Experimental and robust modeling approach for lead(II) uptakeby alginate gel beads: Influence of the ionic strength and medium
composition
Salvatore Cataldo a, Antonio Gianguzza a, Marcello Merli b, Nicola Muratore a, Daniela Piazzese a,⇑,Maria Liria Turco Liveri a
aDipartimento di Fisica e Chimica, Università degli Studi di Palermo, Viale delle Scienze, I-90128 Palermo, ItalybDipartimento di Scienze della Terra e del Mare, Università degli Studi di Palermo, Via Archirafi 22, I-90123 Palermo, Italy
a r t i c l e i n f o
Article history:
Received 5 May 2014
Accepted 28 July 2014
Available online 4 August 2014
Keywords:
Alginate gel beads
Robust statistics
Metal adsorption
Wastewater treatment
Ionic strength
Differential Pulse Anodic Stripping
Voltammetry
Adsorption isotherms
Adsorption kinetics
Lead
Heavy metal
a b s t r a c t
Systematic kinetic and equilibrium studies on the lead ions removal ability by Ca-alginate gel beads have
been performed by varying several internal parameters, namely, number of gel beads, nature and com-
position of the ionic medium and pH, which allowed us to model a wastewater in order to closely repro-
duce the composition of a real sample. Moreover, the effects brought about the different ionic species
present in the reacting medium have been evaluated.
Differential Pulse Anodic Stripping Voltammetry (DP-ASV), has been systematically used to perform
kinetic and equilibrium measurements over continuous time in a wide range of concentration. Kinetic
and equilibrium data have been quantitatively analyzed by means of robust approach both for the
non-linear regression and the subsequent residuals analysis in order to significantly improve the results
in terms of precision and accuracy.
Alginate gel beads have been characterized by SEM and an investigation on their swelling behavior has
also been made. Removal efficiency of the calcium-alginate gel beads has been calculated and results
obtained have showed a relevant dependence on ionic strength, composition of ionic media, pH of solu-
tion and number of gel beads. The number of gel beads takes part as key crucial components, i.e., the
higher the number of beads the greater the amount of Pb(II) species removed from the sample, the lower
the time needed to reach the maximum removal efficiency of 90%.
� 2014 Elsevier Inc. All rights reserved.
1. Introduction
The discharge of wastewaters from various industrial activities
to the environment is more and more rigorously controlled. The
cogent problem arises from the inadequate disposal of wastewater
mainly contaminated with heavy metals, being a risk for both the
aquatic ecosystems and human health [1]. In fact, presence of
heavy metals in the aquatic ecosystem leads unambiguously to
harmful effects to living organisms because these metals are not
biodegradable and can be bio-accumulated through the food chain.
Among the heavy metals present in the wastewater a great atten-
tion has been paid to lead, which is one of the most useful metals
not only for its wide presence but also for both its effortless extrac-
tion and handle [2]. Unless these benefits, we cannot leave out of
consideration that lead has the most damaging effects on human
health, whereby it can enter the human body through uptake of
food (65%), water (20%) and air (15%) [3]. For all these reasons, lead
is one of the most controlled metal in wastewaters: its maximum
admissible concentration in drinking water was fixed to 10 lg L�1
by European Union. Several classical remediation processes have
been proposed for metal recovery from industrial effluents [4–8],
but in the majority of the situations all of the above reported treat-
ments reveal to be unsuitable, especially for very low-concentra-
tion solutions. This way, the application of these protocols
reflects not only into a waste of both time and money but on envi-
ronmental reasons trough direct or indirect impact by producing
hazardous wastes that need further treatment [9–11]. Thus, in
selecting the proper technique to apply for the wastewater treat-
ment several features have to be taken into account, i.e., environ-
mental impact, costs, competitiveness, technical efficiency, life
cycle and sustainable growth parameters.
A very promising alternative to the above reported methods for
the removal of heavy metals is the bio-sorption based on the use of
http://dx.doi.org/10.1016/j.jcis.2014.07.042
0021-9797/� 2014 Elsevier Inc. All rights reserved.
⇑ Corresponding author.
E-mail address: [email protected] (D. Piazzese).
Journal of Colloid and Interface Science 434 (2014) 77–88
Contents lists available at ScienceDirect
Journal of Colloid and Interface Science
www.elsevier .com/locate / jc is
different materials [12–25]. In particular, bacteria [26], alga [27],
fungi [28], but also sub-products of agriculture [29] or industry,
biopolymers [30–34] have been already applied. These renewable
resources possess peculiar characteristics, which make them very
suitable for sequestering the heavy metals present in the waste.
Although they share some features with conventional resins char-
acterized by the presence of a number of reactive groups, they
present the advantages to remove the heavy metal by synergically
exploiting different processes, namely ion exchange, complexation
and also electrostatic interactions [35,36]. As the attractive interac-
tions between the heavy metal species and the functional groups of
adsorbent drive the process, the effects of the wastewaters ionic
composition cannot be leaved out of consideration because the rel-
evant presence of ionic species clearly interferes with the removal
processes of heavy metal.
Furthermore, particularly in areas of wastewater treatments by
biosorption processes, a knowledge of the kinetics of biomaterial-
metal ion uptake is very important, because knowing the temporal
scale of the process is useful as identifying the final equilibrium
state of the system. In fact, a given adsorbent could have a very
high removing efficiency but on a timeframe very long. This way,
the applied materials for the remediation process would result
both time and money consuming.
Among different biopolymers, alginate can be considered a suit-
able sorbents because of (a) the wide availability, being a major
component of the cell walls of brown algae, (b) the high affinity
for heavy metals [37–41], and (c) the ability to form gel depending
on the experimental conditions of the aqueous alginate solutions
[42].
In order to model the composition of a real wastewater samples
and to obtain a complete picture of the forces that govern the
adsorption of lead ions into alginate gel beads, we planned a sys-
tematic kinetic and equilibrium studies by methodically changing
the ionic strength and ionic medium.
Differential Pulse Anodic Stripping Voltammetry (DP-ASV) has
been used to perform kinetic and equilibrium measurements over
continuous time in a wide range of metal concentration, as this
electrochemical technique shows relevant advantages in terms of
high sensitivity, ease of application even in solutions with high
ionic strength values, low cost, high selectivity and accuracy [43].
Up to now, to the best of our knowledge, only few researchers
[44,45] have employed electrochemical techniques to study waste-
waters treatment by biosorption processes due to the contamina-
tion on the sensitive electrode by the adsorption of organic
matter in samples. In this investigation we have preliminarily ver-
ified the possibility to use the DP-ASV for direct measurements of
lead ions in solution and experimental data obtained have demon-
strated a good reproducibility and no interference by organic mat-
ter in the experimental electrochemical conditions adopted for this
study.
Both the kinetic and equilibrium data have been quantitatively
analyzed by means of the Ordinary Least-Squares regressions (OLS)
and (robust) Iteratively Reweighted Least-Squares regressions
(IRLS) by using the typical models. For the kinetic data the
pseudo-first order, pseudo-second order, double exponential
model, Higuchi and Weibull models have been applied while the
sorption characteristics were examined by using the classical mod-
els, i.e., Langmuir, Freundlich, and some others, e.g., Langmuir–Fre-
undlich, Sips, double Langmuir. The affinity of the Pb(II) species for
the binding sites was evaluated by using the Scatchard plot analy-
sis [46] that allows to gather useful information about affinity phe-
nomena of adsorbent toward the adsorbate.
The analysis of the experimental data allowed us not only to
elucidate the mechanism pathways concerning the adsorption pro-
cesses but also to establish the nature of the interactions which
govern the removing process. Complementary studies on the algi-
nate gel beads swelling behavior and their SEM characterization
provided important information for corroborating the mechanism
pathways trough which the metal ion adsorption take place.
2. Materials and methods
2.1. Chemicals
Commercial alginic acid (AA, molecular weight in the range 70–
100 kDa), as sodium salt extracted from Macrocystis pyrifera, with
an average content of mannuronic and guluronic acids of 61%
and 39%, respectively, was supplied by Sigma (lot. 60K1443). Nitric
acid and sodium hydroxide solutions used to fix the pH of the
metal ion aqueous standard solutions were prepared by diluting
concentrated Fluka ampoules and standardized against sodium
carbonate and potassium hydrogen phthalate, respectively.
Sodium nitrate, sodium chloride, lead nitrate and calcium chloride
dehydrate were supplied by Fluka and were always dried at 110 �C
before use. The purity of calcium chloride and lead nitrate was
checked by Inductively Coupled Plasma Optical Emission Spectros-
copy (ICP-OES) measurements. All the solutions were prepared
using freshly prepared CO2-free ultra pure water (qP 18 MX cm)
and grade A glassware.
2.2. Experimental procedures for the preparation and the
characterization of sorbent material
Calcium alginate gel beads (Ca-A) were prepared at the concen-
tration of 2%, using the dropping technique, as already reported in
a recent paper [47]. Different analysis were carried out in order to
determine the density, the water content, the weight and the
mechanical resistance of gel beads and the detailed experimental
procedures were recently described by Cataldo et al. [47].
The degree of swelling of calcium alginate gel beads was deter-
mined gravimetrically. Different number of calcium alginate gel
beads (NB), specifically 25, 50 and 100 beads, were placed in
25 mL of NaCl aqueous solutions at different ionic strength values
(0.1 6 I/mol L�16 0.8), at pH 5.0. The beads were allowed to swell,
under gentle stirring at room temperature for 6 h. The swollen
beads were taken out from the solution, completely dried using a
filter paper in order to remove the excess of water on their surface
and afterwards weighed. The swelling ratio (SR) was determined
by considering the following relationship
SR ¼ ws �wd
wd
ð1Þ
where ws and wd are the weights of the swollen and dry beads,
respectively.
The surface morphology and the size of calcium alginate gel
beads after lead sorption (Ca-A/Pb) was observed by using an elec-
tronic microscope ESEM FEI QUANTA 200F coupled with an EDX
(Energy Dispersive X-ray spectroscopy) system. The gel beads were
dried at T = 80 �C and their surface was coated with gold in the
presence of argon by an Edwards Sputter Coater S150A in order
to prevent charging under electronic beam. The electron beam
was opportunely set in order to avoid the damage of the samples.
SEM micrographs were registered within the micrometer range.
2.3. Experimental procedures for kinetic and equilibrium
investigations
Lead nitrate aqueous solutions have been prepared by weighing
the salt and dissolving it with the aqueous HCl or HNO3 solution at
the required pH and ionic strength. The kinetic investigations have
been performed under constant stirred conditions at room temper-
78 S. Cataldo et al. / Journal of Colloid and Interface Science 434 (2014) 77–88
ature at pH = 5.0 and keeping constant the heavy metal amount
(50 mg L�1):
1. In the absence of added electrolytes by keeping constant NB
at 25.
2. By varying the NB in the range 25–100 on varying the ionic
strength values (0.1 6 I/mol L�16 0.8) with NaCl.
3. In the presence of NaNO3 medium at I = 0.1 and 0.8 mol L�1
by keeping constant NB at 25.
The kinetic experiments have also been carried out in the pres-
ence of 25 beads
1. At pH = 3.0 in the presence of NaCl medium at I = 0.1 and
0.8 mol L�1.
2. At pH 5.0 on varying the ionic strength in the range 0.1 6
I/mol L�16 0.8 in a mixed ionic media, consisting of
mixture of NaCl + CaCl2 at different Na+/Ca2+ ratios.
The kinetic investigations were performed by monitoring the
time evolution of the Pb2+ concentration (C0 = 50 mg L�1) in the
solution during the adsorption onto the calcium alginate gel beads
by using a polarographic system (Metrohm 663 VA STAND) con-
trolled by the Autolab potentiostat in conjunction with the
IME663 interface. The whole apparatus was controlled by NOVA
1.6 software (by Metrohm Autolab). The VA STAND is fitted with
a three electrode system which consists of Multi-Mode Electrode
Pro (Metrohm, code 6.1246.120) working in the Static Mercury
Drop Electrode (SMDE) mode, a glassy carbon auxiliary electrode
(code 6.1247.000) and a double junction Ag/AgCl/KCl (3 mol L�1)
reference electrode (code 6.0728.030).
The DP-ASV measurements were performed after bubbling
purified N2 gas into the solutions for 150 s and the peak current
of lead was recorded every 3.5–7 min. The experimental electro-
chemical conditions were chosen in order to optimize the quality
parameters, as signal/noise ratio, repeatability, accuracy, etc., and
to avoid interferences; so a deposition potential of �0.55 V was
applied under stirring for 1 s; after an equilibration time of 10 s
the voltammogram was registered from �0.55 V to �0.20 V with
a scan rate of 0.01 V s�1 and a step potential of 5 mV. The modula-
tion amplitude was 50 mV with a modulation time of 0.01 s and a
time interval of 0.5 s. The hydrogen ion concentration of the solu-
tions was checked by a potentiometer (Metrohm, Model 654)
equipped with a combined ISE-H+ glass electrode (Ross type
8102). No significant pH changes have been detected during the
lead(II) sorption processes. Moreover, the applied pH values are
well below the pHpzc 6.5 ± 0.1 [48].
The equilibrium studies were carried out by means of batch
experiments, putting a variable number of beads (from NB = 5 up
to 100) in 25 mL of solution containing Pb(NO3)2 at the concentra-
tion of 200 mg L�1; experiments have been made in the single
(NaCl) and mixed (NaCl + CaCl2) ionic media at a selected ionic
strength values, i.e., 0.1 mol L�1. The solutions have been shaken
for 24 h by using an orbital rotator apparatus and then filtered.
The residual concentration of Pb2+ ion in the filtrates has been
determined by using Differential Pulse Anodic Stripping Voltam-
metry (DP-ASV) at the same experimental conditions as for kinetic
experiments.
2.4. Robust data analysis
In this investigation both the kinetic and the equilibrium mod-
els have been fitted by means of Ordinary Least-Squares regres-
sions (OLS) and (robust) Iteratively Reweighted Least-Squares
regressions (IRLS). In order to check the reliability of the regression
results and to detect the possible dangerous outliers in the data set,
subsequent residuals analysis has been performed for each regres-
sion run, using some robust regression diagnostics techniques.
Moreover, the choice of the best model has been carried out by
means of advanced decision tools belonging to the family of the
Bayesian Information Criteria, as well as the classical ANOVA com-
parison tests. We used the Excel utility SOLVERSTAT [49], in which
all of these advanced statistical procedures are implemented. In
Table. 1 the kinetics and equilibrium models used in this work
are listed.
The IRLS procedures have been performed adopting as M-esti-
mator function the Huber’s one [50] with a tuning constant
c = 1.345 (i.e., the constant which sets the Gaussian efficiency of
the M-estimator to the 95%), and using the Standardized Residuals
as a scale measure of the M-estimator, while the robust r-estima-
tor MAD (namely the Median Absolute Deviation of the residuals)
has been adopted. Note that as concerns the kinetic model regres-
sions, in this work we present just the results obtained by IRLS for
the sake of brevity. The (robust) regression diagnostics procedure
to detect the outliers in the data set have been performed by
means of statistical descriptors such as Studentized Residuals,
Cook’s distances and COVRATIO values, following the protocol
elaborated in Merli et al. [51].
2.4.1. Monitoring the regression quality
Following Neter et al. [52], the conditions that we tried to sat-
isfy (in addition to the ‘‘classical’’ observation of R, R2 and SSR
and of the standard uncertainty associated to each variable
together with the related t-test) to get the best least-squares
regression were:
(1) Absence of multi-collinearity (the presence of high or near-
perfect inter-correlations between or among the indepen-
dent variables) in the case of multiple regression analysis.
(2) Residuals normality.
(3) Residuals homoscedasticity.
(4) Independency of the residuals from each other.
Condition (1) has been verified by means of the Variance Infla-
ction Factor (VIF), i.e., a test based on the R2 obtained when each
variable is regressed on the remaining independent variables. As
well known, collinearity arises from sampling methodology,
over-defined model, model choice and presence of outliers. Multi-
collinearity, in turn, determines inaccurate estimates of the regres-
sion parameters, alters the result of various tests and degrades the
predictability of the model. A suitable upper bound for each VIF
values is 10.
The normality of the residuals (2) has been checked by mean of
the so-called Jarque–Bera Statistic (JB) based on the coefficient of
skewness and kurtosis. JB is distributed as a v2 distribution with
Table 1
Kinetic and equilibrium models tested in this work.
Kinetic model Equation
First order model (FOM) qt ¼ qeð1� e�ktÞSecond order model (SOM) qt ¼ q2e kt
ð1þqektÞDouble exponential model (DEM) qt ¼ A1ð1� e�k1tÞ � A2ð1� e�k2 tÞHiguchi model (HM) qt ¼ kH
ffiffi
tp
þ c
Weibull model (WM) qt ¼ ð1þ eð�tb=kÞ þ cÞqeEquilibrium model Equation
Langmuir Cs ¼ qm �KL �Ce
1þKL �Ce
Freundlich Cs ¼ KF � Cne
Sips qe ¼ qm �KS �CmS
1þKS �CmS
Double Langmuir Cs ¼ qm1 �K1 �Ce
1þK1 �Ceþ qm2 �K2 �Ce
1þK2 �Ce
S. Cataldo et al. / Journal of Colloid and Interface Science 434 (2014) 77–88 79
two degrees of freedom. The homoschedasticity, i.e., the constant
variance of the residuals, has been tested by means of a Modified
Levene Test (MLT. See Levene [53] for further details). The MLT
value should be less than a critical value to ensure that the resid-
uals are homoschedastic.
The independence of the residuals (4) has been tested with the
Durbin–Watson statistics (DW. See Durbin and Watson [54] and
Howard and Cassidy [55], for a practical application).
2.4.2. Choice of the model
As above mentioned, the model tested in this work were FOM,
SOM. DEM and WM among the usual kinetic models commonly
adopted in kinetics studies, while HM has been tested in order to
check if multilinear pathways occur.
The choice of the model among different tests has been per-
formed by means of a number of statistical decisions tools. The
classical, not fully satisfactory, approach is based on the evaluation
of the residuals and the error values associated to the minimized
parameters. Referring to the SOLVERSTAT manual [49], other use-
ful information can be obtained by some advanced statistics such:
(1) R2adj [52] (in addition to the usual R and R2). While the clas-
sical coefficients of determination R and R2 indicate the frac-
tion of total variability in the data shown by the regression
model, R2adj is particularly useful when comparing models
with different number of degrees of freedom, since it is sen-
sitive to addition of irrelevant variables.
(2) Predicted Residual Sums of Squares (PRESS) statistic, which
is a measure of the predictive power of the model. This sta-
tistic tell us how well the model would predict each of the
points in the data set if they were not included in the regres-
sion. In comparison with other experiments, the PRESS value
should be the smallest one.
(3) Cross-Validation Mean Error of Prediction (MEP), which
gives a rough description of the performance of the model,
and it depends on the unknown response function and the
unknown variance of the observations. Best models have
lower MEP [56].
(4) R2 prediction, which reflects the prediction ability of the
model. It is possible to have a high R2 and a low R2 predic-
tion. In a similar way as R2adj this statistic shows a maximum
for the best predictive model. The difference is that R2 pre-
diction is based on the predictive power of the model.
Higher R2 prediction values are preferred.
(5) The family of the Bayesian Information Criterion (BIC), such
as Schwartz criterion (SBC), Akaike Information Criterion
(AIC), Hannan-Quinn Information Criterion (HQC) as well
as their ‘‘corrected’’ version (AICc, HQCc, SBCc), i.e., corrected
for the number of degrees of freedom [57]. They are gener-
ally based on measuring the fit given by SSE and correcting
it for the number of regressors. In this sense, these statistics
give an idea of the relative quality of a statistical model, pro-
viding a means for model selection. These model selection
strategies deal with the trade-off between the goodness of
fit of the model and the complexity of the model itself, and
hinge on information entropy. Note that these criteria do
not provide a test of a model in the sense of testing a null
hypothesis; thus, they cannot give information about the
quality of the model in an absolute sense. The best model
will show low values of these statistical tools. For the sake
of brevity, in this work we present the results related to
AIC and AICc tests.
(6) A ‘‘classical’’ (but actually effective) F-test as commonly
adopted in econometrics. When the regression has a low
explanatory power (i.e., low R2) the F-test values will be
close to zero, while if R2 is close to 1, then the F-test value
tends to be very large. The larger is its value, the best is
the model.
An example of application of the procedure to choose the
kinetic model performed in one selected case among those investi-
gated is reported in the Supplementary Material section.
2.4.3. The outliers recognition
As mentioned above, in order to check for the presence of dan-
gerous outliers in the data set, a robust analysis of the residuals has
been performed.
In general, if one or more figures of merit among those listed
above show unacceptable values, particular care must be paid to
the residuals behavior, or, most of all, to some scaled measure of
them. In the outlier recognition particularly useful are some
(robust) regression diagnostics such as leverage, Cook’s distance,
COVRATIO and – for instance – Studentized Residuals. Combining
the analysis of these estimators one is allowed to detect in a rigor-
ous way the presence of an actually influent outlier, whose the fit-
ting would turn into an inevitable worsening of the results (overall
of the parameter estimates). A suitable regression diagnostic could
be the so-called William graph, which plots Cook’s distance and
COVRATIO vs. leverage. An outlier is present if simultaneously (i)
leverage is > 3p/n, Cook’s distance > F(0.5,n�p,n) and (iii) COVRA-
TIO < 1–3p/n. Values of COVRATIO > 1 + 3p/n (i.e., its upper bound)
are not necessarily related to outliers, but just to strongly influenc-
ing data points. Once the presence of outliers is detected, if the IRLS
procedure is not able to underweight each aberrant observation,
one is suggested to remove them from the data set.
A complete procedure to identify and remove outliers in one
selected case among those investigated is reported in Supplemen-
tary Material section.
3. Results and discussions
3.1. Swelling behavior of Ca-A gel beads
The swelling behavior of 2% calcium alginate gel beads were
investigated in NaCl aqueous solutions over a wide range of ionic
strength (0.1 6 I/mol L�16 0.8), at pH = 5.0 and room temperature.
Results obtained, expressed as swelling ratio SR according to Eq.
(1), are collected in Table 2.
The obtained results suggested that the ionic strength plays a
relevant role in influencing the swelling behavior to an extent that
depends on the number of beads. In particular, for the different
systems investigated the increase in ionic strength hampers the
swelling process by a factor of about 20% for the system containing
25 gel beads, 36% and 40% for those containing 50 and 100, respec-
tively. The effect of the ionic strength on the swelling behavior can
be reasonably explained by considering that the shrinking process
occurs due to a decrease in the repulsive interactions of negatively
charged carboxyl groups on the alginate caused by the interaction
with the positively charged Na+ from the ionic medium. Further-
Table 2
Swelling ratio for the calcium alginate gel beads in NaCl medium, at different ionic
strength values and at room temperature.
I/mol L�1 SR
NB
25 50 100
0.1 33.2 28.6 19.4
0.5 27.0 20.5 13.5
0.8 26.4 17.2 11.8
80 S. Cataldo et al. / Journal of Colloid and Interface Science 434 (2014) 77–88
more the swelling is always greater for a number of Ca-A equal to
25, probably because of the higher surface area in contact with the
aqueous solution.
3.2. Scanning Electron Microscopy (SEM)
Micrographs from Scanning Electron Microscopy (SEM) analysis
show wrinkled surfaces of gel beads with increases of pores in the
presence of Pb2+ ions with respect to the blank Ca-A gel beads. This
morphology may be attributed to the shrinkage of the beads at
higher metal concentrations [58]. As an example SEM surface
micrographs of Ca-A biopolymers after contacting with 200 mg L�1
lead(II) solutions for 24 h are depicted in Fig. 1.
SEM analysis was coupled with EDX measurements which
allow to obtain semi-quantitative results on the metal content in
the different dried gel beads. EDX results for the system investi-
gated were reported in Fig. 1, in comparison with analogous
obtained for Ca-A system, recently reported [47]. They clearly show
that calcium percentage in gel beads lowers when the calcium–
biopolymer system is in contact with the solutions containing lea-
d(II) ions. These results prove that the heavy metal species displace
to a certain extent the Ca2+ ions from the alginate gel beads.
3.3. Sorption kinetic data
In Fig. 2 is reported, as an example, the time evolution of the
current intensity (I) of the Pb2+ aqueous solution containing 25 cal-
cium alginate gel beads in NaCl 0.1 mol L�1.
Since the removal of the heavy metal species by the calcium
alginate gel beads takes place, as can be expected, the current
intensity of Pb2+ decreases as function of time till it reaches a con-
stant value. The obtained trends clearly suggest that the uptake
process is complete and no desorption occurs in the timeframe of
observation. The current intensities data have been converted into
quantity of lead ion removed qt for all systems examined.
The effects of both the nature and composition of ionic medium
on the total adsorption of Pb2+ have been evaluated by following
the sorption kinetic profiles for the aqueous system containing
25 Ca-A gel beads at pH = 5.0 both in the absence of fixing ionic
medium and in the presence of NaCl over a wide range of ionic
strength, i.e., 0.1 6 I/mol L�16 0.8 (Fig. 3a); moreover, in the same
interval of ionic strength the influence of the different number of
beads on the lead(II) biosorption at has been investigated. The
obtained results are depicted in Fig. 3b. Perusal of figures clearly
demonstrate that presence of NaCl in the reacting medium plays
a relevant role, particularly the progressively increase of the elec-
trolyte concentration in solution leads to a drastic reduction of
the Pb2+ uptake by alginate gel beads. This effect can be explained
according to literature [59–61] and our swelling results by
Inte
nsi
ty(C
ou
nts
s-1
1000
2000
3000
4000
5000
6000
7000
8000
Inte
nsi
ty(C
ou
nts
s)
0,00
0
C
O
1,00 2,00
Energy (KeV) Energy (KeV)
u
3,00
Ca
4,00
Au
(a)
5,00
It
itC
t-1
1000
1500
2000
2500
3000
Inte
ns i
ty (
Cou
nt
s)
0,000
500
C
1,00 2,00
A
Pb
3,00
Ca
4,00
Au
(b)
5,00
u
A
O
(c) (d)
Fig. 1. EDX spectra of Ca-A (a) and Ca-A/Pb (b); SEM images at 10,000� of magnification (c) of Ca-A gel bead surface and (d) after sorption of Pb2+ ions.
0 50 100 150 200 250 300
1
2
3
4
5
6
7
8
9
10
I (µ
A)
t (min)
Fig. 2. Adsorption kinetics of Pb2+ 50 mg L�1 onto 25 Ca-A beads in NaCl
0.1 mol L�1.
S. Cataldo et al. / Journal of Colloid and Interface Science 434 (2014) 77–88 81
considering that the presence of both additional cations and anions
induces double consequences. In fact, the interaction between the
negative charge present in the alginate chain back-bone and the
anions in the nearness of the polymer chains will keep the anions
concentration in the near-neighbor of the surface lower than that
of the bulk solution. Nevertheless, this difference vanishes on
increasing the ionic strength of the medium. Moreover, the Na+
ions of the salt used to adjust the ionic strength will compete with
the heavy metal for the sorption sites. Indeed, it cannot leaved out
of consideration that the increase in the concentration of the
applied salt induces a shrink of the beads, as a consequence, the
adsorption sites become less accessible for the lead ion uptake.
Thus, the complete balance of these effects reflects into a signifi-
cant decrease of the Pb(II) loaded into the gel beads.
To further establish the influence of the ionic strength on the
lead biosorption rate by Ca-A gel beads a mixed ionic media, i.e.,
NaCl + CaCl2, has been applied. The kinetic profiles have been gath-
ered for the system containing 25 beads in a wide range of ionic
strength (0.1 6 I/mol L�16 0.8) at pH = 5.0, and for I = 0.5 mol L�1
for two different Na+/Ca2+ ratios. Fig. 3b clearly shows that, analo-
gously to the ionic strength effect discussed above, the contempo-
rary presence of both Na+ and Ca2+ ions in solution brings about the
same decreasing uptake effect on the biosorption process whose
extent is significantly enhanced by an increase in the Ca2+/Na+
ratios in the ionic medium.
Once established the role played by the ionic strength on the
Pb(II) loading gel beads process, the kinetic investigation have
been carried out by keeping constant the ionic strength, ionic med-
ium nature and Pb2+ concentration, on varying the number of gel
beads from 25 up to 100. This way, a complete picture of the mech-
anism pathways trough which the beads-metal ion uptake occurs
could be drawn. For all cases examined the time necessary to the
biomaterial for achieving the complete removal of the undesired
metal was determined. The trends shown in Fig. 3b emphasize that
for the same initial concentration of metal ions, for the same con-
ditions of ionic strength, by considering the same range of time, the
removing heavy metal adsorption process is strongly dependent on
the number of applied gel beads, i.e., the higher the number of
beads the greater the amount of Pb(II) species removed.
The analysis of kinetics based on advanced statistical diagnos-
tics and robust fitting techniques suggested that experimental data
are better reproduced by the DEM which implies two reaction
pathways. One possibility to justify the DEM is that two different
species, most likely diverse type of lead(II) complexes, which
would be absorbed by the alginate gel beads with different rate.
However, this pathways have been ruled out on the base of the
kinetic data obtained in the presence of different ionic medium
(see discussion below). This way, we have reasonably proposed
that the sorption mechanism occurs via two parallel reactions
involving the lead(II) species and two non energetically equivalent
binding sites, namely the specific and non specific binding sites.
A schematic representation of the reaction pathways to which
different rate constant values are associated is depicted in Fig. 4.
It is reasonable to assume that the complex formation between
the heavy metal and the surface functional groups present in the
polymer backbone is faster with respect to the cation exchange
process, i.e., the Ca2+ displacement by the Pb2+ species. These
hypothesis take into consideration that the functional groups are
prone to form the complex with the metal ion due to their avail-
able position on the beads surface, while the calcium cation linking
–COO� groups belonging to different chains exchanges with the
Pb(II) with low rate.
The above depicted reaction pathways have been proposed on
the base of preliminary kinetic investigation on the Ca2+ ion release
by the Ca-A gel beads. The measurements have been performed
both in absence (blank) and in the presence of Pb2+ ions under
some representative experimental conditions of ionic strength,
ionic medium and pH used in the full work, e.g., NaNO3 and NaCl
0.1 mol L�1. Since the Ca2+ ion concentration cannot be determined
by the voltammetric technique the ICP-OES method was applied.
Quantitative analysis of the kinetic data unveiled that, even
though, a relationship between moles of Pb2+ adsorbed and Ca2+
released so far exists it does not respect a 1:1 ratio, i.e., the extent
of lead(II) ions uptake is higher than that of the calcium ions
release. These obtained trends suggested us that lead(II) adsorp-
tion must occur not only via the cationic-exchange mechanism
but an additional pathway could justify the extra uptake. By con-
sidering the –COO� available groups in the alginate chains we have
reasonably ascribed the additional process to the complex forma-
tion between these groups and the undesired metal ions. The attri-
bution of k1 and k2 to the fast complex formation and to the slightly
cation exchange has been intuitively proposed because the Ca2+
displacement from its binding site need more energy and time
with respect to that necessary for the complex formation.
The fast and slow kinetic constant values obtained for the
uptake of heavy metal by 25 Ca-A beads in the absence of added
electrolyte are shown in Table 3. It can be noticed that the rate con-
stant for the complex formation is roughly 6 times that of the cat-
ion exchange.
As concerns the rate of the processes, comparison of the data of
Table 4 reveals that the kinetic constant values strongly depend on
the different experimental conditions applied, namely, NB and
amount of added electrolyte. In particular the presence of NaCl in
the reaction medium leads to higher removal rates. These results
0 50 100 150 200 250
0,0
0,2
0,4
0,6
0,8
1,0
qt
t (min)
(a)
0 50 100 150 200 250
0,0
0,2
0,4
0,6
0,8
1,0
qt
t (min)
(b)
Fig. 3. (a) 25 Beads of Ca-A in 25 mL for different value of ionic strength (NaCl): 0.1 (�); 0.5 (N); 0.8 (.) mol L�1 and no salt added (j); (b) different number of beads (25 (j),
50 (d) and 100 (N)) in 25 mL of Pb 50 ppm and 0.1 mol L�1 NaCl. At room temperature and pH = 5.
82 S. Cataldo et al. / Journal of Colloid and Interface Science 434 (2014) 77–88
seem apparently in contradiction with the expected decrease of the
rate constant on augmentation of the ionic strength, however
might be justified by considering that this dependence is usually
observed for homogeneous medium, while in the present work,
the aqueous gel beads solution constitute an heterogeneous react-
ing aqueous medium. This way, the observed behavior can be
ascribed to the interplay between the diverse swell to which
undergoes the beads in the medium with different salinity and
the electrostatic effects brought about the monovalent anions.
The ‘‘sum’’ of all effects superimposes that in the presence of NaCl
the alginate chain of the gel beads offers to the Pb(II) species more
available binding sites, thus, making easy and fast the lead(II) load-
ing processes.
It has to be noticed that, at each NB and ionic strength values,
the k1 is always lower than k2, indicating that the complex forma-
tion mechanism is favorite with respect to the ion exchange.
Moreover, the obtained k1 values follows the order
k1(NB = 25) > k1(NB = 100) > k1(NB = 50) while the complex forma-
tion rate constants increases pursuing the order k2(NB = 25) < k2(NB = 50) > k2(NB = 100). The latter trends completely match the
swelling gel beads previously observed. This way, it might be rea-
sonably concluded that the peculiar rearrangements of both shape
and size of alginate gel beads induced by the presence of interact-
ing cations and anions may either favor or hamper a given process.
In order to test how the removal process is influenced by pH
changes we studied the lead(II) adsorption process in the presence
of NB = 25 at I (NaCl) = 0.1 mol L�1 and pH = 3.0. Under this exper-
imental condition, functional groups in the backbones of gel beads
are present mainly in the protonated form, i.e., –COOH [62]. Com-
parison of rate constant values determined for NB = 25, at NaCl
0.1 mol L�1 at different pH values shows that k1 at pH = 3 (see
Tables 4 and 5) decreases respect to the value reported at pH = 5,
but k2 associated to the complex formation mechanism is constant
as the pH decreases. The behavior of k2 value can be reasonably
explained by considering that the competition between the H+
and Pb2+ ions for the carboxylic groups favors at low pH the forma-
tion of Pb2+-AA complex species, as the stability of the complex is
greater than the stability of the protonated species.
These results clearly suggest that the lead(II) removal process is
not only governed by the size of the gel beads (either like they are
prepared or modified by the added electrolyte), but also the nature
of additional cations and anions have to play a relevant role.
With this aim the adsorption process has been followed on
varying either the nature of the anions, i.e., the chloride has been
replaced by the nitrate; or by differently dosing the amount of
Ca2+ ions. The obtained results are collected in Tables 5–7. Interest-
ing and clarifying results have been provided by these studies. In
particular, by comparing the effect of the nitrate with that of the
Fig. 4. Reaction scheme.
Table 3
IRLS regression figures of merit and parameters values
for the case with no salt addition, at pH = 5.
I 0
R2 0.9996
R2pred 0.9995
JB test (<9.2) 0.35
Parameters a
k1 0.0133 ± 0.0043
k2 0.0789 ± 0.0039
a min�1.
Table 4
Selected IRLS regression figures of merit and parameters values for the case NaCl at
different ionic strength values and at room temperature, at pH = 5.
Medium
NaCl 0.1 mol L�1 0.5 mol L�1 0.8 mol L�1
NB = 25
R2 0.9985 0.9966 0.9801
R2pred 0.9982 0.9902 0.9739
JB test (<9.2) 2.6 0.4 0.9
Parametersa
k1 0.01580 ± 0.00090 0.01790 ± 0.00070 0.0255 ± 0.0024
k2 0.1217 ± 0.0046 0.367 ± 0.028 0.464 ± 0.088
NB = 50
R2 0.9996 0.9979 0.9947
R2pred 0.9995 0.9975 0.9930
JB test (<9.2) 4.7 0.6 3.7
Parametersa
k1 0.0352 ± 0.0011 0.0190 ± 0.0010 0.0158 ± 0.0010
k2 0.2420 ± 0.0057 0.1537 ± 0.0074 0.270 ± 0.018
NB = 100
R2 0.9996 0.9978 0.9989
R2pred 0.9977 0.9929 0.9971
JB test (<9.2) 1.4 0.96 1.33
Parametersa
k1 0.0726 ± 0.0030 0.0291 ± 0.0241 0.03220 ± 0.00090
k2 0.528 ± 0.016 0.380 ± 0.031 0.441 ± 0.015
a min�1.
S. Cataldo et al. / Journal of Colloid and Interface Science 434 (2014) 77–88 83
chloride anions it can be evidenced that the presence of NaNO3
favors the complex formation between the heavy metal and the
anionic group of the gel beads with respect to the ionic-exchange.
These trends are in line with the different affinity of the two kind of
anions towards the Pb2+ ions. In particular, the Cl� anions exhibits
the ability to form weak complexes with lead ions, thus, its reac-
tions become competitive with those of carboxylic groups present
in alginate beads. This way, the rate of the complex formation is
enhanced by the presence of non-complexing nitrate anions while
that of the ion-exchange with calcium(II) cation is slowed down.
Moreover, both process are inhibited by the increase in the ionic
strength concentration.
Finally, to further corroborate the proposed lead(II) loading
mechanism the kinetic of the heavy metal uptake has been fol-
lowed by applying an ionic medium having different Na+/Ca2+
ratio. Indeed, all the investigated conditions resemble the real
wastewater sample composition.
As concern the alginate gel beads lead(II) removal rate on vary-
ing the composition of a mixture of electrolytes, namely NaCl and
CaCl2, by comparing these data with those above discussed in the
absence of CaCl2 it is worth noting that the simultaneous presence
of both cations affects to a different extent the kinetics of the two
processes. In particular, both uptake processes are faster than
those observed in the absence of the calcium cations, however,
while the kinetic constant associate to the complex formation
increases on increasing the ratio Na+/Ca2+ (by keeping constant
the Ca2+ concentration) the rate of the ionic exchange remains
almost the same. Moreover, an augmentation of the ratio
Na+/Ca2+ (by increasing the concentration of both cations) leads
to a significant enhancement of the removal rates.
Since it would be expected that the simultaneous presence of
the Na+ and Ca2+ cations would slow down the rate of both pro-
cesses (due to the competition with the leads(II) for the binding
sites) and, particularly, the rate of the complex formation has to
be inhibited to a larger extent while the converse has been
detected a ‘‘realistic’’ explanation is necessary.
Most likely, the negative charges of the –COO� groups in the
polymer chains are shielded by the cation species keeping the ionic
strength, even though the major role is played by the calcium(II)
ions, precisely, the higher the Ca2+ concentration the higher the
effect on the rate enhancement. These trends might be explained
by considering that a so high electrolyte content may induce con-
formational transitions in the polymer backbone and in the size of
the beads, which make the sites much more accessible for the
uptaking of the lead(II) ions by the gel beads. Although, the pres-
ence of competitive cations in the reaction medium decreases
the amount of Pb(II) uptaken by the alginate gel beads, the cations
shielding effects on the polymer backbones favor the approaching
of the lead(II) ions, thus, speeding up the bio-adsorption processes.
This way, wherever the adsorption site, namely specific and
non-specific, the heavy undesired metal is faster removed in a
medium whose electrolyte content resemble to those of the real
wastewater sample.
3.3.1. Removal efficiency
The above discussed results unveiled that the amount of loaded
lead ions into the gel beads is strongly dependent on the applied
experimental conditions. Thus, the experimental sorption kinetic
profiles have been exploited to calculate, in analogy with the drug
release profiles already reported [63,64], a parameter, the percent
removal efficiency (RE(%)) which is strictly correlated to the time
required for the removal of lead(II) from the solutions. The removal
efficiency has been calculated by means of the following equation:
REð%Þ ¼ 100SA
R
where SA corresponds to the shaded area under the adsorption
profile up to a fixed time and R is the rectangle area (y100 * t)
described by 100% adsorption in the same time.
The calculated RE% values for all the experimental condition
discussed above are shown in Table 8. It can be easily notated that
this parameter reflects the observed kinetic trends, indeed, is
strongly sensible to both the amount and the kind of electrolyte
forming the ionic reacting medium and the number of applied
gel beads, the pH also play a relevant role. The RE% dependence
from the electrolytes presence in the wastewater model samples
allow to draw the conclusion that whatever the ionic strength is
varied the lead(II) removal is hindered due to the electrostatic
interactions between the undesired metal and the alginate gel
beads. Nevertheless, it is necessary to face with the real sample
composition. However, an interesting remark regards the effect
of the number of beads on the R.E.% parameter; calculations have
given evidence that a growing number of Ca-A gel beads ensures
better performance, i.e., the higher the NB of gel beads the higher
the amount of Pb(II) removed from the sample, the lower the time
needed to reach the maximum removal.
The parameter RE% above described contributes to easily and
simple interpret the adsorption mechanism and to obtain a
detailed knowledge of the most important factors (pH, ionic
strength, composition of ionic medium, etc.) that can influence
the adsorption processes of lead onto alginate gel beads. This infor-
mation is relevant especially in the planning stage of a removal
process of lead ions from wastewaters. Moreover, comparison of
the results obtained in the present work, in the situation mimick-
ing a real wastewater sample, reveals that the uptake capacities of
our alginate gel beads result to be superior to those showed by
some other systems previously studied [65].
3.4. Sorption isotherms
The adsorption isotherms have been constructed by determin-
ing the lead(II) equilibrium concentration (Ce) in the range
3–170 mg L�1, then, the difference between the initial heavy metal
concentration and that left in the aqueous solution was used to
estimate the amount of adsorbed metal (qe) onto the gel beads.
By taking into account the results obtained from the kinetic
Table 5
Selected IRLS regression figures of merit and parameters values for
the case NaCl 0.1 mol L�1, number of beads 25 and pH = 3.0.
NaCl 0.1
R2 0.9493
R2pred 0.9438
JB test (<9.2) 8.1
Parametersa
k1 0.0096 ± 0.0028
k2 0.123 ± 0.032
a min�1.
Table 6
Selected IRLS regression figures of merit and parameters values for the case NaNO3
0.1 and 0.8 mol L�1 and at room temperature, at pH = 5.0.
NaNO3
NB = 25
I 0.1 0.8
R2 0.9949 0.9899
R2pred 0.9946 0.9819
JB test (<9.2) 0.3 1.6
Parametersa
k1 0.02890 ± 0.00080 0.0178 ± 0.0012
k2 1.05 ± 0.89 0.448 ± 0.069
a min�1.
84 S. Cataldo et al. / Journal of Colloid and Interface Science 434 (2014) 77–88
measurements, i.e., the higher the amount of gel beads the higher
the removed metal ion concentration and, indeed, the faster the
uptake velocity; we have used a significant elevated amount of
both the sorbent and sorbate to improve the loading efficiency of
the alginate gel beads, namely, the lead ions was set at 200 mg L�1
while the beads quantities was varied over the wide range
(5 6 NB 6 100). Moreover, by considering the peculiar trends
obtained for the kinetic constant as a function of the type of salt
used to fix the medium ionic strength, the isotherms have been
gathered for NaCl at 0.1 mol L�1 and for NaCl + CaCl2 at 0.1 mol L�1
([NaCl] = 0.01 mol L�1 and [CaCl2] = 0.03 mol L�1) while the pH was
fixed at 5.0.
The equilibrium voltammetric values, obtained under the dif-
ferent experimental conditions, have been used to construct the
adsorption isotherms depicted in Fig. 6, where the equilibrium
amount of heavy metal adsorbed into the gel bead (qe, mg g�1) is
plotted as a function of Ce.
Perusal of Fig. 5 clearly evidences that the amount of adsorbed
Pb(II) species increases on increasing the equilibrium metal con-
centration and depends to a significant extent on the nature of
the inorganic salt used to keep constant the ionic strength. In par-
ticular, for each Ce value the corresponding qe is higher when only
the NaCl regulates the ionic strength, while the presence of mixed
electrolytes implies a less Pb(II) uptake into the gel beads. This
result is in line with our explanation given in the previous section
dealing with the kinetic evidence of the effect of the mixed med-
ium. We briefly recall that both the Na+ and Ca2+ ions free in the
aqueous suspension compete with the heavy metal for the avail-
able active sorption sites, which in turn reflects into a noticeable
decrease of the maximum removing quantities of the heavy metal
from the aqueous solution. Moreover, by neutralizing the negative
charges present in the polymer chains they may lead to a contrac-
tion of the beads, which, in turn, reflects into a lower metal ions
uptake.
Modeling the adsorption data in terms of adsorption isotherm
equation is a cogent and crucial task in the design of application
of proper protocols for the wastewater treatment.
Several adsorption models reported in the literature [66,67] are
based either on one-binding sites or two-binding sites, in the for-
mer the sites of the adsorbent are homogeneously equivalent,
independent and characterized by the same energy while in the
latter the solid adsorbent exhibits two types of sites to which cor-
respond strong and weak binding affinities toward the adsorbate,
being consequently different energies.
The adsorption isotherm models reported in Table 1 have been
applied to analyze our data. The obtained adsorption parameters
are collected in Table 9.
Inspection of Table 9 and Fig. 5 evidences that the adsorption
isotherm model of Sips and Freundlich, for the systems NaCl and
NaCl + CaCl2, respectively, exhibit a good fit to experimental data.
These empirical equations are based on the assumption that the
adsorption process takes place on two different populations of
binding sites of the adsorbent.
The Sips equation overcame the problem of the Freundlich
equation of the continuing increase in the adsorbed amount with
an increase in concentration. In fact, the two equations are similar
Table 7
Selected IRLS regression figures of merit and parameters values for the case ‘‘Mixed NB = 25’’ for the concentrations 0.1, 0.5 (0.03 + 0.41), 0.5 (0.163 + 0.01) and 0.8 (0.03 + 0.71).
Medium
NaCl + CaCl2 0.1 mol L�1 0.5 mol L�1 (0.03 + 0.41) 0.8 mol L�1 (0.03 + 0.71)
NB = 25
R2 0.9925 0.9974 0.9913
R2pred 0.9911 0.9953 0.9818
JB test (<9.2) 4.3 8.0 1.1
Parametersa
k1 0.0221 ± 0.0012 0.02170 ± 0.00080 0.0227 ± 0.0018
k2 0.494 ± 0.070 0.597 ± 0.039 0.417 ± 0.036
NaCl + CaCl2 0.5 mol L�1 (0.163 + 0.01)
NB = 25
R2 0.9904
R2pred 0.9669
JB test (<9.2) 5.4
Parametersa
k1 0.0359 ± 0.0031
k2 0.88 ± 0.19
a min�1.
Table 8
Percent removal efficiency for the systems investigated.
Medium I/mol L�1 RE%
NB
25 50 100
No salt 0 92
NaCl 0.1 81 90 94
0.5 62 72 84
0.8 54 69 81
NaCl (pH = 3) 0.1 64
NaNO3 0.1 83
0.8 70
NaCl + CaCl2 0.1 76
0.5 (0.41 + 0.03) 58
0.5 (0.01 + 0.163) 53
0.8 (0.71 + 0.03) 44
0 40 80 120
150
300
450
600
qe (
mg
g-1
)
Ce (mg L-1)
Fig. 5. Adsorption isotherms of Pb2+ ions in NaCl (square) and NaCl + CaCl2 (circle)
0.1 mol L�1 at room temperature.
S. Cataldo et al. / Journal of Colloid and Interface Science 434 (2014) 77–88 85
but the Sips has a finite limit when the adsorbate concentration is
sufficiently high.
Comparison of the obtained binding constant values reveals
that the alginate gel beads adsorb the Pb(II) in sites with different
binding affinities, however, the estimated binding constant values
represent the average value of each binding value in each site. The
value of n lower than 1 clearly indicate that the adsorption sites in
the gel beads are heterogeneous in nature.
Thus, in the light of the kinetic results, the complementary
equilibrium data suggest that the alginate gel beads offer two types
of binding sites, heterogeneous in nature and with different affin-
ities, for the removal of lead(II) from a water waste model accord-
ingly to two pathways mechanism which occur at different rate
(see kinetic data section for the discussion).
However, to unambiguously verify these assessment we have
treated the equilibrium data through the Scatchard plot analysis,
which is also known as independent site-oriented model. The Scat-
chard equation, below reported, derives from the mathematical
transformations of the classical Langmuir equation where there
exist a relationship between amount of the adsorbed per unit con-
centration of aqueous bulk solution (qe/Ce) and qe
qe=Ce ¼ qmKL � KLqe ð2Þ
Thus, the qe/Ce values must depend linearly on qe. It is important
to evidence that the mathematical transformation of the Langmuir
equation results to be a very useful and informative process for
two main reasons. First, by normalizing the qe value the effect of
concentration on the shape of Scatchard plot is theoretically elim-
inated; second, if deviations in the qe/Ce versus qe plot from the
linearity are observed they can be attributed to different binding
sites available to the adsorbate, which, in other words, correspond
to diverse affinities phenomena.
From the Scatchard analysis of our data shown in Fig. 6(a and b)
we can highlight that the curvature of the curves is upward which
is indicative of negative cooperations. These type of interactions
implies that once the uptake of the Pb(II) species occurs, indepen-
dently of the binding sites, their presence on the alginate gel beads
hampers the further adsorption, as a consequence, the removed
heavy metal amount decreases. Moreover, the existence of two
regions of behavior can be detected. Usually, the region with high
slope is associate to specific binding site (high affinity), while that
with low slope corresponds to non-specific binding (low affinity).
It can be noticed that, at low Pb(II) concentration, the binding is
specific while at high concentration the binding becomes non-spe-
cific. These trends can be explained by considering that the Pb(II)
ions at low concentration adsorb into the gel beads in sites, being
high affinities, by forming the complex with the available –COO�
groups of the alginate chains ion while, at high heavy metal ions
concentration, the metal cations displace the Ca2+ crosslinked in
the alginate chains. As more metal ions are bound to alginate,
the alginate chains have to rearrange to coordinate them. This
way, the polymer backbones undergo to a tighter conformation
that hinders further chain rearrangement hence making some
sorption sites non-accessible.
Finally, in order to obtain information about the nature of the
heavy metal/gel beads interaction, the analysis of the equilibrium
data has been also performed by means of the Dubinin–Radushke-
vich (DR) equation:
100 200 300 400 500 600 700
0
10
20
30
40
50
60
70
80q
e/C
e (
L g
-1)
qe (mg g
-1)
(a)
100 200 300 400 500
0
5
10
15
20
qe
(mg g-1
)
qe/C
e (
L g
-1)
(b)
0,0 0,5 1,0 1,5 2,0 2,5
4,5
5,0
5,5
6,0
6,5
ln q
e
e2
(kJ2 mol
-2)
(c)
0,00 0,05 0,10 0,15 0,20 0,25
4,5
5,0
5,5
6,0
6,5
ln q
e
ε2
(kJ2 mol
-2)
(d)
Fig. 6. Scatchard plots of Pb2+ adsorption at t = 25 �C in 0.1 mol L�1 of NaCl (a) and NaCl + CaCl2 (b); Dubinin–Radushkevich adsorption isotherms of Pb2+ at t = 25 �C in
0.1 mol L�1 of NaCl (c) and NaCl + CaCl2 (d).
86 S. Cataldo et al. / Journal of Colloid and Interface Science 434 (2014) 77–88
ln qe ¼ ln qm � ke2 ð3Þwhere k is a constant related to adsorption energy and e is RT ln
(1 + 1/Ce).
The DR approach gave rise to curved plots which means that
free energy of adsorption changes on changing the amount of sor-
bate already adsorbed (Fig. 6(c) and (d)). Thus, we can say that
each equilibrium data treatment drives to the conclusion that the
lead(II) adsorption into alginate gel beads takes place trough two
different pathways at two independent binding sites. At this stage,
we can conclude that the affinity analysis represents an efficient
tool for setting up the proper experimental conditions to be
applied for achieving a quantitative removing of the heavy metal
system. Moreover, the gathered information could be fruitful
exploited to significantly diminish both the cost and the time of
the whole process.
4. Conclusion
In the present work we have realized a very efficient, perfor-
mant and low cost system for removing undesired heavy metal
ions, namely Pb(II) species, from a model wastewater sample.
The information provided by kinetic and equilibrium comple-
mentary studied have been exploited to elucidate the adsorption
mechanism of the lead(II) species into alginate gel beads.
The proposed mechanism pathways have been corroborated
through the SEM characterization of the blank and Pb(II)-loaded
gel beads. Swelling behavior of the alginate gel beads has com-
pleted the picture.
DP-ASV can be considered an appropriate experimental tool to
perform kinetic and equilibrium investigations in terms of accu-
racy, high sensitivity and selectivity.
The obtained removal efficiency of the calcium-alginate gel
beads has been found to strongly depend on the internal parame-
ters, i.e., number of gel beads, nature and concentration of the
added electrolyte and pH, of the applied system. All parameters
play a role in influencing the wastewater treatment process, how-
ever, the number of gel beads take part as key crucial components,
i.e., the higher the number of beads the greater the amount of
Pb(II) species removed from the sample, the lower the time needed
to reach the maximum removal.
The effectiveness of the robust procedures involved in this work
suggests to systematically adopt robust data treatment in regres-
sion and related diagnostics in a research, in order to draw a com-
plete picture for real processes.
Acknowledgments
The authors carried out this work thanks to a grant from the
project ‘‘Development of innovative technologies for the treatment
of fluid wastes from shipping activities and for marine environ-
ment protection’’ (PON02_00153_2849085 ‘‘Ricerca e competiti-
vità 2007–2013, asse 1’’).
Appendix A
n number of observations
p number of refined parameters
SSM Model Sum of Squares
SSR Regressional Sum of Squares
SST Total Sum of squares
R correlation coefficient
R2 coefficient of determination
R2adj adjusted R-squared
p1, p2, p3,
p4
parameter 1–4 refined. Referring to Table 2., for
FOM p1 = Qe, p2 = k1; for SOM p1 = Qe, p2 = k; for
DEM p1 = A1, p2 = k1, p3 = A2, p4 = k2; for HM
p1 = kH, p2 = C; for WM p1 = k, p2 = b, p3 = C
s.u. (pn) standard uncertainty for the nth parameter
t (pn) t-statistic value for the nth parameter
VIF variance inflation factors for each parameter,
defined as in this work the threshold of 5 has been
adopted (VIF < 5)
ME mean error
r standard deviation of the residuals
DW Durbin–Watson test for the autocollinearity of the
residuals
MLT Modified Levene Test for constant variance [53]
(continued on next page)
Table 9
Sorption isotherm parameters of Pb2+ by alginate gel beads at room temperaturea. Symbols as in Appendix A.
Langmuir Freundlich
NaCl NaCl + CaCl2 NaCl NaCl + CaCl2
qm (mg g�1) 673 ± 20 540 ± 43 KF (mg(1�n) g�1 Ln) 139 ± 24 47.0 ± 2.0
KL (L g�1) 0.117 ± 0.011 0.030 ± 0.010 n 0.331 ± 0.040 0.463 ± 0.010
R2 0.99399 0.97688 R2 0.93456 0.99911
AICc 45.16 44.49 AICc 61.88 24.90
Sips
NaCl NaCl + CaCl2
qm (mg g�1) 728 ± 24 2300 ± 1000
KS (L mg�1) 0.090 ± 0.010 0.00050 ± 0.00070
n 0.840 ± 0.044 0.526 ± 0.036
R2 0.99848 0.99956
AICc 42.54 30.64
Double Langmuir
NaCl NaCl + CaCl2
qm1 (mg g�1) 580 ± 150 790 ± 100
K1 (L g�1) 0.069 ± 0.028 0.0050 ± 0.0010
qm2 (mg g�1) 130 ± 160 158 ± 23
K2 (L g�1) 0.61 ± 0.87 0.192 ± 0.047
R2 0.998857 0.99982
AICc 54.51 55.36
a qm, qm1 and qm2 are maximum adsorption capacities while K (with the proper subscript) state for equilibrium constant.
S. Cataldo et al. / Journal of Colloid and Interface Science 434 (2014) 77–88 87
Res+,Res- number of the residuals greater than ME and less
than ME respectively for the Residuals Run Test
(Wald Wolfowitz One Tail Distributions Runs
Test)
P. prob. Passed probability for the sign Residuals Run
Tests
PRESS Predicted Residual Sums of Squares
RMSE Root Mean Square Error, defined as (SSR/n)1/2
RMSP Root Mean Square Press error, defined as (PRESS/
n)1/2
R2 pred R2 prediction, defined as [1 � (PRESS/SST)] * 100
AIC Akaike Information Criterion, defined as
n�(log(SSR/n) + 2(p/n))
AICc corrected Akaike Information Criterion defined as
n�(log(SSR/n) + 2(p/n)�log(log(n)))F-test F statistics defined as R2/p/(1�R2)/(n�p�1)
Appendix B. Supplementary material
Supplementary data associated with this article can be found, in
the online version, at http://dx.doi.org/10.1016/j.jcis.2014.07.042.
References
[1] B. Amarasinghe, R.A. Williams, Chem. Eng. J. 132 (2007) 299–309.[2] J. Lenihan, W.W. Fletcher, The Chemical Environment, first ed., Academic Press,
New York & San Francisco, 1977.[3] V.M. Nurchi, I. Villaescusa, Coord. Chem. Rev. 252 (2008) 1178–1188.[4] N.A. Badawy, A.A. El-Bayaa, A.Y. Abdel-Aal, S.E. Garamon, J. Hazard. Mater. 166
(2009) 1266–1271.[5] M.V. Dinu, E.S. Dragan, A.W. Trochimczuk, Desalination 249 (2009) 374–379.[6] K. Fujinaga, H. Nagura, R. Yamasaki, H. Kokusen, Y. Komatsu, Y. Seike, M.
Okumura, Solvent. Extr. Res. Dev. 13 (2006) 175–184.[7] O. Kebiche-Senhadji, L. Mansouri, S. Tingry, P. Seta, M. Benamor, J. Membr. Sci.
310 (2008) 438–445.[8] I. Komjarova, R. Blust, Anal. Chim. Acta 576 (2006) 221–228.[9] S.S. Ahluwalia, D. Goyal, Bioresour. Technol. 98 (2007) 2243–2257.[10] M.A. Dubois, J.F. Dozol, C. Nicotra, J. Serose, C. Massiani, J. Anal. Appl. Pyrolysis
31 (1995) 129–140.[11] T.A. Kurniawan, G.Y.S. Chan, W.-H. Lo, S. Babel, Chem. Eng. J. 118 (2006) 83–98.[12] V.K. Gupta, S. Agarwal, T.A. Saleh, J. Hazard. Mater. 185 (2011) 17–23.[13] V.K. Gupta, S.K. Srivastava, D. Mohan, S. Sharma, Waste Manage. 17 (1997)
517–522.[14] T.A. Saleh, V.K. Gupta, Environ. Sci. Pollut. Res. 19 (2012) 1224–1228.[15] V.K. Gupta, R. Jain, A. Mittal, M. Mathur, S. Sikarwar, J. Colloid Interface Sci. 309
(2007) 464–469.[16] V.K. Gupta, R. Jain, S. Varshney, J. Colloid Interface Sci. 312 (2007) 292–296.[17] V.K. Gupta, A. Mittal, L. Krishnan, J. Mittal, J. Colloid Interface Sci. 293 (2006)
16–26.[18] V.K. Gupta, A. Mittal, L. Kurup, J. Mittal, J. Colloid Interface Sci. 304 (2006) 52–
57.[19] V.K. Gupta, A. Mittal, A. Malviya, J. Mittal, J. Colloid Interface Sci. 335 (2009)
24–33.[20] A. Mittal, D. Kaur, A. Malviya, J. Mittal, V.K. Gupta, J. Colloid Interface Sci. 337
(2009) 345–354.[21] A. Mittal, J. Mittal, A. Malviya, V.K. Gupta, J. Colloid Interface Sci. 344 (2010)
497–507.[22] A. Mittal, J. Mittal, A. Malviya, D. Kaur, V.K. Gupta, J. Colloid Interface Sci. 343
(2010) 463–473.[23] A. Mittal, J. Mittal, A. Malviya, D. Kaur, V.K. Gupta, J. Colloid Interface Sci. 342
(2010) 518–527.[24] V.K. Gupta, I. Ali, T.A. Saleh, A. Nayak, S. Agarwal, RSC AdV. 2 (2012) 6380–
6388.
[25] A. Mittal, J. Mittal, A. Malviya, V.K. Gupta, J. Colloid Interface Sci. 340 (2009)16–26.
[26] K. Vijayaraghavan, Y.-S. Yun, Biotechnol. Adv. 26 (2008) 266–291.[27] Y.N. Mata, M.L. Blazquez, A. Ballester, F. Gonzalez, J.A. Munoz, J. Hazard. Mater.
158 (2008) 316–323.[28] S. Tunali, T. Akar, A.S. Ozcan, I. Kiran, A. Ozcan, Sep. Purif. Technol. 47 (2006)
105–112.[29] A. Demirbas, J. Hazard. Mater. 157 (2008) 220–229.[30] S. Cataldo, A. Gianguzza, A. Pettignano, I. Villaescusa, React. Funct. Polym. 73
(2013) 207–217.[31] W.S. Ngah, S. Fatinathan, J. Environ. Sci. 22 (2010) 338–346.[32] K. Yu, J. Ho, E. McCandlish, B. Buckley, R. Patel, Z. Li, N.C. Shapley, Colloids Surf.,
A 425 (2013) 31–41.[33] W.M. Algothmi, N.M. Bandaru, Y. Yu, J.G. Shapter, A.V. Ellis, J. Colloid Interface
Sci. 397 (2013) 32–38.[34] A. Bée, D. Talbot, S. Abramson, V. Dupuis, J. Colloid Interface Sci. 362 (2011)
486–492.[35] S. Materazzi, J. Finamore, R. Risoluti, A. Napoli, Microchem. J. 115 (2014) 27–
31.[36] B. Volesky, Sorption and Biosorption, BV Sorbex Inc, Montréal, St. Lambert,
Québec, Canada, 2003.[37] T.A. Davis, F. Llanes, B. Volesky, G. Diaz-Pulido, L. McCook, A. Mucci, Appl.
Biochem. Biotech. 110 (2003) 75–90.[38] N. Emmerichs, J. Wingender, H.C. Flemming, C. Mayer, Int. J. Biol. Macromol. 34
(2004) 73–79.[39] A. Haug, Acta Chem. Scand. 15 (1961) 1794–1795.[40] C. Lamelas, F. Avaltroni, M. Benedetti, K.J. Wilkinson, V.I. Slaveykova,
Biomacromolecules 6 (2005) 2756–2764.[41] L.A. Rodrigues, L.J. Maschio, R.E. da Silva, M.L. da Silva, J. Hazard. Mater. 173
(2010) 630–636.[42] C.H. Goh, P.W.S. Heng, L.W. Chan, Carbohydr. Polym. 88 (2012) 1–12.[43] J. Wang, B. Tian, J. Wang, J. Lu, C. Olsen, C. Yarnitzky, K. Olsen, D.
Hammerstrom, W. Bennett, Anal. Chim. Acta 385 (1999) 429–435.[44] G. Guibaud, E. van Hullebusch, F. Bordas, Chemosphere 64 (2006) 1955–
1962.[45] X. Li, W. Wei, X. Zeng, J. Zeng, J. Yin, L. Wu, World J. Microbiol. Biotechnol. 23
(2007) 1465–1471.[46] D.R. Narine, R.D. Guy, Soil Sci. 133 (1982) 356–363.[47] S. Cataldo, G. Cavallaro, A. Gianguzza, G. Lazzara, A. Pettignano, D. Piazzese, I.
Villaescusa, J. Environ. Chem. Eng. 1 (2013) 1252–1260.[48] C. Escudero, N. Fiol, I. Villaescusa, J. Bollinger, J. Hazard. Mater. 164 (2009)
533–541.[49] C. Comuzzi, P. Polese, A. Melchior, R. Portanova, M. Tolazzi, Talanta 59 (2003).[50] P.J. Huber, E.M. Ronchetti, Robust Statistics, second ed., John Wiley & Sons Inc.,
Hoboken, NJ, 2009.[51] M. Merli, L. Sciascia, M.L. Turco, Int. J. Chem. Kinet. 42 (2010) 587–607.[52] J. Neter, M.H. Kutner, C.J. Nachtsheim, W. Wasserman, Applied Linear
Regression Models, third ed., Irwin, Chicago, 1996.[53] H. Levene, Contributions to Probability and Statistics: Essays in Honor of
Harold Hotelling, in: I. Olkin, S.G. Ghurye, W. Hoeffding, W.G. Madow, H.B.Mann (Eds.), Stanford University Press, 1960, pp. 278–292.
[54] J. Durbin, G.S. Watson, Testing for Serial Correlation in Least SquaresRegression. I, in: Department of Applied Economics, University ofCambridge, 1951, pp. 409–428.
[55] E. Howard, J. Cassidy, J. Chem. Educ. 77 (2000) 409.[56] O. Bunke, Selecting Variables and Models in Regression Analysis: Some Tools
and Suggestions for a Strategy, Sektion. Univ, Berlin, 1984.[57] T.P. Ryan, Modern Regression Methods, Wiley, 1997.[58] T. Gotoh, K. Matsushima, K. Kikuchi, Chemosphere 55 (2004) 57–64.[59] S.K. Papageorgiou, E.P. Kouvelos, F.K. Katsaros, Desalination 224 (2008) 293–
306.[60] V.J.P. Vilar, C.M.S. Botelho, R.A.R. Boaventura, Process Biochem. 40 (2005)
3267–3275.[61] S. Schiewer, M.H. Wong, Chemosphere 41 (2000) 271–282.[62] C. De Stefano, A. Gianguzza, D. Piazzese, S. Sammartano, Anal. Bioanal. Chem.
383 (2005) 587–596.[63] P. Costa, J.M. Sousa, Eur. J. Pharm. Sci. 13 (2001) 123–133.[64] I. Calabrese, G. Cavallaro, C. Scialabba, M. Licciardi, M. Merli, L. Sciascia, M.L.
Turco, Int. J. Pharm. 457 (2013) 224–236.[65] R. Lagoa, J.R. Rodrigues, Appl. Biochem. Biotech. 143 (2007) 115–128.[66] O. Hamdaoui, E. Naffrechoux, J. Hazard. Mater. 147 (2007) 401–411.[67] O. Gezici, A. Ayar, Clean-Soil Air Water 37 (2009) 349–354.
88 S. Cataldo et al. / Journal of Colloid and Interface Science 434 (2014) 77–88
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