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ENVIRONMENTAL BIOTECHNOLOGY
Influence of metal ionic characteristics on their biosorption
capacity by Saccharomyces cerevisiae
Can Chen &Jianlong Wang
Received: 19 September 2006 /Revised: 24 October 2006 /Accepted: 27 October 2006 / Published online: 30 November 2006
#Springer-Verlag 2006
Abstract The influence of metal ionic characteristics on
their biosorption capacity was analyzed using quantitative
structure–activity relationships model. The waste biomass
of Saccharomyces cerevisiae was used as biosorbent to
adsorb 10 kinds of metal ions, and their maximum
biosorption capacity (q
max
) was determined by the Lang-
muir isotherm model. The values of q
max
decreased in the
following order (in millimole per gram): Pb
2+
(0.413)>Ag
+
(0.385)> Cr
3+
(0.247)> Cu
2+
(0.161)> Zn
2+
(0.148)> Cd
2+
(0.137)>Co
2+
(0.128)>Sr
2+
(0.114)> Ni
2+
(0.108)>Cs
+
(0.092). Twenty-two parameters of physiochemical char-
acteristics of metal ions were selected and correlated with
q
max
, i.e., OX, AN, r(Å), ΔIP (eV), ΔE
0
(V), X
m
,|log
K
OH
|, X2
mr,Z
2
/r, AN/ΔIP, sr,AR,AW,IP,AR/AW,Z/r
2
,
Z/AR
2
,Z/r,Z/AR, Z*
2
/r·, Z*, N. The linear regression analysis
showed that the covalent index X2
mrwas correlated well
with q
max
for all metal ions tested in the following equation:
q
max
=0.029+0.061 (X2
mr)(R
2
=0.70). It suggested that the
greater the covalent index value of metal ion was, the
greater the potential to form covalent bonds with biological
ligands, such as sulphydryl, amino, carboxyl, hydroxyl
groups, etc. on the biomass surface, and the higher the
metal ion biosorption capacity was. Classification of metal
ions, for divalent ion or for soft–hard ion could improve the
linear relationship (R
2
=0.89). The equation could be used
to predict the biosorption capacity of metal ions.
Keywords Biosorption .Ionic characteristics .
Saccharomyces cerevisiae .QSAR
Introduction
Heavy metal pollution has become one of the most serious
environmental problems in China (Wang 2002). Biosorp-
tion, using biomaterials such as bacteria, fungi, yeast, and
algae, is regarded as a cost-effective biotechnology for the
treatment of high volume and low concentration complex
wastewaters containing heavy metal(s) in the order of 1 to
100 mg/l (Wang and Chen 2006).
Biosorptive capacity is mainly influenced by three kinds
of influential factors: metal ionic characteristics (e.g.,
atomic weight, ion radius, valence, etc.), the nature of the
biosorbents (e.g., cell age), and biosorption conditions (e.g.,
pH, temperature, contact time, etc.). Obviously, metal ionic
properties in aqueous solution are an important inherent
factor to influence ion uptake (Tobin et al. 1984; Remacle
1990; Brady and Tobin 1995; Tsezos et al. 1996). To date,
environmental conditions (such as pH value) have been
discussed widely in most references on biosorption.
Mechanisms of metal biosorption (such as ion exchange,
chelation, microprecipitation, etc.) were also explored
through instrumental analysis technology, such as Fourier-
transformed infrared spectroscopy (FTIR), scanning electron
microscopy (SEM), transmission electron microscopy
(TEM), etc. to some extent although a limited extent (Wang
and Chen 2006; Volesky 1990). For example, Raize et al.
(2004) demonstrated that Ni ion was removed mainly
through the mechanism of ion exchange by the nonliving
brown marine macroalga. However, ionic characteristics
have never been fully investigated in the field of metal
biosorption. Despite the quite extensive literature available
on metal–microbe interactions, few researchers have attempt-
ed to relate differing mechanism and/or relative levels of
metal uptake or toxicity to the chemical characteristics of
the metals under investigation (Avery and Tobin 1993).
Appl Microbiol Biotechnol (2007) 74:911–917
DOI 10.1007/s00253-006-0739-1
C. Chen :J. Wang (*)
Laboratory of Environmental Technology, INET,
Tsinghua University,
Beijing 100084, People’s Republic of China
e-mail: wangjl@tsinghua.edu.cn
In some relevant references published so far, only about
one to three metal ionic characteristics were discussed
accounting for metal uptake, e.g., ionic radius (r), covalent
index (X2
mr, where X
m
is electronegativity) and the first
hydrolysis constant (log K
OH
). Biosorption of metal cations
such as Sr
2+
,Mn
2+
,Zn
2+
,Cu
2+
,Cd
2+
, and Pb
2+
by freeze-
dried Rhizopus arrhizus was observed to be related to
covalent index (X2
mr) (Brady and Tobin 1995). Tobin et al.
(1984) reported that the amount of uptake of the cations by
dry cells of R. arrhizus was directly related to ionic radius
of metals, such as La
3+
,Mn
2+
,Cu
2+
,Zn
2+
,Cd
2+
,Ba
2+
,Hg
2+
,
Pb
2+
,UO
2þ
2,andAg
+
, which could be explained by a com-
plexation mechanism involving sites in the biomass contain-
ing carboxylate, phosphate, and other functional groups.
However, the ionic radius did not appear to reflect the
biosorption capacities when Pb
2+
,Cu
2+
,Zn
2+
,Cd
2+
,and
Ni
2+
were adsorbed onto sugar beet pulp (Reddad et al.
2002). In this case, the log of first hydrolysis constant
showed a linear relationship with the initial sorption rate and
the maximum sorption capacities, respectively.
However, many other parameters of metal ionic charac-
teristics (such as electronic configuration, ionization poten-
tial, etc.) nearly have not been mentioned as far as we know
in the field of biosorption of metal ions. Walker et al.
(2003) discussed about 20 physical and chemical properties
of cations, and examined the relationships between those
properties and their toxic effects. Now significant progress
has been made in solving the technical problems with
quantum chemical computations of transition metals. Thus,
the tools are available for computing more properties of
aqueous ions for use in quantitative structure–activity
relationships (QSARs) (Capitani and DiToro 2004).
QSARs have been applied widely to predict bioactivity
(toxicity or bioavailability) of organic compounds in
pharmacology and toxicology. In contrast, models correlat-
ing metal ionic characteristics with their bioactivity remain
poorly explored and underutilized (Ownby 2002). Devel-
oping and validating quantitative cationic–activity relation-
ships or QCARs to predict the toxicity metals is
challenging because of issues associated with metal
speciation, complexation, and interactions within biological
systems and the media used to study these interactions
(Walker et al. 2003).
Newman and coworkers (McCloskey et al. 1996;
Newman and McCloskey 1996; Newman et al. 1998;
Tatara et al. 1998) developed a novel quantitative ion
character–activity relationships (QICARs) as a useful tool
to predict the relative toxicity of metal ions based on metal–
ligand binding tendencies. The first hydrolysis constant
reflects a metal ion’s tendency to bind to intermediate
ligands such as biochemical functional groups with oxygen
donor atoms. Intermediate ligands including carboxyls,
hydroxyls, aldehydes, ketones, and amino compounds
(Tatara et al. 1997). The softness parameter sr, a measure
of the ability of a metal ion to give up its valence electrons,
was also a measure of metal ion affinity to soft donor
atoms, like S-containing ligands. McKinney et al. (2000)
also discussed structure activity relationship or SAR
(qualitative and quantitative modeling methods relating
chemical structure to biological activity) to analyze biolog-
ical activity of metal ions. The QICARs approach, based on
metal-ligand binding tendencies, has been applied success-
fully to a wide range of effects, species, and media on a
single metal basis. Ownby and Newman further demon-
strated that the QICARs approach was also suitable for
prediction of toxicity in binary metal mixtures (Ownby
2002; Ownby and Newman 2003).
The objective of this study was to correlate the metal
ionic properties with their biosorption capacity during
biosorption process to explore the metal–biomass interac-
tions using QSARs model.
Materials and methods
Preparation of biosorbent
Dry yeast (Saccharomyces cerevisiae) was friendly sup-
plied as a waste by a local brewery in China. The yeast was
ground in a mortar, and passed through a screener with the
order of 150 μm. Then, the yeast cells were stored in
desiccators for future use.
Metal solution
The nitrate salts of Ag(I), Cs(I), Zn(II), Pb(II) Ni(II), Cu(II),
Co(II) Sr(II), Cd(II), and Cr(III), were used to prepare stock
solution with deionized water. The stock solution was
diluted to a designed value with deionized water before
experiments. Real concentration of metal ion was deter-
mined using ICP-AES or AAS. The pH was adjusted to
about 4.0 with dilute nitric acid and sodium hydroxide to
avoid or decrease the possible precipitation of metal ions.
Nitrate ion forms only weak complexes with metal ions, so
nitrate salts were used in our study. Buffers could not be
used to avoid their possible complexation with metal ions.
Biosorption experiments
A series of flasks (100 ml) containing 0.1 g of dried
biomass were prepared. A 50-ml metal ion solution with
known initial concentration was added, and the flasks were
shaken (150 r/min) at 30°C for a certain time. The
equilibrium time for the metal ions tested by the biomass
cells was confirmed at 3 h except for Cr
3+
(18 h) according
to the preexperiments. During experiments, the pH value
912 Appl Microbiol Biotechnol (2007) 74:911–917
during the process of biosorption was not controlled. The
biomass was separated by centrifugation (12,000 r/min,
5 min) and the supernatants were used to analyze the metal
ion concentration. The experiments were conducted in
duplicate, and control experiments without biomass were
also performed.
Metal analysis
The concentration of Ag
+
,Cs
+
,Zn
2+
,Pb
2+
,Ni
2+
,Cu
2+
,
Co
2+
,Sr
2+
,Cr
3+
, and Cd
2+
in supernatant was determined
using ICP-AES method (Thermo IRIS Intrepid II XSP) or
by an atomic absorption spectrometric method with flame
atomization (AAS 6 Vario).
Metal ionic characteristics and correlation approach
Mainly according to the references (McCloskey et al. 1996;
Lewis et al. 1999; Walker et al. 2003; Wolterbeek and
Verburg 2001; Zhang and Xiao 2004; Jiang et al. 2006), We
selected 22 variables (physiochemical properties) of metal
ions, i.e., OX, AN, r(Å), ΔIP (eV), ΔE
0
(V), X
m
, |log
K
OH
|, X2
mr,Z
2
/r, AN/ΔIP, sr, AR, AW, IP, AR/AW, Z/r
2
,Z/
AR
2
,Z/r, Z/AR, Z*
2
/r·, Z*, N. These abbreviations repre-
sented the ion properties as the following: OX = oxidation
number; AN = atomic number; r= ionic radius (Å); ΔIP =
change in ionization potential (eV); ΔE
0
= electrochemical
potential of the ion and its first stable reduced state (V);
X
m
=electronegativity;|logK
OH
| = log of the first hydrolysis
constant; X2
mr= covalent index; Z= ion charge; Z
2
/r=the
cation polarizing power; sr= softness index; defined as:
(coordinate bond energy of the metal fluoride)–(coordinate
bond energy of the metal iodide)/(coordinate bond energy of
the metal fluoride). AR = atomic radius; AW = atomic
weight; IP = ionization potential; Z* = effective ion charge;
and N= the number of valence shell electrons.
The relationship between ionic characteristics and q
max
was established with regression analysis using the software
EViews 4.0. The level of significance was set as α=0.05.
The contribution of a variable (ionic property) to each
equation was firstly tested for statistical significance
(Fstatistic) and ttest (tstatistic). Then, based on one
variable regression analysis, some of equations with two
variables were generated. In multilinear regression of
statistics, adjusted R(R= correlation coefficient), Akaike
information criterion (AIC), and Schwarz criterion (SC)
could be used for evaluating the predictive models. Newman
group specially supposed the AIC as a more rigorous
criterion so that models with different complexity (one or
two explanatory variable) could be compared. The model
with the smallest AIC was judged to have the most
information (Newman and McCloskey 1996).
The following statistic parameters were computed by the
software of EViews:
n= number of metals considered; R= correlation
coefficient; R
2
= coefficient of determination; R2
adj:¼
adjusted Rsquare; SE = standard error of regression; F
ratio (Ftest); P= probability of significance; AIC = Akaike
information criterion; and MAPE = mean absolute percent
error between observed and predicted values in prediction.
All listed equations were statistical significance (F
statistic and tstatistic) at α=0.05. Some were not
significant; however, they also listed for convenience of
comparing the fitting results for different class of metals.
Results
Maximum biosorptive capacity
To determine the maximum biosorption capacity (q
max
)of
metal ions by the yeast cells, the equilibrium isotherms of
metal biosorption was fitted with Langmuir model:
qe ¼qmax bCe=1þbCe ð1Þ
For the fitting of the experimental data, we used the
following equation of linear form of the Langmuir model:
1=qe ¼1=qmax þ1=qmaxbðÞCe ð2Þ
where q
e
is equilibrium metal uptake (millimole per gram),
C
e
is the equilibrium concentration in solution (millimole
per liter), q
max
represents the maximum uptake capacity of
metal ions, and bis a constant, ratio of the adsorption/
desorption rates, i.e., related to the energy of adsorption
through the Arrhenius equation, (liter per millimole).
The maximum metal uptake q
max
was determined by
fitting the data with the Langmuir isotherm model Eq. 2.
The resulting values of q
max
were listed in Table 1.
Table 1 Maximum uptake capacity (q
max
)byS. cerevisiae obtained
from Langmuir model
Metal ions q
max
(mmol/g) R
2
Class
Pb
2+
0.413 0.917 Class B (soft)
Ag
+
0.385 0.991 Class B (soft)
Cr
3+
0.247 0.935 Borderline
Cu
2+
0.161 0.976 Borderline
Zn
2+
0.148 0.956 Borderline
Cd
2+
0.137 0.954 Borderline
Co
2+
0.128 0.967 Borderline
Sr
2+
0.114 0.973 Class A (hard)
Ni
2+
0.108 0.977 Borderline
Cs
+
0.092 0.982 Class A (hard)
Appl Microbiol Biotechnol (2007) 74:911–917 913
It can be seen that q
max
decreased in the following order
(on mole basis):
Pb2þ>Agþ>Cr3þ>Cu2þ>Zn2þ>Cd2þ>Co2þ
>Sr2þ>Ni2þ>Csþ
The highest uptake of 0.413 mmol/g was observed for
Pb
2+
, whereas the lowest uptake was 0.092 mmol/g for Cs
+
,
a kind of alkali metal ion. The parameter q
max
reflects the
metal affinity to the sites of yeast cells. The yeast cells really
showed a preferential binding capacity for Pb
2+
from the
values of q
max
. This result was also in agreement with the
many previous reports published (Wang and Chen 2006).
On mole basis, this affinity order was basically similar to
the absorption by freeze dried R. arrhizus Pb
2+
>Cu
2+
>
Cd
2+
>Zn
2+
>Mn
2+
>Sr
2+
(Brady and Tobin 1995) except
for Cd
2+
and Zn
2+
. Reddad et al. (2002) also reported the
same order (maximum uptake by Langmuir model): Pb
2+
>
Cu
2+
>Zn
2+
>Cd
2+
>Ni
2+
when using sugar beet pulp as
biosorbent. Tobin et al. (1984) reported the similar order for
the metal uptake affinity of R. arrhizus:UO
2þ
2>Cr
3+
>Pb
2+
>Ag
+
>Ba
2+
>La
3+
>Zn
2+
>Hg
2+
>Cd
2+
>Cu
2+
>Mn
2+
>Na
+
,
K
+
,Rb
+
,Cs
+
(=0), except Cr
3+
and Cu
2+
.
The difference in biosorption capacity among Ag
+
,Cs
+
,
Zn
2+
,Pb
2+
,N
i2+
,Cu
2+
,Co
2+
,Sr
2+
,Cr
3+
, and Cd
2+
for a
given strain (S. cerevisiae) under similar environmental
conditions could be attributed to different ionic character-
istics of these meal ions.
Correlation ionic properties with q
max
For all metals tested (n=10), only one property of covalent
index X2
mrshowed very statistical significant (at the level of
significance 0.002) on the maximum uptake capacities
q
max
:
qmax ¼0:029 þ0:061 X2
mr
ð3Þ
X2
mralone accounted for 67% of the variation in q
max
values for all metals ions. AR/AW and Z* also showed
moderate significance (α=0.05), but had less influence on
metal uptake than X2
mr(see model 2 and 3 in Table 2).
The model 1 (Table 2) indicated that the mean absolute
percent error (MAPE) between observed and predicted
values was 27%. The inequality coefficient was 0.14 and
the covariance proportion was near to 1 (0.91).
The relationship between q
max
and X2
mrwas depicted in
Fig. 1.
It can be seen that the greater the covalent index value of
a metal ion, the greater its biosorptive capacity by the
biomass.
Newman group work showed that modeling mono-, di-,
and trivalent metal ions separately for toxicity assessment
could improve the models (McCloskey et al. 1996; Tatara et
al. 1998). Therefore, we generated models using the data of
divalent metal ions. In this study, we did not make models
for mono- and trivalent metal ions because the number of
them was small. The modeling results showed that not only
the model with the one variable X2
mr, AR/AW, or Z* did
improve the fit, but also more variables became statistically
significant, i.e., AN, AN/ΔIP,andAW.Thestatistic
parameters, for example, R2
adj changed from 0.67 to 0.87,
and Fvalue from 19.04 to 39.77 when fitting data from all
metals to only divalent metals for the model with one
variable X2
mr, (comparing Table 2, model 1 and Table 3,
model 5). The MAPE decreased from 27% for all metals
(model 1) to 22% for divalent metal ions (model 5).
Table 3showed that metal uptake capacities of divalent
cations increased with increase of atomic number (AN),
covalent index (X2
mr), (AN/ΔIP), atomic weight (AW), or
effective ionic charge (Z*), but decreased with increase of
AR/AW. Table 3also showed that the highest fitting value
for one variable model was also with X2
mrfor divalent
cations (R2
adj ¼0:87, AIC=−3.39, MAPE=22.30). Howev-
er, using MAICE, the model 10 with two independent
variable gave the best fitting (R2
adj ¼0:91, AIC=−3.74,
MAPE=15.70):
qmax ¼0:018 þ0:05 X2
mrÞþAN=ΔIP
ð4Þ
Equation 4offered better fitting than the model with one
variable X2
mr.
Discussion
The modeling results could be improved when the metal
ions were classified according to the valence of metal ions
(divalent ions) or hard/soft ions. It was found that different
metal ionic characteristics played different role in metal
biosorption for different class of metal ions, which implied
that different mechanisms involved in metal biosorption.
Table 2 Linear regression analysis of relationship between q
max
and metal ionic characteristics for all metal ions tested (n=10)
Model (q
max
=) RR
2
R2
adj:SE FPAIC MAPE
1 0.029+ 0.061 (X2
mr) 0.84 0.70 0.67 0.067 19.04 0.002 −2.38 27.36
2 0.47−14.9 (AR/AW) 0.68 0.46 0.39 0.091 6.84 0.030 −1.78 37.82
3−0.127+ 0.023 (Z*) 0.64 0.41 0.34 0.095 5.61 0.045 −1.70 39.97
914 Appl Microbiol Biotechnol (2007) 74:911–917
All metal ions tested in this study was basically
classified as class A (hard), borderline and class B (soft)
according to Nieboer and Richardson (1980), but with some
variation. Pb
2+
was classified as class B (soft) in our study
rather than in borderline group by Pearson (1963) and by
Nieboer and Richardson (1980). Cd
2+
was classified as
borderline ion rather than as soft ion by Pearson. Our study
showed that Pb
2+
as soft ion and Cd
2+
as borderline ion
were more applicable for biosorption.
Pb
2+
owned the highest character of class B (soft). In
fact, many references reported that Pb
2+
exhibited the
similar toxicity to soft ions (McCloskey et al. 1996). In
many multimetal biosorption studies, Pb
2+
generally
showed high biosorptive capacity by various biomass. Cd
2+
was classified as soft ion by Pearson. However, Niebore
and Richardson especially pointed out that Cd
2+
falls into
the borderline metals. Avery and Tobin (1993) demonstrated
that Cd
2+
classified as borderline ion, intended to be more
applicable to biological systems, correlates more closely
with the results when the metal ion, such as Sr
2+
,Mn
2+
,
Zn
2+
,Cu
2+
,Cd
2+
, and Tl
+
, was absorbed by metabolism-
independent S. cerevisiae. Therefore, Cd
2+
was regarded as
borderline ion in our study. Therefore, it sounded reason-
able for such classification for Pb and Cd ions.
Metal biosorptive capacity by the dry yeast cells (q
max
)
decreased in the following order (in millimole per gram):
Pb2þ>Agþ>Cr3þ>Cu2þ>Zn2þ>Cd2þ>Co2þ
>Sr2þ>Ni2þ>Csþ
This tendency showed that the biomass preferentially
absorb soft ions (Pb
2+
and Ag
+
), then borderline ions (Cr
3+
,
Cu
2+
,Zn
2+
,Cd
2+
, and Co
2+
), and last hard ions (Sr
2+
and
Cs
+
). Ni
2+
was unexpected lower, even lower than that of
hard ion Sr
2+
. Kogej and Pavko (2001) also found the lower
uptake of Ni ion than that of Sr ion (Fe
2+
>Ag
+
>Fe
3+
>Pb
2+
>Cu
2+
>Cd
2+
>Sr
2+
>Zn
2+
>Ni
2+
>Li
+
>Al
3+
)byRhizopus
nigricans (Kogej and Pavko 2001). Reddad et al. (2002)
also reported that lowest uptake for Ni
2+
when the waste
sugar beet pulp absorb several cations (Cd
2+
,Cu
2+
,Pb
2+
,
Zn
2+
, and Ni
2+
) separately. Ni
2+
seemed to be exclusively
adsorbed by an ion exchange mechanism. In fact, Malik
(2004) pointed out that the low binding capacity of biomass
for certain metal ion such Ni. Raize et al. (2004) also
reported the lowest uptake for Ni ion comparing to Pb and
Cd ions. They demonstrated that nickel cation sequestration
mechanism was mainly ion exchange when nickel ion
interacted with the nonliving brown marine macroalga,
Sargassum vulgaris, in its natural form by employing the
following instrumental and chemical techniques: FTIR,
SEM, X-ray photoelectron spectroscopy (XPS), and extrac-
tion of alginic acid and sulfated polysaccharides, which act
as metal-binding moieties present in cell wall (Raize et al.
2004). However, cadmium cation was mainly removed by
apparently chelation, and lead removed through the
combination of ion exchange, chelation, and reduction
reactions, and precipitation. The fact of ion exchange
played important role in Ni removal implied that Ni ion
exhibited the great extent of hard ion characteristic. Sr
2+
,a
hard ion, showed a degree of covalence although the ionic
bonding was much important than that of covalent bonding
to the overall adsorption of the biomass (Avery and Tobin
Table 3 Linear regression analysis of relationship between q
max
and metal ionic characteristics for divalent cations (n=8)
Model (q
max
=) RR
2
R2
adj SE FPAIC MAPE
4−0.026+ 0.005 (AN) 0.91 0.83 0.79 0.049 24.01 0.004 −2.96 24.20
5 0.060 (X2
mr) 0.94 0.89 0.87 0.039 39.77 0.001 −3.39 22.30
6 0.039+0.026 (AN/ΔIP) 0.77 0.59 0.50 0.076 7.07 0.045 −2.08 32.30
7 0.002+0.002 (AW) 0.92 0.84 0.81 0.047 25.94 0.004 −3.03 23.14
8 0.457−15.56 (AR/AW) 0.84 0.70 0.64 0.064 11.87 0.018 −2.42 23.42
9−0.24+ 0.028 (Z*) 0.81 0.65 0.58 0.069 9.35 0.028 −2.26 30.38
10 −0.018+ 0.05 (X2
mr)+ 0.01(AN/ΔIP)
a
0.97 0.94 0.91 0.032 31.66 0.004 −3.74 15.70
a
Variable was not significant on q
max
(α=0.05).
0
0.1
0.2
0.3
0.4
0.5
02468
X
m2
r
q
max
(mmol/g)
Pb
2+
Ag
+
Cr
3+
Sr
2+
Cs
+
Zn
2+
N
i
2+
Co
2+
Cd
2+
Cu
2+
Fig. 1 Maximum biosorptive capacities plotted against corresponding
covalent index values
Appl Microbiol Biotechnol (2007) 74:911–917 915
1993). The above facts may explain the low uptake capacity
of Ni ion. Tobin et al. (1984) reported that the R. arrhizus
did not absorb alkaline metal ions, such as K
+
,Na
+
, and
Cs
+
, which were usually hard ions. More reports basically
demonstrated this tendency in metal biosorption by various
biaomss, including S. cerevisiae,R. arrhizu,R. nigricans,
sugar beet pulp waste, and the like.
For all metals tested (n=10), or for divalent metals, or
for soft ions plus borderline ions, X2
mrshowed the best
fitting. The maximum biosorptive capacity increased with
the increase of the covalent index values (see Fig. 1). Kogej
and Pavko (2001) reported that the maximum biosorption
capacities and the binding constant of R. nigricans were
positively correlated with the covalent index of metal ions
(Fe
2+
,Ag
+
,Fe
3+
,Pb
2+
,Cu
2+
,Cd
2+
,Sr
2+
,Zn
2+
,Ni
2+
,Li
+
,
and Al
3+
).
The X2
mrwas a measure for a metal ion of the importance
of covalent interactions relative to ionic interactions. The
greater the values of X2
mr, the stronger the covalent bonding
between metal and biomass, and the lesser the contribution
of ionic bonding to metal biosorption, which resulted in the
higher q
max
. According to Nieboer and Richardson (1980),
the larger the X2
mr, the more characteristics of class B, and
the metal ions preferentially interacted with the functional
group in the following order: S- > N- > O- containing
group. Class B preferred to form the most stable complexes
with the groups, such as CO, S
2−
,RS
−
,R
2
S, CN
−
,H
−
,R
−
,
then R
2
NH, R
3
N, =N–,–CO–N–R, RNH
2
, and the least
stable complexes with ROH, RCOO
−
, C=O, ROR, HPO2
4,
OH
−
,O
2−
,H
2
O, NO
3, ROSO
3,CO
2
3, etc. In contrast,
class A ion with small value of X2
mr, preferred to form
stable complex with the metal binding donor atom in
ligands in the order: O > N > S. Borderline ions were able
to form stable complexes with all categories of ligands.
Preferences did exist, which reflected the degree of class A
or B character of the particular borderline metal and the
relative availability of the different ligands in a system.
X2
mrdid not display significant linear correlation with
q
max
for borderline or plus hard ions, but could account for
metal ions containing soft ions Pb
2+
and Ag
+
. It suggested
that X2
mrwas suitable for explaining biosorption phenom-
ena of soft ions, in which covalent bonding contributed
much in metal–biomass interaction.
Conclusions
The maximum biosorptive capacity q
max
by S. cerevisiae
was determined by the Langmuir model, which decreased
in following order (on mol basis): Pb
2+
>Ag
+
>Cr
3+
>
Cu
2+
>Zn
2+
>Cd
2+
>Co
2+
>Sr
2+
>N
i2+
>Cs
+
.
The q
max
were correlated with 22 ionic properties, i.e.,
OX, AN, r(Å), ΔIP (eV), ΔE
0
(V), X
m
,|logK
OH
|, X2
mr,
Z
2
/r, AN/ΔIP, sr,AR,AW,IP,AR/AW,Z/r
2
,Z/AR
2
,Z/r,
Z/AR, Z*
2
/r·, Z*, N. The covalent index X2
mroffered best
fitting for soft ions. The greater the covalent index value of
a metal ion, the greater its potential to form covalent bonds
with biological ligands, generally in order: S > N > O.
Models could be improved based on metal classification,
such as divalent cations according to valence or soft/hard
ions according to Niebero and Richardson classification.
In a word, QSARs used in metal toxicity assessment
could be applied to correlating the metal ionic character-
istics with their biosorptive capacity. It offered a new way
and idea to predict the biosorption capacity and to explore
the metal–biomass interaction.
Acknowledgments The work was financially supported by the
National Natural Science Foundation of China (grant no. 50278045)
and the Basic Research Fund of Tsinghua University (grant no.
JC2002054).
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