Preparation of lignin nanoparticles by chemical modification

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Iranian Polymer Journal ISSN 1026-1265Volume 23Number 5 Iran Polym J (2014) 23:355-363DOI 10.1007/s13726-014-0232-0

Preparation of lignin nanoparticles bychemical modification

Iulian Andrei Gilca, Roxana ElenaGhitescu, Adrian Catalin Puitel &Valentin I. Popa

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ORIGINAL PAPER

Preparation of lignin nanoparticles by chemical modification

Iulian Andrei Gilca • Roxana Elena Ghitescu •

Adrian Catalin Puitel • Valentin I. Popa

Received: 9 August 2013 / Accepted: 21 February 2014 / Published online: 12 March 2014

� Iran Polymer and Petrochemical Institute 2014

Abstract Lignin is the main natural aromatic polymer

and consists of about a quarter of lignocellulosic biomass.

That is why products obtained from lignin are very

attractive research topics, but it is also a very complex

issue due to its complicated structure which depends on the

separation method and plant species. To become a widely

used raw material, the basic characteristics and structure-

dependent properties must be elucidated, initially. For this

reason, it is necessary to obtain lignin with superior prop-

erties, to be able to compete with fossil resources. This

paper presents a chemical method to modify lignin by

hydroxymethylation to obtain nanoparticles. Nanotechnol-

ogy allows using chemical, physical and biological effects

that do not occur outside the nanoscale world. To find the

best conditions (from the average particle size distribution

point of view), three reaction parameters were varied: ratio

of lignin/aldehyde, pH and temperature. The following

output value has been the average particle size distribution.

The obtained data were processed with software in demo

version (Modde 9) and resulted regression equation allows

us to establish the optimum conditions. At the same time,

the reaction products thus synthesized were characterized

by FTIR spectroscopy, GPC, and 31PNMR spectroscopy.

The results confirm that using this reaction, it is possible to

synthesize nanoparticles from hydroxymethylated lignin.

Lignin derivatives containing high hydroxyl group content

have a potential utilization as phenol substitute in the

phenol formaldehyde resin synthesis, composites, biocides,

etc.

Keywords Hydroxymethylation � Lignin � Optimization �Nanolignin

Introduction

Nowadays, conventional raw material resources are limited

and become difficult to assure. Therefore, renewable

resources play an important role in the sustainable devel-

opment concept. Lignin is a biopolymer, the main aromatic

component in vegetal biomass, and it is the second most

abundant component of lignocellulosic biomass [1–3]

(about 28–30 % lignin content for softwood [4], 20–28 %

for hardwood [5], 15–21 % for annual plants [6, 7]) after

cellulose. The reactivity of lignin is determined by its

origin and particular structure and also by the chemical

structural modifications induced by separation methods.

At the same time, the reactivity of lignin can be increased

using different reactions such as hydroxymethylation [8],

epoxidation [9], carboxymethylation [10], esterification [11]

oxidation and sulfonation [12], a.s.o. The main advantage of

the hydroxymethylated lignin is its high content of hydroxyl

groups which allows using it as phenol substitute in phenol

formaldehyde resin synthesis [13], composites, biocides

systems and bioremediation [14–16]. For instance, when

lignin is hydroxymethylated, the resulting product is repor-

ted to replace up to 50 % of phenol in the phenol formal-

dehyde resins, without loss of properties [13, 17].

By modification of the parameters of hydroxymethylation

reaction (time, temperature, catalyst type, formaldehyde/

I. A. Gilca (&) � R. E. Ghitescu � A. C. Puitel � V. I. Popa

Department of Natural and Synthetic Polymers, Faculty of

Chemical Engineering and Environmental Protection, Gheorghe

Asachi Technical University of Iasi, 700050 Iasi, Romania

e-mail: [email protected]

R. E. Ghitescu

Empa, Swiss Federal Laboratories for Materials Science and

Technology, Laboratory for Protection and Physiology,

Lerchenfeldstrasse 5, 9014 Saint Gall, Switzerland

Iran Polymer and

Petrochemical Institute 123

Iran Polym J (2014) 23:355–363

DOI 10.1007/s13726-014-0232-0

Author's personal copy

phenol ratio), lignin derivatives with different characteristics

can be obtained. Thus, Schilling developed a method of

obtaining a submicron (about 300 A) lignin-based binder

resins for flexographic water-borne black ink formulation

using hydroxymethylation reaction [18].

Nanodispersions based on organic compounds, com-

pared with other substances are not persistent in the envi-

ronment. These nanodispersions can be used to achieve:

biocides, pharmaceuticals/antioxidants, plant bioregulators,

paints, etc. [14, 16, 18]. Taking into account the method

proposed by Schilling, we carried out a study to establish

the most convenient conditions for the synthesis of micro-

and nano-scale particle size. The obtained lignin deriva-

tives were characterized by FTIR spectroscopy, gel per-

meation chromatography and 31P NMR spectroscopy

techniques and the resulted modifications were confirmed,

as well.

Experimental

Materials

Sarkanda grass lignin (L2) obtained by an alkaline proce-

dure of delignification was supplied by Granit Recherche

Development Co., Switzerland. The characteristics of lig-

nin (1.05-OHtotal groups/C9; 0.91-OHphenolic/C9; 0.96-OCH3/

C9; 0.88-alkyl/aryl ratio; 0.88-carbonyl/C9; 0.82-syringyl/

guaiacyl ratio) were determined in previous work [8]. All

the other materials used were reagent grade.

Methods

A glass reactor equipped with a mechanical stirrer was

immersed in a water bath. A quantity of 10 g of lignin

(absolutely dry) was suspended in 47 mL of distilled water

and stirred for 2 h at room temperature. After that, the

heating was started to the set temperature. The reaction

time was kept constant at 4 h and the temperature was

varied between 50 and 95 �C. After the lignin suspension

reached the desired temperature, a 50 % sodium hydroxide

solution and an ammonia hydroxide (25 %) solution

(depending on pH) were added and stirred for another 2 h.

The pH of the mixture was varied in the range of 7.5–12.0

during the experiments. After 2 h of stirring, a formalde-

hyde solution (37 %) (depending on lignin/formaldehyde

mass ratio) was added to the reaction mixture. The lignin/

aldehyde ratio was varied between 0.5 and 1.5. A central

composite factorial (CCF) experimental design was used to

study the dependence of the particle size on varied reaction

parameters. The obtained product was recovered by pre-

cipitation at pH 2 with a solution of hydrochloric acid

(2 %), separated by centrifugation, washed and dried.

Characterization

Particle size distribution analysis of a suspension in dis-

tilled water of the modified lignin nanoparticles was carried

out using a Shimadzu SALD 7001 (Japan) particle size

analyzer. A single light source of violet laser combined

with a single optical system allows the size determination

of particles from 15 nm to 500 lm by laser diffraction

method.

FTIR spectra were recorded with a Digilab Scimitar

FTS2000 (USA) spectrophotometer using the KBr pellet

method. The data acquisition conditions were: spectral

width of 4,000–400 cm-1, 64 scans at a resolution of

4 cm-1. The pellet was prepared with a mixture of 200 mg

KBr and 2 mg of lignin sample. FTIR data were processed

using ACD Labs software.

Gel permeation chromatography was performed using a

Shimadzu LC 20AT (Japan) liquid chromatograph with a

SPD M20A ultraviolet diode array (UV) detector set at

280 nm. Two columns connected in series were used:

Agilent PL gel MIXED-D 5 lm, 1–40 K and PL gel 5 lm,

MW range 500 Da–20 kDa. Complete calibration was

achieved using polystyrene standards provided by Fluka

(Switzerland). THF was used as eluent, at flow rate of

0.5 mL/min. Before the injection, the lignin samples were

acetylated with acetyl bromide following the procedure

described by Lu and Ralph [19].

In the second GPC analysis variant, non-acetylated lig-

nin samples analysis was carried out using TSK-gel

GMPWxl GPC column (300 9 7, 8 mm, 500 Da–

8,000 kDa). Elutions were performed with a pH 11 phos-

phate buffer solution, at a flow rate of 0.5 mL/min. Cali-

bration curve was obtained using standards of poly(styrene

sulfonic acid) sodium salt, provided by Fluka

(Switzerland).

For 31PNMR, the lignin samples were derivatized with

2-chloro-4,40,5,50-tetramethyl-1,3,2-dioxaphospholane.

Samples were dissolved in a solvent (1.6/1: pyridine/chlo-

roform ratio) [20]. The 31P NMR spectra were performed on

a Bruker 300 NMR (Germany) spectrometer. The NMR

spectra were performed by Bruker Topspin software.

Results and discussion

Formaldehyde reacts with lignin in the presence of alkali

both by substituting the free 5 position in the phenolic

(guaiacyl) nuclei, and by Tollens reaction of the side chains

bearing carbonyl groups (Scheme 1). This reaction may be

continued with a condensation resulting in the decrease of

hydroxyl group content [21, 22].

During the hydroxymethylation process, formaldehyde

is added to lignin in alkaline medium. Three reactions can

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take place. The main one is Lederer–Manasse reaction,

where hydroxymethyl groups are incorporated to the lignin

aromatic rings (Scheme 1, reaction a), increasing the

reactivity of the molecule. Nondesirable reactions are the

Cannizzaro reaction, in which formaldehyde reacts with

itself, and Tollens reaction in which lignin side chains are

Scheme 1 Lignin

hydroxymethylation reactions (a

and b) and further condensation

reaction (c)

Table 1 Experimental variables and their normalization according to the experimental design model

Exp. no. Lignin/formaldehyde

ratio

Normalized

value [x]

pH Normalized

value [y]

Temperature

(�C)

Normalized

value [T]

Average particle

size (nm) [z]

1. 0.5 -1 7.5 -1 50 -1 0.189

2. 1.5 1 7.5 -1 50 -1 0.221

3. 0.5 -1 12 1 50 -1 0.26

4. 1.5 1 12 1 50 -1 0.279

5. 0.5 -1 7.5 -1 95 1 0.301

6. 1.5 1 7.5 -1 95 1 0.252

7. 0.5 -1 12.0 1 95 1 0.262

8. 1.5 1 12.0 1 95 1 0.145

9. 0.5 -1 9.75 0 72.5 0 0.24

10. 1 1 9.75 0 72.5 0 0.245

11. 1 0 7.5 -1 72.5 0 0.221

12. 1 0 12.0 1 72.5 0 0.201

13. 1 0 9.75 0 50 -1 0.101

14. 1 0 9.75 0 95 1 0.092

15 1 0 9.75 0 72.5 0 0.098

16. 1 0 9.75 0 72.5 0 0.081

17. 1 0 9.75 0 72.5 0 0.096

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substituted by aliphatic methylol groups (Scheme 1, reac-

tion b). Increasing temperature, hydroxymethyl groups

react at free positions of other lignin units or phenol to

form methylene bonds (Scheme 1, reaction c). The reac-

tivity of lignins in hydroxymethylation or cross-linking

depends on its source (softwood, hardwood or grass) on the

pulping conditions (pH, temperature, pressure) and on the

reaction conditions.

Optimization of reaction conditions

The lignin separated from Sarkanda grass by an alkaline

procedure was hydroxymethylated in different conditions

(Table 1). With the aim to establish optimum conditions to

synthesize nanoparticles based on hydroxymethylation of

lignin, the following parameters were considered: the lig-

nin/aldehyde mass ratio, pH and temperature. The depen-

dent variable was the average particle size. The obtained

data were used to optimize the hydroxymethylation reac-

tion conditions and to determine the correlation equation

between parameters and particle size. A demo version of

adequate modeling software was used.

Response surface methodology can be employed to

explore the contribution of a given set of independent vari-

ables (process parameters or system inputs) on a set of

dependent ones (process results or system outputs). The

coefficient of determination, R2, designates the fitness

between practical results and mathematical model. To

determine the model predictive power, the parameter Q2 is

calculated. This reflects the validation of prediction.

Parameter Q2 is the fraction of the variation of the response

predicted by the model according to cross validation and

expressed in the same units as R2. These parameters are used

together as diagnostic tool for the model. Values close to 1

are desirable, because they indicate an excellent model. In

practice, a Q2 value higher than 0.5 designates a good model.

The difference between R2 and Q2 should not to be more

than 0.2–0.3. The validation of prediction may be esti-

mated using Eq. (1):

Q2 ¼SStot � SSpred

� �

SStot

ð1Þ

where, SStot is the total sum of the squares of the difference

of the dependent variable and its grand mean; SSpred is the

prediction residual sum of squares, computed by squared

difference between observed dependent variable Y and

predicted dependent variable Y [23].

The multiple linear regression (MLR) fitting method

was used to validate experimental data and to generate a

mathematical equation which could be used for optimiza-

tion or for forecast results of hydroxymethylation reaction.

Based on statistical analysis results (p \ 0.05; F [ 1), a

particular form (a second-degree polynomial) for the

dependence of a system response as function of selected

independent variables was found as described by Eq. (2).

Thus, it was possible to obtain equation coefficients that

describe the mathematical model (Table 2) as follows:

z ¼ a1x2 � a12xy � a11x � a21y� a3T2 þ a31T

þ a2y2 a13xT a23yT þ C ð2Þ

where z, average particle size; x, lignin/formaldehyde mass

ratio; y, pH; T, temperature.

In Fig. 1 are represented, for example, average particle

size distributions for some samples that resulted in differ-

ent conditions of reactions (experiments 1, 2, 3, 4, 5, and 7)

as compared to that of control sample (6). From this data,

we can appreciate that by hydroxymethylation it is possible

to obtain nanosized lignin particles. The Eq. (2) was used

Table 2 Values of the coefficients of the resulted model equation—normalized values were used

Characteristics Abbreviation Coeff. Sca Std. Err.b Conf. int(±)c

Constant Cst 0.120521 0.013241 0.0313103

Lignin/formaldehyde x -0.011 0.00978536 0.023139

pH y -0.00369998 0.00978536 0.023139

Temperature T 0.000199991 0.00978536 0.023139

Lignin/aldehyde 9 lignin/aldehyde x2 0.100338 0.0189047 0.0447032

pH 9 pH y2 0.068838 0.0189047 0.0447032

Temperature 9 temperature T2 -0.045662 0.0189047 0.0447032

Lignin/aldehyde 9 pH xy -0.010125 0.01009404 0.0258702

Lignin/aldehyde 9 temperature xT -0.027125 0.01009404 0.0258702

pH 9 temperature yT -0.034375 0.01009404 0.0258702

R2 = 0.926; Q2 = 0.607a Coefficient scatteredb Standard errorc Confidence interval

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to study the influence of pH and lignin/formaldehyde ratio

(L/A) for temperatures of 50, 72.5 and 90 �C (Figs. 2, 3, 4).

From this graphical representation, the optimum conditions

to obtain a convenient particle size distribution were

established as: temperature = 72 �C, pH = 9.8, and lignin/

formaldehyde ratio = 1.

Fig. 1 Average particle size

distribution analysis; samples 1,

2, 3, 4, 5, 7 (according to the

Table 1) and sample 6 as

control sample

Fig. 2 Response surface

representation of average lignin

particle size (APS) distribution

as functions of pH and L/A ratio

at 50 �C





at 72.5 �C

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Characterization of reaction products

FTIR analysis

FTIR spectroscopy was used to characterize the raw

material and to analyze the changes in the lignin structure

during the hydroxymethylation (Table 3) [24–26]. The

FTIR spectra obtained are characterized by a broad O–H

band at 3,400 cm-1 (which were increased in the case of

modified samples), an intense C–H band at 2,927 cm-1,

and another at 2,854 cm-1, typical of methoxyl groups and

also hydroxymethyl groups. The absorbance values at these

wavenumbers were increased for hydroxymethylated

samples. The aromatic skeletal vibration occurs at 1,600

and 1,500 cm-1. The band at 1,600 cm-1 was used for

normalization and its intensity was always set to 1.00. The

C–H deformations of asymmetric methyl and methylene

appeared at 1,470–1,460 cm-1, and the ether carbon–

oxygen bands appeared at 1,400–1,000 cm-1. The band at

1,370 cm-1 was due to the bending vibration of the phe-

nolic OH group. Bands at 1,140 and 1,035 cm-1 are

characteristic to secondary and primary OH groups,

respectively [27]. Also, a band at 836 cm-1, which is due

to the aromatic C–H out of plane vibration in p-hydroxy-

phenyl propane, was also detected (Fig. 5). More exact and

complete information was provided by 31P NMR spec-

troscopy technique.

GPC analysis

It is known that lignin molecular mass and its distribution

are parameters related to the physico-chemical properties

which are very important in the development of potential

applications. Molecular mass distribution may be used to

monitor the modification of lignin during performed

physico-chemical treatments [28].

After reaction, the resulted hydroxymethylated samples

along with non-modified lignin were characterized by GPC

using non-acetylated and acetylated derivatives. The main

advantages of gel permeation chromatography are acces-

sibility, short time of analysis, small quantities of sample

and the possibility of determining the molecular mass on a

very large scale. The results obtained from GPC are pre-

sented in Figs 6 and 7 and Tables 4 and 5. Both applied

variants evidenced that hydroxymethylation of lignin took

place by modification of average macromolecular mass.

According to calculation of the values obtained for Sark-

anda grass lignin (L2) and two hydroxymethylated samples

(selected from those corresponding from dimensional point

of view, Ex4, Ex7) was observed a decrease of the weight

average molecular mass (Mw) and at the same time an

increase of the number average molecular mass (Mn) for

both types of analysis. These data confirm the results

reported in a previous work [13]. Thus, the polydispersity





at 90 �C

Table 3 Assignment of infra-red absorptions of lignin and its

derivatives

Characteristic bands

(cm-1)

Band origin

3410–3,460 Phenolic hydroxyl groups and aliphatic

structures stretching

2,945–2,920 C–H stretching of methyl or methylene group

2,840–2,835 Unconjugated C–O stretching

1,730–1,675 C=O stretching

1,610–1,695 Aromatic skeletal vibration

1,515–1,505 C–C stretching of aromatic skeletal

1,470–1,460 C–H stretching of aromatic skeletal

1,420–1,400 CH vibration of methyl group

1,370–1,330 Syringyl units vibration

1,330–1,325 Guaiacyl units vibration

1,160–1,130 OH stretching of secondary alcohol

1,044 OH stretching of primary alcohol

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index (PID) decrease proved that by hydroxymethylation, a

nanostructured polymer with a more uniform molecular

mass can be obtained.

However, there are some differences between the values

resulted using the two techniques. These can be explained

by association phenomena by hydrogen bonds including

fragments of lignin with different molecular mass of non-

acetylated samples, which are characterized by lower val-

ues. In case of acetylated lignin derivatives, the hydroxyl

groups were blocked, preventing molecular association and

thus the values of molecular mass and polydispersity

indices were higher.

Fig. 5 FTIR spectra of

Sarkanda grass lignin

unmodified (L2) and

hydroxymethylated (Ex4 and

Ex7) samples

Fig. 6 Molecular mass distribution of Sarkanda grass lignin unmod-

ified (L2) and hydroxymethylated (Ex4 and Ex7) samples for non-

acetylated step

Fig. 7 Molecular mass distribution of Sarkanda grass lignin unmod-

ified (L2) and hydroxymethylated (Ex7 and Ex4) samples for

acetylated step

Table 4 Molecular mass values of Sarkanda grass lignin unmodified

(L2) and hydroxymethylated (Ex7, Ex4) samples in non-acetylated

step

Sample

code

Average

molecular mass

(Mw) (g/mol)

Numeric average

molecular mass (Mn)

(g/mol)

Polydispersity

index (PID)

(Mw/Mn)

L2 135,424 14,485 9.34

Ex7 88,651 20,322 4.36

Ex4 115,883 26,409 4.38

Table 5 Molecular mass values of Sarkanda grass lignin unmodified

(L2) and hydroxymethylated (Ex7, Ex4) samples in acetylated step

Sample Average

molecular mass

(Mw) (g/mol)

Numeric average

molecular mass (Mn)

(g/mol)

Polydispersity

index (PID)

L2 190,240 17,784 10.69

Ex7 186,828 27,809 6.71

Ex4 117,333 16,273 7.20

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31P NMR analysis

NMR spectroscopy is a useful technique to observe the

evolution of functional groups during lignin modification.

The use of phosphorous-containing derivatizing reagents

for lignin analysis has grown in importance. The sensitivity

of a 31PNMR experiment is about 15 times less than that of

a proton NMR experiment. The range of 31P chemical

shifts is more than 1,000 ppm for a variety of phosphorous

compounds and the average line width is about 0.7 Hz

[29]. This technique was used to characterize non-modified

and hydroxymethylated lignin samples (Fig. 8; Table 6)

after derivatizing them with phosphorous compounds. The

results obtained allow to distinguish the different types of

functional groups which can be quantified by integrating

the following regions for: aliphatic between 150.8 and

146.3 ppm, condensed phenolic units (diphenylmethane

type) between 144.3 and 142.8 ppm, syringyl phenolic

units between 143.7 and 142.2 ppm, condensed phenolic

units (4-O-50 type) between 142.8 and 141.7 ppm, con-

densed phenolic units (5-50 type) between 141.7 and

140.2 ppm, guaiacyl and demethylated phenolic units

between 140.2 and 138.4 ppm, p-hydroxyphenolic units

between 138.6 and 136.9 ppm and carboxylic acids

between 135.6 and 133.7 [30–32].

After the hydroxymethylation reaction, spectra integra-

tion for unmodified Sarkanda grass lignin (L2) and modi-

fied by hydroxymethylation (samples Ex 4, Ex7), it was

observed that the peak area in chemical shift range

140–137 ppm decreases simultaneously with the increasing

of the peak area in 144,3–142,8 ppm chemical shift. Thus,

the introduction of hydroxymethyl groups in lignin mac-

romolecules was confirmed by 31PNMR spectra analysis

and also further condensation with diphenylmethane bonds

formation (Table 6).

Conclusion

Using hydroxymethylation reaction of lignin, it was pos-

sible to obtain nanoparticles and by mathematical modeling

optimum reaction conditions were established as being:

temperature = 72 �C, pH = 9.8, lignin/formaldehyde

ratio = 1. The nanoparticles were characterized from

Fig. 8 31PNMR spectra of

Sarkanda grass lignin

unmodified (L2) and

hydroxymethylated (Ex4 and

Ex7) samples

Table 6 Aliphatic, phenolic, and carboxylic hydroxyl groups content

determined in Sarkanda grass lignin unmodified (L2) and hydrox-

ymethylated (Ex4 and Ex7) samples

Functional groups L2 Ex4 Ex7

(OH, mmol/g) (OH, mmol/g) (OH, mmol/g)

Aliphatic 1.4615 2.4742 2.6285

Condensed phenol 1.0873 1.9238 1.9531

Guaicyl phenolic 0.6862 0.3675 0.6769

p-OH fenol 0.3898 0.2900 0.3329

Carboxylic acids 0.6594 0.8585 1.1405

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dimensional point of view. The modifications determined

by hydroxymethylation reaction were confirmed by FTIR

spectroscopy, GPC-chromatography and 31P-NMR spec-

troscopy techniques. All of the performed analysis show

that use of the proposed chemical method lead to hydrox-

ymethylated lignin nanoparticles.

Acknowledgments This paper was realised with the support of

POSDRU CUANTUMDOC ‘‘DOCTORAL STUDIES FOR EURO-

PEAN PERFORMANCES IN RESEARCH AND INNOVATION’’

ID79407 project funded by the European Social Found and Romania

Government. The authors are grateful to Dr. Claudia Crestini and Dr.

Federica Melone from University of Rome ‘‘Tor Vergata’’ for support

in GPC acetylated and 31P-NMR analyses.

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