Bioresource Technology 102 (2011) 2990–2997

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Bioresource Technology

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Full description of copper uptake by algal biomass combining an equilibriumNICA model with a kinetic intraparticle diffusion driving force approach

Roberto Herrero a, Pablo Lodeiro a,⇑, Lino J. García-Casal a, Teresa Vilariño a, Carlos Rey-Castro b,Calin David b, Pilar Rodríguez a

a Departamento de Química Física e Enxeñería Química I, Universidade da Coruña, Alejandro de la Sota 1, 15008 A Coruña, Spainb Departament de Química, Universitat de Lleida, Rovira Roure 191, E-25198, Lleida, Spain

a r t i c l e i n f o

Article history:Received 18 June 2010Received in revised form 30 September 2010Accepted 2 October 2010Available online 8 October 2010

Keywords:CopperSargassum muticumLDF

0960-8524/$ - see front matter � 2010 Elsevier Ltd. Adoi:10.1016/j.biortech.2010.10.007

⇑ Corresponding author. Tel.: +34 981 167000x219E-mail address: plodeiro@udc.es (P. Lodeiro).

a b s t r a c t

In this work kinetic and equilibrium studies related to copper binding to the protonated macroalgaSargassum muticum are reported. An intraparticle-diffusion linear driving force (LDF) model has beenchosen for the quantitative description of the kinetics at several initial metal concentrations. Copperintraparticle homogeneous diffusion coefficient (Dh) obtained is in the range 0.2–0.9 � 10�10 m2 s�1.NICA isotherm is demonstrated to constitute a substantial improvement with respect to a simplerLangmuir competitive equation. The binding parameters were chosen to provide the best simultaneousdescription of the equilibrium experiments. Values of log eK Cu (4.3), nCu (1) and p (0.31) in NICA isotherm,and log KCu (3.5–5) in Langmuir competitive model, have been obtained. These parameters have been alsoused to predict the competition between copper and cadmium for binding sites. Two acids, HNO3 andHCl, have been tested to evaluate their effectiveness to release copper from the metal-laden biomass.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

The incidences of different pollution sources, that are harmfulto the environment, have been gradually increased over time dueto rapid urbanization and industrialization. Industrial activitieshave been recognized as a major contributor to a variety of waterpollution problems. Moreover, their rapid growth has produced anincrease in the volume and toxicity of residues, among which, li-quid effluents containing metals are of special interest.

Metals have a high degree of toxicity, which can be harmful forhuman beings and the environment. According to EPA (Environ-mental Protection Agency), copper is an abundant trace elementpresent in earth’s crust and surface waters. Copper is not regardedas highly toxic, and only at elevated concentrations may becomehazardous to some forms of aquatic life. However, treatment ofwastewaters containing these metals is of importance both for envi-ronmental quality and for water reuse. The effluents from mining,leather, fabricated metal products, and electric equipment repre-sent the most important sources of copper pollution. The EuropeanPollutant Emission Register (EPER), which comprises 50 substancesthat have to be reported by industrial facilities if their emissions ex-ceed certain threshold values, establishes for copper that the limitsfor air, water and land are, respectively, 0.1, 0.05 and 0.05 t. per year

ll rights reserved.

9; fax: +34 981 167065.

(Parliament and Council, 2006). As an example, these values are tentimes higher than those for cadmium discharges.

Although various conventional methods, as ion-exchange, li-quid extraction, precipitation, electrodialysis, etc., could be appliedto treat wastewaters, most of these available physicochemicaltechnologies are expensive or ineffective when they are appliedto metal ions removal at low concentrations. Alternative low-costtechnologies are needed to reduce heavy metal concentrations inthe environment to acceptable levels (Srivastava and Majumder,2008).

Biosorption, the passive non-metabolically mediated process ofmetal binding by dead biomass, has a great potential to reach theseobjectives (Lodeiro et al., 2006; Volesky, 2003). Among the advan-tages offered by this technique are the high purity achieved bytreated wastewaters or the use of inexpensive materials as biosor-bents. Waste products from other industries or natural abundantbiomass can be quoted as an example (Demirbas, 2008). The brownmacroalga Sargassum muticum, an invasive species in Europe, hasbeen the biosorbent employed in this work. Its native habitatsare Japanese and Chinese waters, where its presence is much smal-ler than in the European coasts. This alga is an alien species thatinterferes with recreational use of waterways, blocking propellersand intakes. It is also a fouling organism in oyster beds and anuisance to commercial fishermen.

The algal cell wall plays an important role in metal binding, dueto its high content in polysaccharides with acid functional groups.In brown algae, the cell wall is mainly comprised of alginates,

R. Herrero et al. / Bioresource Technology 102 (2011) 2990–2997 2991

which usually constitute about 20–40% of the total dry weight, inaddition to fucoidans (Davis et al., 2003). The carboxyl groups ofalginates are likely to be the main functionalities involved in metalbinding reactions because of their abundance with regard to bothcarboxyl and amine groups of the proteins.

The present work reports a study of Cu adsorption by non-livingbiomass of the brown marine macroalgae S. muticum. The alga waspreviously protonated in order to increase the retention capacity ofthe raw biomass, and to achieve a further stabilization of the bio-material. The process has been analysed through batch experi-ments with regard to the influence of initial metal concentration,pH and the presence of cadmium as competing cation. Both kineticand equilibrium aspects have been discussed. Desorption processhas also been tested. Mathematical models for the quantitativedescription of the biosorption process have been employed in or-der to predict the dynamics and equilibrium behaviour.

2. Experimental methods

2.1. Materials

Fresh samples of brown marine alga S. muticum were collectedfrom the coast of A Coruña (NW Spain). The samples were washedextensively with running and deionised water to removed adher-ing particles, and oven-dried at 60 �C overnight.

Dried samples were then crushed with an analytical mill (IKA A10) and sieved to a size range of 0.5–1 mm. After being sieved, thefollowing pre-treatment was performed (Figueira et al., 2000). Thebiomass was protonated in 0.1 M HNO3 (10 g of biomass/L) for 4 hat room temperature, washed with deionised water, filtered anddried overnight at 60 �C. This biomass was stored in polyethylenebottles until use.

2.2. Chemicals

Analytical grade Cu(NO3)2�3H2O, NaNO3, HNO3, Cd(NO3)2�4H2Oand HCl (Merck), NaOH (Panreac) were used in this work. Cellulosenitrate membrane filters were purchased from Whatman andAlbet; N2 C-55 (99.9995%) was from Carburos Metálicos.

2.3. Methods

2.3.1. Kinetic studiesA copper ion selective electrode (ISECu, Radiometer Analytica)

with a Ag|AgCl reference electrode, previously calibrated in copperconcentration, was employed to analyse the copper evolutionduring the kinetic experiments. The experiments were monitoredpotentiometrically using a homemade program. This techniqueallows a great number of experimental points to be obtainedeasily and quickly without need to withdrawn solution for themeasurements.

Experiments were carried out in a glass cell furnished with athermostated jacket and a nitrogen stream to remove dissolvedO2 and CO2. The ionic strength was adjusted to 0.05 M with NaNO3.All measurements were performed at least in duplicate.

2.3.2. Effect of initial metal concentrationProtonated S. muticum samples (0.25 g) were added to 100 mL

of Cu(II) solution at constant temperature (25.0 ± 0.1 �C) andnatural pH (around 3). The initial concentration of tested coppersolutions were 0.25, 0.50, 1.00 and 3.00 mmol L�1.

2.3.3. Equilibrium studiesAll batch equilibrium studies were carried out in 100 mL Erlen-

meyer flasks containing 0.1 g of the alga to which 40 mL of Cu(II)

solutions were added. The mixtures were agitated on a rotator sha-ker at 150 rpm for 3 h, in order to ensure that equilibrium wasreached. The solution pH was adjusted by using HNO3 or NaOHduring the equilibrium period; from these additions and the quan-tity of Cu(NO3)2 in solution, the ionic strength was calculated foreach sample. The experiments were performed at room tempera-ture. The algal biomass was removed by filtration through a0.45 lm membrane filter. In all equilibrium experiments the pres-ence of organic matter was avoided by UV-digestion of the aliquotsfor 75 min at 90 �C (705 UV Digester, Metrohm); afterwards ali-quots were analyzed for Cu(II) released into solution by differentialpulse anodic stripping voltammetry (DPASV) using a 757 VAComputrace (Metrohm). All batch experiments were carried outat least in duplicate.

2.3.4. Influence of pH on metal biosorptionThe effect of solution pH was studied by using eight Cu(II) solu-

tions (40 mL, 2.22 mmol L�1) added to flasks containing 0.1 g of drybiomass. The solution pH was adjusted to 1.5, 2.5, 3, 3.5, 4.0, 4.5,5.0 and 5.5 by addition of HNO3 or NaOH solutions.

2.3.5. Adsorption isothermsForty millilitres of nine Cu(II) solutions of several concentra-

tions (0.1, 0.2, 0.5, 1.0, 1.5, 2.0, 2.5, 3.5 and 5.0 mmol L�1) wereplaced in Erlenmeyer flasks containing 0.1 g of alga. The solutionpH was adjusted to 4.0 by addition of NaOH solution.

2.3.6. Effect of Cd(II) ions on Cu(II) biosorptionThe competition effect of cadmium ion was tested through

batch sorption experiments carried out with 0.1 g of protonatedS. muticum in contact with binary mixtures (40 mL) of several cop-per ion concentrations (the same used in adsorption isotherms)and the competitor metal ion at two different initial concentrations(0.1 and 5.0 mmol L�1). The solution pH was adjusted to 4.0 byaddition of NaOH solution.

2.3.7. Desorption experimentsFor batch desorption experiments, copper-loaded S. muticum

samples (obtained from previous adsorption process with 0.1 g ofbiomass, at a fixed pH of 4 and initial copper concentration of2.5 mmol L�1) were placed in 100 mL Erlenmeyer flasks and con-tacted with 10 mL of two different desorbents (HCl and HNO3) atthree different concentrations (0.05, 0.1 and 0.5 mol L�1). The mix-tures were agitated on a rotary shaker for 2 h at room temperature.

From these experiments, the optimal eluent concentration wasselected (0.05 mol L�1) and then, the effect of contact time wasstudied. In this case, the volume of HCl or HNO3 solutions was in-creased to 20 mL, in order to ensure better contact between algaeand solution and to facilitate the samples extraction. Aliquots of100 lL of solution were analyzed after 2 and 4 h of contact.

3. Results and discussion

3.1. Kinetic studies

3.1.1. Mathematical modelKinetic experiments are the first necessary stage in every bio-

sorption study. They are needed to determine the time requiredfor sorption equilibrium to be reached and, moreover, the resultingdata may be used to extract kinetic parameters for the modelling ofcolumn biosorption experiments. Several independent processes,including transport phenomena and chemical reaction kinetics,which usually act in conjunction, determine the dynamics of metalbiosorption. In the case of porous sorbents, the following steps maybe present (Garcia-Reyes and Rangel-Mendez, 2010; Volesky,

Fig. 1. Sorption of copper as a function of contact time at different initial metalconcentrations, for aqueous suspensions of the protonated S. muticum in0.05 mol L�1 NaNO3 (temperature of 25.0 ± 0.1 �C, alga dose 2.5 g L�1). The symbolscorrespond to the experimental points at natural pH (see Fig. 2) and differentcopper initial concentrations: 0.25 mmol L�1 (squares), 0.50 mmol L�1 (up trian-

2992 R. Herrero et al. / Bioresource Technology 102 (2011) 2990–2997

2003): transport of sorbate within the bulk solution (advection anddiffusion), transfer of sorbate from bulk solution to the sorbent sur-face through the boundary layer of fluid immediately adjacent tothe particle (external film diffusion), diffusion of sorbate withinthe particle (intraparticle diffusion), and chemical reaction of thesorbate with the binding sites of the biomass.

The experimental conditions and setup are very often chosen sothat mass transfer resistance, due to metal transport in the bulksolution, and film diffusion through the boundary layer of biosor-bent are minimized. In particular, an adequate mixing, created byproper agitation, allows a fast transport of metal in bulk solution,and, at the same time, it can contribute to suppress the boundarylayer surrounding the particles. In these cases, intraparticle diffu-sion and/or chemical reaction may be the rate-limiting steps inthe sorption kinetics. However, in biosorption of metal ions fromaqueous solution it is common to assume that the overall rate ofuptake is controlled mainly by the diffusivity of the sorbate withinthe particle, whereas the chemical reaction of binding with the sor-bent sites is assumed to be relatively faster.

In the present work an intraparticle-diffusion linear drivingforce model (LDF) was chosen for the quantitative description ofthe copper uptake kinetics. Further details about this model canbe found in bibliography (Tien, 1994; Vilar et al., 2006). In sum-mary, this model is based on the following assumptions:

- The rate of metal uptake is controlled by the homogeneous dif-fusion of the sorbed species within the biomass particles. Theexternal film diffusion resistance is assumed negligible in theactual conditions selected for the experiments (agitation rate:180 rpm, biomass particle size: 0.5–1 mm). Similar conditionswere already tested in bibliography for Sargassum biomass(Yang and Volesky, 1999) and they were proved to ensure theexclusion of these kinetic limitations.

- The particles are modelled as homogeneous thin plates of thick-ness 2L. Therefore, the uni-dimensional diffusion of the sorbedmetal ion along the direction normal to the particle surfacedetermines the overall diffusion rate (Volesky, 2003).

- The equilibrium concentration of sorbed copper at the particleinterface is described by the Langmuir isotherm (see Section3.2.1 below).

- Based on the model assumptions, the mass balance equationsfor copper in the batch reactor are as follows:

V � dCCu;t

dtþms �

dqCu;t

dt¼ 0 ð1Þ

dqCu;t

dt¼ Dh

LdqCu;t;z

dz

� �z¼L

ð2Þ

where qCu;t represents the average metal concentration in theparticle, CCu,t the metal concentration in solution at any time t,and Dh is the intraparticle homogeneous diffusion coefficientof the sorbed species within the algae.From the assumption that the sorbed copper concentration pro-

file, qCu;t;z, is parabolic, the following expression (known as lineardriving force approximation) can be derived for the variation ofthe copper uptake with time (Tien, 1994; Vilar et al., 2006):

dydt¼ k � yeq � y

� �ð3Þ

where y ¼ qCu;tQmax;Cu

; yeq ¼ QCuQmax;Cu

and k is a rate constant; Qmax,Cu is themaximum copper sorption capacity and QCu is the equilibriumsorption capacity of the alga.From the mass balance (Eq. (1)) atevery instant we get:

V � CCu;t þms � qCu;t ¼ V � CCu;i ð4Þ

Reordering this equation and introducing the definition ofx ¼ CCu;t

CCu;i, where, CCu,i is the initial copper concentration in solution,

and n0 ¼ V �CCu;ims �Qmax;Cu

(the ratio between the initial total amount of cop-

per and the maximum quantity that can be sorbed in a mass, ms, ofsorbent), the following expression is obtained:

y ¼ n0 � ð1� xÞ ð5Þ

Substituting this equation in Eq. (3) and using Langmuir isotherm,we obtain the following expression:

dydt¼ k � 1� x� b � CCu;i � x

n0 � 1þ b � CCu;i � x� �" #

ð6Þ

where b is the Langmuir affinity constant (see Eq. (7)). The initialconditions are: t = 0, x = 1, y = 0. This ordinary differential equationwas solved numerically using ode45 routine from Matlab (MATLAB�

v.2008b, The Mathworks Inc.), which is based on the Runge–Kuttaalgorithm.

Experimental kinetic data were fitted to this model by a least-squares minimization procedure using fminsearch routine fromMatlab. Only one parameter (k) was adjusted to fit the four exper-imental data series simultaneously. The Qmax,Cu (0.32 ± 0.02mmolg�1) and b (0.9 ± 0.1 L mmol�1) parameters were taken fromfitting results of equilibrium isotherm data obtained at pH 3 usingLangmuir model (r2 = 0.994).

3.1.2. Copper kinetic sorption rate descriptionExperimental data show that the equilibrium is attained within

1 h, and no further significant adsorption is noted beyond this per-iod (Fig. 1). In addition, it can be observed that 50% of the total cop-per uptake occurred within 10 min, showing that the rate of copperuptake is rather fast. It can also be observed that the percentage ofcopper removed from solution diminishes as the initial copper con-centration increases (Table 1). The evolution of solution pH wasalso followed. As it is shown in Fig. 2, the pH was around 3 in allthe kinetic experiments, and only a slight increase was observedwith time.

Fig. 1 shows the copper kinetic data at four different initial me-tal concentrations and their adjustment using the linear drivingforce model, as described above. As it is denoted, this simple model

gles), 1.00 mmol L�1 (circles) and 3.00 mmol L�1 (down triangles). Lines representmodelled results calculated using Eq. (6).

Table 1Kinetic rate constant for copper uptake by protonated Sargassum muticum at severalinitial metal concentrations (T = 298 K, pH = 3), obtained by fitting experimental datato Eq. (6) (LDF model). The percentage of Cu removed from solution at the end of thekinetic process is also included.

Ci (mmol L�1) %Cu removed k (min�1) r2

0.25 43 0.0642 0.910.50 35 0.0642 0.981.00 30 0.0642 0.963.00 18 0.0642 0.992

Fig. 2. Evolution of solution pH values with time at different initial metalconcentrations: 0.25 mmol L�1 (dash line), 0.50 mmol L�1 (solid line),1.00 mmol L�1 (dot line) and 3.00 mmol L�1 (dash-dot line), for aqueous suspen-sions of the protonated S. muticum in 0.05 mol L�1 NaNO3 (temperature of25.0 ± 0.1 �C, alga dose 2.5 g L�1).

R. Herrero et al. / Bioresource Technology 102 (2011) 2990–2997 2993

could describe kinetic data accurately. The calculated determina-tion coefficients (r2), used as an indication of model goodness,are showed in Table 1.

The rate constant, k, regressed from the LDF model was0.0642 min�1. The validity of this model is also demonstrated bythe fact that the same rate constant is valid to describe the exper-imental points for different initial metal concentrations. The rateconstant can be related with the intraparticle homogeneous diffu-sion coefficient, Dh, as: k = 3Dh/L2. Since L (half of the particle thick-ness) has an estimated average value between 0.25 and 0.5 mm,then Dh must be in the range 0.2–0.9 � 10�10 m2 s�1. This parame-ter is independent of concentration and, as expected, much lowerthan the molecular diffusion coefficient for Cu in water(7.2 � 10�10 m2 s�1) (Heyrovsky and Kuta, 1966). The fact that S.muticum seaweed material is a porous gel-like particle makes Dh

for Cu to be about 10 times lower than the corresponding diffusioncoefficient in pure water or aqueous salt solutions.

3.2. Equilibrium studies

3.2.1. Mathematical modelsThe development of a technology based on biosorption implies

the use of adequate models for the metal ion binding to biomate-rials. These models can be employed to analyse equilibrium dataand to compare quantitatively different biosorbents under severalconditions. Ideally, they would constitute a useful tool to predictthe metal biosorption, to deduce the binding mechanism and todetermine the influence on biosorption of variables such as pH, io-

nic strength or the presence of competing species (Lodeiro et al.,2006).

The most common isotherms used in sorption studies, Lang-muir–Freundlich (Eq. (7)) and Langmuir (Eq. (7) with n0 ¼ 1), areable to accuracy reproduce equilibrium experimental data if envi-ronmental parameters, such as pH, are controlled carefully duringexperiments (Carro et al., 2009; Lodeiro et al., 2004).

QCu ¼Qmax;CuðbCCuÞ

1n0

1þ ðbCCuÞ1n0

ð7Þ

where b represents the affinity for the sorbate, which can be used tocompare the adsorption performance, n0 is an empirical parameterthat varies with the degree of heterogeneity, and CCu is the copperconcentration in solution at equilibrium.

In order to account for stoichiometry and pH effects, a modifiedcompetitive Langmuir sorption model, Eq. (8), was proposed bySchiewer and Wong (1999). The metal binding at equilibrium is de-scribed as a function of pH and free metal ion concentration insolution.

QCu ¼ nQmax;HKCu CCuð Þn

1þ KHCH þ KCuCCuð Þnð8Þ

where KCu and KH are the equilibrium constants for the binding ofcopper and protons, respectively; CH is the proton concentrationin solution, the parameter n defines the stoichiometry ratio, 1:1(n = 1) or 1:2 (n = 0.5), and Qmax,H is the maximum binding capacityfor protons, which has been calculated from the equivalence pointof the acid–base titrations in absence of heavy metal.

However, another aspects that characterize algal biomass, suchas chemical heterogeneity, polyelectrolytic effects and conforma-tional changes (Buffle, 1988), are not considered by these equa-tions. As a consequence, new models with additional parameters,that reflect the complexity of the system, would be required.

The NICA model, developed by Kinninburg (Kinniburgh et al.,1999) for the description of metal adsorption in heterogeneousmaterials, addresses to binding site heterogeneity, interactions be-tween ionic species and reaction stoichiometry. It is a semi-empir-ical, competitive, non-ideal and thermodynamically consistentmodel, whose application is fairly simple. This model, which is ableto describe different types of experiments (acid–base titrations,influence of pH on biosorption, metal sorption and competitionwith other metals in solution), constitutes a powerful tool to de-scribe biosorption processes with both great accuracy and rela-tively small number of parameters (see below) (Herrero et al.,2006; Lamelas et al., 2005; Pagnanelli et al., 2005). However, de-spite the obtained encouraging results, the knowledge of the geo-metric parameters that determine the electrostatic description ofthe system would be required in order to derive the intrinsic bind-ing parameters (i.e., independent of the bulk ionic strength) (Li andEnglezos, 2005; Rey-Castro et al., 2004, 2003).The basic NICA equa-tion for the overall binding of species i in the competitive situationis:

hi ¼~K ici

� �ni

Pi

~K ici

� �ni�

Pi

~K ici

� �ni� p

1þP

i

~K ici

� �ni� p ð9Þ

where hi is the coverage fraction of the species i, ~K i is the medianvalue of the affinity distribution for species i, p is the width of thedistribution (usually interpreted as a generic or intrinsic heteroge-neity seen by all ions), ni is an ion-specific non-ideal term and ci

is the local concentration of species i at the binding site.The follow-ing normalization condition is used to calculate the amount of spe-cies i bound, Qi:

2994 R. Herrero et al. / Bioresource Technology 102 (2011) 2990–2997

Q i ¼ hini

nH

� �Q max;H ð10Þ

The ratio ni/nH has been interpreted by Kinniburgh et al. (1999) interms of stoichiometry and cooperativity. When this ratio is less thanone, then the maximum binding of species i is lower than the totalamount of sites (defined as the amount of titratable protons), whichwould be a consequence of certain degree of multidentism. On theother hand, a value of ni/nH greater than one would reflect some de-gree of cooperativity. Finally, if ni/nH = 1, it can be demonstrated thatthe maximum proton/metal exchange ratio is one.If only the protonbinding is considered (i.e., absence of competing ions), Eqs. (9) and(10) simplify to the Langmuir–Freundlich (LF) isotherm:

Q H ¼ Q max;H

~KHcH

� �mH

1þ ~KHcH

� �mHð11Þ

where this time the heterogeneity parameter mH describes the com-bined effect of nH and p (mH = nH�p). In the case of a homogeneoussystem (where all the binding sites behave as independent, chemi-cally equivalent sites) ni and p are 1, and then the mono or multi-component Langmuir isotherms are obtained.

The development of a physico-chemical model to describe me-tal ion binding to seaweed biomass needs a previous description ofproton binding as a function of pH in 1:1 electrolytes. Rey-Castroet al. (2003) reported proton binding data from potentiometrictitrations of biomass from different seaweed species (Sargassum,Cystoseira and Saccorhiza sp.). Moreover, in a previous work, Lode-iro et al. (2005) obtained the maximum amount of acid functionalgroups of the algae S. muticum (2.61 mmol g�1), through acid–basetitrations of protonated biomass samples. The fit of the protonbinding data to an isotherm model allowed the estimation of anaverage acid constant KH, referred to NaNO3 0.05 mol L�1, with avalue of 103.8, and the heterogeneity parameter, 0.54, calculatedfrom least-squares fit of the LF isotherm. These results were usedin this work for the interpretation of both proton and metal bind-ing in terms of competitive adsorption isotherms.

3.2.2. Copper uptake and the effect of pHThe copper uptake capacity of the biomass was tested using the

empirical Langmuir–Freundlich (Eq. (7)) and Langmuir isotherms(Eq. (7) with n0 ¼ 1). As it is showed in Fig. 3 (pH 4), both models

Fig. 3. Copper biosorption isotherms for suspensions of protonated S. muticum (algadose: 2.5 g L�1) at pH 4.0 ± 0.1 (open triangles) at 25 �C. Lines represent the fits tothe Langmuir model, Eq. (7) with n0 = 1, (dashed line) and Langmuir–Freundlich(solid line) model, Eq. (7).

describe with great accuracy the experimental points and identicalfitted parameters are obtained (Qmax,Cu = 1.12 ± 0.02 mmol g�1 andLog b = 1.4 ± 0.3), since the heterogeneity parameter (n0) has a va-lue about the unity, which turns Langmuir–Freundlich equationinto Langmuir one. However, one must be aware that the validityof these models or their underlying assumptions are not proved.

The maximum uptake capacity obtained for copper is around1.1 mmol g�1, which corresponds to 71 mg g�1 (equivalent to 7%of the total dry weight of the alga). This value is comparable withmaximum capacities obtained with other marine algae, such as Fu-cus serratus (1.60 mmol g�1) (Ahmady-Asbchin et al., 2008), Sargas-sum filipendula (1.30 mmol g�1) (Luna et al., 2007) or Posidoneaoceanica (1.35 mmol g�1) (Izquierdo et al., 2010). Moreover, thereare a great variety of useful materials proposed for copper removalfrom solution with different maximum metal uptake capacities,such as: spent–grain (0.165 mmol g�1) (Lu and Gibb, 2008), acti-vated poplar sawdust (0.085–0.21 mmol g�1) (Acar and Eren,2006), Trametes versicolor (0.63 mmol g�1) (Sahan et al., 2010),Mansonia wood sawdust (0.67 mmol g�1) (Ofomaja et al., 2010)or chitosan (1.59 mmol g�1) (Paulino et al., 2008).

The effect of pH on Cu(II) ion adsorption capacity of S. muticumwas studied at 2.22 mmol L�1 of initial Cu(II) concentration. Heavymetal sorption studies have shown that pH is one of the most impor-tant parameters affecting the process (Schiewer and Volesky, 2000).Both algae structure and copper speciation are affected by solutionpH. The structure of the alga can be damaged by extremely acidicpH, due to the acid ability to dissolve certain groups of polysaccha-rides found on the surface of the biomass. The pH can also changethe state of the active binding sites of the algae, mainly the carboxylgroups of alginates. Moreover, the MINEQL + speciation programmeshows that insoluble CuO appears at pH values greater than 5.5,decreasing free Cu(II) ion concentration.

As seen from Fig. 4, the copper uptake capacity is almost negligi-ble at pH values less than 2.5, the metal adsorption increases sharplybetween pH values 2.5 and 4.0, whereas a plateau is reached aroundpH 4.5. To explain this behaviour, it is important to consider both themetal speciation in solution and the ionic state of cell wall functionalgroups, mainly carboxyl groups, at various pH values (Davis et al.,2003). Since Cu(II) is present in its free ionic form (Cu2+) at pH valueslower than 5, copper biosorption depends on the protonation ordeprotonation state of the cell wall polymer functional groups,which have a pK value of 3.8 (Lodeiro et al., 2005).

Fig. 4. Effect of pH on copper adsorption by 2.5 g L�1 of protonated S. muticum at25 �C, with initial copper concentrations of 2.22 mmol L�1 (open squares). Linesrepresent the fit of the data to different equations: NICA isotherm, Eqs. (9) and (10)(solid line), competitive Langmuir isotherm assuming 1:1 stoichiometry (dottedline) and assuming 1:2 stoichiometry (dashed line), Eq. (8).

Fig. 5. Copper binding by S. muticum at pH = 4.0 ± 0.1. Symbols represent exper-imental points (the same showed in Fig. 3), solid line is the fitted NICA isotherm,Eqs. (9) and (10), dotted line is the competitive Langmuir isotherm assuming 1:1stoichiometry and dashed line assuming 1:2 stoichiometry, Eq. (8).

R. Herrero et al. / Bioresource Technology 102 (2011) 2990–2997 2995

At pH values less than 2.0, these functional groups are clearlyassociated with hydrogen ions, restricting the approach of Cu(II)cations; so low uptake capacities could be explained by the metalion binding to strongly acidic groups that do not become proton-ated at these pH, like sulfonic groups from fucoidans, that areknown to be present in Sargassum biomass (Davis et al., 2003).As the pH increases, more carboxylate groups would be exposedand the available negative charges would lead to a rise in the bind-ing of Cu(II) ions. Above pH 4.5, the increase in the Cu(II) sorptionis almost negligible and the uptake reaches a plateau. Similarbehaviour has been reported for Cd(II) ion biosorption by proton-ated Sargassum (Lodeiro et al., 2004, 2005).

The single component (proton) model was then extended to thegeneral multi-component case (interpretation of competitive ionbinding). The conditional proton binding parameters obtained inEq. (11) (Qmax,H, log ~KH and mH) were assumed to apply also inthe presence of copper. The values of Qmax,H, and mH were takenas fixed in all subsequent calculations using the NICA model.Thebinding parameters for the copper ion were chosen to providethe best simultaneous description of the isotherm at constant pH(4) and the data of copper adsorption vs. pH. The values of log~KCu, nCu and p, in the NICA isotherm, or log KCu, in the Langmuircompetitive models, were first optimized by least-squares fit foreach data set, and then average values (see Table 2) were used tofit the experimental data according to the different models, asshown in Figs. 5 and 6. In the NICA model, the separation of nH

and p was made using the constraint mH = nH�p.The fits to NICA and Langmuir competitive models of the copper

binding data at different pH values are shown in Fig. 4. It can be ob-served that the NICA equation is able to reproduce experimentaldata satisfactory. On the other hand, Langmuir competitive 1:2model describes with accuracy the ‘‘S-shaped” curve in Fig. 4, butit does not reproduce the plateau reached at pH values higher than4, whereas the Langmuir competitive 1:1 model is not able toreproduce the pH effect.

Experimental data from isotherm at pH 4 were also fitted usingNICA and Langmuir competitive models (Fig. 5). The obtained re-sults demonstrate, like in the study for the adsorption of copperas a function of pH, that only NICA model can explain experimentaldata properly, employing the same constants attained through pro-ton binding studies.

Fig. 6. Effect of cadmium ions competition on copper elimination by S. muticum atpH = 4.0 ± 0.1 at 25 �C. Symbols represent experimental points at two differentcadmium initial concentrations: 0.1 mmol L�1 (filled circles) and 5 mmol L�1 (opencircles). Solid lines represent the fitted NICA isotherm.

3.2.3. Cadmium competition on copper uptakeThe values listed in Table 2, together with the obtained in a pre-

vious article for cadmium adsorption (log ~KCd = 3.1 and nCd = 1.8)(Lodeiro et al., 2005) were also used to predict the competition be-tween copper and cadmium ions for the biosorbent binding sites.The comparison between experimental results from copper iso-therms (at two different initial cadmium concentration) and NICAmodel prediction is shown in Fig. 6. Note that there is good agree-ment between model and experimental data, using the same mod-el parameters, estimated from the batch sorption experiments andacid–base titrations in the absence of metal.

One must be aware that the use of the initial concentration ofthe competitor metal (cadmium) does not reflect the sorptionequilibrium; however, for modelling purposes only final equilib-

Table 2Optimal parameters estimated for copper binding by the acid-treated biomass.

Copper binding parameters

Log ~KCu/log KCu nCu/n Heterogeneity parameter, p

NICA fit 4.3 ± 0.1 1.05 ± 0.05 0.31 ± 0.02Lang. fit 3.5 ± 0.1 1Lang. fit 5.0 ± 0.5 0.5

rium concentrations were considered. In general terms, it can beobserved that as copper initial concentration is incremented, theeffect of cadmium competition decreases. So that, little differencebetween the two isotherms is observed at the highest initial copperconcentration. Moreover, the copper isotherm is practically identi-cal to the respective one in absence of the competitive cation atany copper concentration for the lowest initial cadmium concen-tration (compare experimental points in Fig. 5 and filled circle datain Fig. 6). This result demonstrates a greater affinity of the activebinding sites in the alga for copper ions than for cadmium ions.This fact is also supported by the median values of the affinity dis-tribution for cadmium and copper determined with the NICA mod-el, 103.1 (Lodeiro et al., 2005) and 104.3 (Table 2), respectively. Thisaverage Cu binding affinity reported lies between the values of 5.0and 3.6 for the formation constant of Cu–alginate complexes(assuming langmuirian complexation) reported by De Stefanoet al. (2010) at infinite dilution and 0.1 M ionic strength, respec-tively. It is also probe that the main responsible for the metal sorp-tion in brown algae is alginate.

Table 3Percentage of copper released from protonated S. muticum biomass (previously metal-loaded), employing HNO3 and HCl acids as desorbent agents at different concentra-tions, solution volumes and contact times.

Concentration (mol L�1) Contact time (h) Volume (mL) %Cu removed

HCl0.05 2 10 780.1 2 10 730.5 2 10 760.05 2 20 860.05 4 20 94

HNO3

0.05 2 10 690.1 2 10 750.5 2 10 800.05 2 20 870.05 4 20 95

2996 R. Herrero et al. / Bioresource Technology 102 (2011) 2990–2997

3.3. Desorption studies

In general, the application of biosorption as a useful alternativein wastewater treatment implies the sorbent regeneration, in orderto recover the bounded metal and to reduce process costs.

Desorption studies require a great amount of experimentalwork to determine both the ideal desorbent and its best conditionsof use. In batch studies, it must be taken into account that the me-tal, once desorbed, remains in solution and it continues in contactwith the biosorbent, so a new adsorption equilibrium could beestablished, affecting desorption process.

An important parameter that requires special consideration isthe solid/liquid ratio (biosorbent mass/solution volume). Ideally,this relation should be as high as possible, which implies lowdesorbent volume and/or a great amount of biomass.

Two acids, HNO3 and HCl, were both tested in batch studies andevaluated according to their effectiveness to release copper from themetal-laden biomass, estimating the percentage of metal desorbed(Table 3). The acid elution efficiency was based on the competitionbetween protons and the heavy metal ions bound to active sites,which will be released if eluant concentration is high enough andthere is not steric impediment. Both acids were found to be verypowerful metal-desorbing agents (Lodeiro et al., 2006; Vilar et al.,2007), with the added advantage that both the release of metaland the regeneration of the alga can be achieved just in one step.

First of all, three acid concentrations (0.05, 0.1 and 0.5 mol L�1)were tested at fixed both contact time (2 h) and solution volume(10 mL). As it is shown in Table 3, the percentage of desorbed cop-per was similar for the different concentrations of both acids,although not as high as it was desirable (around 75% of copper re-moval). So that, the lowest acid concentration was selected and thesolution volume was increased to 20 mL in order to improve themix, making that the solution was not so dense. In this way, thepercentage of desorbed copper was raised up to 87%. As the contacttime was augmented to 4 h, percentages very close to the completecopper desorption were achieved (Table 3).

To summarize, it can be said that the effectiveness of HNO3 andHCl acids is very similar. Their optimal conditions of use are an acidconcentration of 0.05 mol L�1, a solid/liquid ratio of 5 g/L and a bio-mass-desorbent contact time of 4 h.

4. Conclusions

Kinetic experiments showed that equilibrium was attainedwithin 1 h. An intraparticle-diffusion linear driving force model(LDF) was able to correctly explain these dynamic experiments.

A simple Langmuir or Langmuir–Freundlich isotherm can beused to accuracy describe equilibrium experiments. However, onlythe application of a model that takes into account the complexity

of macromolecular systems, e.g., NICA model, allows a gooddescription of all equilibrium experiments tested (isotherm, pHinfluence and competition between copper and cadmium) employ-ing the same constants attained through proton binding stud-ies.Batch studies proved the high efficiency of HNO3 and HClacids as copper desorbing agents.

Acknowledgements

This work was funded by the projects CTM2006-03142/TECNO(from the Ministerio de Educación y Ciencia of Spain) and PGDIT06-TAM00401CT (from Xunta de Galicia). The authors would like tothank Dr. I. Bárbara and Dr. J. Cremades (University of A Coruña)for the collection and classification of the algae species. Pablo Lode-iro gratefully acknowledges financial support through Ángeles Al-variño project AA 10.02.56B.444.0 and the grant for research stayoutside Galicia 10.02.561B.480.0 (from Xunta de Galicia), bothco-funded by 80% with European Social Funds.

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