algas sargassum

Biosorption of binary mixtures of Cr(III) and Cu(II) ions
by Sargassum sp
E.A.Silva I ; E.S.Cossich II ; C.G.Tavares II ; L.Cardozo Filho II* ; R.Guirardello III*
I School of Chemical Engineering, UNIOESTE, Rua da Faculdade 645, 85903-000,
Phone: (+55) (45) 252-3535, Toledo - PR, Brazil
II Department of Chemical Engineering, UEM, Av. Colombo 5790, 87020-900, Phone:
(+55) (44) 226-2727, Maringá - PR, Brazil, E:mail:cardozo@deq.uem.br
III School of Chemical Engineering, UNICAMP, Av. Albert Einstein 500, 13083-970,
Phone: (+55) (19) 3788-3955, Campinas - SP, Brazil, E-mail:guira@feq.unicamp.br
ABSTRACT
The adsorption of two metal ions, Cr(III) and Cu(II), in single-component and binary
systems by Sargassum sp., a brown alga, was studied. Equilibrium batch sorption
studies were carried out at 30 o C and pH 3.5. Kinetic tests were done for a binary
mixture (chromium + copper) for a contact time of 72 hours to guarantee that
equilibrium was reached. The monocomponent equilibrium data obtained were
analyzed using the Langmuir and Freundlich isotherms. The binary equilibrium data
obtained were described using four Langmuir-type and Freundlich isotherms. The F-
test showed a statistically significant fit for all binary isotherm models. The parameters
for isotherms of the Langmuir-type were used to determine the affinity of one metal
for the biosorbent in the presence of another metal. The chromium ion showed a
greater affinity for Sargassum sp.than the copper ion.
Keywords: biosorption, chromium, copper, isotherm, multicomponent, Sargassum.
INTRODUCTION
The increase in metal consumption on an industrial scale is an important
environmental issue. Conventional methods for removing metals, such as chemical
precipitation and sedimentation, oxidation, reduction and separation by membranes
and ionic resins, may be expensive and sometimes ineffective depending on
concentration. Biosorption processes have been proposed as an alternative method for
recovering and removing metals from industrial effluents with metal concentrations in
the range of 1 to 100 mg/L (Volesky, 1990).
The objective of this work was to determine the isotherms for copper and chromium
ions by the marine algaSargassum sp., using equilibrium data for solutions of these
metal ions. These isotherms may provide useful information for the design of
processes to remove these metal ions from industrial effluents at low concentrations.
Biosorbents made from marine algae biomass have been studied due to their wide
availability and low cost. Although thousands of species of algae have been identified
in the last two centuries, very little has been investigated in order to determine their
relative capacity to retain toxic metallic ions (Çetinkaya et al., 1999). The brown

marine alga Sargassum can be found in large amounts in the south and southwest
Brazilian coast (Silva, 2001).
Equilibrium Isotherms
Biosorptive metal uptake can be quantitatively evaluated using experimental
biosorption equilibrium isotherms. The two widely accepted isotherm models for singlesolute
systems are the Langmuir and Freundlich isotherms, described by equations (1)
and (2), respectively:
where q max and k are the Langmuir constants, and
where a and n are the Freundlich constants.
Multimetal systems are often encountered in industrial operations. Evaluation,
interpretation and representation of biosorption results for two-metal systems is more
difficult. Generally the results obtained for a single metal in solution cannot be used to
predict the behavior of two-metal systems.
The effects of metal mixtures on a biosorption system can be complex, and three kinds
of possibilities can arise (Ting et al., 1991): (i) the effect of the mixture is bigger than
the sum of the effects of the components of the mixture (synergism); (ii) the effect of
the mixture is smaller than the sum of the effects of the components of the mixture
(antagonism); (iii) the effect of the mixture is the same as the sum of the effects of
the components of the mixture (noncompetitive).
Reliable knowledge, correlation, and analysis of multicomponent equilibrium data are
essential for achieving an understanding of biosorption dynamics and for further
development of biosorption separation processes which represent a whole new area in
environmental biotechnology (Chong and Volesky, 1995). Therefore, isotherm models
for equilibrium of multicomponent mixtures should be used.
The Langmuir isotherm for a binary mixture is represented by the following equation:
where q max , k 1 and k 2 are binary Langmuir constants.
The Langmuir model has also been used to analyze multicomponent biosorption
equilibrium data (Chong and Volesky, 1995; Sag and Kutsal, 1996; Chong and
Volesky, 1996; Sánchez et al., 1999; Figueira et al., 1997).

Analysis of binary biosorption data for metallic ions has also been carried out by using
modified Langmuir models (Chong and Volesky, 1995; Sánchez et al., 1999). These
models are represented by equations (4) through (6).
Equation (4) was originally developed to describe the noncompetitive inhibition in
kinetic enzymatic studies (Bailey and Ollis, 1986). This equation is similar to the
Langmuir model, with additional terms in the numerator and the denominator and an
additional parameter, K:
By incorporating new constants (β 1 , β 2 ) as exponents for each equilibrium
concentration in the denominator of the Langmuir isotherm, the following
mathematical expression may be obtained:
Using constants β 1 and β 2 as exponents in both the numerator and denominator of the
Langmuir isotherm, the Langmuir-Freundlich isotherm is obtained, and it is
represented by the following mathematical expression (Ruthven, 1984):
Sag et al. (1998) used the Freundlich empirical model to describe the biosorption
equilibrium data in binary systems, whose mathematical representation is given by
equations (7) and (8):
where a 1 , n 2 , a 2 and n 2 are Freundlich isotherm constants, obtained from the individual
component equilibrium data. The other constants, α 11 , α 12, α 21 , α 21 , a 12 and a 21 , are
determined from equilibrium binary data.

Metal Biosorption by Biomass
The first step in the design of a process for metal removal by biosorption, through ion
exchange or adsorption, is the choice of the biosorbent material. The main criteria for
the selection of the biosorbent material are cost, removal capacity and selectivity. The
metal ion removal capacity is determined through experimental equilibrium data, which
are also useful in the design and simulation of fixed bed columns for removal of
metallic ions from the solution. A number of researchers in the literature have studied
metal biosorption by biomass.
Kratochvil et al. (1998) studied chromium(III) removal by
protonated Sargassum biomass (H 2 SO 4 0.2 M) and the same biomass linked with
calcium (Ca(OH) 2 ). The equilibrium concentration of the chromium ion in the solution
ranged from 0 to 9 mmol/L. These experiments were carried out at pH 4.0, and the
biomass was able to retain around 0.769 mmol/g. Kratochvil et al. (1997) studied
copper(II) ion removal by protonated (HCl 0.1 M) Sargassum fluitans seaweed biomass
linked with calcium (CaCl 2 ). The interval of the chromium ion equilibrium concentration
in the solution was similar to the one used in the present work. With pH 4.5 the
biomass was able to retain around 1.18 mmol/g.
Chong and Volesky (1995) correlated sorption equilibrium data for binary metallic
systems (Cu + Zn), (Cu + Cd) and (Zn + Cd) by FCAN2 biomass (manufactured
from Ascophyllum nodosum marine alga) using equations (3), (4) and (5). Chong and
Volesky (1995) fitted their data to the same models in a manner very similar to that
used in the current work. The model with the lowest number of parameters was
chosen.
Sánchez et al. (1999) correlated equilibrium data on sorption for a binary metallic
system (Cu+Zn) byCymodocea nodosa seaweed, pH 4.5, using equations (3), (4) and
(5) as adsorption isotherms. The three models tested also represented the binary
equilibrium data in a very similar manner.
Sag et al. (1998) tested equations (3), (5) and (6) to describe binary equilibrium data
for sorption of Cu(II) and Zn by Rhizopus arrhizus fungus at pH 4.0 and 5.0 and a
temperature of 25 o C and observed that of the isotherm models tested, only the
Freundlich model could effectively represent the data.
The affinity of the metallic ions for the sites on the biosorbent material depends on
several factors, such as ionic radii, pH and interaction of the ion with the biomass.
There are few studies in the literature about the effect of temperature on the
biosorption, such as the one by Cossich (2000) who studied the biosorption of cromium
ion by Sargassum sp.
The dependency of pH on the biosorption capacity indicates that weakly acid carboxyl
groups are the probable sites of ion exchange (Kratochvil and Volesky, 1998).
Carboxylate has been identified as the chemical group responsible for capturing the
metallic ions in the seaweed and in the gram-positive bacteria (Churchill et al., 1995;
Kratochvil and Volesky, 1998). The biosorbent used in this work also has carboxylate
in its cell wall.
Churchill et al. (1995) studied the removal of Cr +3 , Co +2 , Ni +2 and Cu +2 ions by
biosorbents prepared from biomass of gram-positive and gram-negative bacteria. The

esults obtained by the study of sorption at equilibrium showed the following affinity
series: Cr +3 > Cu +2 > Co +2 > Ni +2 .
In this work, experiments were conducted to determine the length of time required for
equilibrium of the metal ion to be reached in the solution and the Sargassum
sp. biomass, and the equilibrium data for different concentrations of solutions of
chromium, copper and a mixture of both. The results were used to fit the model
parameters, using isotherm models represented by equations (3) through (8).
MATERIAL AND METHODS
The methodology used in this work is similar to the one used by a number of
researchers in the literature (Chong and Volesky, 1995; Chong and Volesky, 1996;
Cossich, 2000; Kratochvil et al., 1998; Sag and Kutsal, 1996; Sag et al., 1998;
Sánchez et al., 1999; Volesky, 1990). The procedure is described as follows:
Biomass
The biomass used in the experiments was the brown Sargassum sp. It was washed in
water, rinsed with distilled water and dried in an oven at 60 o C during 24 hours. The dry
weight of the biomass was determined after drying at 105 o C during 24 hours. The dry
biomass was then chopped and sieved to different fraction sizes. Dry particles with an
average diameter of 0.625 mm were used for the sorption experiments.
Preparation of Chromium and Copper Solutions
Solutions of Cr(III) in distilled water were prepared using chromium and potassium
sulfate salt (CrK(SO 4 ) 2 .12H 2 O – Sigma).
Solutions of Cu(II) in distilled water were prepared using copper sulfate salt
(Cu(SO 4 ).5H 2 O – Merck).
Kinetic Experiments
The kinetics of biosorption of chromium and copper and a mixture of both by
the Sargassum sp. biomass was evaluated in 2000 mL Erlenmeyer flasks with 1000 mL
of solution and 1.5 g of biomass (dry weight). Two kinetic tests were carried out for
each ion in the following concentrations: 3.10 and 6.32 mmol Cr/L, 2.92 and 5.49
mmol Cu/L. For the binary mixture (chromium + copper), two kinetic tests were also
carried out in the following concentrations: 1.10 mmol Cr/L and 0.93 mmol Cu/L, 3.17
mmol Cr/L and 2.8 mmol Cu/L.
The flasks were maintained at 30 o C under constant agitation (140 rpm) on a rotary
shaker (Marconi MA830). A series of 1 mL samples of solution was removed from the
flasks at predetermined time intervals and analyzed by atomic absorption spectroscopy
(AA) to ascertain metal concentrations.
Sorption Equilibrium Experiments
Batch equilibrium sorption experiments were carried out in 125 mL Erlenmeyer flasks
containing 50 ml of the metal solution to which 0.15 g of dry biomass particles had

een added. The suspensions were agitated on a rotary shaker at 160 rpm at 30 o C and
pH 3.5. When sorption equilibrium was reached (according to the time determined by
the kinetic experiments) the solution was filtered and then analyzed by atomic
absorption spectroscopy.
The initial pH of the chromium (III) solution ranged from 3.3 to 3.7, whereas the
chromium concentration ranged from 0.5 to 3 mmol/L. However, Cossich (2000)
observed the formation of precipitate in solutions containing 1 mmol/L of chromium
when the pH was adjusted to 4.0 by adding NaOH. Microprecipitation can produce a
distortion of sorption results and hinder determination of the amount of metal uptake.
Thus, it was decided to work at pH 3.5. The pH was adjusted to 3.5 before and during
the sorption experiments by adding 0.1 N NaOH or 0.1 N H 2 SO 4 , as required.
The biomass was removed by vacuum filtration. Initial and final concentrations for the
metallic ions in the solution in each flask were measured by atomic absorption
spectroscopy (Varian SpectrAA-10 plus).
The equilibrium concentration of metallic j ion in the solid biomass phase (
) was
calculated from the initial concentration ( ) and the equilibrium concentration ( ) in
each flask, using the following equation:
where V is the volume of the solution and m s , the biosorbent mass (dry weight).
The same methodology was used to obtain the equilibrium data for the biosorption of
the chromium-copper binary system by Sargassum sp. The concentration of binary
solutions ranged from 1 to 10 mmol/L.
All equilibrium sorption experiments were carried out in duplicate.
Statistical Methods
The following definitions and statistical methods were used to evaluate the binary
isotherm models given by equations (3) through (8) in the representation of
experimental equilibrium data:
The average of the absolute value of the relative deviation (AD) is defined by equation
(10):

where and are the experimental equilibrium uptake values and the values predicted
using the isotherm model for each point i, respectively, and ne is the number of
experimental points. For replications and repeated experiments,
(11):
is given by equation
which is the average of the experimental equilibrium uptake values ( k=1,...,nr i )
for the replications of each experiment i ( i=1,...,ne ). In this work, all sorption
experiments were carried out in duplicate (nr i = 2 ).
The quadratic sum of the residue (SQ r ) is defined by equation (12):
The quadratic average of the residue (MQ r ) is defined by equation (13):
where p is the number of parameters in the model and .
The quadratic sum of the regression (SQ R ) is defined by equation (14):
where is the average of all experimental values , given by equation (15):
The quadratic average of the regression (MQ R ) is defined by equation (16):
The multiple coefficient of determination (R 2 ) is defined by equation (17):

The model parameters were determined by the criterion of least squares using the
objective function given by equation (18), which is the sum of the squares of the
relative errors:
The procedure used to obtain the minimum value for F obj was the simplex method
(Nelder and Mead, 1965). For replications and repeated experiments,
equation (11).
is given by
The models were analyzed using the F-test with a confidence level of 95%. As a
criterion, Box and Wetz (1973) suggest that the ratio MQ R / MQ r must be at least 4 or 5
times greater than the value expected for the F-distribution, where MQ R and MQ r are
the quadratic averages of the regression and the residue, respectively, so that
regression can be statistically significant and useful for making predictions.
The models were also analyzed using the average of the absolute value of the relative
deviation (AD) and the multiple coefficient of determination (R 2 ). When multiplied by
100, R 2 represents the percentage of variability in the observed variable that is
explained by the regression (Anderson et al., 1991).
RESULTS AND DISCUSSION
Kinetic Experiments
Figures 1 and 2 show the results obtained in the kinetic tests of sorption for chromium
and for copper at different concentrations. The metallic j ion fraction removed from
solution was calculated by the following expression:

where the equilibrium concentration is the ion concentration in the solution at the end
of the experiment.
It can be observed from Figure 1 that the system with chromium attained final
equilibrium after a contact time of 48 hours. Also, from Figure 2 it can be observed
that the system with copper attained final equilibrium after a contact time of 8 hours.
Therefore, in order to guarantee that equilibrium was reached, contact times of 72 and
48 hours were used in the chromium and copper equilibrium sorption experiments,
respectively.

Contact time for equilibrium is a function of several factors: biomass type (number and
types of metal-binding sites), size and forms of biomass, state of biomass (active or
inactive, free or immobilized), etc. The results of kinetic biosorption tests showed that
copper removal is faster than chromium removal. The greater mobility of the copper
ion is probably due to its smaller ionic radius, since the chromium ion is in a hydrated
form in the conditions tested and therefore has a greater ionic radius than the copper
ion.
After 6 hours, approximately 70% of the chromium had been removed when C 0 = 6.32
mmol/L, and 80% when C 0 = 3.10 mmol/L. However, irrespective of the value for
initial concentration, the time required for the system to reach equilibrium was about
the same.
After 30 minutes, approximately 87% of the copper had been removed when C 0 =
10.97 meq/L, and 82% when C 0 = 5.84 meq/L. However, irrespective of the value for
initial concentration, the time required for the system to reach equilibrium was about
the same.
The results of the kinetic tests for the binary mixture (chromium + copper) are shown
in Figures 3 and 4. It can be verified that a minimum contact time of 48 hours was
necessary for the system to reach equilibrium. Thus, in the equilibrium experiments a
contact time of 72 hours was allowed to guarantee that equilibrium would be reached.

The two kinetic curves for the binary mixture show that the initial concentration of
metallic ions affects the shape of the curve, and at the beginning, the copper ion is
captured faster than the chromium ion. In addition, it can be verified that after a short
interval of time the copper concentration in the solution had increased. This indicates
that copper is being released by the biosorbent.
At the beginning there are many sites available, and since the availabilities of the ions
are similar and the mobility of copper is greater, it occupies more sites than chromium.
Later on, the chromium ions migrate to the biosorbent surface to occupy more
available sites, including some that were previously occupied by copper, because
chromium has a higher affinity for Sargassum sp. than copper has. When this happens,
an increase in copper concentration in the solution is observed, as illustrated in Figures
3 and 4.
Individual Sorption of Chromium and Copper Ions by Sargassum sp.
The data on sorption of chromium and copper ions by Sargassum sp. were used to fit
the model parameters in the Langmuir and Freundlinch isotherms. The results, which
are shown in Table 1, were calculated by fitting the curves to the experimental data
using the simplex method with the criterion of least squares (Nelder and Mead, 1965).

Values for the correlation coefficient (R 2 ) and for the average of the absolute value of
the relative deviation (AD) for the Langmuir and Freundlich models used to represent
single-component equilibrium data are shown inTable 2.
The Freundlich model was the one that better represented the equilibrium data on
chromium ion sorption because it had the largest correlation coefficient and the
smallest average deviation. The equilibrium data on copper ion sorption were better
represented by the Langmuir model.
The results obtained in this work indicate that the Sargassum sp. biomass has a
chromium removal capacity of around 1.30 mmol/g and a copper removal capacity of
around 1.08 mmol/g, both at 30 °C and pH 3.5.
Binary Sorption of Chromium and Copper Ions by Sargassum sp
The experimental equilibrium data for the sorption of a binary mixture of chromium
and copper ions bySargassum sp. were compared using five different isotherm models,
represented by equations (3) through (8). These models and their parameters are
shown in Table 3.
The results for Langmuir-type and Freundlich isotherm parameters are shown in Table
4. The k j constants from the Langmuir-type isotherm are the ratio between desorption
and sorption rates. Therefore, low values of these constants indicate a strong metal
affinity for the sites on the adsorbent material. From the results shown inTable 4 for
models 1, 2, 3 and 4, it can be observed that the Cr +3 ion had a stronger affinity
for Sargassum sp.than the Cu +2 . The same affinity was obtained by Churchill et al.
(1995), who used a biomass that also had carboxylate in its cell wall.

The Langmuir-type isotherm described by model 2 is based on a kinetic model of
sorption that allows the formation of the following complexes: B – M 1 , which
represents the sites occupied by metal M 1 ; B – M 2 , which represents the sites occupied
by metal M 2 , and B – M 1 – M 2 , which represents the sites occupied simultaneously by
metals M 1 and M 2 . The formulation and development of the equilibrium relationships
for this model are presented in Chong and Volesky (1995) and Sánchez et al. (1999).
The K constant for model 2 is related to the ratio between desorption and adsorption
rates for the B – M 1 – M 2 complex. By the results in Table 4, it can be observed that
the value for K is higher than the value for k 1 and lower than the value for k 2 , showing
that the formation of the B – M 1 – M 2 complex is less likely than the formation of the B
– M 1 complex but more likely than the formation of the B – M 2 complex, where M 1 is
Cr +3 and M 2 is Cu +2 .
Parameter values for the Freundlich isotherm are shown in Table 4, and since the
formulation of the isotherm is empirical, its constants have no physical signification.
However, this model showed the best numerical fit (Tables 5, 6 and 7).

Table 5 shows the percentages of points whose experimental equilibrium concentration
in the biosorbent deviated less than 10% from the calculated concentration values
using the five models.
Values for the quadratic sum of the residue (SQ r ), the average deviation (AD) and the
sum of the squared relative residue (objective function F obj ) for each of the models
used to represent binary equilibrium data are shown in Table 6.
The models were analyzed using the F-test with a confidence level of 95%, and the
results are shown in Table 7. The criterion suggested by Box and Wetz (1973) was
satisfied in all models in this work (the ratio MQ R / MQ r was at least 4 or 5 times greater
than the value expected for the F-distribution).
The experimental equilibrium uptake values and the values predicted using the five
isotherm models described in Table 3 are shown in Figures 5 through 9. It can be
observed that the least dispersion of data was seen for models 4 and 5, in agreement
with the statistical analyses presented in Tables 6 and 7.

Selection of the best model was based on the results of an analysis of variance of the
models tested. The Freundlich and Langmuir-Freundlich isotherms were the ones which
best represented the binary equilibrium data on biosorption, as can be seen in Tables
6 and 7. These models showed the least average deviation and the highest percentage
of explained variation.
The graphic representation of sorption isotherms for binary systems is a threedimensional
surface, where the equilibrium concentration of the target metal in the
biosorbent is described as a function of the equilibrium concentration of metallic ions in
solution. It was observed that, for high levels of total concentration of metallic ions in
solution, the biosorbent reaches the level of saturation easily, resulting in an extensive
plateau on these surfaces. Table 8 shows the experimental data on sorption of
chromium and copper ions by Sargassum sp. and theoretical equilibrium values
calculated using the Freundlich binary isotherm model.

The effect that different levels of secondary metal have on primary metal uptake in a
binary system at equilibrium can be better evaluated by fixing the primary metal
concentration on the adsorption isotherms.Figure 10 illustrates the effect of the
reduction in copper uptake on the biosorbent with the increase in the concentration of
chromium ion in solution. For example, for the low equilibrium concentration of copper
in solution of 0.5 mmol/L, the amount of copper adsorbed by alga is 0.80 mmol/g, in
the absence chromium. When 0.5 mmol/L Cr or 4.0 mmol/L Cr are present in solution,
the amount of copper adsorbed decreases to 0.20 mmol/g (reduction of 75.0%) or
0.11 mmol/g (reduction of 86.3%), respectively.

For the high equilibrium concentration of copper in the fluid phase of 3.0 mmol/L, the
amount of copper adsorbed by alga is 0.98 mmol/g, in the absence chromium. When
0.5 mmol/L Cr or 4.0 mmol/L Cr are present in solution, the amount of copper
adsorbed decreases to 0.50 mmol/g (reduction of 49.0%) or 0.34 mmol/g (reduction
of 65.3%), respectively.
The reduction in adsorption is also observed by fixing the concentration of chromium in solution
(0.5 and 3.0 mmol/L) and using different concentrations of copper. However, the effect is much
less pronounced than in the previous case. It can be observed that for the levels of
concentration studied (0.5 mmol/L and 3.0 mmol/L), the amount of copper uptake was more
sensitive to the presence of chromium than the reverse. This significant reduction in ion removal
capacity is due to the effect of competition, indicating that chromium has a greater affinity for
the biosorbent than copper.

CONCLUSIONS
Batch equilibrium experiments showed that the maximum capacities of Sargassum
sp. biomass for chromium and copper were 1.30 mmol/g and 1.08 mmol/g,
respectively, at 30°C and pH 3.5.
A number of Langmuir-type and Freundlich models was used to correlate equilibrium
data on sorption of chromium-copper metallic ions by Sargassum sp. at 30 o C and pH
3.5. The F-test showed a statistically significant fit for all binary isotherm models.
However, the Freundlich and Langmuir-Freundlich isotherms represented the binary
equilibrium data better than other models, according to the results of the analysis of
variance. The Freundlich isotherm represented the equilibrium data slightly better than
the Langmuir-Freundlich model.
Langmuir isotherm parameters were used to determine the affinity of one metal for the
biosorbent in the presence of the other metal, showing that the chromium ion had a
greater affinity for Sargassum sp. than the copper ion.
NOMENCLATURE

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