano, R. Samuel, C. E. Wyman, J. He and A. J. Ragauskas, Bioresource technology,

2015, 197, 128-136.

64. H. L. Trajano, S. Pattathil, B. A. Tomkins, T. J. Tschaplinski, M. G. Hahn, G. J. Van Berkel and C. E.

Wyman, Bioresource Technology, 2015, 179, 202-210.

65. H. L. Trajano, N. L. Engle, M. Foston, A. J. Ragauskas, T. J. Tschaplinski and C. E. Wyman, Biotechnology

for biofuels, 2013, 6, 110.

66. M. J. Antal, W. S. Mok and G. N. Richards, Carbohydrate research, 1990, 199, 91-109.

67. C. J. Knill and J. F. Kennedy, Carbohydrate Polymers, 2003, 51, 281-300.

68. J. Li, G. Henriksson and G. Gellerstedt, Bioresource Technology, 2007, 98, 3061-3068.

69. M. Wayman and J. H. Lora, Journal of Applied Polymer Science, 1980, 25, 2187-2194.

70. Q. Sun, Y. Pu, X. Meng, T. Wells and A. J. Ragauskas, ACS Sustainable Chemistry & Engineering, 2015, 3,

2203-2210.

71. F. S. Chakar and A. J. Ragauskas, Industrial Crops and Products, 2004, 20, 131-141.

72. M. J. Antal Jr, W. S. Mok and G. N. Richards, Carbohydrate research, 1990, 199, 91-109.

73. R. S. Ramirez, M. Holtzapple and N. Piamonte, Aqueous Pretreatment of Plant Biomass for Biological and

Chemical Conversion to Fuels and Chemicals, 145-167.

74. M. Möller and U. Schröder, RSC Advances, 2013, 3, 22253-22260.

75. S. Park, J. O. Baker, M. E. Himmel, P. A. Parilla and D. K. Johnson, Biotechnology for biofuels, 2010, 3, 1.

76. M. Wada, Journal of Polymer Science Part B: Polymer Physics, 2002, 40, 1095-1102.

77. K. Meyer and L. Misch, Helv. Chim. Acta, 1937, 20, 232-244.

78. Y. Nishiyama, P. Langan and H. Chanzy, Journal of the American Chemical Society, 2002, 124, 9074-9082.

79. Y. Nishiyama, J. Sugiyama, H. Chanzy and P. Langan, Journal of the American Chemical Society, 2003,

125, 14300-14306.

80. K. Schenzel, S. Fischer and E. Brendler, Cellulose, 2005, 12, 223-231.

81. B. Hinterstoisser and L. Salmén, Vibrational Spectroscopy, 2000, 22, 111-118.

82. K. Schenzel and S. Fischer, Cellulose, 2001, 8, 49-57.

292

83. H. Kim and J. Ralph, RSC Advances, 2014, 4, 7549.

84. S.-Y. Ding and Y.-S. Liu, in Biomass Conversion: Methods and Protocols, ed. E. M. Himmel, Humana

Press, Totowa, NJ, 2012, DOI: 10.1007/978-1-61779-956-3_3, pp. 23-30.

85. A. L. Barnette, L. C. Bradley, B. D. Veres, E. P. Schreiner, Y. B. Park, J. Park, S. Park and S. H. Kim,

Biomacromolecules, 2011, 12, 2434-2439.

86. M. Langholtz, M. Downing, R. Graham, F. Baker, A. Compere, W. Griffith, R. Boeman and M. Keller, SAE

International Journal of Materials & Manufacturing, 2014, 7, 115-121.

87. A. J. Ragauskas, G. T. Beckham, M. J. Biddy, R. Chandra, F. Chen, M. F. Davis, B. H. Davison, R. A.

Dixon, P. Gilna and M. Keller, Science, 2014, 344, 1246843.

88. J. Rencoret, G. Marques, A. Gutiérrez Suárez, L. Nieto Garrido, J. Jiménez-Barbero, Á. T. Martínez and J.

C. d. Río Andrade, 2008.

89. C. Heitner, D. Dimmel and J. Schmidt, Lignin and lignans: advances in chemistry, CRC press, 2010.

90. M. Lawoko, G. Henriksson and G. Gellerstedt, Biomacromolecules, 2005, 6, 3467-3473.

91. A. Bjorkman, Nature, 1954, 174, 1057-1058.

92. C. I. Ishizawa, T. Jeoh, W. S. Adney, M. E. Himmel, D. K. Johnson and M. F. Davis, Cellulose, 2009, 16,

677-686.

93. W. G. Glasser and S. Sarkanen, Lignin, properties and materials, Washington, DC (USA); American

Chemical Society, 1989.

94. H. Hatakeyama and T. Hatakeyama, in Biopolymers, Springer, 2009, pp. 1-63.

95. H. Hatakeyama and T. Hatakeyama, Lignin and Lignans: Advances in Chemistry. CRC Press, New York,

2010, 301-316.

96. T. Hatakeyama and H. Hatakeyama, Thermal properties of green polymers and biocomposites, Springer

Science & Business Media, 2006.

97. J. A. Schmidt, Lignin and lignans: Advances in chemistry, 2009, 49.

98. J. Lora, B. Naceur and A. Gandini, Industrial commercial lignins: sources, properties and applications,

Elsevier, Amsterdam, 2008.

99. J. Li, G. Gellerstedt and K. Toven, Bioresource technology, 2009, 100, 2556-2561.

100. P. Sannigrahi, A. J. Ragauskas and S. J. Miller, Bioenergy Research, 2008, 1, 205-214.

101. S. V. Pingali, V. S. Urban, W. T. Heller, J. McGaughey, H. O'Neill, M. Foston, D. A. Myles, A. Ragauskas

and B. R. Evans, Biomacromolecules, 2010, 11, 2329-2335.

102. S. Y. Lin and C. W. Dence, Methods in lignin chemistry, Springer, 1992.

103. M. J. Selig, S. Viamajala, S. R. Decker, M. P. Tucker, M. E. Himmel and T. B. Vinzant, Biotechnology

293

Progress, 2007, 23, 1333-1339.

104. B. S. Donohoe, S. R. Decker, M. P. Tucker, M. E. Himmel and T. B. Vinzant, Biotechnol Bioeng, 2008, 101,

913-925.

105. H. Li, Y. Pu, R. Kumar, A. J. Ragauskas and C. E. Wyman, Biotechnology and bioengineering, 2014, 111,

485-492.

106. P. Sannigrahi, D. H. Kim, S. Jung and A. Ragauskas, Energy & Environmental Science, 2011, 4, 1306-

1310.

107. B. S. Donohoe, S. R. Decker, M. P. Tucker, M. E. Himmel and T. B. Vinzant, Biotechnology and

Bioengineering, 2008, 101, 913-925.

108. R. Sun and J. Tomkinson, Separation and Purification Technology, 2001, 24, 529-539.

109. A. Toledano, A. García, I. Mondragon and J. Labidi, Separation and Purification Technology, 2010, 71, 38-

43.

110. K. R. Colyar, J. Pellegrino and K. Kadam, Separation Science and Technology, 2008, 43, 447-476.

111. Y. Chen, R. R. Sharma-Shivappa, D. Keshwani and C. Chen, Applied Biochemistry and Biotechnology,

2007, 142, 276-290.

112. V. S. Chang, B. Burr and M. T. Holtzapple, in Biotechnology for Fuels and Chemicals, Springer, 1997, pp.

3-19.

113. S. Kim and M. T. Holtzapple, Bioresource Technology, 2005, 96, 1994-2006.

114. F. Teymouri, L. Laureano-Perez, H. Alizadeh and B. E. Dale, Applied Biochemistry and Biotechnology,

2004, 113, 951-963.

115. O. Sánchez, C. J. Alméciga-Díaz and R. Sierra, Delignification process of Agro-Industrial wastes an

alternative to obtain fermentable carbohydrates for producing fuel, INTECH Open Access Publisher, 2011.

116. Y. Sun and J. Cheng, Bioresource technology, 2002, 83, 1-11.

117. X. Zhao, K. Cheng and D. Liu, Applied microbiology and biotechnology, 2009, 82, 815-827.

118. R. W. Thring, E. Chornet and R. P. Overend, Biomass, 1990, 23, 289-305.

119. X. Pan, N. Gilkes and J. N. Saddler, Holzforschung, 2006, 60, 398-401.

120. P. Sannigrahi and A. J. Ragauskas, Aqueous pretreatment of plant biomass for biological and chemical

conversion to fuels and chemicals, 2013, 201-222.

121. T. J. McDonough, The chemistry of organosolv delignification, IPST technical paper series; number 455,

1992.

122. R. El Hage, N. Brosse, P. Sannigrahi and A. Ragauskas, Polymer Degradation and Stability, 2010, 95, 997-

1003.

294

123. P. Sannigrahi, A. J. Ragauskas and S. J. Miller, Energy & Fuels, 2009, 24, 683-689.

124. R. El Hage, N. Brosse, L. Chrusciel, C. Sanchez, P. Sannigrahi and A. Ragauskas, Polymer Degradation

and Stability, 2009, 94, 1632-1638.

125. L. J. Jönsson, B. Alriksson and N.-O. Nilvebrant, Biotechnology for biofuels, 2013, 6, 1.

126. K. V. Sarkanen and C. H. Ludwig, Lignins: occurrence, formation, structure and reactions., 1971.

127. C. Pouteau, P. Dole, B. Cathala, L. Averous and N. Boquillon, Polymer Degradation and Stability, 2003,

81, 9-18.

128. A. Sluiter, B. Hames, R. Ruiz, C. Scarlata, J. Sluiter and D. Templeton, National Renewable Energy

Laboratory, Golden, CO, 2006.

129. K. Iiyama and A. F. Wallis, Journal of the Science of Food and Agriculture, 1990, 51, 145-161.

130. A. Sluiter, B. Hames, R. Ruiz, C. Scarlata, J. Sluiter, D. Templeton and D. Crocker, Report N. TP-510-

42618, 2011, 17.

131. T. Standard, Acid-insoluble lignin in wood and pulp Tappi, T222 om-02, 2002.

132. C. W. Dence, in Methods in lignin chemistry, Springer, 1992, pp. 33-61.

133. F. Beall, Wood Fiber, 1969, 1, 215-226.

134. M. G. Grønli, G. Varhegyi and C. Di Blasi, Industrial & Engineering Chemistry Research, 2002, 41, 4201-

4208.

135. Z. Strassberger, P. Prinsen, F. van der Klis, D. S. van Es, S. Tanase and G. Rothenberg, Green Chemistry,

2015, 17, 325-334.

136. F. Martin, C. Saiz-Jimenez and F. Gonzalez-Vila, Holzforschung, 1979, 33, 210-212.

137. G. Brunow, Biopolymers Online, 2005.

138. L.-S. Johansson, J. M. Campbell, K. Koljonen and P. Stenius, Applied Surface Science, 1999, 144–145, 92-

95.

139. T. Haensel, M. Reinmöller, P. Lorenz, W. J. Beenken, S. Krischok and S. I.-U. Ahmed, Cellulose, 2012, 19,

1005-1011.

140. D. Takada, K. Ehara and S. Saka, Journal of Wood Science, 50, 253-259.

141. Y. Pu, S. Cao and A. J. Ragauskas, Energy & Environmental Science, 2011, 4, 3154-3166.

142. J. Ralph and L. L. Landucci, 2010.

143. S. Li and K. Lundquist, Nordic Pulp & Paper Research Journal, 1994, 9, 191-195.

144. Y. Pu, B. Hallac and A. J. Ragauskas, in Aqueous Pretreatment of Plant Biomass for Biological and

Chemical Conversion to Fuels and Chemicals, 2013, pp. 369-390.

295

145. L. Fu, S. A. McCallum, J. Miao, C. Hart, G. J. Tudryn, F. Zhang and R. J. Linhardt, Fuel, 2015, 141, 39-45.

146. A. T. Martínez, A. González, M. Valmaseda, B. Dale, M. Lambregts and J. Haw, Holzforschung-

International Journal of the Biology, Chemistry, Physics and Technology of Wood, 1991, 45, 49-54.

147. S. Raiskila, M. Pulkkinen, T. Laakso, K. Fagerstedt, M. Loija, R. Mahlberg, L. Paajanen, A.-C. Ritschkoff

and P. Saranpaa, Silva Fennica, 2007, 41, 351.

148. A. Karmanov and O. Y. Derkacheva, Russian Journal of Bioorganic Chemistry, 2013, 39, 677-685.

149. U. P. Agarwal and S. A. Ralph, Holzforschung, 2008, 62, 667-675.

150. A. W. Pelzer, M. R. Sturgeon, A. J. Yanez, G. Chupka, M. H. O’Brien, R. Katahira, R. D. Cortright, L.

Woods, G. T. Beckham and L. J. Broadbelt, ACS Sustainable Chemistry & Engineering, 2015, 3, 1339-

1347.

151. C. Rolando, B. Monties and C. Lapierre, in Methods in Lignin Chemistry, eds. S. Y. Lin and C. W. Dence,

Springer Berlin Heidelberg, Berlin, Heidelberg, 1992, DOI: 10.1007/978-3-642-74065-7_23, pp. 334-349.

152. K. M. Holtman, H.-M. Chang, H. Jameel and J. F. Kadla, Journal of agricultural and food chemistry, 2003,

51, 3535-3540.

153. G. Gellerstedt, in Methods in Lignin Chemistry, Springer, 1992, pp. 322-333.

154. J. I. Hedges and J. R. Ertel, Analytical Chemistry, 1982, 54, 174-178.

155. E. Säwén, 2011.

156. P. Karhunen, P. Rummakko, J. Sipilä, G. Brunow and I. Kilpeläinen, Tetrahedron Letters, 1995, 36, 169-

170.

157. J. Ralph, T. Akiyama, H. Kim, F. Lu, P. F. Schatz, J. M. Marita, S. A. Ralph, M. S. Reddy, F. Chen and R.

A. Dixon, Journal of Biological Chemistry, 2006, 281, 8843-8853.

158. L. Zhang and G. Gellerstedt, Magnetic Resonance in Chemistry, 2007, 45, 37-45.

159. J. Ralph, J. M. Marita, S. A. Ralph, R. D. Hatfield, F. Lu, R. M. Ede, J. Peng and L. L. Landucci, 1999.

160. S. B. Kim and Y. Y. Lee, Bioresource Technology, 2002, 83, 165-171.

161. J. S. Luterbacher, J.-Y. Parlange and L. P. Walker, Biotechnology and Bioengineering, 2013, 110, 127-136.

162. J. Y. Zhu and X. J. Pan, Bioresource Technology, 2010, 101, 4992-5002.

163. Z.-H. Liu, L. Qin, F. Pang, M.-J. Jin, B.-Z. Li, Y. Kang, B. E. Dale and Y.-J. Yuan, Industrial Crops and

Products, 2013, 44, 176-184.

164. I. F. Cullis, J. N. Saddler and S. D. Mansfield, Biotechnology and bioengineering, 2004, 85, 413-421.

165. J. D. DeMartini, M. Foston, X. Meng, S. Jung, R. Kumar, A. J. Ragauskas and C. E. Wyman,

Biotechnology for Biofuels, 2015, 8, 1-16.

296

166. J. F. Saeman, Industrial & Engineering Chemistry, 1945, 37, 43-52.

167. S. Harun, V. Balan, M. S. Takriff, O. Hassan, J. Jahim and B. Dale, Biotechnology for biofuels, 2013, 6, 40.

168. S. B. Kim and Y. Lee, Bioresource Technology, 2002, 83, 165-171.

169. B. Yang, Z. Dai, S.-Y. Ding and C. E. Wyman, Biofuels, 2011, 2, 421-450.

170. C. Somerville, S. Bauer, G. Brininstool, M. Facette, T. Hamann, J. Milne, E. Osborne, A. Paredez, S.

Persson, T. Raab, S. Vorwerk and H. Youngs, Science, 2004, 306, 2206-2211.

171. C. M. Lee, A. Mittal, A. L. Barnette, K. Kafle, Y. B. Park, H. Shin, D. K. Johnson, S. Park and S. H. Kim,

Cellulose, 2013, 20, 991-1000.

172. J. Hayashi, A. Sufoka, J. Ohkita and S. Watanabe, Journal of Polymer Science: Polymer Letters Edition,

1975, 13, 23-27.

173. R. H. V. Atalla, David L., Science, 1984, 223, 283-285.

174. B. H. Henri Chanzy, FEBS Lett, 1985, 184, 285–288.

175. Y. Habibi, L. A. Lucia and O. J. Rojas, Chemical reviews, 2010, 110, 3479-3500.

176. A. N. Fernandes, L. H. Thomas, C. M. Altaner, P. Callow, V. T. Forsyth, D. C. Apperley, C. J. Kennedy and

M. C. Jarvis, Proceedings of the National Academy of Sciences, 2011, 108, E1195-E1203.

177. L. H. Thomas, V. T. Forsyth, A. Sturcova, C. J. Kennedy, R. P. May, C. M. Altaner, D. C. Apperley, T. J.

Wess and M. C. Jarvis, Plant physiology, 2013, 161, 465-476.

178. M. E. Himmel, S. Y. Ding, D. K. Johnson, W. S. Adney, M. R. Nimlos, J. W. Brady and T. D. Foust,

Science, 2007, 315, 804-807.

179. S.-Y. Ding and Y.-S. Liu, in Biomass Conversion, ed. M. E. Himmel, Humana Press, 2012, vol. 908, ch. 3,

pp. 23-30.

180. R. R. Lahiji, X. Xu, R. Reifenberger, A. Raman, A. Rudie and R. J. Moon, Langmuir, 2010, 26, 4480-4488.

181. Y. Pu, B. Hallac and A. J. Ragauskas, in Aqueous Pretreatment of Plant Biomass for Biological and

Chemical Conversion to Fuels and Chemicals, John Wiley & Sons, Ltd, 2013, DOI:

10.1002/9780470975831.ch18, pp. 369-390.

182. R. Samuel, Y. Pu, M. Foston and A. J. Ragauskas, Biofuels, 2010, 1, 85-90.

183. H. E. Larsson PT, Wickholm K, Pettersson E, Iversen T, Solid State Nuclear Magnetic Resonance, 1999,

15, 31-40.

184. M. Dick-Pérez, Y. Zhang, J. Hayes, A. Salazar, O. A. Zabotina and M. Hong, Biochemistry, 2011, 50, 989-

1000.

185. A. Isogai, M. Usuda, T. Kato, T. Uryu and R. H. Atalla, Macromolecules, 1989, 22, 3168-3172.

186. C. Y. Liang and R. H. Marchessault, Journal of Polymer Science, 1959, 39, 269-278.

297

187. P. J. H. Lynette M. Daviesa, Roger H. Newman, Carbohydr Res, 2002, 337, 587-593.

188. J. Mann and H. J. Marrinan, Journal of Polymer Science, 1958, 32, 357-370.

189. H. J. Marrinan and J. Mann, Journal of Polymer Science, 1956, 21, 301-311.

190. H. Morikawa, R. Hayashi and M. Senda, Plant Cell Physiol., 1978, 19, 1151-1159.

191. M. L. Nelson and R. T. O'Connor, Journal of Applied Polymer Science, 1964, 8, 1311-1324.

192. R. H. Newman, M.-A. Ha and L. D. Melton, Journal of Agricultural and Food Chemistry, 1994, 42, 1402-

1406.

193. M. Tsuboi, Journal of Polymer Science, 1957, 25, 159-171.

194. S. Park, J. O. Baker, M. E. Himmel, P. A. Parilla and D. K. Johnson, Biotechnology for biofuels, 2010, 3,

10.

195. P. Zugenmaier, 2007, DOI: 10.1002/bbpc.19850891120.

196. A. L. Barnette, C. Lee, L. C. Bradley, E. P. Schreiner, Y. B. Park, H. Shin, D. J. Cosgrove, S. Park and S. H.

Kim, Carbohydrate Polymers, 2012, 89, 802-809.

197. K. Kafle, X. Xi, C. M. Lee, B. R. Tittmann, D. J. Cosgrove, Y. B. Park and S. H. Kim, Cellulose, 2013, 21,

1075-1086.

198. S. Kim, C. Lee and K. Kafle, Korean J. Chem. Eng., 2013, 30, 2127-2141.

199. C. M. Lee, N. M. A. Mohamed, H. D. Watts, J. D. Kubicki and S. H. Kim, Journal of Physical Chemistry

B, 2013, 117, 6681-6692.

200. Y. B. Park, C. M. Lee, B. W. Koo, S. Park, D. J. Cosgrove and S. H. Kim, Plant physiology, 2013, 163,

907-913.

201. C. M. Lee, K. Kafle, Y. B. Park and S. H. Kim, Physical Chemistry Chemical Physics, 2014, 16, 10844-

10853.

202. Y. R. Shen, The Principles of Nonlinear Optics, Wiley-Interscience, New York, 1984.

203. Y. R. Shen, Nature, 1989, 337, 519-525.

204. K. B. Eisenthal, Abstracts of Papers of the American Chemical Society, 1997, 213, 69-COLL.

205. Y. R. Shen, Surface spectroscopy by nonlinear optics, 1994.

206. H.-F. Wang, W. Gan, R. Lu, Y. Rao and B.-H. Wu, Int. Rev. Phys. Chem., 2005, 24, 191-256.

207. W. E. Asher and E. Willard-Schmoe, Applied Spectroscopy, 2013, 67, 253-260.

208. H. Kim, A. Lagutchev and D. D. Dlott, Propellants Explos. Pyrotech., 2006, 31, 116-123.

209. E. Surber, A. Lozano, A. Lagutchev, H. Kim and D. D. Dlott, J. Phys. Chem. C, 2007, 111, 2235-2241.

298

210. X. Zhuang, P. B. Miranda, D. Kim and Y. R. Shen, Physical Review B, 1999, 59, 12632-12640.

211. L. Velarde and H.-F. Wang, Physical Chemistry Chemical Physics, 2013, DOI: 10.1039/C3CP52577E.

212. C. M. Lee, N. M. Mohamed, H. D. Watts, J. D. Kubicki and S. H. Kim, Journal of Physical Chemistry B,

2013, 117, 6681-6692.

213. L. Velarde and H. F. Wang, Chemical Physics Letters, 2013, 585, 42-48.

214. L. Velarde and H. F. Wang, Journal of Chemical Physics, 2013, 139, 084204.

215. L. Velarde, X. Y. Zhang, Z. Lu, A. G. Joly, Z. M. Wang and H. F. Wang, Journal of Chemical Physics, 2011,

135, 241102.

216. T. Imai, J. Sugiyama, T. Itoh and F. Horii, Journal of Structural Biology, 1999, 127, 248-257.

217. S. K. Yoshiharu N, Masahisa W, Takeshi O, Macromolecules, 1997, 30, 6395–6397.

218. J. Sugiyama, H. Chanzy and G. Maret, Macromolecules, 1992, 25, 4232-4234.

219. S.-Y. Ding, Q. Xu, M. K. Ali, J. O. Baker, E. A. Bayer, Y. Barak, R. Lamed, J. Sugiyama, G. Rumbles and

M. E. Himmel, BioTechniques, 2006, 41, 435.

220. A. R. Felmy, O. Qafoku, B. W. Arey, J. Z. Hu, M. Hu, H. Todd Schaef, E. S. Ilton, N. J. Hess, C. I. Pearce,

J. Feng and K. M. Rosso, Geochimica et Cosmochimica Acta, 2012, 91, 271-282.

221. H.-F. Wang, W. Gan, R. Lu , Y. Rao and B.-H. Wu, Int. Rev. Phys. Chem., 2005, 24, 191-256.

222. R. Lu, W. Gan, B. H. Wu, H. Chen and H. F. Wang, Journal of Physical Chemistry B, 2004, 108, 7297-

7306.

223. R. Lu, W. Gan, B. H. Wu, Z. Zhang, Y. Guo and H. F. Wang, Journal of Physical Chemistry B, 2005, 109,

14118-14129.

224. S.-Y. Ding, S. Zhao and Y. Zeng, Cellulose, 2013, 21, 863-871.

225. Kovalenko, Russian Chemical Reviews, 2010, 79, 231-241.

226. M. G. Paterlini, T. B. Freedman and L. A. Nafie, Journal of the American Chemical Society, 1986, 108,

1389-1397.

227. T. Taniguchi and K. Monde, Organic & biomolecular chemistry, 2007, 5, 1104-1110.

228. C. M. Lee, K. Kafle, Y. B. Park and S. H. Kim, Physical chemistry chemical physics : PCCP, 2014, 16,

10844-10853.

229. K. Kafle, R. Shi, C. M. Lee, A. Mittal, Y. B. Park, Y.-H. Sun, S. Park, V. Chiang and S. H. Kim, Cellulose,

2014, 21, 2219-2231.

230. Y. B. Park, C. M. Lee, K. Kafle, S. Park, D. J. Cosgrove and S. H. Kim, Biomacromolecules, 2014, 15,

2718-2724.

299

231. G. Ma and H. C. Allen, The Journal of Physical Chemistry B, 2003, 107, 6343-6349.

232. U. Bornscheuer, K. Buchholz and J. Seibel, Angewandte Chemie International Edition, 2014, 53, 10876-

10893.

233. H. Zhao, J. H. Kwak, Z. C. Zhang, H. M. Brown, B. W. Arey and J. E. Holladay, Carbohydrate polymers,

2007, 68, 235-241.

234. K. Igarashi, T. Uchihashi, A. Koivula, M. Wada, S. Kimura, T. Okamoto, M. Penttilä, T. Ando and M.

Samejima, Science, 2011, 333, 1279-1282.

235. D. Klemm, F. Kramer, S. Moritz, T. Lindström, M. Ankerfors, D. Gray and A. Dorris, Angewandte Chemie

International Edition, 2011, 50, 5438-5466.

236. H. Chanzy, Journal, 1993.

237. D. Klemm, B. Heublein, H. P. Fink and A. Bohn, Angewandte Chemie International Edition, 2005, 44,

3358-3393.

238. L. H. Sperling, Introduction to physical polymer science, John Wiley & Sons, 2005.

239. M. Wada, Journal of Polymer Science Part B: Polymer Physics, 2002, 40, 1095-1102.

240. M. Wada, T. Kondo and T. Okano, Polym J, 2003, 35, 155-159.

241. M. Wada, R. Hori, U.-J. Kim and S. Sasaki, Polymer Degradation and Stability, 2010, 95, 1330-1334.

242. E. Debzi, H. Chanzy, J. Sugiyama, P. Tekely and G. Excoffier, Macromolecules, 1991, 24, 6816-6822.

243. H. Yamamoto, F. Horii and H. Odani, Macromolecules, 1989, 22, 4130-4132.

244. S. L. Price, Physical Chemistry Chemical Physics, 2008, 10, 1996-2009.

245. J. F. Matthews, M. E. Himmel and M. F. Crowley, Cellulose, 2012, 19, 297-306.

246. J. Sugiyama, J. Persson and H. Chanzy, Macromolecules, 1991, 24, 2461-2466.

247. A. Watanabe, S. Morita and Y. Ozaki, Biomacromolecules, 2007, 8, 2969-2975.

248. A. Watanabe, S. Morita and Y. Ozaki, Biomacromolecules, 2006, 7, 3164-3170.

249. F. Horii, H. Yamamoto, R. Kitamaru, M. Tanahashi and T. Higuchi, Macromolecules, 1987, 20, 2946-2949.

250. T. R. Bhuiyan and N. Hirai, Journal of Wood Science, 2005, 51, 42-47.

251. M. T. R. Bhuiyan, N. Hirai and N. Sobue, Journal of Wood Science, 46, 431-436.

252. J. Sugiyama, R. Vuong and H. Chanzy, Macromolecules, 1991, 24, 4168-4175.

253. O. Biermann, E. Hädicke, S. Koltzenburg and F. Müller‐Plathe, Angewandte Chemie International Edition,

2001, 40, 3822-3825.

300

254. A. A. Baker, W. Helbert, J. Sugiyama and M. J. Miles, Journal of structural biology, 1997, 119, 129-138.

255. A. Baker, W. Helbert, J. Sugiyama and M. Miles, Biophysical Journal, 2000, 79, 1139-1145.

256. A. Baker, W. Helbert, J. Sugiyama and M. Miles, Applied Physics A, 1998, 66, S559-S563.

257. W. L. Earl and D. VanderHart, Macromolecules, 1981, 14, 570-574.

258. K. Wickholm, P. T. Larsson and T. Iversen, Carbohydrate Research, 1998, 312, 123-129.

259. H. Yamamoto and F. Horii, Macromolecules, 1993, 26, 1313-1317.

260. R. J. Viëtor, R. H. Newman, M. A. Ha, D. C. Apperley and M. C. Jarvis, The Plant Journal, 2002, 30, 721-

731.

261. T. Imai and J. Sugiyama, Macromolecules, 1998, 31, 6275-6279.

262. B. G. Saar, Y. Zeng, C. W. Freudiger, Y. S. Liu, M. E. Himmel, X. S. Xie and S. Y. Ding, Angewandte

Chemie International Edition, 2010, 49, 5476-5479.

263. H. Chanzy, M. Dube, R. Marchessault and J. Revol, Biopolymers, 1979, 18, 887-898.

264. M. C. Jarvis and D. C. Apperley, Plant physiology, 1990, 92, 61-65.

265. S. Park, J. O. Baker, M. E. Himmel, P. A. Parilla and D. K. Johnson, Biotechnol Biofuels, 2010, 3.

266. H.-F. Wang*, W. Gan†‡, R. Lu†‡ §, Y. Rao†‡¶ and B.-H. Wu†, International Reviews in Physical

Chemistry, 2005, 24, 191-256.

267. Y. Shen, Applied Physics A Solids and Surfaces, 1994, 59, 541-543.

268. L. Zhang, Z. Lu, L. Velarde, L. Fu, Y. Pu, S.-Y. Ding, A. J. Ragauskas, H.-F. Wang and B. Yang, Cellulose,

2015, 22, 1469-1484.

269. J. Löbau and K. Wolfrum, JOSA B, 1997, 14, 2505-2512.

270. M. Flörsheimer, C. Brillert and H. Fuchs, Langmuir, 1999, 15, 5437-5439.

271. M. S. Yeganeh, S. M. Dougal and B. G. Silbernagel, Langmuir, 2006, 22, 637-641.

272. Y. Liu and M. C. Messmer, The Journal of Physical Chemistry B, 2003, 107, 9774-9779.

273. S. B. Waldrup and C. T. Williams, Catalysis Communications, 2007, 8, 1373-1376.

274. C. T. Williams, Y. Yang and C. D. Bain, Langmuir, 2000, 16, 2343-2350.

275. S. Kweskin, R. Rioux, H. Song, K. Komvopoulos, P. Yang and G. Somorjai, ACS Catalysis, 2012, 2, 2377-

2386.

276. K. Gautam, A. D. Schwab, A. Dhinojwala, D. Zhang, S. Dougal and M. Yeganeh, Physical review letters,

2000, 85, 3854.

301

277. Z. Lu, A. Karakoti, L. Velarde, W. Wang, P. Yang, S. Thevuthasan and H.-f. Wang, The Journal of Physical

Chemistry C, 2013, 117, 24329-24338.

278. L. Velarde, X.-y. Zhang, Z. Lu, A. G. Joly, Z. Wang and H.-f. Wang, The Journal of chemical physics, 2011,

135, 241102.

279. Y. B. Park, C. M. Lee, B.-W. Koo, S. Park, D. J. Cosgrove and S. H. Kim, Plant physiology, 2013, 163,

907-913.

280. B. Yang, D. M. Willies and C. E. Wyman, Biotechnology and Bioengineering, 2006, 94, 1122-1128.

281. R. D. Preston and J. Cronshaw, Nature, 1958, 181, 248-250.

282. L. H. Sperling, Introduction to physical polymer science, John Wiley & Sons, 2015.

283. H. Chum, L. Douglas, D. Feinberg and H. Schroeder, Evaluation of pretreatments of biomass for enzymatic

hydrolysis of cellulose.[Organosolv process, wet oxidation, and steam explosion of wood chips] , Solar

Energy Research Inst., Golden, CO (USA); Colorado State Univ., Fort Collins (USA), 1985.

284. X. Zhuang, P. Miranda, D. Kim and Y. Shen, Physical Review B, 1999, 59, 12632.

285. R. Lu, W. Gan, B.-h. Wu, H. Chen and H.-f. Wang, The Journal of Physical Chemistry B, 2004, 108, 7297-

7306.

286. P. Zugenmaier, Crystalline Cellulose and Derivatives, Springer Berlin Heidelberg, 2008.

287. M. G. Paterlini, T. B. Freedman and L. A. Nafie, Biopolymers, 1986, 25, 1751-1765.

288. C. Y. Liang and R. H. Marchessault, Journal of Polymer Science, 1959, 37, 385-395.

289. Y. Guo and P. Wu, Carbohydrate Polymers, 2008, 74, 509-513.

290. T. Kondo, Cellulose, 1997, 4, 281-292.

291. K. Tashiro and M. Kobayashi, Polymer, 1991, 32, 1516-1526.

292. F. Shimazu and C. Sterling, Journal of Food Science, 1966, 31, 548-551.

293. A. Rollett, F. Humphreys, G. S. Rohrer and M. Hatherly, Recrystallization and related annealing

phenomena, Elsevier, 2004.

294. F. Shafizadeh, Thermal uses and properties of carbohydrates and lignins, Elsevier, 2012.

295. B. Henrissat, Cellulose, 1994, 1, 169-196.

296. B. Yang, Z. Dai, S.-Y. Ding and C. E. Wyman, Biofuels, 2011, 2, 421-449.

297. K. Igarashi, M. Wada, R. Hori and M. Samejima, Febs Journal, 2006, 273, 2869-2878.

298. N. Hayashi, J. Sugiyama, T. Okano and M. Ishihara, Carbohydrate Research, 1997, 305, 261-269.

299. W. E. Mabee, D. J. Gregg, C. Arato, A. Berlin, R. Bura, N. Gilkes, O. Mirochnik, X. Pan, E. K. Pye and J.

302

N. Saddler, Applied Biochemistry and Biotechnology, 2006, 129, 55-70.

300. M. Galbe and G. Zacchi, Applied microbiology and biotechnology, 2002, 59, 618-628.

301. J. Sun, S.-Y. Ding and J. Doran-Peterson, Biological Concerstion of Biomass for Fuels and Chemicals:

Exploration from Natural Utilization Systems, Royal Society of Chemistry, 2013.

302. D. Fengel and G. Wegener, Wood: chemistry, ultrastructure, reactions, Walter de Gruyter, 1983.

303. E. Adler, Wood Sci. Technol., 1977, 11, 169-218.

304. A. J. Panshin and C. d. Zeeuw, Textbook of wood technology. Volume I. Structure, identification, uses, and

properties of the commercial woods of the United States and Canada., 1970.

305. Q. A. Nguyen, M. P. Tucker, F. A. Keller and F. P. Eddy, Applied Biochemistry and Biotechnology, 2000,

84-6, 561-576.

306. B. Yang, A. Boussaid, S. D. Mansfield, D. J. Gregg and J. N. Saddler, Biotechnology and bioengineering,

2002, 77, 678-684.

307. L. Kumar, V. Arantes, R. Chandra and J. Saddler, Bioresource technology, 2012, 103, 201-208.

308. L. Kumar, R. Chandra, P. A. Chung and J. Saddler, Bioresource technology, 2010, 101, 7827-7833.

309. Y.-C. He, Y. Ding, Y.-F. Xue, B. Yang, F. Liu, C. Wang, Z.-Z. Zhu, Q. Qing, H. Wu and C. Zhu, Bioresource

technology, 2015, 193, 324-330.

310. e. Karp, M. G. Resch, B. S. Donohoe, P. N. Ciesielski, M. H. O'Brien, J. E. Nill, A. Mittal, M. Biddy and G.

T. Beckham, ACS Sustainable Chemistry & Engineering, 2015.

311. J. Lehto, H. Pakkanen and R. Alén, Journal of Wood Chemistry and Technology, 2015, 35, 337-347.

312. R. Sierra, M. T. Holtzapple and C. B. Granda, AIChE Journal, 2011, 57, 1320-1328.

313. V. S. Chang and M. T. Holtzapple, 2000.

314. R. Gupta and Y. Y. Lee, Bioresource Technology, 2010, 101, 8185-8191.

315. M. Pedersen, A. Viksø-Nielsen and A. S. Meyer, Process Biochemistry, 2010, 45, 1181-1186.

316. M. Pedersen and A. S. Meyer, New Biotechnology, 2010, 27, 739-750.

317. R. S. Ramirez, M. Holtzapple and N. Piamonte, in Aqueous Pretreatment of Plant Biomass for Biological

and Chemical Conversion to Fuels and Chemicals, John Wiley & Sons, Ltd, 2013, DOI:

10.1002/9780470975831.ch8, pp. 145-167.

318. H. L. Trajano and C. E. Wyman, in Aqueous Pretreatment of Plant Biomass for Biological and Chemical

Conversion to Fuels and Chemicals, John Wiley & Sons, Ltd, 2013, DOI: 10.1002/9780470975831.ch6,

pp. 103-128.

319. A. V. Bandura and S. N. Lvov, Journal of Physical and Chemical Reference Data, 2006, 35, 15-30.

303

320. B. S. Dien, H.-J. G. Jung, K. P. Vogel, M. D. Casler, J. F. Lamb, L. Iten, R. B. Mitchell and G. Sarath,

Biomass and Bioenergy, 2006, 30, 880-891.

321. D. Guay, B. Cole, R. Fort Jr, J. Genco and M. Hausman, Journal of Wood Chemistry and Technology, 2000,

20, 375-394.

322. J. Gierer, Wood Sci. Technol., 1985, 19, 289-312.

323. E. M. Karp, B. S. Donohoe, M. H. O’Brien, P. N. Ciesielski, A. Mittal, M. J. Biddy and G. T. Beckham,

ACS Sustainable Chemistry & Engineering, 2014, 2, 1481-1491.

324. E. A. Capanema, M. Y. Balakshin and J. F. Kadla, Journal of agricultural and food chemistry, 2004, 52,

1850-1860.

325. D. D. Laskar, M. P. Tucker, X. Chen, G. L. Helms and B. Yang, Green Chemistry, 2014, 16, 897-910.

326. Y.-m. Zhang, Y. Peng, X.-l. Yin, Z.-h. Liu and G. Li, Journal of Chemical Technology & Biotechnology,

2014, 89, 1954-1960.

327. D. A. Baker and T. G. Rials, Journal of Applied Polymer Science, 2013, 130, 713-728.

328. J.-W. Jeon, L. Zhang, J. L. Lutkenhaus, D. D. Laskar, J. P. Lemmon, D. Choi, M. I. Nandasiri, A. Hashmi,

J. Xu, R. K. Motkuri, C. A. Fernandez, J. Liu, M. P. Tucker, P. B. McGrail, B. Yang and S. K. Nune,

ChemSusChem, 2015, 8, 428-432.

329. H. Wang, H. Ruan, H. Pei, H. Wang, X. Chen, M. p. Tucker, J. R. Cort and B. Yang, Green Chemistry,

2015, DOI: 10.1039/C5GC01534K.

330. Application: WO2014163652 A1 Pat., 2013-US38927

2014163652, 2014.

331. J. Ralph, K. Lundquist, G. Brunow, F. Lu, H. Kim, P. F. Schatz, J. M. Marita, R. D. Hatfield, S. A. Ralph

and J. H. Christensen, Phytochemistry Reviews, 2004, 3, 29-60.

332. S. D. Mansfield, C. Mooney and J. N. Saddler, Biotechnology progress, 1999, 15, 804-816.

333. S. I. Aronovsky and R. A. Gortner, Industrial & Engineering Chemistry, 1930, 22, 264-274.

334. W. S. L. Mok and M. J. Antal Jr, Industrial & Engineering Chemistry Research, 1992, 31, 1157-1161.

335. D. Robert, M. Bardet, C. Lapierre and G. Gellerstedt, Cellulose chemistry and technology, 1988.

336. R. Samuel, M. Foston, N. Jiang, L. Allison and A. J. Ragauskas, Polymer Degradation and Stability, 2011,

96, 2002-2009.

337. D. D. Laskar, B. Yang, H. Wang and J. Lee, Biofuels, Bioproducts and Biorefining, 2013, 7, 602-626.

338. S. Cao, Y. Pu, M. Studer, C. Wyman and A. J. Ragauskas, Rsc Advances, 2012, 2, 10925-10936.

339. M. H. Studer, J. D. DeMartini, M. F. Davis, R. W. Sykes, B. Davison, M. Keller, G. A. Tuskan and C. E.

Wyman, Proceedings of the National Academy of Sciences, 2011, 108, 6300-6305.

304

340. Q. Sun, M. Foston, X. Meng, D. Sawada, S. V. Pingali, H. M. O’Neill, H. Li, C. E. Wyman, P. Langan and

A. J. Ragauskas, Biotechnology for biofuels, 2014, 7, 150.

341. C. Fu, J. R. Mielenz, X. Xiao, Y. Ge, C. Y. Hamilton, M. Rodriguez, F. Chen, M. Foston, A. Ragauskas and

J. Bouton, Proceedings of the National Academy of Sciences, 2011, 108, 3803-3808.

342. H. Hisano, R. Nandakumar and Z.-Y. Wang, In Vitro Cellular & Developmental Biology-Plant, 2009, 45,

306-313.

343. A. Ragauskas, Y. Pu, R. Samuel, N. Jiang, C. Fu and Z.-Y. Wang, Frontiers in Energy Research, 2014, 1,

14.

344. H. Shen, M. Mazarei, H. Hisano, L. Escamilla-Trevino, C. Fu, Y. Pu, M. R. Rudis, Y. Tang, X. Xiao and L.

Jackson, The Plant Cell Online, 2013, 25, 4342-4361.

345. L. Yan, L. Zhang and B. Yang, Biotechnology for biofuels, 2014, 7, 76.

346. O. Bobleter, G. Bonn and R. Concin, 1980.

347. C. Liu and C. E. Wyman, Bioresource Technology, 2005, 96, 1978-1985.

348. F. Hu and A. Ragauskas, Rsc Advances, 2014, 4, 4317-4323.

349. H. Shen, C. R. Poovaiah, A. Ziebell, T. J. Tschaplinski, S. Pattathil, E. Gjersing, N. L. Engle, R. Katahira,

Y. Pu, R. Sykes, F. Chen, A. J. Ragauskas, J. R. Mielenz, M. G. Hahn, M. Davis, C. N. Stewart, Jr. and R.

A. Dixon, Biotechnology for Biofuels, 2013, 6.

350. A. J. Ragauskas, G. T. Beckham, M. J. Biddy, R. Chandra, F. Chen, M. F. Davis, B. H. Davison, R. A.

Dixon, P. Gilna, M. Keller, P. Langan, A. K. Naskar, J. N. Saddler, T. J. Tschaplinski, G. A. Tuskan and C.

E. Wyman, Science, 2014, 344, 709-+.

351. S. G. Allen, D. Schulman, J. Lichwa, M. J. Antal, E. Jennings and R. Elander, Industrial & Engineering

Chemistry Research, 2001, 40, 2352-2361.


353. A. Sluiter, B. Hames, R. Ruiz, C. Scarlata, J. Sluiter, D. Templeton and D. Crocker.

354. R. Overend, E. Chornet and J. Gascoigne, Philosophical Transactions of the Royal Society of London.

Series A, Mathematical and Physical Sciences, 1987, 321, 523-536.

355. K. Iiyama and A. F. A. Wallis, Journal of the Science of Food and Agriculture, 1990, 51, 145-161.


for Biofuels, 2013, 6, 110.



for Biofuels, 2013, 6.

359. K. K. Pandey and A. J. Pitman, International Biodeterioration & Biodegradation, 2003, 52, 151-160.

305

360. M. Åkerholm, B. Hinterstoisser and L. Salmén, Carbohydrate Research, 2004, 339, 569-578.

361. O. Faix and J. H. Böttcher, Holz als Roh- und Werkstoff, 1992, 50, 221-226.

362. A. T. Martı́nez, G. Almendros, F. J. González-Vila and R. Fründ, Solid State Nuclear Magnetic Resonance,

1999, 15, 41-48.

363. A. Gutierrez, P. Bocchini, G. C. Galletti and A. T. Martinez, Applied and environmental microbiology,

1996, 62, 1928-1934.

364. D. Ibarra, J. C. del Río, A. Gutiérrez, I. M. Rodríguez, J. Romero, M. J. Martínez and Á. T. Martínez,

Journal of Analytical and Applied Pyrolysis, 2005, 74, 116-122.

365. S. D. Mansfield, H. Kim, F. Lu and J. Ralph, Nature protocols, 2012, 7, 1579-1589.

366. S. D. Mansfield, H. Kim, F. Lu and J. Ralph, Nat. Protocols, 2012, 7, 1579-1589.

367. J. H. Lora and M. Wayman, Canadian Journal of Chemistry, 1980, 58, 669-676.

368. T. Q. Yuan, S. N. Sun, F. Xu and R. C. Sun, J Agric Food Chem, 2011, 59, 10604-10614.

369. J. C. Del Río, J. Rencoret, P. Prinsen, A. n. T. Martínez, J. Ralph and A. Gutiérrez, Journal of agricultural

and food chemistry, 2012, 60, 5922-5935.

370. J.-L. Wen, S.-L. Sun, B.-L. Xue and R.-C. Sun, Materials, 2013, 6, 359-391.

371. J. Rencoret, G. Marques, A. Gutierrez, D. Ibarra, J. Li, G. Gellerstedt, J. I. Santos, J. Jimenez-Barbero, A.

T. Martinez and J. C. del Rio, Holzforschung, 2008, 62, 514-526.

372. A. Guerra, L. A. Lucia and D. S. Argyropoulos, Holzforschung, 2008, 62, 24-30.

373. A. Guerra, I. Filpponen, L. A. Lucia and D. S. Argyropoulos, Journal of Agricultural and Food Chemistry,

2006, 54, 9696-9705.

374. C. Olsen, V. Arantes and J. Saddler, Bioresource technology, 2015, 187, 288-298.

375. W.-S. Lim and J.-W. Lee, Bioresource technology, 2013, 140, 306-311.

376. M. Paananen and H. Sixta, Bioresource Technology, 2015, 193, 97-102.

377. S. Agnihotri, I. A. Johnsen, M. S. Bøe, K. Øyaas and S. Moe, Wood Sci. Technol., 2015, 1-16.

378. C. Asada, C. Sasaki, Y. Uto, J. Sakafuji and Y. Nakamura, Biochemical engineering journal, 2012, 60, 25-

29.

379. W. Zhu, J. Zhu, R. Gleisner and X. Pan, Bioresource Technology, 2010, 101, 2782-2792.

380. Q. Nguyen, M. Tucker, B. Boynton, F. Keller and D. Schell, in Biotechnology for Fuels and Chemicals,

Springer, 1998, pp. 77-87.

381. A. Fissore, L. Carrasco, P. Reyes, J. Rodríguez, J. Freer and R. T. Mendonça, Journal of industrial

microbiology & biotechnology, 2010, 37, 893-900.

306

382. X. Pan, J. F. Kadla, K. Ehara, N. Gilkes and J. N. Saddler, Journal of Agricultural and Food Chemistry,

2006, 54, 5806-5813.

383. X. Pan, D. Xie, R. W. Yu, D. Lam and J. N. Saddler, Industrial & Engineering Chemistry Research, 2007,

46, 2609-2617.

384. B. Yang, A. Boussaid, S. D. Mansfield, D. J. Gregg and J. N. Saddler, Biotechnology and Bioengineering,

2002, 77, 678-684.

385. M. Normark, S. Winestrand, T. A. Lestander and L. J. Jönsson, BMC biotechnology, 2014, 14, 1.

386. X. Li, X. Luo, K. Li, J. Zhu, J. D. Fougere and K. Clarke, Applied biochemistry and biotechnology, 2012,

168, 1556-1567.

387. Q. A. Nguyen, M. P. Tucker, F. A. Keller, D. A. Beaty, K. M. Connors and F. P. Eddy, Applied biochemistry

and biotechnology, 1999, 77, 133-142.

388. R. P. Chandra, Q. Chu, J. Hu, N. Zhong, M. Lin, J.-S. Lee and J. Saddler, Bioresource technology, 2016,

199, 135-141.

389. R. T. Elander, B. E. Dale, M. Holtzapple, M. R. Ladisch, Y. Y. Lee, C. Mitchinson, J. N. Saddler and C. E.

Wyman, Cellulose, 2009, 16, 649-659.

390. F. Hu, S. Jung and A. Ragauskas, Bioresource technology, 2012, 117, 7-12.

391. B. Yang and E. Wyman Charles, Biotechnol Bioeng, 2004, 86, 88-95.

392. L. Yan, Y. Pu, M. Bowden, A. J. Ragauskas and B. Yang, Acs Sustain Chem Eng, 2015, DOI:

10.1021/acssuschemeng.5b01021.

393. M. Foston, H. L. Trajano, R. Samuel, C. E. Wyman, J. He and A. J. Ragauskas, Bioresource Technology,

2015, 197, 128-136.

394. J. Gierer, The reactions of lignin during pulping. A description and comparison of conventional pulping

processes, DTIC Document, 1970.

395. J. Gierer, Wood Sci. Technol., 1980, 14, 241-266.

396. M. Wayman and J. Lora, Tappi Tech Assoc Pulp Paper Ind, 1978.

397. M. Drumond, M. Aoyama, C.-L. Chen and D. Robert, Journal of wood chemistry and technology, 1989, 9,

421-441.

398. X. Pan, D. Lachenal, V. Neirinck and D. Robert, Journal of wood chemistry and technology, 1994, 14, 483-

506.

399. D. Robert, in Methods in lignin chemistry, Springer, 1992, pp. 250-273.

400. A. Tolbert, H. Akinosho, R. Khunsupat, A. K. Naskar and A. J. Ragauskas, Biofuels, Bioproducts and

Biorefining, 2014, 8, 836-856.

401. C. Tengborg, M. Galbe and G. Zacchi, Enzyme and microbial technology, 2001, 28, 835-844.

307

402. B. Yang, Y. Lu and C. E. Wyman, Biofuels from Agricultural Wastes and Byproducts, 2010, 175-200.

403. L. R. Lynd, M. S. Laser, D. Bransby, B. E. Dale, B. Davison, R. Hamilton, M. Himmel, M. Keller, J. D.

McMillan, J. Sheehan and C. E. Wyman, Nat Biotechnol, 2008, 26, 169-172.

404. L. R. Lynd, C. E. Wyman and T. U. Gerngross, Biotechnol Prog, 1999, 15, 777-793.

405. C. E. Wyman, Trends Biotechnol, 2007, 25, 153-157.

406. S. P. S. Chundawat, G. T. Beckham, M. E. Himmel and B. E. Dale, Annual Review of Chemical and

Biomolecular Engineering, 2010, 2, 1-25.

407. L. da Costa Sousa, S. P. S. Chundawat, V. Balan and B. E. Dale, Current Opinion in Biotechnology, 2009,

20, 339-347.

408. M. Galbe and G. Zacchi, in Biofuels, ed. L. Olsson, Springer Berlin Heidelberg, 2007, vol. 108, ch. 70, pp.

41-65.

409. P. Alvira, E. Tomás-Pejó, M. Ballesteros and M. J. Negro, Bioresource Technology, 2010, 101, 4851-4861.

410. J. Zhu, X. Pan and R. S. Zalesny Jr, Applied microbiology and biotechnology, 2010, 87, 847-857.

411. L. Cadoche and G. D. López, Biological Wastes, 1989, 30, 153-157.

412. H. Lu, E. Ip, J. Scott, P. Foster, M. Vickers and L. L. Baxter, Fuel, 2010, 89, 1156-1168.

413. T. Pattanotai, H. Watanabe and K. Okazaki, Fuel, 2013, 104, 468-475.

414. K. R. Gaston, M. W. Jarvis, P. Pepiot, K. M. Smith, W. J. Frederick and M. R. Nimlos, Energy & Fuels,

2011, 25, 3747-3757.

415. S. P. S. Chundawat, B. Venkatesh and B. E. Dale, Biotechnology and Bioengineering, 2007, 96, 219-231.

416. E. M. K. Joseph Shekiro III*, Nicholas J Nagle, Melvin P Tucker, Richard T Elander and Daniel J Schell,

Biotechnology for biofuels, 2014, 7.

417. K. H. Kim, M. P. Tucker and Q. A. Nguyen, Biotechnology Progress, 2002, 18, 489-494.

418. K. M. F. Kazi, P. Jollez and E. Chornet, Biomass and Bioenergy, 1998, 15, 125-141.

419. S. A. Hosseini and N. Shah, Bioresour Technol, 2009, 100, 2621-2628.

420. P. Kumar, D. M. Barrett, M. J. Delwiche and P. Stroeve, Industrial & Engineering Chemistry Research,

2009, 48, 3713-3729.

421. I. Ballesteros, J. M. Oliva, M. J. Negro, P. Manzanares and M. Ballesteros, Process Biochemistry, 2002, 38,

187-192.

422. A. M. Elshafei, J. L. Vega, K. T. Klasson, E. C. Clausen and J. L. Gaddy, Bioresource Technology, 1991,

35, 73-80.

423. B. Yang and C. E. Wyman, Methods in molecular biology, 2009, 581, 103-114.

308

424. J. Shekiro III, E. Kuhn, N. Nagle, M. Tucker, R. Elander and D. Schell, Biotechnology for biofuels, 2014, 7,

23.

425. M. R. Ehrhardt, T. O. Monz, T. W. Root, R. K. Connelly, C. T. Scott and D. J. Klingenberg, Appl Biochem

Biotechnol, 2010, 160, 1102-1115.

426. N. Szijarto, M. Siika-aho, T. Sontag-Strohm and L. Viikari, Bioresour Technol, 2011, 102, 1968-1974.

427. S. Viamajala, J. D. McMillan, D. J. Schell and R. T. Elander, Bioresour Technol, 2009, 100, 925-934.

428. A. Esteghlalian, A. G. Hashimoto, J. J. Fenske and M. H. Penner, Bioresource Technology, 1997, 59, 129-

136.

429. A. Sluiter, B. Hames, R. Ruiz, C. Scarlata, J. Sluiter and D. Templeton.

430. Y. Kim, R. Hendrickson, N. S. Mosier and M. R. Ladisch, Methods in molecular biology, 2009, 581, 93-

102.

431. A. Esteghlalian, A. G. Hashimoto, J. J. Fenske and M. H. Penner, Bioresource Technology, 1997, 59, 129-

136.

432. T. A. Lloyd, Dartmouth College, 2005.

433. W. Mabee, D. Gregg, C. Arato, A. Berlin, R. Bura, N. Gilkes, O. Mirochnik, X. Pan, E. Kendall Pye and J.

Saddler, Applied Biochemistry and Biotechnology, 2006, 129, 55-70.

434. W. C. Brennan MA, Applied Biochemistry and Biotechnology, 2004, 113-116.

435. K. Hammes, R. J. Smernik, J. O. Skjemstad and M. W. I. Schmidt, Applied Geochemistry, 2008, 23, 2113-

2122.

436. S. Brown, Estimating Biomass and Biomass Change of Tropical Forests: a Primer. (FAO Forestry Paper -

134), 1997.

437. R. Kumar, G. Mago, V. Balan and C. E. Wyman, Bioresour Technol, 2009, 100, 3948-3962.

438. Y. Pu, F. Hu, F. Huang, B. H. Davison and A. J. Ragauskas, Biotechnology for biofuels, 2013, 6, 15.

439. J. Li, G. Henriksson and G. Gellerstedt, Bioresour Technol, 2007, 98, 3061-3068.

440. S. J. B. Duff and W. D. Murray, Bioresource Technology, 1996, 55, 1-33.

441. T. A. Lloyd and C. E. Wyman, Bioresour Technol, 2005, 96, 1967-1977.

442. C. E. Wyman, B. E. Dale, V. Balan, R. T. Elander, M. T. Holtzapple, R. S. Ramirez, M. R. Ladisch, N. S.

Mosier, Y. Y. Lee, R. Gupta, S. R. Thomas, B. R. Hames, R. Warner and R. Kumar, in Aqueous

Pretreatment of Plant Biomass for Biological and Chemical Conversion to Fuels and Chemicals, John

Wiley & Sons, Ltd, 2013, DOI: 10.1002/9780470975831.ch12, pp. 239-259.

443. R. Davis, L. Tao, C. Scarlata, E. Tan, J. Ross, J. Lukas and D. Sexton, 2015.

444. L. Laureano-Perez, F. Teymouri, H. Alizadeh and B. Dale, in Twenty-Sixth Symposium on Biotechnology

309

for Fuels and Chemicals, eds. B. Davison, B. Evans, M. Finkelstein and J. McMillan, Humana Press, 2005,

DOI: 10.1007/978-1-59259-991-2_91, ch. 91, pp. 1081-1099.

445. H. Wang, L. Zhang, T. Deng, H. Ruan, X. Hou, J. R. Cort and B. Yang, Green Chemistry, 2016, DOI:

10.1039/C5GC02967H.

446. J. F. Saeman, Industrial & Engineering Chemistry, 1945, 37, 43-52.

447. J. R. Gray, in Environmental Instrumentation and Analysis Handbook, John Wiley & Sons, Inc., 2005, DOI:

10.1002/0471473332.ch23, pp. 491-510.

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