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Separation Science and Technology

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Removal of Zinc from Aqueous Solution by MetalResistant Symbiotic Bacterium Mesorhizobiumamorphae

Xiuli Hao , Osama Abdalla Mohamad , Pin Xie , Christopher Rensing &Gehong Wei

To cite this article: Xiuli Hao , Osama Abdalla Mohamad , Pin Xie , Christopher Rensing &Gehong Wei (2014) Removal of Zinc from Aqueous Solution by Metal Resistant SymbioticBacterium Mesorhizobium amorphae , Separation Science and Technology, 49:3, 376-387, DOI:10.1080/01496395.2013.843195

To link to this article: http://dx.doi.org/10.1080/01496395.2013.843195

Accepted author version posted online: 26Sep 2013.

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Removal of Zinc from Aqueous Solution by Metal ResistantSymbiotic Bacterium Mesorhizobium amorphae

Xiuli Hao,1 Osama Abdalla Mohamad,2 Pin Xie,1 Christopher Rensing,3 andGehong Wei11College of Life Sciences, Shaanxi Key Laboratory of Molecular Biology for Agriculture,Northwest A & F University, Yangling, Shaanxi, China2Faculty of Agriculture and Environmental Sciences, Suez Canal University, El-Arish,North Sinai, Egypt3Department of Plant and Environmental Sciences, University of Copenhagen, Denmark

Biosorption of zinc by living biomasses of metal resistantsymbiotic bacterium Mesorhizobium amorphae CCNWGS0123was investigated under optimal conditions at pH 5.0, initial metalconcentrations of 100mgL�1, and a dose of 1.0 gL�1.M. amorphaeexhibited an efficient removal of Zn2þ from aqueous solution withmaximum biosorption capacity of 120.85mg g�1. Moreover, morethan 70% Zn2þ could be recovered from Zn-loaded biomass atpH 1.0. Both the Langmuir and Freundlich isotherms provided abetter fit to experimental data for Zn2þ sorption with correlationcoefficients of 0.9885. Kinetics models suggested there was morethan one step involved in the Zn2þ sorption process, while apseudo-second-order model was more suitable to describe the kineticbehavior accurately, indicating a chemisorption process. Carbonyl,amino, carboxyl, and aromatic groups were responsible for the bio-sorption of Zn2þ by M. amorphae. Cellular deformation, precipi-tate, and damage were found after Zn2þ treatment. Competitivesorption revealed Cu2þ, Cd2þ, and Ni2þ were competed with Zn2þ

for adsorption sites with the order: Cu2þ>Cd2þ>>Ni2þ.

Keywords biosorption; isotherms; kinetics; M. amorphae; zinc

INTRODUCTION

Due to increasing agricultural and industrial activitiesincluding pesticides and fertilizer use, dyes, mining, indus-trial, and municipal effluent (1,2), a great volume of efflu-ent contaminated with toxic metals such as copper, zinc,cadmium, lead, and chromium has been released into theenvironment (3). The presence of excessive heavy metalsin aqueous ecosystem has greatly threatened aquatic lives

and also the lives of human beings, which can lead toaccumulative poisoning, cancer, and brain damage viathe food chain (4). Zinc is an essential element for allorganisms, since it participates in protein and nucleic acidmetabolism, stimulates the activity of hundreds ofenzymes, and controls the activity of the immunologicalsystem (3). However, it is toxic for humans at levels of100–500mg per day and high intake of zinc can cause liver,kidney, and pancreas damage (4). Therefore, zinc removaland recovery is extremely important for our environmentand for human health (5,6).

Compared with conventional physical methods (extrac-tion, ion exchange, membrane technologies, and sorption)and chemical methods (chemical precipitation and electroly-sis) for the removal of heavy metal (7), biosorption, a termdescribing a process in which contaminants were bindingto non-living biomass passively, has generally been viewedas a highly efficient, easily handable, and low cost removalpathway (8,9). Various economic, natural, and environmen-tally friendly materials including agricultural and industrialwaste, bacteria, fungi, and algae are currently under study asbiosorbents for the potential ability to eliminate heavymetals from aqueous solution (4). Generally, suitable bio-sorbents are often biopolymers possessing an abundanceof negatively charged ligands such as hydroxyl, carboxyl,and uronic acids (10,11). Nitrogen fixing rhizobia, a signifi-cant symbiont with leguminous plants, have traditionallybeen used in agricultural practice to provide nitrogen toplant. Because of the production of extracellular polymers(EPS) such as capsular exopolysaccharides, it is also a poten-tial efficient and cheap biosorbent for metal sorption fromsolution (12). Chen et al. (13) found free-livingC. taiwanensiscells could adsorb 50.1, 19.0, and 19.6mgg-1 of Pb, Cu,and Cd, respectively. Cotoras et al. (14) reported Rhizobiumtrifolii could reduce 0.4mM UO2

2þ by 60%.Metal resistance of microbes attributes to different mecha-

nisms including precipitation, efflux and accumulation,

Received 2 April 2013; accepted 7 September 2013.Xiuli Hao and Osama Abdalla Mohamad contributed equally

to this work.Address correspondence to Gehong Wei, College of Life

Sciences, Shaanxi Key Laboratory of Molecular Biology forAgriculture, Northwest A & F University, Yangling, Shaanxi712100, China. E-mail: weigehong@nwsuaf.edu.cn

Color versions of one or more of the figures in the article canbe found online at www.tandfonline.com/lsst.

Separation Science and Technology, 49: 376–387, 2014

Copyright # Taylor & Francis Group, LLC

ISSN: 0149-6395 print=1520-5754 online

DOI: 10.1080/01496395.2013.843195

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etc., which directly affect sorption capacities of microbes.Because different microbes used for biosorption had differentand sometimes uncertain resistant mechanisms, previousreports on the relationship between resistance and biosorp-tion capacity of different biosorbents were conflicting (15–17). In this study, living Mesorhizobium amorphae cells werechosen to investigate the potential adsorption capacity ofZn due to the following reasons. First, Zn resistance of M.amorphae enable the survival and growth of strain to functionwell in the wastwater. Second, more extracellular metabolitessuch as EPS will be produced by living cells to response tometal stress, with a corresponding increase of metal sorptioncapacity. Finally, no functional efflux systems for Zn such asP-type ATPase were proved in M. amorphae (data notshown), whichmeans using living cells ofM. amorphae as bio-sorbent will not reduce Zn accumulation. The objective of thiswork was to investigate the optimal zinc sorption condition,sorption capacity, kinetic and equilibrium of Mesorhizobiumsystematically, reveal the probable biosorption mechanismby Fourier transform infrared spectra (FT-IR) analysis, Scan-ning Electron Microscopy (SEM), and Energy DispersiveX-ray Scanning (EDS).

MATERIALS AND METHODS

Biosorbent and Reagents Preparation

Mesorhizobium amorphae CCNWGS0123 used through-out this study was previously isolated from nodules ofRobinia pseudoacacia growing on a mine tailing in Gansuprovince, northwestern China (18). Minimum inhibitoryconcentration (MIC) which defined as the lowest concen-tration at which no growth observed were determined usingTY agar medium (5 g Tryptone, 3 g yeast extract, 0.46 gCaCl2, and 20 g agar per liter) amended with different con-centrations of Zn2þ and incubated at 28�C for two weeks.The effect of different zinc concentrations on the growth ofCCNWGS0123 in TY broth (19) was determined by absor-bance at 600 nm (OD600) with a UV=VIS spectrophot-ometer (PerkinElmer, USA). All cultures were incubatedat 28�C with 1% inoculation and agitation at 120 rmin�1.Samples were taken during interval periods and three repli-cates for each sample were performed. Physiological andbiochemical tests were carried out according to themethods described by Gao (20).

Cells of M. amorphae used for zinc biosorption wereprecultured in TY medium in shaker flasks at 28�C untilearly stationary phase was reached. Then log-phase cellswere harvested by centrifugation (13,201� g, 15min).After removing the supernatant, the collected cells wererinsed with deionized water three times prior to being usedfor biosorption tests.

The Zn(II) stock solution (1.0 g=L) was prepared by dis-solving ZnCl2 � 4H2O into distilled water with several dropsof HCl to avoid precipitation. 50mM phosphate buffer was

used in the batch experiments to keep a constant pH value.The pH values adjusted with 0.1M HCl or 0.1M NaOHwere measured by pH meter Type PHS3-C instruments(Shanghai, China). All chemicals used throughout thisstudy were of analytical-reagent grade.

Effects of pH, Biosorbent Dose, Initial MetalConcentration, and Contact Time on Zinc Biosorption

In order to determine the optimum biosorption con-ditions, effects of pH, biomass dose, initial concentration,and contact time on the zinc biosorption capacity weredetermined first. The pH effect was carried out in the rangeof 3–7 for zinc, using 1 gL�1 biosorbent and zinc concen-tration at 100mgL�1. Meanwhile, the effect of biosorbentdose from 0.5 to 3.0 gL�1 and initial zinc concentrationsfrom 50 to 500mgL�1 were performed in parallel bybatch method with an initial pH 5.0, a dosage of 1.0 gdry cells L�1, and 100mgL�1 zinc ion solution used asstandard conditions. All the samples were agitated at 120r min�1 and 28�C for 24 h. Eventually, the residualzinc concentration in the supernatant was analyzed by anAtomic Absorption Spectrophotometer (AAS, ShimadzuModel-AAA-6650, Japan) and the amount of zincadsorbed Qe (mg g�1 dry biomass) and removal ratio g(%) were calculated with the following equation:

Qe ¼ ðC0 � CeÞ=X ð1Þ

g ¼ 100ðC0 � CeÞ=C0 ð2Þ

where C0 (mgL�1) and Ce (mgL�1) are the initial and equi-librium concentrations of metal ions respectively; X (g drycell L�1) is the biosorbent concentration.

Biosorption Kinetic and Equilibrium Experiments

Biosorption kinetic experiments were carried out at aconcentration of each biosorbent of 1 gL�1 using differentinitial zinc concentration of 50, 100, 200mgL�1. The cellsuspension was incubated at a constant agitation speed of120 r min�1 at 28�C. Samples were taken at different timeintervals within 24 h and centrifuged at 13,201� g for15min. The residual zinc ions were measured as describedabove.

Similarly, the equilibrium experiments were performedin zinc solution by 1 gL�1 biosorbent with an initialconcentration varying from 50 to 200mgL�1 at gentle agi-tation for 24 h. The other operational conditions werethose optimized in earlier tests. Equilibrium concentrationof zinc ion was determined by Atomic AbsorptionSpectroscopy (AAS, Shimadzu Model-AAA-6650, Japan).

Competitive Biosorption and Desorption Experiments

Competitive biosorption experiments were carried outin a 50mL solution with equimolar concentrations

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(100mgL�1) of Zn2þ and different groups of Cu2þ, Cd2þ,and Ni2þ. The competitive biosorption conditions were setaccording to optimum sorption condition tested above.The analysis method of each metal ion was as describedabove.

Zn-loaded biosorbent used after biosorption experimentswas collected, rinsed, and resuspended with double deio-nized water. Desorption was proceeded at 120 rmin�1

and 28�C for 24 h under different pH value range from1–8 adjusted by 0.1mol L�1 HCl. After centrifugation,the Zn concentration in the supernatant was measuredand the desorption efficiency was calculated as follows:

g ¼ Mr=Ms ð3Þ

where Mr (mg) and Ms (mg) are the amount of releasedand initially sorbed Zn on the biosorbent, respectively; g(%) is the desorption efficiency.

FT-IR and SEM-EDX Analysis

The analysis of Fourier transform infrared spectra (FT-IR) of the dried M. amorphae samples (raw and Zn-loadedafter 24 h adsorption) were performed using a 330 spectro-meter (NICOLET AVATAR, USA). 1mg dry powderedsamples were mixed with 100mg spectrometry grade KBrand spectrums at 4000–400 cm�1 were recorded.

In order to better understand the mechanism of interac-tion between zinc and M. amorphae cells, the surface struc-ture and morphology of M. amorphae treated with andwithout 1.0mM Zn2þ in TY medium were observed byScanning Electron Microscopy (SEM). The X-ray elemen-tal spectrum was obtained by an EDXA=EDS system.Samples used for SEM-EDX were prepared as in themethod described by Fan et al. (21).

RESULTS AND DISCUSSION

Growth and Characteristic of Mesorhizobiumamorphae CCNWGS0123

Mesorhizobium amorphae CCNWGS0123 isolated fromlead-zinc mine tailing in northwestern China had a MICof 1.6mM for zinc. The effect of Zn2þ on the growth ofCCNWGS0123 showed there was no significant influenceon the OD600 of cultures grown with 0.5mM Zn2þ addedafter 14 d incubation (Fig. S1). The growth ofCCNWGS0123 with 1.0mM Zn2þ was delayed and thebiomass was nearly 50% lower compared to the biomassin TY medium without Zn2þ after 14 d. Moreover, physio-logical and biochemical characteristics of CCNWGS0123showed the strain had a pH tolerance range from 4 to 11.

Factors Affecting Zinc Biosorption by CCNWGS0123

The factors which affect biosorption performance sig-nificantly include the type of biomass (functional groups),

solution pH (physicochemistry of the metal ions and theactivity of biosorbents), initial heavy metal concentrationand biosorbent dosage (competition for binding sites)(22,23). Figure 1 shows marked influences of initial pH(3–7), adsorbent dose (0.5–2.5 gL�1), initial zinc concen-tration (50–500mgL�1), and contact time on the zincadsorption by M. amorphae. All obvious influences causedby different factors could be explained by the interactionbetween metal ions and available binding sites on thebiomass (24,25).

In general, the increase of pH leads to deprotonation ofmetal ion binding sites exposed by cellular surfaces, whilethe decrease of pH causes competition between protonsand positively charged metal ions. On the other hand, thesolubility of complexes of metals decreases as the pHincreases, which would cause precipitation of cationicmetals (26). There are four zinc species present in the aque-ous solution at pH 1–7: Zn2þ, ZnOHþ, Zn(OH)2 andZn(OH)3. At higher Hþion concentration, Hþions competewith Zn2þ for the binding sites on the surface of the biosor-bent, while with an increase of pH, the negative charge den-sity on the biosorbent surface increases making thebiosorption of positively charged Zn2þ and ZnOHþ speciesare more favorable (24). At the same time, the speciation ofZn(OH)2 and Zn(OH)3 led to the decrease of biosorption athigher pH. Therefore, the optimum pH for zinc uptake wasfound to be 5 and further biosorption investigations wereperformed at this pH value.

An increase in the biomass dose from 0.5 to 2.5 gL�1

resulted in a decrease of zinc biosorption capacity from52.74 to 26.60mg g�1. While the percentage of removalwas simultaneously raised from 26.37% to 66.50%. These

FIG. S1. Growth of M. amorphae CCNWGS0123 in the presence of dif-

ferent concentrations of zinc (0–1.0mM).

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results demonstrate that there was more sorption surfaceand sites available with an increase of biosorbents leadingto the upwards trend of removal efficiency. However, afurther increase of excess biosorbent caused a decrease inthe amount of metal adsorbed onto unit weight of biomass(27). In order to obtain a high biosorption capacity com-bined with a high removal efficiency, 1.0 gL�1 adsorbentdosage was chosen in further study.

The equilibrium sorption capacity increased and theremoval rate decreased with metal concentration varyingfrom 50 to 500mgL�1. An increase in the initial concen-tration resulted in an increase in biosorption capacity from24.13 to 97.02mg g�1, whereas the removal rate of Zn(II)decreased from 50.25% to 18.41%. It has been reported thatthe increased metal uptake was the result of an importantdriving force provided by increasing initial concentration(28). At lower concentrations, all metal ions present in sol-ution could interact with the binding sites adequately andthus the efficiency of biosorption was higher than thoseat higher initial metal ion concentrations. However, the

number of metal ions increased to an extent that exceededthe available binding site in M. amorphae, which means thebinding site had tended to be saturated at higher concentra-tions. Hence, the lack of sufficient surface area led to adecrease in the percentage of adsorption. Therefore,100mgL�1 was the optimum initial concentration whichcould interact with the binding sites adequately and efficiently.

Overall, the optimal Zn biosorption conditions forCCNWGS0123 were determined finally based on the effectof every factor as pH 5.0, initial zinc concentration of100mgL�1 and using a biosorbent dosage of 1.0 g L�1 at120 rmin�1. And biosorption equilibrium was attainedwithin 40, 90, and 120min at 50, 100, and 200mgL�1

Zn2þ, respectively.

Biosorption Kinetics

Several kinetic models are available to understand thebehavior of the biosorbent and also help to reveal thepotential controlling mechanism. It was reported that therewere four steps included in the biosorption process: sorbate

FIG. 1. Effects of pH (Initial metal concentration: 100mgL�1; biosorbent dose: 1.0 gL�1; 28�C; 120 rmin�1) (a), biosorbent dose (pH: 5.0; Initial

metal concentration: 100mgL�1; 28�C; 120 rmin�1) (b), initial metal concentration (pH: 5.0; biosorbent dose: 1.0 gL�1; 28�C; 120 rmin�1) (c) and

contact time (pH: 5.0; biosorbent dose: 1.0 gL�1; 28�C; 120 rmin�1) (d) on zinc biosorption capacity by M. amorphae CCNWGS0123.

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transport in bulk solution (rapid), film diffusion (rapid),intra-particle diffusion (slow), and binding reaction (slow)(29). In order to investigate the rate of zinc biosorptiononto M. amorphae and the potential rate controlling steps,the kinetics of biosorption data were fitted by the followingkinetic models: pseudo first order kinetic model, pseudosecond order kinetic model, Elovich kinetic model, andIntra-particle diffusion model.

The Pseudo First Order Model and the Pseudo Second OrderModel

The assumptions of the two models (the pseudo firstorder and the pseudo second order models) were shownin equations as follows (30):

SðsolidÞ þMe2þðaqueousÞ ! SMe2þðadsorbed � phaseÞð4Þ

2SðsolidÞ þMe2þðaqueousÞ ! S2Me2þðadsorbed � phaseÞð5Þ

The first and second order rate equations are usuallyexpressed in linear forms as follows (29,31):

log10ðQe �QtÞ ¼ log10 Qe � K1t=2:303 ð6Þ

t=Qt ¼ 1=ðK2Q2eÞ þ t=Qe ð7Þ

where Qe and Qt are the solid phase ion concentrations atequilibrium and at time t, respectively (mg g�1), K1 and K2

are the first and second order rate constants, respectively(min�1).

The values of Qe, K1 of the first order model and Qe, K2

of the second order model can be computed from theintercept and slope of the plot of Eq. (6) and Eq. (7),respectively. When t approaches to 0, the initial sorptionrate V0 (mg (g �min)�1) could be calculated by K2Q

2e.

Generally, the pseudo first order model is applicableover the initial 20–30min of the sorption process, whilechemisorption tends to be slower over the whole range ofcontact time. So the amount of Qe of the pseudo first order

model is significantly smaller than the equilibrium amount(32). As shown in Table 1, the theoretical Qe valuesobtained were not in good agreement with the correspond-ing experimental Qe values: 24.87, 43.49, 62.50mg g�1 atinitial zinc concentrations of 50, 100, 200mgL�1, respect-ively. Therefore, although the correlation coefficients ofpseudo first order for metals were high, the adsorptionreaction could still not be described with this model. TheQe values of the pseudo second order model obtained fromFig. 2 were 24.75, 43.48, 62.89mg g�1 for 50, 100, and200mgL�1 zinc, respectively. These values were very closeto the experimental values Qe (24.87, 43.49, 62.50mg g�1)(Table 1). Moreover, the values of the correlation coef-ficient were extremely high, even more than 0.999, whichconfirmed the applicability of this model. The biosorptionrate V0 in Table 1 appeared to be slower at higher initialmetal concentration because of the limited binding sites(33,34). Therefore, the pseudo second order model wasmore appropriate for the kinetic behavior of zinc biosorp-tion by M. amorphae CCNWGS0123, indicating that thechemisorption process may be the controlling step accord-ing to the pseudo second order reaction mechanism (25).

Elovich Kinetic Model

The Elovich kinetic equation is usually expressed asfollows:

dQt=dt ¼ a expð�bQtÞ ð8Þ

where a is the initial chemisorption rate (mg (g �min)�1)and b is the desorption constant (g mg�1). To simplifythe Elovich equation, Chien and Clayton assumed ab>>tand applied the boundary conditions Qt¼ 0 at t¼ 0 andQt¼Qt at t¼ t, then the form of Eq. (8) could be expressedas follows (35):

Qt ¼ 1=b lnðabÞ þ 1=b ln t ð9Þ

Thus the kinetic constants a and b could be obtainedfrom the linear relationship showed in Fig. S2.

The correlation between the theoretical curves and theexperimental data at different initial zinc concentrations

TABLE 1Kinetic parameters for the pseudo-first-order, pseudo-second-order and Elovich kinetic model at an initial Zn2þ

concentration of 50, 100, 200mgL�1

MetalConcentration

(mgL�1)ExperimentalQ (mg g�1)

Pseudo-first-orderkinetics

Pseudo-second-orderkinetics Elovich kinetics

Qe

(mg g�1)K1

(min�1) r2Qe

(mg g�1)K2 (g

(mg �min)�1)V0 (mg

(g �min)�1) r2a (mg

(g �min)�1) b r2

Zn 50 24.8698 10.3586 0.09074 0.9953 24.7525 0.04364 26.7380 1 765.7423 0.3856 0.9716100 43.4896 12.8056 0.04284 0.9784 43.4783 0.01746 33.0033 1 378.8860 0.1888 0.9665200 62.5000 35.0187 0.03155 0.9591 62.8931 0.003502 13.85042 1 139.1355 0.1294 0.9658

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looked excellent (Table 1) (r2 values form 0.93 to 0.98),which confirmed that the Elovich model was also appli-cable. However, the pseudo second order kinetic model fit-ted better when compared to the Elovich model. The valuesof a and b decreased as initial zinc concentration increasedfrom 50 to 200mgL�1, which were in agreement withprevious report (34).

Intra-Particle Diffusion Model

The intra-particle diffusion would become a ratelimiting step on condition that the agitation rate is so highthat film diffusion could be ignored. The equation of thismodel could be expressed as follows (36):

Qt ¼ Kdt1=2 ð10Þ

where Kd is the intra-particle rate constant(mg (g

ffiffiffiffiffiffiffiffiffi

minp

)�1).If intra-particle diffusion occurred and was the only

rate-limiting step in the adsorption by CCNWGS0123,the plot of Qt vs.

ffiffi

tp

would be linear and the line wouldpass through the origin. Otherwise, other mechanismswould also be involved. Figure 3 illustrates the effect ofintra-particle diffusion on zinc biosorption at differentinitial concentrations of 50, 100, and 200mgL�1, respect-ively. The plots of the experimental data represented amulti-linear shape over the entire time range which didnot pass through the origin, suggesting more than one stepwas involved in the whole sorption process (37). As shownin Fig. 3, three steps were involved in the whole zincadsorption by M. amorphae as follows: First, transfer zincions from bulk solution to external surface of biosorbentparticle; then the distinct low velocity sorption followsdue to intra-particle diffusion; finally, biosorption reachesequilibrium because of the concentration gradient betweenbulk solution and biosorbent particle.

FIG. 2. Pseudo-second-order kinetic model for zinc biosorption at initial zinc concentrations of 50, 100 and 200mgL�1.

FIG. S2. Elovich kinetic model for zinc biosorption at initial zinc

concentrations of 50, 100 and 200mgL�1.

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Sorption Isotherm

Langmuir and Freundlich Model

The Langmuir model assumed that there is no inter-ference among adsorbate molecules during the sorptionprocess (homogeneous surface hypothesis) (38). Whilethe empirical Freundlich model considered that themolecules attached to binding site would affect the next site(heterogeneous distribution hypothesis) (39). Basically, theLangmuir model equation is expressed as shown below:

Qe ¼ QmaxKLCe=ð1þ KLCeÞ ð11Þ

where Qmax represents the maximum binding capacity(mgg�1), andKL is the Langmuir affinity constant (Lmg�1).

Another essential parameter of the Langmuir isothermis RL. The dimensionless adsorption intensity RL indicatingfavorable isotherm when 0<RL< 1, could be calculatedby (40):

RL ¼ 1=ð1þ KLC0Þ ð12Þ

The Freundlich isotherm is defined using Eq. (13)as follows:

Qe ¼ KFC1=ne ð13Þ

where KF (mg g�1) and n (dimensionless) reflect thecharacteristics of the sorption system.

Figure 4 shows the biosorption equilibrium of zinc ontoM. amorphae according to the initial zinc concentrations.The calculated results of the Langmuir and Freundlich

models are given in Table 2. The predicted zinc LangmuirQmax values and the Langmuir constant KL ofM. amorphaewere 120.85mg g�1 and 0.00866Lmg�1, respectively. Themean RL values was 0.4228 for the range of initial zincconcentrations in this study (50–500mgL�1), reflectingthe favorable adsorption process. For the Freundlichmodel, the value of 1=n for zinc ion biosorption on toM. amorphae (0.4784) was between 0.1 and 1.0, which indi-cated that zinc was favorably adsorbed by M. amorphae atthe condition studied (41). The adsorption capacity valuesKF obtained from the Freundlich model which indicatedthe biosorption capacity was 5.89mg g�1. In the view ofthe correlation coefficient, the correlation coefficient ofLangmuir and Freundlich models was both 0.9885, indicat-ing that the data fitted both the Langmuir and Freundlichmodels well. Zinc biosorption behavior could be betterdescribed by both isotherms implied that the binding ofzinc proceeded as a monolayer sorption hypothesis (42).

Dubinin-Radushkevich Model

The Dubinin-Radushkevich (D-R) model was generallyused to estimate the energy of adsorption of the sorbent,helping to determine which kind of process (physical orchemical) the adsorption belonged to. The linear form ofthe model is given as:

lnQe ¼ lnQm � be2 ð14Þ

where Qe is adsorption capacity at equilibrium (mmol g�1),Qm is the maximum sorption capacity (mmol g�1), b repre-sents the D-R isotherm constant (mol2 kJ�2), and e is thePolanyi sorption potential (43), which could be calculated

FIG. 3. Intra-particle diffusion model for zinc biosorption at initial zinc

concentrations of 50, 100 and 200mgL�1.

FIG. 4. Langmuir and Freundlich fitting plots of zinc biosorption onto

M. amorphae CCNWGS0123.

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by Eq. (15):

e ¼ RT lnð1þ 1=CeÞ ð15Þ

The mean free energy E (kJmol�1) can be calculatedusing the equation given below:

E ¼ ð�2bÞ�1=2 ð16Þ

Normally, E< 8 kJmol�1 means the sorption process isphysical in nature, while 8<E< 16 kJmol�1 indicates theadsorption occurs through chemical ion exchange (43).

As can be seen in Table 2, the sorption capacity Qm was4.2928mmol g�1, which were all higher than those obtainedfrom the Langmuir model (1.8593mmol g�1). As reportedpreviously, this is due to different kinds of hypothesis wereused as precondition when formulating these isotherms(34). The mean free energy of adsorption E for zinc wasbetween 8 and 16 kJmol�1, reflecting a chemical ionexchange process. Based on the correlation coefficient,

the zinc adsorption by M. amorphae CCNWGS0123followed Langmuir, Freundlich, and D-R isotherm models.

Competitive Sorption and Desorption Efficiency

Figure 5 showed the competitation of Zn2þ with Cu2þ,Cd2þ, and Ni2þ in different combination. The removalefficiency of Zn2þ by M. amorphae decreased from52.49% to 23.30%, 39.03%, and 41.94% when added withCu2þ, Cd2þ, and Ni2þ, respectively, indicating Cu2þ hadstronger adsorption competition with Zn2þ than Cd2þ

and Ni2þ. Much lower removal efficiency of Zn2þ wasobtained when three or four metal ions were combinedtogether. However, Ni2þ seemed to have little cooperativeeffect with other ions to compete with Zn2þ. Competitivesorption results revealed ions competed with Zn2þ follow-ing the order: Cu2þ>Cd2þ>>Ni2þ, which was agree withthe results described by De Carvalho et al. (44).

The recycle of heavy metal and the renew of biosorbentsis necessary for the application of biosorption. Variouseluants such as EDTA, Na2CO3, various acids (HCl,HNO3, H2SO4) and citric acid have been used in thedesorption process widely (24). In this study, HCl was

FIG. 5. Effect of competing ions (Cu2þ, Cd2þ and Ni2þ) on Zn2þ

removal efficiency by M. amorphae CCNWGS0123 (The patterns of com-

peting ions combination were: 1: Zn2þ; 2: Zn2þþCu2þ; 3: Zn2þþCd2þ; 4:

Zn2þþNi2þ; 5: Zn2þþCu2þþCd2þ; 6: Zn2þþCu2þþNi2þ; 7:

Zn2þþCd2þþNi2þ; 8: Zn2þþCu2þþCd2þNi2þ). The concentration

of each ion was 100mgL�1.

FIG. S3. Desorption efficiency of zinc from zinc-loaded biomass of M.

amorphae CCNWGS0123 at different pH.

TABLE 2Fitting parameters for the Langmuir, Freundlich, and Dubinin–Radushkevich models of zinc biosorption

MetalLangmuir Freundlich Dubinin–Radushkevich

KL

(Lmg�1)Qm

(mg g�1) RL r2KF

(L g�1) 1=n r2Qm

(mmol g�1) bE

(kJmol�1) r2

Zn 0.00866 120.8543 0.4228 0.9885 5.8880 0.4784 0.9885 4.2928 0.00623 8.9586 0.9962

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FIG. 6. FT-IR spectrum of biosorbents with and without zinc loaded (The order was M. amorphae CCNWGS0123 without metal, Zn-loaded

CCNWGS0123 from top to bottom).

FIG. 7. SEM micrographs of M. amorphae CCNWGS0123 cell surface treated without (a) and with (b) 1.0mM Zn2þ together with EDX

spectra (c: 0mM Zn2þ ; d: 1.0mM Zn2þ ).

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selected to elute metal ions from the biomass. As revealedin Fig. S3, the desorption efficiencies for zinc decreased asthe pH increased from 1 to 7. The desorption efficiencykept stable at around 11.72% for zinc with increasing pHstarting at 4.0 and more than 70% of Zn2þ adsorbed onM. amorphae were released at pH 1.0.

FT-IR and SEM/EDS Analysis

Polysaccharides, lipids, and protein on the cell wallswere suggested to participate in metal-binding previously(45–47). The FT-IR spectra of biosorbents with andwithout zinc loaded in the range of 400–4000 cm�1 werepresented in Fig. 6, where the differences caused by zincloaded were analyzed. The broad brand region for �OHor �NH stretching was shifted from 3397.01 cm�1 (raw)to 3424.72 cm�1 (zinc-loaded). There was no significantdifference for the zinc-loaded biosorbent compared to thatof the metal free control at 1654.10 cm�1 corresponding tothe amid I band. Moreover, when loaded with Zn2þ, thepeaks at 1458.39 cm�1, 1396.2 cm�1, and 1230.38 cm�1

almost disappeared, indicating the formation of a complex

between metal and the CH2 group of lipids, the C�O groupof polysaccharides and �COOH group of the protein.Compared to the intensities of the C�O stretching ofthe hydroxyl group (C�OH) from ploysaccharides at1062.50 cm�1, the peak shifted to 1069.40 cm�1 for thezinc-loaded sample, which demonstrated that hydroxylgroups were involved in zinc biosorption. In addition, therewere complex substitution reactions on benzene rings atlower wave numbers (under 700 cm�1) after zinc absorp-tion (48). All results above showed the interaction betweenZn2þ and functional groups on the surface of M. amorphaein the biosorption process.

Compared to functional groups of M. amorphaeinvolved in Cu2þ adsorption (18), �OH or �NH stretchingat around 3397–3428 cm�1, C�O stretching of the hydroxylgroup (C�OH) from ploysaccharides at 1057–1069 cm�1

and benzene rings at lower wave numbers below 1000 cm�1

involved in both Zn2þ and Cu2þ adsorption; while, a complexbetween Zn2þ and the CH2 group of lipids, the C�O groupof polysaccharides and �COOH group of the proteinwas formed during Zn2þ adsorption, but not significant

TABLE 3Zinc biosorption capacity of different bacterial species

Bacteria

Biosorption optimum conditionsBiosorptioncapacity(mg g�1

biomass) ReferencepH BiomassZn2þ

(mgL�1) Time

Agitationrate

(rmin�1)

Pseudomonas putida CZ1 5.0 0.1 gL�1 (living) 65.3 – 200 27.4 (Chen et al., 2005)Pseudomonas putida CZ1 5.0 0.1 gL�1

(non-living)65.3 – 200 17.7 (Chen et al., 2005)

Pseudomonas putida 7.0 0.001 gL�1 0.1 10min – 6.9 (Pardo et al., 2003)Pseudomonas aeruginosaAT18

7.0 0.01 gL�1 70 72 h 150 87.72 (Perez Silva et al.,2009)

Streptoverticilliumcinnamoneum

5.5 2 gL�1 (boiling waterpretreatment)

100 30min 150 21.3 (Puranik andPaknikar, 1997)

Streptomyces rimosus 6.5 3 gL�1

(non-living)100 – 250 30 (Mameri et al.,

1999)Streptomyces zinciresistens 5.0 1% (v=v) 130 24 h 120 299.85 (Lin et al., 2011)Streptomyces ciscaucasicus 5.0 2 gL�1 (living) 150 24 h 90 42.75 (Li et al., 2010)Streptomyces ciscaucasicus 5.0 2 gL�1 (non-living) 150 24 h 90 54 (Li et al., 2010)Thiobacillus ferrooxidans 6.0 0.2 gL�1 100 30min 300 82.61 (Celaya et al.,

2000)Thiobacillus ferrooxidans 6.0 0.3 gL�1 (NaOH

pretreatment)– 2 h – 172.4 (Liu et al., 2004)

Brevibacterium sp. HZM-1 3.0 0.4% w=v 325 12 h – 41.85 (Taniguchi et al.,2000)

Mesorhizobium amorphaeCCNWGS0123

5.0 1 gL-1 100 24 h 120 120.85 This study

�Shot dash means the data were not available.

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in Cu2þ adsorption; Moreover, no C=O, amide I, II, III,and unsaturated alkenes, alkyls groups were predictedduring Zn2þ adsorption.

The SEM micrographs showed changes of cellularmorphology before and after Zn2þ loaded. Cells growingunder 1mM Zn2þ were much shorter and globularcompared to cells without Zn2þ treatment (Fig. 7b).Granular precipitations on the surface of M. amorphaecells were also observed. It seemed Zn may induce celldeforming and some cells could resist zinc by producingsecretions to precipitate zinc ion on the surface. EDXSspectra also conformed the zinc ions absorbed on the cellsurface (Fig. 7d).

CONCLUSIONS

Using bacteria with the capacity of producing polysac-charide to immobilize metals had been reported previously(49,50). Zn2þ biosorption capacity varied from 17.7 to299.85mg g�1 dry biomass according to different bacterialspecies (Table 3). In this study, M. amorphae strainCCNWGS0123 isolated from nodules of Robinia pseudoa-cacia growing on mine tailing was able to survive in themetal (loid) contaminated environment, and also had thecapacity for removal of metal (loid) from the contaminatedenvironment. The potential removal of zinc onto biomassof metal resistant strain M. amorphae CCNWGS0123was described. The biosorption capacity of zinc by M.amorphae was influenced by pH, ion concentration, biosor-bent dose, and contact time. Equilibrium time needed byZn2þ adsorption (90min) was much longer than Cu2þ

(25–30min) at initial concentration of 100mgL�1 Zn2þ=Cu2þ (18), which corresponding to the result that Cu2þ

had stronger adsorption competition with Zn2þ. Withmuch higher r2 values and more accurate prediction ofQe, the pseudo second order kinetic model was moresuitable to depict the absorption process compared to thepseudo-first-order kinetic model and Elovich kineticmodel. Moreover, the intra-particle diffusion modelindicated that the sorption of zinc onto M. amorphae wasproceeded by surface chemisorption and more than onestep was involved in the whole sorption process. Thedescribed experiments revealed that the zinc adsorptiondata had an excellent compatability with both Langmuirand Freundlich models, and the D-R isotherm depicteda chemical process for biosorption. The analysis by FT-IRexhibited several functional groups including �C=O,�COOH, �NH, and �C6H5 were active in the zincbiosorption by M. amorphae, which were much lessthan functional groups involved in copper adsorption.Moreover, SEM=EDS analysis showed changes of cellularmorphology and produce of exocellular sectetion byM. amorphae CCNWGS0123 under the stress of Zn.

FUNDING

This work was funded by the 973 Project of China(2010CB126502), National Science Foundation of China(30970003, 30900215, and 30630054), InternationalScience & Technology Cooperation Program of China(2010DFA91930), and 13115 Project of Shaanxi(2015ZDKG-59).

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