A breakthrough biosorbent in removing heavy metals: Equilibrium, kinetic, thermodynamic and mechanism analyses in a lab-scale study

TL;DR: This novel MMBB can effectively be utilized as an adsorbent to remove heavy metal ions from aqueous solutions and calculated thermodynamic parameters indicated feasible, spontaneous and exothermic biosorption process.

About: This article is published in Science of The Total Environment.The article was published on 2016-01-15 and is currently open access. It has received 125 citations till now. The article focuses on the topics: Biosorption & Adsorption.

Summary

1. Introduction

• Heavy metals are discharged to aquatic environments from various industries such as paper, textile, plastic, ceramic and cement manufacturing, mining and electronics plating.
• The significant difference between previous studies and current work is gaining the advantages and also using the biosorptive potentials of various biosorbents in a combination.
• In addition, thermodynamic parameters were determined for the sorption of all metal ions to explain the process feasibility.

2.1. Preparation of adsorbents and heavy metal-containing effluent

• All the reagents used for analysis were of analytical reagent grade from Scharlau and Chem-Supply Pty Ltd. .
• The metal concentration was analyzed by Microwave Plasma-Atomic Emission Spectrometer, MP-AES, (Agilent Technologies, USA).
• The biosorbents were applied in metal removal process for selecting the best ones in term of biosorption capacity sawdust (SD), sugarcane (SC), corncob (CC), tea waste (TW), apple peel (AP), grape stalk (GS), palm tree skin (PS), eucalyptus leaves (EU), mandarin peel (MP), maple leaves (ML) and garden grass (GG).
• After using or removing their useable parts, they were washed by tap and distilled water to remove any dirt, color or impurity and then dried in the oven (Labec Laboratory Equipment Pty Ltd., Australia) at 105 °C overnight.

2.2. Biosorption studies in batch system

• The tests were performed with synthetic multi-metal stock solution with concentration of 3000 mg/L for each metal, prepared by dilution in Milli-Q water.
• Solution pH was adjusted with 1 M HCl and NaOH solutions.
• After equilibration, to separate the biomasses from solutions, the solutions were filtered by Whatman™ GF/C-47 mm/circle (GE Healthcare, Buckinghamshire, UK) filter paper and final concentration of metal was measured using MPAES.
• All the experiments were carried out in duplicates.
• The statistical analysis was performed by analysis of variance .

2.3. Characterization of adsorbents by FTIR and SEM

• To determine the functional groups involved in biosorption of Cd(II), Cu(II), Pb(II) and Zn(II) onto MMBB, a comparison between the Fourier Transform Infrared Spectroscopy (FTIR) before and after meal loading was done using Shimadzu FTIR 8400S (Kyoto, Japan).
• Metal-loaded biosorbent were filtered and dried in the oven.
• The small amount of samples was placed in the FTIR chamber on the KBr plates for analyzing the functional groups involving in biosorbent process by comparing with unused multi-metal biosorbent.

3.1. Selection of adsorbents

• Eleven different natural biosorbents, namely sawdust, sugarcane, corncob, tea waste, apple peel, grape stalk, palm tree skin, eucalyptus leaves, mandarin peel, maple leaves and garden grass, individually were compared in regard to the biosorption capacities for Cd(II), Cu(II), Pb(II) and Zn(II) uptake in Fig.
• The results indicate TW, ML and MP showed satisfying biosorptive capacity for all heavy metal ions (cadmiu , copper, lead and zinc).
• TW:ML:MP combination was selected to apply for further batch experiments.
• Apparently, there are no significant differences between the equal proportions of 1:1:1 and the others, especially for lead and copper.
• This wadespite the fact that ANOVA results for each metal indicated the rejection of the null hypothesis due to P value was less than 0.05.

3.2. Characterization of adsorbents by FTIR

• The FTIR spectrum of MMBB exhibited a large number of absorption peaks, indicating the complexity in nature of this adsorbent.
• The shift of some functional groups bands and their intensity significantly changed after heavy metal biosorption (Table 1).
• These shifts may be attributed to carboxylic (C O) and hydroxylic (O–H) groups on the MMBB's surface.

3.3. SEM analysis

• From Table 1, SEM depicts the morphology changes of unloaded and loaded biosorbent.
• After biosorption of heavy metal ions, the surface became smoother with less porosity with probable metal entrapping and adsorbing on biosorbent.
• The SEM/EDS was reported in previous study (Abdolali et al., 2015).

3.4.1. Influence of pH

• The initial pH values above 5.5 are not preferable du to the observed presence of metal hydroxide precipitation, so as the experiments were not conducted beyond pH 5.5.
• The results indicated that the optimum pH value was 5.5 for all metals.

3.4.2. Influence of contact time

• It is evident from Fig. 3(b) that the rate of metal uptake was very fast within first 30 min as a result of the exuberant number of available active sit s on adsorbent surfaces and then decreased until equilibrium was reached.
• Biosorption capacity leveled off at equilibrium state within 180 min.
• Therefore, the biosorption time was set to 180 min in each experiment.

3.4.3. Influence of adsorbent dose

• Biosorption capacity was also affected by biosorptin dose and amount of available active sites and this effect is shown in Fig. 3(c).
• The experimental results indicate that the percentage removal of all metal ions on MMBB represents an equilibrium pattern for biosorbent amounts of 5 g/L and more.
• Furthermore, the removal efficien y decreased by increasing initial metal ion solution with similar trends.

3.4.4. Effect of biosorbent particle size

• The effect of particle size of biosorbent was conducted for 5 g/L adsorbent dose and an initial concentration of 50 mg/L.
• It was found that biosorpti n capacity did not significantly change by varying particle sizes.
• The reason was that these particle size distributions were very small (less than 300 µm).
• The smaller biosorbent size exhibits better performance in regard with metal removal due to a higher surface area for metal adsorption; however the mechanical stability reduces particularly in column (Liu et al., 2012).

3.5. Adsorption kinetics

• A kinetic investigation was carried out to quantify the adsorption rate controlling steps in Cd(II), Cu(II), Pb(II) and Zn(II) uptake on MMBB.
• The pseudo-first-order kinetic model known as the Lagergren equation and takes the form as: (2) where, qt and qe are the metal adsorbed at time t and equilibrium, respectively, and K1 (min− 1) is the first-order reaction rate equilibrium constant.
• The experimental data and obtained parameters of these models were measured by MATLAB® and summarized in Table 2.
• As shown in Table 2, with comparison between adsorption rate constants, the estimated q and the coefficients of correlation associated with the Lagergren pseudo-first-order and the pseudo-secnd-order kinetic models at room temperature for MMBB, it is obvious that both kinetic models well described all metal biosorption.
• The coefficients of correlation (R2) of pseudo-second-order kinetic model were slightly larger than those of pseudo-first-order kinetic model for Cu in all initial concentrations.

3.6. Adsorption isotherm

• The correlation between the adsorbed and the aqueous metal concentrations at equilibrium has been described by the Langmuir, Freundlich, Dubinin–Radushkevich, Sips, Redlich– Peterson and Khan adsorption isotherm models.
• Furthermore, residual root mean square error (RMSE), error sum of square (SSE) and correlation of determination (R2) were used to measure the exactness of fitting.
• Among three-parameter isotherm models, for Cu(II) and Zn(II), Khan isotherm describes biosorption conditions moderately better than Sips and Redlich–Peterson models, while for Cd(II) and Pb(II), the Sips model was found to provide the best correlation of the biosorption equilibrium data.
• Various kinds of agro-industrial wastes and by-products were studied for heavy metal removal.
• A comparison between maximum adsorptive capa ities of MMBB and some other adsorbents is shown in Table 4.

3.7. Biosorption mechanism

• The main mechanisms known for metal sorption on ligocellulosic biosorbents are chelating, ion exchanging and making complexion with functional groups and releasing [H3O] + into aqueous solution.
• Ionic exchange is known as a mechanism which involves electrostatic interaction between positive metallic cations and the negatively charged groups in the cell walls.
• On the other hand, many characterization studies confirmed that ion exchange mechanism was included in heavy metal biosorption process rather than complexation with functional groups on the biosorbent surface and also showed the role of sodium, potassium, calcium and magnesium present in the adsorbent in ion exchange mechanism (Ding et al., 2012 and Akar et al., 2012).
• In addition, the mean free energy of adsorption calcul ted from Dubinin– Radushkevich isotherm can evaluate sorption properties and main mechanism.
• With respect to kinetic modeling, it also established that metal uptake by the micro-organisms takes place in two consecutive stages: a passive and quick uptake that follows by an active and very slow uptake.

3.8. Adsorption thermodynamics

• The experimental results indicated dependency of adsorption on the temperature and are listed in Table 5.
• The Gibbs free energy indicates the degree of spontaneity of sorption process, and the higher negative value reflects a more energetically favorable sorption.
• ∆H° and ∆S° were obtained from the slop and intercept of the Van't Hoff plots (Fig. 5).
• I addition, the low value of ∆S° may imply that no remarkable change in entropy occurred during the sorption of Cd, Cu, Pb and Zn ions on MMBB.

4. Conclusions

• The present work explores a new economical and selective lignocellulosic biosorbent containing tea waste, maple leaves and mandarin peels as an alternative to costly adsorbents for the removal of Cd(II), Cu(II), Pb(II) and Zn(II) ions.
• The low cost, rapid attainment of phase equilibrium (within 3 h) and high sorption capacity values may be cited among the main advantages.
• Adsorption kinetics follows a pseudo-second-order kinetic model and negative values of ∆H° and ∆G° prove the exothermic and spontaneous nature of the biosorption phenomenon.
• Hence, this novel MMBB can be a promising adsorbent to eliminate heavy metal ions from aqueous solutions.

Figures

Fig. 1. Biosorption capacity of different agro-industrial wastes and by-products.

Fig. 4. Comparison of (a) individual and (b) total metal ions adsorbed and released in biosorption process (initial heavy metal conc.: 50 mg/L).

Table 4. Biosorption capacities of various adsorbents.

Table 3. Isotherm constants of two- and three-parameter models for Cd(II), Cu(II), Pb(II) and Zn(II) adsorption (initial metal conc.: 10–500 mg/L).

Table 1. FTIR spectra and SEM images of (a) unloaded and (b) metal loaded-biosorbents.

Fig. 5. Van't Hoff plots for Cd(II), Cu(II), Pb(II) and Zn(II) adsorption(initial pH 5.5 ± 0.1; initial metal conc.: 1–50 mg/L; contact time: 3 h; biosorbent dose: 5 g/L).

Table 2. Adsorption rate constants, the estimated qe and the coefficients of correlation associated with the Lagergren pseudo-first and pseudo-second order, kinetic models.

Fig. 3. Effect of (a) initial pH of solution, (b) contact time, (c) biosorbent dose and (d) biosorbent particle size on Cd(II), Cu(II), Pb(II) and Zn(II) adsorption (room temperature, 22 ± 2 °C; initial metal conc.: 50 mg/L).

Table 5. Thermodynamic parameters, ∆G° (kJ/mol), ∆H° (kJ/mol) and ∆S° (kJ/mol K), for adsorption of Cd(II), Cu(II), Pb(II) and Zn(II) adsorption.

Frequently asked questions

Q1. What have the authors contributed in "A breakthrough biosorbent in removing heavy metals: equilibrium, kinetic, thermodynamic and mechanism analyses in a lab-scale study" ?

For Cu ( II ) and Zn ( II ), the Khan isotherm describes better biosorption conditions while for Cd ( II ) and Pb ( II ), the Sips model was found to provide the best correlation of the biosorption equilibrium data.