cd (ii) and zn (ii) biosorption on lactarius piperatus macrofungus:...

Cd (II) and Zn (II) Biosorption on Lactarius

piperatus Macrofungus: Equilibrium Isotherm and

Kinetic StudiesBoldizsar Nagy, Botond Szilagyi, Cornelia Majdik, Gabriel Katona,Cerasella Indolean, and Andrada M�aic�aneanuFaculty of Chemistry and Chemical Engineering, Babes-Bolyai University, RO-400028, Cluj-Napoca, Romania;[email protected] (for correspondence)

Published online 14 November 2013 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ep.11897

In this study, biosorption of cadmium (Cd) (II) and zinc(Zn) (II) ions from synthetic wastewater was investigatedusing Lactarius piperatus macrofungus biomass in batchconditions. The presence of amino, carboxylic, sulfonate,and phosphate groups was identified along with shifts anddecreased intensities of the main peaks (Fourier transforminfrared spectroscopy), and deformations of macrofunguscell walls after heavy metals biosorption (scanning electronmicroscopy) were observed. The effects of stirring rate, bio-mass quantity, initial metal ion concentration, contact time,pH, and temperature were studied. The optimum parameterswere established as follows: 700 rpm, 2 g (for Cd) and 5 g(for Zn) biosorbent, pH in the range of 5.49–5.72, and 296K. By comparing various kinetic models, the biosorption pro-cess was found to follow the pseudo-second-order kinetics.Isotherm models were tested using linear and nonlinear(Covariance Matrix Adaptation Evolution Strategy optimiza-tion algorithm) regression analyses. Maximum adsorptioncapacities calculated using Langmuir isotherm were 10.65mg/g for Cd (II) and 7.54 mg/g for Zn (II). Results alsoshowed that nonlinear regression analysis has better per-formances, with Sips model, describing process the best. Theresults indicated that L. piperatus can be used as a cost-effective biosorbent for the removal of Cd (II) and Zn (II)ions from aqueous solution. VC 2013 American Institute ofChemical Engineers Environ Prog, 33: 1158–1170, 2014

Keywords: biosorption, metal ions, Lactarius piperatus,regression analysis, CMA-ES algorithm

INTRODUCTION

With the increasing of industrial activities, nowadays, theenvironment is heavily exposed to different kinds of pollu-tion. One of the biggest global problems could be consid-ered the heavy metal pollution. These toxic contaminantsreleased by different industries are discharged in the environ-ment with risks for all living organism (terrestrial or aquatic).

Cadmium (Cd) is one of the most toxic metals found inindustrial effluents. This dangerous pollutant is produced bymetal plating, battery production, mining and other indus-tries operation, energy and fuel production, fertilizer andpesticide industry, electrolysis, electro-osmosis, leatherwork-ing, photography, aerospace, atomic energy installation, andso forth [1]. Zinc (Zn) is an essential requirement for goodhealth but excess can be harmful. Different industrial processsuch as steel works with galvanizing lines, electroplating,and viscose rayon units are major sources for wastewatercontamination with Zn.

Biosorption has gained an important credibility because ofits eco-friendly nature, low operating cost, short operationtime, and no production of secondary compounds that may betoxic. As it is known, biosorption can be defined as theremoval of metal or metalloid species, compounds, and partic-ulates from solution by biological material [2]. In the last fewyears, a variety of biomaterials and micro-organisms havebeen explored by the researchers including algae [3], fungi [4],aquatic mosses [5], sawdust [6], but mushrooms which belongto the category of macrofungi are far from being thoroughlyinvestigated [4,7,8]. Macrofungi are edible fungi of commercialimportance and their cultivation has emerged as a promisingagro-based land-independent enterprise. More than 2000 spe-cies of macrofungi exist in nature but only 22 species areextensively cultivated for commercial purposes [9].

Fruit bodies of macrofungi (mushrooms) are consideredideal as biosorbents, because it has been demonstrated thatmany fungal species exhibit high biosorptive potential [10].Mushrooms are important sources of nutrients and manymedicinal properties have been credited to them such assuppression of platelet aggregation, reduction of blood cho-lesterol, prevention of heart disease, and reduction of bloodglucose level [11].

Lactarius piperatus was chosen as a biosorbent materialin this study because it is green and economical, and thereare relatively little information in the literature regarding theremoval of the heavy metal ions from aqueous solution.

The linear regression analysis has been the most com-monly used technique to evaluate the fit of experimental dataand isotherm models for sorption systems [12]. The linearleast-squares method with linearly transformed kinetic rateequation has been also widely applied for confirming the

This article was published online on 14 November 2013. An errorwas subsequently identified. This notice is included in the onlineand print versions to indicate that both have been corrected 28November 2013.

VC 2013 American Institute of Chemical Engineers

Environmental Progress & Sustainable Energy (Vol.33, No.4) DOI 10.1002/ep1158 December 2014

experimental data using coefficients of determination [13].However, during the last few years, a development of interestin the utilization of nonlinear optimization modeling hasbeen reported [14]. This is mainly because the transformationof nonlinear equations to linear forms implicitly alters theirerror structure and may also violate the error variance andnormality assumptions of standard least squares [15].

The aim of this study was to investigate the biosorptionpotential of L. piperatus for the removal of Cd (II) and Zn (II)ions from aqueous solution. The effects of stirring rate, bio-mass quantity, initial metal ion concentration, contact time,pH, and temperature were studied. Biosorption kinetics datawere fitted using pseudo-first-, pseudo-second-order, intrapar-ticle, and film diffusion models. Isotherm models were testedusing linear and nonlinear regression analyses to determinethe best fitting with experimental data. A nonlinear optimiza-tion algorithm (Covariance Matrix Adaptation Evolution Strat-egy, CMA-ES) was used in describing biosorption.

MATERIALS AND METHODS

BiosorbentThe macrofungus L. piperatus biomass was collected from

a local woodland area near Cluj-Napoca, Cluj County, Roma-nia. Before use, samples were washed several times with dis-tilled water to remove dirt and surface impurities, and dried at70�C for 24 h. The dried samples were grinded and sieved;0.6–1.2-mm fraction was further used in all experiments. Thesieved biomass was then stored in an airtight box before itsutilization.

ChemicalsThe stock solution, 1 g/L of Cd (II) and Zn (II), was pre-

pared by dissolving Cd(NO3)2�4H2O or ZnSO4�7H2O in dis-tilled water. The required concentrations were obtained bydiluting the stock solution to the desired concentrations (50–270 mg/L). HCl (0.1 M) and NaOH (0.1 M) volumetric solu-tions were used to adjust the pH of the solution. All chemi-cals used were of analytical grade.

Biomass Characterization

Microscopy Investigations

Scanning electron microscopy (SEM) images for L. pipera-tus biomass were obtained with a JEOL (USA) JSM 5510 LVapparatus.

Fourier Transform Infrared Spectroscopy Spectral Analysis

Fresh and used (separated from Cd and Zn solution afterbiosorption) macrofungus samples were subjected to Fouriertransform infrared spectroscopy (FTIR) analysis for bothheavy metals. Macrofungus-dried samples were prepared byencapsulating 1.2 mg of finely grounded biomass particles in300 mg of KBr. Infrared spectra were obtained using a JASCO615 FTIR spectrometer 400–4000 cm21 (resolution, 2 cm21)and data were processed with ORIGIN PRO 8.5 software.

Adsorption Experiments

To optimize the experimental conditions and to collectdata for the modeling study, batch experiments were per-formed, contacting various quantities of L. piperatus macro-fungus (1–5 g) at 300–700 rpm (magnetic stirring) with 100mL aqueous solution of Cd (II) or Zn (II) at different initialconcentrations (50–270 mg/L) at 296 K for 240 min to reachequilibrium. To determine the exact concentration of Cd andZn ions and to establish the evolution of the removal pro-cess, samples of 100 lL were collected at different time inter-vals up to 240 min (preliminary experiments showed thatthis time is sufficient for attaining the equilibrium).

At the end of the predetermined time, the suspension wasfiltered and the remaining concentration of metal in theaqueous phase was determined using an Atomic AbsorptionSpectrometer (SensAA Dual GBS Scientific Equipment, Aus-tralia). To evaluate the amount of Cd and Zn ions retainedper unit mass of macrofungus, the adsorption capacity wascalculated using the following equation:

qe5ðC02CeÞV

m(1)

where qe is the amount adsorbed at equilibrium (mg/g), C0

is the initial metal ion concentration (mg/L), Ce is the equi-librium metal ion concentration (mg/L), V is the volume ofthe aqueous phase (L), and m is the biosorbent mass.

Removal efficiency, E (%), was calculated as a ratiobetween Cd (II) and Zn (II) biosorbed at time t (mg/L) andthe initial metal ion concentration (mg/L):

E5C02Ct

C03100 (2)

To study the effect of pH on metal uptake by L. piperatus,the initial pH solution was varied in the range of 2.57–8.34. pHvalue was adjusted using 0.1 M of HCl and 0.1 M of NaOH sol-utions at the beginning of the experiment. pH values weremeasured using a Consort C863 pH-meter. To establish thethermodynamic parameters, temperatures of 296, 306, and 316K were used to conduct biosorption experiments.

Experimental data were used to determine the equilib-rium time, equilibrium concentrations, amounts adsorbed atequilibrium, optimum pH, and the quantity of biosorbent formaximum efficiency. Also, the experimental data were usedto establish isotherm (linear and nonlinear regression) andkinetic models. All the experiments were repeated threetimes; the values presented were calculated using averagedconcentration values.

Biosorption Kinetics

Pseudo-first- (Lagergren) and pseudo-second-order (Ho),intraparticle diffusion (Weber and Morris), and liquid film dif-fusion models (Table 1) were used to test the experimentaldata to investigate the adsorption mechanism and potentialrate determining steps [16,17]. The linear regression analysis(coefficient of determination, R2) was used to analyze the lin-ear forms of kinetic models. Kinetic constants were deter-mined using slope and intercept values of the linear plots.

The pore diffusion coefficient, D (cm2/s), for the removalof metal ions by L. piperatus biomass was calculated (assum-ing a spherical geometry of the biosorbent—average size, 0.9mm) using the following equation [18]:

D50:003r20

t1=2

� �(3)

Based on the pseudo-second-order model, t1/2 was esti-mated using Eq. (4):

t1=251

k2qe(4)

Biosorption Isotherm Models

Adsorption occurs whenever a solid surface is exposed toa gas or liquid and it is defined as the enrichment of materialor increase in the density of the fluid in the vicinity of aninterface. The relationship, at constant temperature, betweenthe amount adsorbed and the equilibrium pressure, or con-centration, is known as the adsorption isotherm [19]. In this

Environmental Progress & Sustainable Energy (Vol.33, No.4) DOI 10.1002/ep December 2014 1159

study, we used some well-known adsorption isotherm (Table2) with two and three parameters to describe the biosorptionprocess of Cd (II) and Zn (II) on L. piperatus macrofungus.

Langmuir model [20] assumes a monolayer adsorption,with no lateral interaction between the adsorbed moleculeson specific sites. Freundlich empirical model [21] can beapplied to multilayer adsorption, with nonuniform distribu-tion of adsorption heat and affinities over the heterogeneoussurface. Dubinin–Radushkevich isotherm [22] is generallyapplied to express the adsorption mechanism with a Gaus-sian energy distribution onto a heterogeneous surface. Tem-kin isotherm [23] contains a factor that explicitly takes intoaccount adsorbent–adsorbate interactions. Hill equation [24]was postulated to describe the binding of different speciesonto homogeneous substrates. Redlich–Peterson isotherm[25] is a hybrid isotherm featuring both Langmuir andFreundlich isotherms, which incorporates three parametersinto an empirical equation. Sips isotherm [26] is a combinedform of Langmuir and Freundlich expressions deduced forpredicting the heterogeneous adsorption systems. Toth iso-therm model [27] is another empirical equation developed toimprove Langmuir isotherm fittings (experimental data), andit is useful in describing the heterogeneous adsorption sys-tems. Khan isotherm [28] is a generalized model suggestedfor pure solutions, whereas Radke–Prausnitz isotherm is aversatile equation, which reduces to linear, Freundlich, orLangmuir isotherms in specific cases [14,29].

Using two- and three-parameter adsorption isothermmodels, adsorption capacity values, qe, can be calculated asa function of equilibrium concentrations, Ce, which can bedetermined experimentally. The goal is the identification of

the model parameters, which gives the minimal differencesbetween the measured and the calculated value of qe. Tradi-tional way to realize parameter estimation is linearizationand linear regression of the isotherm models. In this case,linearity of the plots expressed by coefficient of distribution,R2, can give some information about the fitting between theexperimental data and the isotherm model, the closest to lin-earity could be considered as describing better the adsorp-tion equilibrium in a certain system. The problem is that thelinearization cannot be realized in every case and even if itis possible it should be applied prudently. Nonlinear regres-sion is a more general method that can be used to estimatemodel parameters, and it can be applied even if the isothermmodel cannot be linearized.

In this study, linear and nonlinear regressions were used toobtain isotherm parameter values for Cd (II) and Zn (II) bio-sorption on L. piperatus macrofungus. In case of linear regres-sion, coefficient of determination, R2, was used to analyzeLangmuir and Freundlich isotherm models. In case of nonlin-ear regression, the searching of best-fitting parameters wasdefined as an optimization problem. In this study, as there isno initial guess about the optimal values of the decision varia-bles, instead of inner, deterministic optimization algorithms ofthe MATLAB, we used a stochastic optimization algorithmwhich is the CMA-ES [30]. This algorithm finds the global min-imum of the objective function using statistical computationmethods. Standard deviation, a nonlinear relationship that canbe applied well as an objective function, defined by the fol-lowing equation, was chosen as objective function [31]:

SD5

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1

n

Xn

i51

�qcalcðiÞ2qexpðiÞ

�2

s(5)

From the definition of the standard deviation, it seemsthat the equation is a nonlinear relationship, which can beapplied well as an objective function.

The decision variables of the objective function were theisotherm model parameters:

SDðpar1; par2; par3 Þ¼! min (6)

In the case of nonlinear regression parameter identifica-tion, there is always a risk that the optimization algorithm

Table 1. Kinetic model equations used to investigate Cd (II)and Zn (II) biosorption on L. piperatus macrofungusbiomass.

Kinetic model Nonlinear form Linear form

Pseudo-first-order dqt

dt 5k1ðqe2qtÞ ln ðqe2qtÞ5ln ðqe2k1tÞ

Pseudo-kinetic-order dqt

dt5k2ðqe2qtÞ2 t

qt5 1

k2q2e1 t

qe

Intra-particlediffusion

— qt5kipt0:5

Liquid film diffusion — ln ð12FÞ52kfdt

Table 2. Equilibrium model equations used to investigate Cd (II) and Zn (II) biosorption on L. piperatus macrofungusbiomass.

Isotherm Nonlinear form Linear form

Langmuir qe5qmax KLCe

11KLCe1qe

5 1qmax

1 1qmax KL

3 1Ce

� �Freundlich qe ¼ KFC

1=ne

log qe5log KF1 1n log Ce

Dubinin–Radushkevich

qe5qs3exp ð2kadE2Þ ln qe5ln qs2kadE2

Tempkin qe5RTbT

ln AT Ce qe5RTbT

ln AT 1 RTbT

� �ln Ce

Hill qe5qsH C

nHe

KD1CnHe

log qe

qsH 2qe

� �5nH log Ce2log KD

Redlich–Peterson qe5KRCe

11aRCge ln KR

Ce

qe21

� �5gln Ce1ln aR

Sips qe5KS C

bSe

11aS CbSe

bS ln Ce52ln KS

qe

� �1ln aS

Toth qe5 KT Ce

ðaT 1CeÞ1=t ln qe

KT

� �5ln Ce2

1t3�ln ðaT 1CeÞ

�Khan qe5

qS bK Ce

ð11bK CeÞaK—

Radke–Prausnitz qe5aRP rRC

bRe

aRP 1rRCbR21e

—


will find a local minimum as the solution. All of the inneroptimization functions of the MATLAB program use deter-ministic algorithms (fminsearc, Nelder-Mead simplex algo-rithm; fminbnd, golden-section method; fmincon, sequentialquadratic programming, etc.). These methods can be usedsuccessfully if the starting point is near to the global mini-

mum, and hence if we have an initial guess about the opti-mal values of the decision variables.

The traditional way to characterize the model accuracyfor nonlinear regression is by the coefficient of determi-nation. This coefficient is calculated by most of the com-mercial statistical software packages (MS Excel, Origin,

Figure 1. SEM images of the L. piperatus macrofungus before (a), after Cd (II) (b), and after Zn (II) (c) biosorption.

Figure 2. FTIR spectra of the L. piperatus macrofungus before (a), after Cd (II) (b), and after Zn (II) (c) biosorption. [Color fig-ure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]


wileyonlinelibrary.com

etc.) and its calculation is based on the followingrelationships:

SSerr5Xn

i51

ðyi2fiÞ2 (7)

SStot5Xn

i51

yi21

N

Xn

j51

yj

!(8)

R2512SSerr

SStot(9)

In the cases of Langmuir and Freundlich isotherms, themodel parameters were calculated by both the linear and thenonlinear regression methods as described above.

RESULTS AND DISCUSSION

Biosorbent CharacterizationThe macrofungus, L. piperatus, commonly known as the

pappery milk-cap is a semi-edible basidiomycete fungus nat-ural and readily available biosorbent. L. piperatus is found inEurope in the Black Sea region on the floor in deciduouswoodland. It is a relatively large size mushroom, which hasa funnel-shaped when mature, with exceptionally crowdedgills [32].

SEM AnalysisSEM studies of the L. piperatus were conducted to exam-

ine the textural and surface morphologies of the biomass.Based on the SEM images (Figure 1a), it can be concludedthat macrofungus has an intact structure with a large numberof voids. Figures 1b and 1c show SEM images of the L. piper-atus biomass after Cd (II) and Zn (II) biosorption. Theseimages show some changes and deformation of cell walls.

FTIR Spectral AnalysisFTIR spectral analysis was used to collect more informa-

tion about the characteristic functional groups, which havean important role in the biosorption process of the metalions. As shown in Figure 2, the spectra display several vibra-tional bands, indicating the complex nature of the materialexamined. To determine the functional groups, fresh (Figure2a) and used (metal ions loaded) L. piperatus biomass, Cd(II) (Figure 2b) and Zn (II) (Figure 2c), samples were sub-jected to FTIR analysis. The complex spectra obtained con-firmed the presence of R-NH2 amino groups (amino acids,proteins, glycoproteins, etc.), carboxylic acids (fatty acids,lipopolysaccharides, etc.), sulfonate, and phosphate [33]. Thebroad and strong band observed at 3418 cm21 for fresh,3431 cm21, and 3416 cm21 for Cd (II)-loaded and Zn (II)-loaded was owing to bonded hydroxyl (AOH) and amino(ANHA) groups. The changes in AOH absorption peakshowed that metals binding decreased the degree of the

Figure 4. Influence of the initial Cd (II) concentration overthe (a) time evolution and (b) adsorption capacity on the L.piperatus macrofungus; Ci 5 50–270 mg/L, 0.6 < d < 1.2mm, 296 K, pH 5.72, and 700 rpm. [Color figure can beviewed in the online issue, which is available atwileyonlinelibrary.com.]

Figure 3. The effect of the L. piperatus macrofungus quan-tity on (a) Cd (II) and (b) Zn (II) biosorption; Ci 5 50 mg/L,0.6 < d < 1.2 mm, 296 K, 5.72 (Cd)/5.49 (Zn) pH, 700 rpm.[Color figure can be viewed in the online issue, which isavailable at wileyonlinelibrary.com.]




hydroxyl polymerization from lignocellulose present in fun-gal cell wall.

In Figure 2, the peaks observed at 2920 cm21 for (a),2919 cm21 for (b), and 2918 cm21 for (c) spectra can beassigned to the ACH groups of the biomass, closely to theliterature data [4].

The peaks at 1653 cm21 that originate from asymmetricstretching of carboxylic C@O double-bond vibrations,coupled to NAH bending vibrations, indicate the presence ofamide I structure. The amide II bands at 1559 cm21 remainunmodified after Cd (II) and Zn (II) adsorption and arisefrom the NAH bending vibrations coupled to CAN stretchingvibrations. The peaks observed around 1458 cm21 are attrib-uted to the CAO bond in carboxylic groups, coupled withS@O stretching from sulfate groups. At 1395 cm21, a weakpeak appears in case of the fresh sample, which was shiftedafter loading of heavy metals (at 1383 cm21, for both Cd (II)and Zn (II) biosorptions). Peak identified at 1279 cm21—for(a) spectrum—represents the deformation vibrations of C@Ocarboxylic acids and was shifted to 1276 and 1273 cm21,respectively, after uptake of metals.

Also, an average band of CAOAC stretching appearsaround 1075 cm21 before biosorption, at 1076 cm21 after Cd(II), and 1077 cm21 after Zn (II) uptake and could beassigned to CAOAC stretching in sugars. The spectra in<800 cm21 region indicate that phosphate, sulfonate, andsulfide groups are also present in the biomass. Similar FTIRresults were reported for the biosorption of Cd (II) on mac-

rofungus Amanita rubescens, Pleurotus platypus, and Lactar-ius scrobiculatus biomasses [4]. Changes in FTIR spectrawere observed after both Cd (II) and Zn (II) are biosorbed.Shifts and decreased intensities of the main peaks wererecorded after metal ion biosorption. These results suggestthat the presence of these important groups in L. piperatusmacrofungus plays an essential role in binding metals ions,in our case Cd (II) and Zn (II).

Biosorption Results

Effect of Stirring Rate and Biomass Quantity

The stirring rate is an important parameter that may influ-ence the metal ion uptake on L. piperatus biomass. Threedifferent stirring rates (700–500–300 rpm) were tested. Metalion amounts adsorbed increased with the stirring rate toreach maximum at 700 rpm. Lower speeds could cause inef-ficient dispersion of the biomass particles in solution that ledto agglomeration of particles [11] and also could limit theexternal diffusion through the liquid film on the particle sur-face. The obtained values for adsorption capacity decreasedfrom 2.06 to 1.99 mg/g for Cd (II) and from 0.70 to 0.64 mg/g for Zn (II), at 700 and 300 rpm, respectively. Therefore, astirring speed of 700 rpm was selected from these results forboth heavy metals that have been studied.

To determine the optimal biomass quantity, variousamounts of L. piperatus macrofungus (between 1 and 5 g)were contacted with Cd (II) and Zn (II) (50 mg/L) solutions.Biomass quantity influences the removal of Cd (II) and Zn(II) as shown in Figure 3, indicating that as the biosorbentquantity increases, adsorption capacity will decrease (reducedunsaturation, particle agglomeration which will increase diffu-sional path length) [34], and removal efficiency will increaseowing to the increased number of active sites on biosorbentsurfaces and exchangeable ions available for adsorption.

Between the two considered ions, Cd (II) has a higheraffinity toward the biosorbent surface, which reflects inhigher adsorption capacity values obtained. Furthermore, allthe experiments were conducted using 2 and 5 g biomassfor Cd (II) and Zn (II), respectively.

Effect of Initial Metal Ion Concentration and Contact Time

When initial metal ion concentration was considered as aparameter in the biosorption process, the following conclu-sions can be depicted: (a) concentration has an abruptdecrease in the first 10 min from the beginning of the experi-ment; (b) concentration decreases slowly until equilibrium;(c) equilibrium was reached in about 80 min for morediluted solutions (50–100 mg/L), and in about 120 min forother solutions (Figure 4a); and (d) adsorption capacityincreases with the increase in concentration (Figure 4b).

These conclusions indicate that if the metal ion concentra-tion in solution increases, the difference in concentrationbetween bulk solution and surface also increases, intensify-ing the mass transfer processes. Accordingly, a higher quan-tity will be adsorbed, proving that L. piperatus macrofungushas a high adsorption capacity toward metal ions. Adsorptioncapacities up to 7.66 and 2.28 mg/L were calculated for Cd(II) and Zn (II), respectively. Removal efficiencies reached amaximum of 83.85% for Cd (II) and 65.34% for Zn (II).

Effect of Initial pH

The pH value at which biosorption process takes place isan important factor that strongly influences the metal ionremoval process. This parameter is directly related to thecompetition between protons and metal ions to active siteson biosorbent surface [35]. The role of initial hydrogen ionconcentration on the biosorption of Cd (II) and Zn (II) ionsonto L. piperatus was studied within the pH range of 2.57–

Figure 5. (a) Initial and final pH values and (b) the effect ofinitial pH values on removal efficiency values for Cd (II) bio-sorption using L. piperatus macrofungus; Ci 5 50 mg/L, 0.6< d < 1.2 mm, 296 K, and 700 rpm. [Color figure can beviewed in the online issue, which is available atwileyonlinelibrary.com.]



8.34 and the results are shown in Figure 5, as an examplefor Cd (II).

Measuring pH values at the beginning and at the end ofthe biosorption process, we observed, for both metals, thatirrespective of the initial pH value (acid or basic), equilib-rium pH values increase (from the initial acid) and decrease(from the initial basic) toward neutral (slightly acidic) in therange of 5.65–6.18 for Zn (II) and 4.53–6.03 for Cd (II) (Fig-ure 5a).

For both metal ions studied, we can observe that with anincrease of the initial solution pH, the biosorption efficiencyalso increases. In case of Zn (II), the steep increase observedafter pH of 7 is owing to the starting of the precipitation pro-cess. In case of Cd (II) (Figure 5b) minimum removal effi-ciency (62.37%) was calculated at pH of 2.57 and maximumremoval efficiency (94.58%) at pH of 8.34. Further increase

of the pH (>8.34) will lead to Cd precipitation. As the initialpH is lowered, the cell-wall surface-binding sites charge willbecome positive (owing to the fact that protons are favoredin direct competition between them and metal ions ontoactive sites of biosorbent surface), restricting the metal ionsfrom reaching the binding site on the cell wall. As the initialpH increases, the charge of cell-wall-binding sites willbecome less positive, leading to an increase of the biosorp-tion efficiency [35].

Effect of Temperature

To determine the effect of temperature on the biosorptionprocess of Cd (II) and Zn (II) on L. piperatus macrofungus,three different temperatures were considered 23�C (296 K),33�C (306 K), and 43�C (316 K). The influence of

Figure 6. Temperature influence over the (a) adsorption capacity and (b) removal efficiency of Zn (II) on L. piperatus macro-fungus; Ci 5 50 mg/L, 0.6 < d < 1.2 mm, pH 5.49, and 700 rpm. [Color figure can be viewed in the online issue, which isavailable at wileyonlinelibrary.com.]



temperature on adsorption capacity and efficiency of Zn (II)biosorption on L. piperatus macrofungus is shown inFigure 6.

As it can be observed, an increase in temperature led toan increase of the adsorption capacity and efficiency, sug-gesting that the biosorption process is endothermic. Similar

Figure 7. Plots of the (a) pseudo-first-order and (b) pseudo-second-order kinetic model for Cd (II) biosorption using L.piperatus macrofungus; Ci 5 50–270 mg/L, 0.6 < d < 1.2mm, 296 K, pH 5.72, and 700 rpm. [Color figure can beviewed in the online issue, which is available atwileyonlinelibrary.com.]

Table 3. Pseudo-first-order and pseudo-second-order rate constants, calculated and experimental qe values for Cd (II) and Zn(II) biosorption on L. piperatus biomass using different initial concentrations; Ci 5 50–270 mg/L, 2 g (Cd)/5g (Zn), 0.6 < d <1.2 mm, 296 K, 5.72 (Cd)/5.49 (Zn) pH, 700 rpm.

C (mg/L)qe (exp)(mg/g)

Pseudo-first-order Pseudo-second-order

k1 (1/min)qe (calc)(mg/g) R2 k2 (g/mg�min)

qe (calc)(mg/g) R2

Cd (II)50 2.06 2.61 3 1022 0.88 0.9051 10.47 3 1022 2.10 0.9999100 4.12 2.20 3 1022 2.14 0.9591 2.73 3 1022 4.26 0.9998155 5.93 2.30 3 1022 4.01 0.8284 1.51 3 1022 6.12 0.9990200 6.64 2.52 3 1022 4.52 0.9725 1.29 3 1022 6.95 0.9998270 7.66 1.78 3 1022 4.11 0.9290 1.09 3 1022 7.97 0.9995Zn (II)50 0.70 2.75 3 1022 0.61 0.8754 10.18 3 1022 0.74 0.999280 1.09 1.74 3 1022 0.60 0.9355 7.27 3 1022 1.14 0.9998115 1.37 2.31 3 1022 0.83 0.9662 6.44 3 1022 1.43 0.9996160 2.07 2.45 3 1022 1.14 0.9585 5.24 3 1022 2.15 0.9996185 2.38 2.13 3 1022 1.28 0.9560 4.22 3 1022 2.45 0.9997

Figure 8. Plots of the (a) intraparticle diffusion and (b) liq-uid film diffusion model for Cd (II) biosorption using L.piperatus macrofungus; Ci 5 50–270 mg/L, 0.6 < d < 1.2mm, 296 K, pH 5.72, and 700 rpm. [Color figure can beviewed in the online issue, which is available atwileyonlinelibrary.com.]




results were reported in the literature for the biosorption ofCd (II) onto Pycnoporus sanguineus [36] and onto Rhodotor-ula rubra [37]. Higher temperatures were not consideredowing to the fact that the preliminary studies showed thatdesorption process begins after 50�C.

Biosorption KineticsTo determinate the biosorption kinetics of Cd (II) and Zn

(II) ions onto L. piperatus biomass, pseudo-first- and pseudo-second-order, intraparticle diffusion, and liquid film diffusionwere used to test the experimental data to investigate theadsorption mechanism and potential rate-determining steps.Linear regression was used to determine the best-fittingkinetic rate equation (coefficient of determination, R2).

Correlation coefficients obtained when the pseudo-first-order kinetic model was applied to metal ion biosorption(Table 3 and Figure 7a) have very low values. Also, calcu-lated adsorption capacities values show great differences bycomparison to experimental values. Based on this remark, itcan be concluded that the biosorption process cannot beclassified as first order. When pseudo-second-order kineticmodel was applied for the considered adsorption process(Table 3 and Figure 7b), coefficients of determination higherthan 0.99 were obtained. A comparison between calculated

and experimental adsorption capacities (Table 3) showed agood agreement. Therefore, it was concluded that Cu (II)and Zn (II) metal ion biosorption on L. piperatus biomass

Table 4. Intra-particle diffusion rate coefficients for Cd (II) and Zn (II) biosorption on L. piperatus biomass; Ci 5 50–270 mg/L,2 g (Cd)/5 g (Zn), 0.6 < d < 1.2 mm, 296 K, 5.72 (Cd)/5.49 (Zn) pH, 700 rpm.

C (mg/L) D (cm2/s)

Region 1, 10–45 minutesRegion 2, 45–150

minutesRegion 3, 150–240

minutes

kip

(mg/g�min1/2) R2kip

(mg/g�min1/2) R2kip

(mg/g�min1/2) R2

Cd (II)50 8.88 3 1028 0.17 0.9308 0.01 0.9826 0.01 0.8474100 4.71 3 1028 0.37 0.9263 0.08 0.9908 0.02 0.7677155 3.75 3 1028 0.50 0.9661 0.13 0.9593 0.10 0.7851200 3.64 3 1028 0.64 0.9695 0.15 0.9914 0.03 0.9053270 3.54 3 1028 0.78 0.9828 0.15 0.9529 0.10 0.7677

Intercept 0.87 – 1.37 1.85 – 5.54 1.90 – 6.09Zn (II)50 3.04 3 1028 0.07 0.9772 0.02 0.9623 0.05 3 1022 0.767780 3.35 3 1028 0.10 0.9672 0.02 0.9849 1.90 3 1022 0.7677115 3.73 3 1028 0.13 0.9409 0.04 0.9636 0.87 3 1022 0.9716160 4.56 3 1028 0.16 0.9710 0.04 0.896 0.88 3 1022 0.7677185 4.18 3 1028 0.21 0.9760 0.04 0.932 1.36 3 1022 0.8195

Intercept 0.12 – 0.65 0.42 – 1.82 0.69 – 2.17

Table 5. Liquid film diffusion rate coefficients for Cd (II)and Zn (II) biosorption on L. piperatus biomass; Ci 5 50–270mg/L, 2 g (Cd)/5g (Zn), 0.6 <; d < 1.2 mm, 296 K, 5.72(Cd)/5.49 (Zn) pH, 700 rpm.

C (mg/L) kfd (1/min) Intercept R2

Cd (II)50 0.0217 1.23 0.8976100 0.0207 0.81 0.9810155 0.0224 0.48 0.8068200 0.0246 0.47 0.9730270 0.0165 0.78 0.9522Zn (II)50 0.0271 0.24 0.871880 0.0161 0.75 0.9608115 0.0225 0.59 0.9831160 0.0223 0.74 0.9699185 0.0200 0.77 0.9747

Figure 9. Langmuir (a) and Freundlich (b) plots in linearregression analysis, for Cd (II) biosorption using L. piperatusmacrofungus; Ci 5 50–270 mg/L, 0.6 < d < 1.2 mm, 296 K,pH 5.72, and 700 rpm. [Color figure can be viewed in theonline issue, which is available at wileyonlinelibrary.com.]



obeyed the pseudo-second-order kinetic model, suggestingthat the considered process takes place as chemisorption.

Intraparticle diffusion plots for Cd (II) biosorption on L.piperatus biomass are shown in Figure 8a. It is observed thatdata present multilinear plots. Besides the initial region (0–10min), which is usually attributed to bulk diffusion, threeregions were identified (Figure 8a). These regions (three lin-ear plots) are ascribed to boundary layer diffusion (liquidfilm diffusion), intraparticle diffusion, and the final equilib-rium stage [38,39]. Plots of qt against t1/2 are linear, (R2-val-ues, Table 4), but with intercepts ranging between 10.87and 16.09 (plots do not pass through origin). In case of Zn(II), intercept values are smaller, suggesting that in this caseintraparticle diffusion might have an important role in the

biosorption process. Accordingly, smaller adsorption capaci-ties were obtained in this case (Table 3). The calculatedintraparticle diffusion rate constants and pore diffusion coef-ficients (assuming a spherical geometry for the adsorbents)are also listed in Table 4. The pore diffusion coefficientshave values in the range of 3.04 3 1028–8.88 3 1028 cm2/s,which is higher than the rate-determining range (10211–10213 cm2/s) [39]. Therefore, it can be concluded that intra-particle diffusion is not the rate-determining step.

Liquid film model was also applied to metal ion biosorp-tion on L. piperatus biomass, and exemplified in Figure 8b,for two concentrations (50 and 270 mg/L) in case of Cd (II).Linear plots of 2ln(1 2 F) against t were obtained, and rateconstants and intercepts were calculated (Table 5). As noneof these plots exhibits a zero–intercept, it could be suggestedthat kinetics of the considered process is not controlled bydiffusion through the liquid film surrounding the biosorbentgrains.

Taking into consideration all the results obtained byapplying kinetic models on Cd (II) and Zn (II) biosorptionon L. piperatus biomass experimental data, it can be con-cluded that the rate-determining step is the biosorptionprocess.

Biosorption Isotherm ModelsThe equilibrium adsorption study in aqueous phase is an

important step in the design of adsorption systems. In thisstudy, two equilibrium models, Langmuir and Freundlich iso-therms, were considered for linear regression analysis todetermine the best-fitting isotherm model (coefficient ofdetermination, R2) of Cd (II) and Zn (II) biosorption on L.piperatus biomass. Also, for nonlinear regression analysis 10isotherm models (Table 2) were considered. Standard devia-tion (SD) and coefficients of determination were calculated tocompare the isotherm models and the results of linear andnonlinear regression analyses.

The linear plots of the two considered isotherms for Cd(II) biosorption are shown in Figure 9. Langmuir and

Table 6. Langmuir and Freundlich coefficients calculated using linear regression analysis for Cd (II) biosorption on L. piperatusbiomass; Ci 5 50–270 mg/L, 2 g, 0.6 < d < 1.2 mm, 296 K, 5.72 pH, 700 rpm.

Langmuir coefficients Freundlich coefficients

KL (L/mg) qmax (mg/g) R2 nKf (mg(121/n)

L1/n/g) R2

0.032 10.65 0.9778 2.17 1.79 0.9037

Table 7. Adsorption isotherm models and their coefficients calculated using nonlinear regression analysis for Cd (II) biosorp-tion on L. piperatus biomass; Ci 5 50–270 mg/L, 2 g, 0.6 < d < 1.2 mm, 296 K, 5.72 pH, 700 rpm.

Isotherm Calculated parameters SD R2

Langmuir qmax 5 9.0066 KL 5 0.0468 — 0.2471 0.9845Freundlich KF 5 1.357 n 5 2.6848 — 0.5491 0.9232Dubinin–

Radushkevichqs 5 6.9031 kad 5 1.807 3 1025 — 0.5458 0.9241

Tempkin bT 5 1206.18 AT 5 0.4161 — 0.2909 0.9785Hill qsH 5 7.9316 nH 5 4.5484 KD 5 6.4492 0.1987 0.9900Redlich–

PetersonKR 5 0.3761 aR 5 0.0282 g 5 1.0812 0.2333 0.9862

Sips Ks 5 0.2087 b 5 1.2949 aS 5 0.0259 0.1955 0.9903Toth KT 515.397 aT 5 27.804 t 5 0.9086 0.2389 0.9855Khan qs 5 11.0202 bK 5 0.0360 aK 5 1.1006 0.2389 0.9855Radke–

PrausnitzaRP 5 0.4215 rR 5 9.007 br 5 0 0.2471 0.9862

Figure 10. Representation of the Langmuir, Freundlich,Dubinin–Radushkevich, and Sips plots in nonlinear regres-sion analysis in comparison with the experimental data forCd (II) biosorption using L. piperatus macrofungus; Ci 5 50–270 mg/L, 0.6 < d < 1.2 mm, 296 K, pH 5.72, and 700 rpm.[Color figure can be viewed in the online issue, which isavailable at wileyonlinelibrary.com.]



Freundlich coefficients calculated using linear regression arelisted in Table 6.

According to the Langmuir isotherm, the monolayeradsorption capacity was calculated to be 10.65 and 7.54 mg/g for Cd and Zn, respectively, suggesting that the consideredbiosorbents have high ability to retain these ions from aque-ous solutions. The relatively high values of Kf and n Freund-lich constants confirm the fact that the considered biomasshas high adsorption capacity and increased adsorption inten-sity toward the considered metal ions. From the linearity ofthe two plots, expressed by R2 (Table 6), it can be concludedthat as the Langmuir plot is closer to the linearity (higher R2-values), the metal ions biosorption onto L. piperatus biomasscould be described better by the Langmuir model.

The results obtained when nonlinear regression analysis wasapplied to the equilibrium isotherms, as described above Mate-rials and methods, are summarized in Table 7 (isotherm param-eters, standard deviation, and coefficient of determination).

Comparing the standard deviation values obtained foreach of the considered isotherms, the fitting degree followsthe sequence: Sips (best fit) > Hill > Redlich–Peterson >Toth 5 Khan > Langmuir 5 Radke–Prausnitz > Temkin >Dubinin–Radushkevich > Freundlich (Figure 10).

Comparing the R2-values obtained for each of the consid-ered isotherms, the fitting degree follows a similar sequence:Sips (best fit) > Hill > Redlich–Peterson 5 Radke–Prausnitz> Toth 5 Khan > Langmuir > Temkin > Dubinin–Radush-kevich > Freundlich.

As Sips isotherm model is a combined form of Langmuirand Freundlich isotherms, which circumvents the limitationsassociated with Freundlich model, with equation parametersgoverned mainly by the operation conditions [14], will reflectbetter the specific conditions in which the biosorption pro-cess takes place.

When comparing the R2-values obtained by linear andnonlinear regression analyses, it can be observed that nonlin-ear regression analysis gave better results, higher values ofR2, and therefore a better fitting with the experimental data.The cause of the better fitting is that in the case of nonlinearregression the experimental data were fitted directly to themodel but when working with linear regression a modifiedform of the model is used. Moreover, this model modifica-tion is leading to the violation of theories behind the iso-

therm models. The linear regression method approximatesthat the scatter of points around the line follows a Gaussiandistribution and the standard deviation at every value of Ce.In reality, this behavior is impossible with equilibrium iso-therm models. Nonlinear regression method avoids this typeof errors, making this technique the most appropriate to esti-mate the isotherm model parameters [40].

Table 8 compares the results from this study with otherpreviously reported adsorption capacities for Cd (II) and Zn(II) by different low-cost biosorbents. When comparing ourbiosorbent with other used biosorbents from the literature,we can conclude that L. piperatus macrofungus shows asmaller adsorption capacity in comparison with other mush-rooms but highest in comparison with sawdust and interme-diate with other biomaterial residues.

From the obtained results, it is noteworthy that the L.piperatus biomass has a considerable potential for theremoval of Cd (II) and Zn (II) ions from aqueous solution.

CONCLUSIONS

In this study, biosorption of Cd (II) and Zn (II) from syn-thetic wastewater was investigated using L. piperatus (macro-fungus) biomass in batch conditions.

The complex FTIR spectra obtained confirmed the pres-ence of amino, carboxylic acids, sulfonate, and phosphategroups. Also, shifts of the main peaks and decreased inten-sities were observed for the metal-loaded samples, indicatingthat the groups present on the biomass surface wereinvolved in the removal of Cd (II) and Zn (II). Deformationsof macrofungus cell walls after heavy metal biosorption(SEM) were also observed.

After the ranges of values were tested for biosorptionparameters, the following conclusions were drawn: adsorp-tion capacity increases with an increase of stirring rate, initialmetal ion concentration, and temperature. Also, a slightlyacidic toward neutral pH favors the process. A contact timebetween 80 and 120 min was necessary to reach equilibrium,depending on the initial concentration of metal ion.

By comparing various kinetic models, the biosorption pro-cess was found to follow the pseudo-second-order kinetics.Isotherm models were tested using linear and nonlinear(CMA-ES optimization algorithm) regression analyses. Maxi-mum adsorption capacities calculated using Langmuir

Table 8. Previously reported adsorption capacities for Cd (II) and Zn (II) on different biosorbents.

Biosorbent Cd (II) Zn (II) Reference

Lactarius piperatus macrofungus 10.65* 7.54* This studySchizophyllum commune macrofungus — 4.83 [41]Pleurotus platypus mushroom 34.96 —- [42]Agaricus bisporus mushroom 29.67 — [42]Calocybe indica mushroom 24.09 — [42]Cystoseira baccata marine macroalga 0.69 — [43]Codium vermilara algae 21.8 23.8 [44]Spirogyra insignis algae 22.9 21.1 [44]Asparagopsis armata algae 32.3 21.6 [44]Linden sawdust 3.5 2.17 [45]Pinus halepensis sawdust 7.35 — [46]Heartwood powder Areca catechu 10.66 — [47]Fir tree sawdust 2.2 — [48]Wheat bran 15.7 [49]Carrot residues — 29.61 [50]Grapefruit peel 42.09 [51]Coffe husks 6.85 5.56 [52]

*Linear regression results.


isotherm were 10.65 mg/g for Cd (II) and 7.54 mg/g for Zn(II). Results also showed that nonlinear regression analysis hasbetter performances with Sips model, describing process thebest.

All the obtained results confirmed that this efficient andlow-cost biosorbent might be useful for the removal of Cd(II) and Zn (II) ions from aqueous solution.

ACKNOWLEDGMENTS

This study was realized with the financial support of theSectoral Operational Programme for Human ResourcesDevelopment 2007-2013, cofinanced by the European SocialFund, under the project POSDRU/107/1.5/S/76841 with thetitle “Modern Doctoral Studies: Internationalization andInterdisciplinary.”

NOMENCLATURE

aK Khan isotherm model exponentaR Redlich-Peterson isotherm constant (mg/L)aRP Radke-Prausnitz isotherm model constantaS Sips isotherm model constant (mg/L)aT Toth isotherm constant (mg/L)AT Tempkin isotherm equilibrium binding constant

(g/L)bR Radke-Prausnitz isotherm model exponentbS Sips isotherm model exponentbK Khan isotherm model constantbT Tempkin isotherm constantBDR Dubinin-Radushkevich isotherm constantC metal ions concentration (mg/L)Ce equilibrium metal ions concentration (mg/L)Co initial metal ions concentration (mg/L)Ct times t metal ions concentration (mg/L)D Pore diffusion coefficient (cm2/s)E Dubinin-Radushkevich isotherm constantE removal efficiency (%)fi are the calculated qe valuesF fraction attainment at equilibrium (F 5 qt/qe)g Redlich-Peterson isotherm exponentKD Hill constantKF Freundlich isotherm constant (mg(1–1/n) L1/n/g)KL Langmuir isotherm constant (mg/L)KR Redlich-Peterson isotherm constant (mg/L)Ks Sips isotherm model constant (g/L)KT Toth isotherm constant (mg/g)k1 rate constant of pseudo-first-order adsorption (1/min)k2 rate constant of pseudo-second-order adsorption

(mg/g �min)kad Dubinin-Radushkevich isotherm constant (mol2/kJ2)kip intra-particle diffusion rate constant (mg/g �min1/2)kfd liquid film diffusion rate constant (1/min)n biosorption intensitynH Hill cooperativity coefficient of the binding interactionN number of concentration points (5 in this case)qcalc calculated value of the adsorption capacity (mg/g)qe amounts of metal ions adsorbed at equilibrium

(mg/g)qexp experimental value of the adsorption capacity

(mg/g)qmax maximum uptake saturation (mg/g)qs theoretical isotherm saturation capacity (mg/g)qsH

Hill isotherm maximum uptake saturation (mg/L)qt amounts of metal ions adsorbed at time t (mg/g)rR Radke-Prausnitz isotherm model constantR2 coefficient of determinationr0 particle diameter (cm)SD standard deviationSSerr residual sum of squaresSStot total sum of squarest Toth isotherm constant

t1/2 time for half adsorption (s)V volume of the aqueous phase (L)yi are the measured qe values

LITERATURE CITED

1. Wang, J., & Chen, C. (2009). Biosorbents for heavy metalsremoval and their future, Biotechnology Advanced, 27,195–226.

2. Gadd, G.M. (1993). Interactions of fungi with toxic met-als, Phytologist, 124, 25–60.

3. Sheng, P.X., Tim, Y.-P., Chen, J.P., & Hong, L. (2004).Sorption of lead, copper, cadmium, zinc and nickel bymarine algal biomass: Characterization of biosorptivecapacity and investigation of mechanisms, Journal of Col-loid Interface Science, 275, 131–141.

4. Vimala, R., & Das, N. (2011). Mechanism of Cd(II)adsorption by microfungus Pleurotus platypus, Journal ofEnvironmental Science, 23, 288–293.

5. Sari, A., Mendil, D., Tuzen, M., & Solyak, M. (2009). Bio-sorption of palladium(II) from aqueous solution by moss(Racomitrium lanuginosum) biomass: Equilibrium,kinetic and thermodynamic studies, Journal of HazardousMaterial, 16, 874–879.

6. Gupta, S., & Babu, B.V. (2009). Removal of toxic metalCr(VI) from aqueous solution using sawdust as adsorb-ent: Equilibrium, kinetics, and regeneration studies,Chemical Engineering Journal, 150, 352–365.

7. Vetter, J. (1994). The copper, manganese and zinc contentof some edible mushrooms, Z Lebensm Unters Forsch,198, 469–472.

8. Sesli, E., & Tuzen, M. (1999). Levels of trace elements inthe fruiting bodies of macrofungi growing in the EastBlack Sea region of Turkey, Food Chemistry, 65, 453–460.

9. Tuzen, M., Sesli, E., & Solyak, M. (2007). Trace elementlevels of mushrooms species from East Black Sea regionof Turkey, Food Control, 18, 806–810.

10. Townsley, C.C., Ross, I.S., & Atkins, R.J. (1986). Biorecov-ery of metallic residues from various industrial effluentsusing filamentous fungi, Process Metallurgy, 4, 279–289.

11. Javaid, A., Bajwa, R., Shafique, U., & Anwar, J. (2011).Removal of heavy metals by adsorption on Pleurotusostreatus, Biomass and Bioenergy, 35, 1675–1682.

12. Unguresan, M.L., M�aic�aneanu, A., Dulf, F.V., Dulf, E.H., &Gligor, D.M. (2012). Application of linear regression anal-ysis for iron and copper removal process using naturalzeolites, Journal of Thermal Analysis and Calorimetry,110, 1293–1297.

13. Chowdhury, S., & Das, P. (2011). Linear and nonlinearregression analyses for binary sorption kinetics of methyl-ene blue and safranin onto pretreated rice husk, Biore-mediation Journal, 15, 99–108.

14. Foo, K.Y., & Hameed, B.H. (2010). Insights into the mod-elling of adsorption isotherm systems, Chemical Engi-neering Journal, 156, 2–10.

15. Chowdhury S., & Saha, P. (2010). Pseudo-second orderkinetic model for biosorption of methylene blue ontotamarind fruit shell: Comparison of linear and non-linearmethods, Bioremediation Journal, 14, 196–207.

16. Lagergren, S. (1898). Zur theorie der sogenanntenadsorption geloster stoffe, Kungliga SvenskaVetenskapsa-kademiens, Handlingar, Band, 24, 1–39.

17. Ho, Y.S., & McKay, G. (1999). Pseudo-second ordermodel for sorption processes, Process Biochemisty, 34,451–465.

18. Pang, X.-Y., & Gong, F. (2008). Study on the adsorptionkinetics of acid red 3B on expanded graphite, EuropeanJournal of Chemistry, 5, 802–809.


19. Rouquerol, E., Rouquerol, J., & Sing, K. (1999). Adsorp-tion by powders and porous solids. Principles, methodol-ogy and applications, San Diego: Academic Press.

20. Langmuir, I. (1916). The constitution and fundamentalproperties of solids and liquids, Journal of AmericanChemical Society, 38, 2221–2295.

21. Freundlich, H.M.F. (1947). Over the adsorption in solu-tion, Journal of Physical Chemistry, 57, 385–471.

22. Dubinin, M.M., & Radushkevich, L.V. (1947). The equa-tion of the characteristic curve of the activated charcoal,Proceedings of the National Academy of Sciences USSR,55, 331–337.

23. Tempkin, M.I., & Pyzhev, V. (1940). Kinetics of ammoniasynthesis on promoted iron catalyst, Acta PhysicochimicaUSSR, 12, 327–356.

24. Hill, A.V. (1910). The possible effects of the aggregationof the molecules of haemoglobin on its dissociationcurves, Journal of Physiology, 40, iv–vii.

25. Redlich, O., & Peterson, D.L. (1959). A useful adsorptionisotherm, Journal of Physical Chemistry, 63, 1024–1026.

26. Sips, R. (1948). Combined form of Langmuir and Freund-lich equations, Journal of Physical Chemistry, 16, 490–495.

27. Toth, J. (1971). State equations of the solid gas interfacelayer, Acta Chimica Academiae Scientiarum Hungaricae,69, 311–317.

28. Khan, A.R., Ataullah, R., & Al-Haddad, A. (1997). Equilib-rium adsorption studies of some aromatic pollutants fromdilute aqueous solutions on activated carbon at differenttemperatures, Journal of Colloid Interface Science, 194,154–165.

29. Vijayaraghavan, K., Padmesh, T.V.N., Palanivelu, K., &Velan, M. (2006). Biosorption of nickel(II) ions onto Sar-gassum wightii: Application of two-parameter and threeparameter isotherm models, Journal of Hazardous Mate-rial, B133, 304–308.

30. Hansen, N., Mueller, S.D., & Koumoutsakos, P. (2003).Reducing the time complexity of the derandomized evo-lution strategy with covariance matrix adaptation (CMA-ES), Evolutionary Computation, 11, 1–18.

31. Tvrd�ık, J., K�riv�y, I., & Mi�s�ık, L. (2007). Adaptivepopulation-based search: Application to estimation ofnonlinear regression parameters, Computational Statistics& Data Analysis 52/2, 713–724.

32. Marshall, N.L. (1923). The mushrooms book. A popularguide to the identification and study of our commonerfungi (pp. 92–94), Bedford, Massachusetts: ApplewoodBooks.

33. Chergui, A., Kerbachi, R., & Junter, G.-A. (2009). Biosorp-tion of hexacyanoferrate (III) complex anion to dead bio-mass of the basidiomycete Pleurotus mutilus: Biosorbentcharacterization and batch experiments, Chemical Engi-neering Journal, 147, 150–160.

34. Bhattacharyya, K.G., & Gupta, S.S. (2008). Adsorption ofFe(III), Co(II) and Ni(II) on ZrO-kaolinite and ZrO-montmorillonite surfaces in aqueous medium, ColloidsSurfaces A, 317, 71–79.

35. Sari, A., & Tuzen, M. (2009). Kinetic and equilibriumstudies of biosorption of Pb(II) and Cd(II) from aqueoussolution by macrofungus (Amanita rubescens) biomass,Journal of Hazardous Material, 164, 1004–1011.

36. Mashitah, M.D., Azila, Y.Y., & Bhatia, S. (2008). Biosorp-tion of cadmium (II) ions by immobilized cells of Pycno-porus sanguineus from aqueous solution, BioresourceTechnologies, 99, 4742–4748.

37. Salinas, E., de Orellano, E., Reeza, I., Martinez, I.,Marchevsky, E., & Sanz de Tosetti, M. (2000). Removal of

cadmium and lead from dilute aqueous solutions by Rho-dotorula rubra, Bioresource Technologies, 72, 107–112.

38. Dhaouadi, A., Monser, & L., Adhoum, N. (2010). Removalof rotenone insecticide by adsorption onto chemicallymodified activated carbons, Journal of Hazardous Materi-als, 181, 692–699.

39. Ioannou, Z., & Simitzis, J. (2009). Adsorption kinetics ofphenol and 3-nitrophenol from aqueous solutions onconventional and novel carbons, Journal of HazardousMaterials, 171, 954–964.

40. Ncibi, M.C. (2008). Applicability of some statistical toolsto predict optimum adsorption isotherm after linear andnon-linear regression analysis, Journal of HazardousMaterials 153, 207–212.

41. Javaid, A., Bajwa, R., & Javaid, A. (2010). Biosorption ofheavy metals using a dead macro fungus Schizophyllumcommune fries: Evaluation of equilibrium and kineticmodels, Pakistan Journal of Botany, 42, 2105–2118.

42. Vimala, R., & Das, N. (2009). Biosorption of cadmium (II) andlead (II) from aqueous solutions by using mushrooms: A com-parative study, Journal of Hazardous Materials, 168, 376–382.

43. Lodeiro, P., Barriada, J.L., Herrero, R., & Sastre deVicente, M.E. (2006). The marine macroalga Cystoseirabaccata as biosorbent for cadmium(II) and lead(II)removal: Kinetic and equilibrium studies, EnvironmentalPollution, 142, 264–273.

44. Romera, E., Gonz�alez, F., Ballester, A., Bl�azquez, M.L., &Mu~noz, J.A. (2007). Comparative study of biosorption ofheavy metals using different types of algae, BioresourcesTechnology, 98, 3344–3353.

45. Bo�zic, D., Stankovic, V., Gorgievski, M., Bogdanovic, G.,& Kovacevic, R. (2009). Adsorption of heavy metal ionsby sawdust of deciduous trees, Journal of HazardousMaterials, 171, 684–692.

46. Semerjian, L. (2010). Equilibrium and kinetics of cad-mium adsorption from aqueous solutions using untreatedPinus halepensis sawdust, Journal of Hazardous Materi-als, 1173, 236–242.

47. Chakravarty, P., Sarma, N.S., & Sarma, H.P. (2010). Bio-sorption of cadmium(II) from aqueous solution usingheartwood powder of Areca catechu, Chemical Engineer-ing Journal, 162, 949–955.

48. Nagy, B., Maicaneanu, A., Indolean, C., Burca, S., Silaghi-Dumitrescu, L., & Majdik, C. (2013). Cadmium (II) ionsremoval from aqueous solutions using Romanianuntreated fir tree sawdust—A green biosorbent, Acta Chi-mica Slovenica, 60, 263–273.

49. Nouri, L., Ghodbane, I., Hamdaoui, O., & Chiha, M.(2007). Batch sorption dynamics and equilibrium for theremoval of cadmium ions from aqueous phase usingwheat bran, Journal of Hazardous Materials, B149, 115–125.

50. Nasernejad, B., Esslam Zadeh, T., Bonakdar Pour, B.,Esmaail Bygi, M., & Zamani, A. (2005). Comparison forbiosorption modeling of heavy metals (Cr(III), Cu(II),Zn(II)) adsorption from wastewater by carrot residues,Process Biochemistry, 40, 1319–1322.

51. Torab-Mostaedi, M., Asadollahzadeh, M., Hemmati, A., &Khosravi, A. (2013). Equilibrium, kinetic, and thermody-namic studies for biosorption of cadmium and nickel ongrapefruit peel. Journal of the Taiwan Institute andChemical Engineering, 44, 295–302.

52. Oliveira, W.E., Franca, A.S., Oliveira, L.S., & Rocha, S.D.(2008). Untreated coffee husks as biosorbents for theremoval of heavy metals from aqueous solutions, Journalof Hazardous Materials, 152, 1073–1081.


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