Home Synthesis, characterization and application of polypyrrole-cellulose nanocomposite for efficient Ni(II) removal from aqueous solution: Box-Behnken design optimization
Article Open Access

Synthesis, characterization and application of polypyrrole-cellulose nanocomposite for efficient Ni(II) removal from aqueous solution: Box-Behnken design optimization

  • R. Rathika , Oh Byung-Taek , B. Vishnukumar , K. Shanthi , S. Kamala-Kannan EMAIL logo and V. Janaki EMAIL logo
Published/Copyright: February 15, 2018
Download Article (PDF)
Become an author with De Gruyter Brill
Submit Manuscript Author Information
e-Polymers
From the journal e-Polymers Volume 18 Issue 4

Abstract

The role of polypyrrole-cellulose (PPy-Ce) nanocomposite for the removal of Ni(II) from aqueous solution was investigated by batch experiments. The PPy-Ce nanocomposite was prepared by chemical oxidate polymerization of pyrrole monomer with cellulose. Transmission electron micrography (TEM) showed the size of the particles varied from 80 to 95 nm. The characteristic C-O, O-H, C-N and C-C vibrations in the Fourier transform infrared (FTIR) spectra indicate that the cellulose successfully integrated with the pyrrole. Influence of experimental variables such as pH, contact time, adsorbent dose and initial Ni(II) concentration were optimized using the response surface methodology (RSM) based Box-Behnken design (BBD). The optimal conditions for maximum removal of Ni(II) were pH 8, time 65 min, adsorbent dose 0.3 mg/l and Ni(II) concentration 50 mg/l. The maximum removal efficiency under optimized conditions was >94%. The results indicate that BBD could be used to optimize experimental conditions for metal removal from aqueous solution.

Keywords: cellulose; metals; nanocomposites; pyrrole; response surface methodology

1 Introduction

Several public health hazards and deterioration of the ecosystem due to metal contamination have been well documented (1), (2). The metal nickel [Ni(II)] is a serious threat to the environment due to its high utilization in developing countries and its recalcitrant nature, and to human health due to its ability to induce allergies, cardiovascular issues, gastrointestinal irritation, kidney disease and lung fibrosis (3), (4). Mining and metallurgy of Ni(II), various industrial activities and electronic equipment are the major sources of Ni(II) contamination (5), (6). Stringent regulations and restrictions are in place to monitor the discharge of Ni(II) containing effluents into the ecosystem; the acceptable discharge level of Ni(II) from industrial effluents into water bodies is 3 mg/l, and the permissible level in drinking water meant for human consumption is <40 μg/l (7), (8). Therefore, it is essential to manage the level of Ni(II) in industrial effluents before it is discharged into bodies of water. Environmental engineers have been trying to develop innovative technologies to solve this problem. A variety of technologies including chemical reduction, precipitation, evaporation, coagulation, and electrochemical, ion exchange and membrane processes have been studied for Ni(II) ion remediation processes (9), (10). Nevertheless, more research and broader validation are still needed to develop better field applications that are cost effective and eco-friendly.

The adsorption process is a proven technology for treating metal contamination (11), (12), (13), (14) due to its cost-effectiveness, operational flexibility, reusable nature of the treated effluent and its ability to regenerate the adsorbent used. A variety of materials such as agro-industrial wastes, synthetic and natural polymers, polymeric composites, microbial biomass and clayey materials have been assessed for their ability to remove metals from aqueous solutions and/or industrial wastes. In the past decade, attention has been focused on the application of nanomaterials and nanocomposites in the adsorption process because of their enhanced reactivity, high surface area and small internal diffusion resistance. Polypyrrole (PPy) is a semi-conducting polymer that is widely used in material science because of its high electrical conductivity, environmental stability, biocompatibility, electrocatalytic activity, low oxidation potential, easy synthesis, high doping-dedoping potential and low cost. PPy also shows good prospects for the remediation of contaminated water (15), (16). However, poor processability and lack of mechanical properties limit the application of PPy in large/industrial-scale wastewater treatment processes. These disadvantages are overcome by compositing PPy with biological and chemical polymers, agro-industrial wastes and/or other substances (14). For example, PPy-chitosan composite effectively removed 95% Pb(II) and Cd(II) from tap water (18). Under optimal conditions, PPy-sawdust composite removed 94.4% of Zn(II) from aqueous solution and 328, 32.5 and 15.2 mg/l of Cr(VI), Ni and Zn(II), respectively, from plating wastewater (19). Ghorbani and Eisazadeh (20) reported that the PPy-rice husk ash composite removed 96.4% of Cu and 92.4% of Cd from cotton textile wastewater. PPy-bacterial extracellular polysaccharide nanocomposite adsorbed and subsequently reduced 80% of Cr(VI) present in aqueous solution within 30 min (21). Chávez-Guajardo et al. (22) showed that PPy-maghemite composite effectively removes 209 mg/g of Cr(VI) and 171 mg/g of Cu(II) from aqueous solutions.

Cellulose is the most abundant biopolymer in nature. It is a linear homopolymer of β-D-glucopyranose units joined by β-1,4 glycosidic linkages (23). Cellulose and its derivatives have wide-ranging applications in different fields because of their inexpensiveness, renewability, eco-friendly nature and biocompatibility (24), (25). However, native cellulose has limited application in wastewater treatment because of its high crystallinity and supermolecular structure (26). The intra- and intermolecular hydrogen bond network in the polymer decreases the adsorption capacity of cellulose. Therefore, the hydrogen bond network in the cellulose is modified by direct chemical transformation or by composite preparation before its application in the removal of different groups of pollutants from aqueous solution/wastewater (27). Porous cellulose spheres modified with trisodium trimetaphosphate adsorbed 150.6 mg/g of Pb2+ from aqueous solution (28). Janaki et al. (24) reported that the cellulose-polyaniline composite adsorbed 95.9, 91.9, 92.7 and 95.7% of Remazol Brilliant Blue R, Reactive Orange 16, Reactive Black 5, and Remazol Brilliant Violet 5R, respectively, and decolorized 82% of simulated reactive dye bath effluent. Cellulose-acetate/zeolite composite fiber removed 28.57 and 16.95 mg/g of Cu(II) and Ni(II) from aqueous solution (29). Sodium alginate-carboxymethyl cellulose composited adsorbed 99% of Pb(II) from aqueous solution under optimized conditions (30). Stoica-Guzun et al. (31) reported that bacterial cellulose-magnetite composite effectively adsorb Cr(VI) from aqueous solution at pH 4 and subsequently reduced Cr(VI) into Cr(III). However, polypyrrole-cellulose (PPy-Ce) composite has never been assessed for the removal of Ni(II) from aqueous solutions.

Efficiency of the adsorption process is highly influenced by experimental variables such as pH, initial concentration of the adsorbate, adsorbent dosage and contact time (24), (32). Thus, optimization of the experimental conditions is a vital factor for effective adsorption. Conventional and classical methods of optimization do not depict the combined effect of all the experimental variables. Furthermore, it is a time consuming process and requires an increased number of experiments to optimize experimental conditions, which are unreliable. These disadvantages are overcome by optimizing the experimental conditions using a statistics-based experimental design such as response surface methodology (RSM). It is a combination of mathematical and statistical techniques that is widely used for optimizing and improving the process and to understand the interaction among the experimental variables that have to be optimized (33). Hence, the objectives of the present study were (i) to synthesize and characterize PPy-Ce composite, (ii) assess the feasibility of the PPy-Ce composite for the removal of Ni(II) from aqueous solution, and (iii) optimize the experimental variables using the RSM-based Box-Behnken design (BBD).

2 Materials and Methods

2.1 Materials

Pyrrole monomer and cellulose were procured from Sigma-Aldrich (Milwaukee, WI, USA). Ni(II) stock solution (1000 mg/l) was prepared by dissolving a weighed quantity (4.478 g) of nickel sulfate (Sigma-Aldrich, Milwaukee, WI, USA) in double distilled water, and the working concentrations (50–250 mg/l) were prepared by diluting the stock solution with double distilled water. All other chemicals used in this experiment were of analytical grade.

2.2 Preparation of PPy-Ce composite

PPy-Ce composite was prepared by the chemical oxidative polymerization of the pyrrole monomer in the presence of cellulose (24). In brief, cellulose (0.5 g) was dissolved in double distilled water (20 ml) and pyrrole monomer (0.2 m in 1 m HCl) was added and stirred for 15 min to get a homogenous solution. The chemical oxidant ammonium peroxydisulfate was added with continuous stirring at 4°C. The molar ratio of oxidant to monomer was 1:2. The reaction mixture was incubated overnight at 4°C, and the blackish precipitates were separated by centrifugation at 3988 g for 10 min. The precipitates were washed several times with distilled water and methanol until the filtrates became clear. The washed precipitates were freeze dried at −80°C and used for further studies. Polymerization of pyrrole and cellulose is schematically represented in the Figure 1.

Figure 1: Schematic representation of pyrrole cellulose polymerization.
Figure 1:

Schematic representation of pyrrole cellulose polymerization.

2.3 Characterization of PPy-Ce composite

The morphology of the PPy-Ce composite was obtained through transmission electron microscopy (TEM) (JEOL, JEM 2100, Japan). The PPy-Ce composite was mixed with KBr pellets and compressed into films for Fourier transform infrared (FTIR) spectroscopy analysis using a Thermo Scientific Nicolet IR100 spectrometer. The sample was scanned in the range of 400–4000 cm−1. X-ray diffractograms (XRD) of the PPy-Ce composite were obtained using a Cu Kα incident beam (λ=0.1546 nm), monochromated by a nickel filtering wave at a tube voltage of 40 kV and tube current of 30 mA. The scanning was done in the region (2θ) 4–80° at 0.04°/min with a time constant of 2 s.

2.4 Batch experiments

Batch experiments were carried out in a 250 ml Erlenmeyer flask on a shaking incubator (180 rpm) at 26±1°C. RSM-based BBD was used for statistical optimization of Ni(II) removal conditions. Four important independent variables, pH (A), time (B), adsorbent dose (C), and initial Ni(II) concentration (D), were selected based on the preliminary study, and the Ni(II) removal % (Y) was considered as the dependent variable (response). The experiments were designed using Design Expert software (9.0 trial version) and 29 runs were executed to optimize the experimental conditions. At the end of each experiment, samples were withdrawn, centrifuged at 3988 g for 10 min and analyzed for residual Ni(II) concentration using a UV-Vis spectrophotometer (Hach Make: DR/2400 model) at 470 nm. Ni(II) removal (%) was calculated using the following equation:

[1]Removal capacity (%)=[(Co−Ce)/Co]×100

where Co and Ce are the initial and final Ni(II) concentrations (mg/l), respectively. The second-order polynomial equation used to fit the experimental results is depicted in Eq. [2].

[2]% Y=β0+∑βiXi+∑βiiXi2+∑βijXiXj+ε

where Y is the predicted response, β0, βi, βii, and βij are fixed regression coefficients of the model, Xi and Xj represent independent variables, and ε the random error. The adequacy of the proposed model was identified by the diagnostic checking test using analysis of variance (ANOVA), and the property of the fit polynomial model was represented by the coefficient of determination (R2). These analyses were performed by Fisher’s test (F-test) and p-value (probability).

3 Results and discussion

3.1 Characterization of PPy-Ce nanocomposite

A representative transmission electron micrograph of the PPy-Ce composites (Figure 2) confirms that the particles were either spherical or oval in shape. The size of the particles varied from 80 to 95 nm and mostly presented as aggregates. The results are in accordance with previous studies reporting the aggregated nature of PPy composites (20), (21). FTIR spectra of the PPy-Ce nanocomposite are shown in Figure 3A. The peaks at 3358 and 3091 cm−1 are assigned to O-H stretching vibration of polymeric compounds (34). The narrow peaks at 1552 and 1455 cm−1 are ascribed to C-N and C-C ring-vibrations of PPy. The band at 1181 and 1100 cm−1 are the characteristic carbonyl (C-O ester) peaks of cellulose (35). Similarly, the series of peaks at 1314, 1049, 911 and 792 cm−1 are different vibrational modes of PPy (36). In addition, the spectrum showed the presence of alkane peaks at 617 cm−1 (24). The results indicate that cellulose has successfully integrated with PPy. The polymerization process might break the intra- and intermolecular hydrogen bonds in cellulose and, thereby, frees the hydroxyl groups for interaction with metals. To understand the involvement of functional groups in Ni(II) removal, FTIR analysis was carried out in Ni(II) loaded PPy-Ce nanocomposites [composites were dried after contact with initial Ni(II) concentration of 150 mg/l, pH 8 and contact time 10 min], and the results are presented in Figure 3B. The peak assigned to O-H stretching has shifted from 3091 to 3102 cm−1. The adsorption peak attributed to C-N ring-vibrations of PPy has shifted from 1552 to 1543 cm−1. Similarly, the C-O ester peak of cellulose has shifted from 1181 to 1189 cm−1. The results confirmed the involvement of different functional groups in Ni(II) adsorption; electrostatic interactions and some chemical bonds may be formed between PPy-Ce nanocomposites, and Ni(II) ions could be responsible for vibrations in different functional groups. Furthermore, the disappearance of 1109 cm−1 peak in Ni(II) loaded PPy-Ce composites indicate that Ni(II) adsorption altered the functional groups of the composite. Venkatachalam and Seralathan (37) reported a minor shift in the functional groups of benzenesulfonic acid-doped polyaniline nanorods after reactive dye adsorption.

Figure 2: Transmission electron micrograph of PPy-Ce nanocomposite. The particles were circular or spherical in shape.
Figure 2:

Transmission electron micrograph of PPy-Ce nanocomposite. The particles were circular or spherical in shape.

Figure 3: FTIR spectra of PPy-Ce nanocomposite.(A) Before Ni(II) treatment and (B) after Ni(II) treatment.
Figure 3:

FTIR spectra of PPy-Ce nanocomposite.

(A) Before Ni(II) treatment and (B) after Ni(II) treatment.

XRD is widely employed to understand the nature of the materials. Thus, to investigate the nature of PPy-Ce nanocomposite, XRD analysis was carried out and the results are presented in Figure 4. The sharp narrow peaks at 2θ=14.6 and 22.5°, with 6.07 and 3.95 Å d-spacing, suggest the crystalline nature of the PPy-Ce nanocomposite. The results are consistent with previous studies reporting the crystalline nature of the cellulose composites (24), (38).

Figure 4: XRD spectra of PPy-Ce nanocomposite.
Figure 4:

XRD spectra of PPy-Ce nanocomposite.

3.2 Optimization Ni(II) removal

The results for each run performed as per the experimental plan are given in Table 1. The results showed that PPy-Ce nanocomposite removed 95.3–61.1% of Ni(II) from aqueous solution. An empirical relationship between Ni(II) removal (response) and the experimental variables (independent) have been expressed using the following quadratic model:

Table 1:

Box-Behnken design matrix for the four variables with the observed response.

Experiment No.pHTime (min)Adsorbent (g/l)Adsorbate (mg/l)Ni(II) removal (%)
15650.325064.3
28650.325081.2
36.5650.315072.2
46.5100.35071.8
56.51200.115064.2
66.5100.115061.1
76.5650.55083.4
88650.35095.3
98650.115078.9
108100.315084.2
116.5650.315072.0
125650.115061.3
136.5650.315071.6
146.5650.15067.8
156.5650.525071.0
1651200.315068.9
176.5100.515072.0
186.5650.125064.1
196.51200.325071.2
206.5650.315069.8
2181200.315086.6
226.5100.325061.1
236.51200.35075.6
248650.515089.6
255650.515069.8
266.51200.515080.1
276.5650.315071.0
285650.35078.5
295100.315063.2
[3]Ni(II) adsorption% (Y)=71.32+9.15A+2.77B+5.71C −4.96D−0.82AB+0.55AC+0.025AD+1.25BC +1.58BD−2.17CD+6.02A2−1.71B2−1.30C2+1.45D2

In Eq. [3], the magnitude of the coefficient indicates the intensity of the experimental variables on Ni(II) removal, with a positive value indicating a synergistic effect and a negative value indicating an antagonistic effect (39). The coefficients of A (pH), B (time), C (adsorbent dose), two-factor interaction effects (AC, AD, BC and BD), and three curvature effects (D2) were all positive, indicating that Ni(II) removal efficiency of PPy-Ce nanocomposite was improved as the level of these factors increased. However, negative values for other factors (D, AB, CD, B2 and C2) indicate that Ni(II) adsorption rate decreased as the level of these factor increased. High coefficient of A and C indicates that these two factors have a vital role in the removal of Ni(II) using PPy-Ce nanocomposites.

The significance of the second-order quadratic model coefficient was evaluated using ANOVA, and the results are given in Table 2. The predicted R2 (0.8387) and adjusted R2 (0.9415) values for Ni(II) removal were in reasonable agreement with the value of R2 (0.9708), which is closer to 1, indicating better fitness of experimental data with the quadratic model and relationship between experimental variables and the response. The model adequacy was tested through the lack of fit F-test. The F-statistic of the established model was 33.21 with a low probability value (Prob > F 0.0500), indicating that the model is statistically significant. Similarly, the p-values for A, B, C, D and A2 were less than 0.0500, which confirms the influence of pH, contact time, PPy-Ce nanocomposites dosage and initial Ni(II) concentrations to be significant for Ni(II) adsorption onto PPy-Ce nanocomposites. Furthermore, the coefficient of variation (CV) value (2.94%) indicates a satisfying precision and reliability of the model. Thus, the BBD model could provide a reasonable estimation of the response for Ni(II) removal using PPy-Ce nanocomposites.

Table 2:

Analysis of variance (ANOVA) for the removal of Ni(II) onto PPy-Ce nanocomposite.

SourceSum of squaresdfMean squareF-valueProb>F
Model2145.5714153.2633.21<0.0001
A1004.6711004.67217.68<0.0001
B91.85191.8519.900.0005
C391.021391.0284.72<0.0001
D295.021295.0263.92<0.0001
AB2.7212.720.590.4552
AC1.2111.210.260.6166
AD2.500E−00312.500E−0035.417E−0040.9818
BC6.2516.251.350.2640
BD9.9219.922.150.1647
CD18.92118.924.100.0624
A2234.681234.6850.850.0001
B218.97118.974.110.0621
C210.92110.922.370.1463
D213.68113.682.970.1071
Residual64.62144.62––
Lack of fit60.89106.096.530.0427
Pure error3.7340.93––
Cor total2210.1928–––

3.3 Effect of experimental variable and three-dimensional response plots

To understand the interactive effect of experimental variables on the percentage of Ni(II) removal, three-dimensional plots were established (Figure 5). As four different variables were selected for the study, one variable was held as a constant at the central level of each point. Figure 5A shows the combined effect of pH and contact time on the removal of Ni(II) from aqueous solutions. The results showed that Ni(II) removal efficiency increased according to the increase in pH and contact time. For instance, at pH 5 and contact time of 10 min, the removal efficiency was 63.2% and it increased to 86.6% at pH 8 and contact time of 120 min. The decrease in removal efficiency at lower pH was due to the protonation of amino groups in the PPy-Ce nanocomposite as expressed in Eq. [4]:

Figure 5: Three-dimensional response plots showing the influence of experimental variables on Ni(II) removal from aqueous solution.(A) Effect of contact time and pH, (B) effect of adsorbent dose and contact time, (C) effect of adsorbate dose and pH, (D) effect of adsorbate dose and contact time, and (E) effect of adsorbate dose and adsorbent dose.
Figure 5:

Three-dimensional response plots showing the influence of experimental variables on Ni(II) removal from aqueous solution.

(A) Effect of contact time and pH, (B) effect of adsorbent dose and contact time, (C) effect of adsorbate dose and pH, (D) effect of adsorbate dose and contact time, and (E) effect of adsorbate dose and adsorbent dose.

[4]R-NH2+H3O+→ R-NH3++H2O

Protonation leads to a strong electrostatic repulsion between Ni2+ and NH3+ which prevents the adsorption processes. Alternatively, a strong competition between Ni2+ and H+ ions present in the aqueous solution hinders the uptake capacity. However, as the pH increases (5), (6), (7), (8), deprotonation of the nitrogen atom occurs and a higher number of amino groups are chelated with Ni2+ which results in higher uptake capacity. Numerous studies reported that the high alkaline pH (>9) decreases the Ni removal rate, which could be due to the formation of Ni(OH)2 in the reaction mixture (40), (41), (42). Figure 5B represents the interactive effect of adsorbent dose and contact time on Ni(II) removal. The results indicated that Ni(II) removal efficiency was increased according to the increase in the adsorbent dose. This trend could be explained by the fact that high adsorbent dosage increases the number of reactive sites and overall surface area of the adsorbent; thus, the removal efficiency of Ni(II) increased with increase in the PPy-Ce nanocomposite dosage. The results are in accordance with a previous study that reported the removal efficiency of Cu increased with an increase in cyanobacterial dose (32).

The effect of adsorbate dose [initial Ni(II) concentration] and pH is shown in Figure 5C. The graph shows the Ni(II) removal efficiency decreased with an increase in initial Ni(II) concentration and a decrease in pH. For example, at pH 8 and 50 mg/l initial Ni(II) concentration, the removal efficiency was 81.2%, which decreased to 64.3% at pH 5 and 250 mg/l initial Ni(II). The decreased removal at high initial Ni(II) concentration could be due to the competition among Ni(II) ions for available reactive sites in the adsorbent and saturation of reactive sites in the PPy-Ce nanocomposites. The results are consistent with previous study reported a decrease in Sr2+ removal efficiency of polypyrrole-nickel oxide nanocomposite with increase in initial Sr2+ concentrations (43).

The interactive effects of adsorbate dose and contact time are shown in the Figure 5D. The results showed that Ni(II) removal efficiency an increased with an increase in contact time upto 70 min and gradually decreased with an increase in contact time. The initial increase in Ni(II) removal efficiency could be due to the high availability of reactive sites on the PPy-Ce nanocomposites. Gradual occupancy of these sites reduced the reaction rate and, thus, the Ni(II) removal efficiency becomes less efficient. At this point, the amount of Ni(II) being adsorbed onto the PPy-Ce nanocomposite is in dynamic equilibrium with the amount of Ni(II) desorbed form the PPy-Ce nanocomposite. Similar results were reported for Pb(II) and Cu(II) adsorption onto sporopollenin biomass (32). Figure 5E shows the interactive effect of adsorbent and adsorbate dose on Ni(II) removal efficiency. The results indicated that Ni(II) removal efficiency increased according to the adsorbent dose even at high initial Ni(II) concentration. Srivastava et al. (43) reported that Sr2+ removal efficiency increased according to dose of polypyrrole nickel oxide nanocomposite. Furthermore, to support the numerical modeling, confirmatory experiments were performed three times under optimized conditions (pH, 8; time, 65 min; adsorbent dose, 0.3 mg/l; Ni(II) concentration, 50 mg/l). The average Ni(II) removal efficiency was 94.9±0.2%, which is in good agreement with the predicted value 95.3%, suggesting that the model was reliable in this study.

4 Conclusions

The present study attempted to assess the efficiency of PPy-Ce nanocomposite for the removal of Ni(II) from an aqueous solution. The RSB-based BBD design was successfully employed to optimize the experimental conditions for Ni(II) removal using a PPy-Ce nanocomposite. The results indicated that the design is very effective and is a reliable technique for understanding the influence of experimental variables on response. Under optimized experimental conditions, the PPy-Ce nanocomposite removed >94% of Ni(II) from an aqueous solution. Furthermore, easy availability, low cost and ease of synthesis indicate that PPy-Ce nanocomposite could be used for the treatment of Ni(II) bearing wastewaters.

Acknowledgments

This research was supported by “Research Base Construction Fund Support Program” funded by Chonbuk National University in 2017.

References

1. Kamala-Kannan S, Batvari BPD, Lee KJ, Kannan N, Krishnamoorthy R, Shanthi K, Jayaprakash M. Assessment of heavy metals (Cd, Cr and Pb) in water, sediment and seaweed (Ulva lactuca) in the Pulicat Lake, South East India. Chemosphere 2008;71:1233–40.10.1016/j.chemosphere.2007.12.004Search in Google Scholar PubMed

2. Sukumar C, Gowthami G, Nitya R, Janaki V, Kamala-Kannan S, Shanthi K. Significance of co-immobilized activated carbon and Bacillus subtilis on removal of Cr(VI) from aqueous solution. Environ Earth Sci. 2014;72:839–47.10.1007/s12665-013-3007-6Search in Google Scholar

3. Zambelli B, Ciurli S. Nickel and human health. Met Ions Life Sci. 2013;13:321–57.10.1007/978-94-007-7500-8_10Search in Google Scholar PubMed

4. Lee Y, Lim S, Baek BJ, An J, Nam H, Woo K, Cho M, Kim S, Lee S. Nickel(II)-induced nasal epithelial toxicity and oxidative mitochondrial damage. Environ Toxicol Pharmacol. 2016;42: 76–84.10.1016/j.etap.2016.01.005Search in Google Scholar PubMed

5. Janaki V, Kamala-Kannan S, Shanthi K. Significance of Indian peat moss for the removal of Ni(II) ions from aqueous solution. Environ Earth Sci. 2015;74:5351–7.10.1007/s12665-015-4547-8Search in Google Scholar

6. Yang S, Wu Y, Aierken A, Zhang M, Fang P, Fan Y, Ming Z. Mono/competitive adsorption of arsenic(III) and nickel(II) using modified green tea waste. J Taiwan Inst Chem Eng. 2016;60:213–21.10.1016/j.jtice.2015.07.007Search in Google Scholar

7. Rodriguez CE, Quesada A, Rodriguez E. Nickel biosorption by Acinetobacter baumannii and Pseudomonas aeruginosa isolated from industrial wastewater. Braz J Microbiol. 2006;37:456–67.10.1590/S1517-83822006000400012Search in Google Scholar

8. Congeevaram S, Dhanarani S, Park J, Dexilin M, Thamaraiselvi K. Biosorption of chromium and nickel by heavy metal resistant fungal and bacterial isolates. J Hazard Mater. 2007;146:270–7.10.1016/j.jhazmat.2006.12.017Search in Google Scholar PubMed

9. Coman V, Robotin B, Ilea P. Nickel recovery/removal from industrial wastes: a review. Resour Conserv Recycling. 2013;73:229–38.10.1016/j.resconrec.2013.01.019Search in Google Scholar

10. Kazeminezhad I, Mosivand S. Elimination of copper and nickel from wastewater by electrooxidation method. J Magn Magn Mater. 2017;422:84–92.10.1016/j.jmmm.2016.08.049Search in Google Scholar

11. Khosa MA, Wu J, Ullah A. Chemical modification, characterization and application of chicken feathers as novel biosorbents. RSC Adv. 2013;3:20800–10.10.1039/c3ra43787fSearch in Google Scholar

12. Khosa MA, Ullah A. In-situ modification, regeneration, and application of keratin biopolymer for arsenic removal. J Hazard Mater. 2014;278:360–71.10.1016/j.jhazmat.2014.06.023Search in Google Scholar PubMed

13. Arshad M, Khosa MA, Siddique T, Ullah A. Modified biopolymers as sorbents for the removal of naphthenic acids from oil sands process affected water (OSPW). Chemosphere 2016;163:334–41.10.1016/j.chemosphere.2016.08.015Search in Google Scholar PubMed

14. Gupta SS, Bhattacharyya KG. Kinetics of adsorption of metal ions on inorganic materials: a review. Adv Colloid Interface Sci. 2011;162:39–58.10.1016/j.cis.2010.12.004Search in Google Scholar PubMed

15. Wei C, German S, Basak S, Rajeshwar K. Reduction of hexavalent chromium in aqueous solutions by polypyrrole. J Electrochem Soc. 1993;140:L60–2.10.1149/1.2056247Search in Google Scholar

16. Zhang S, Shao YY, Liu J, Aksay IA, Lin YH. Graphene-polypyrrole nanocomposite as a highly efficient and low cost electrically switched ion exchanger for removing ClO−4 from wastewater. ACS Appl Mater Interfaces. 2011;3:3633–7.10.1021/am200839mSearch in Google Scholar PubMed

17. Kumar R, Oves M, Almeelbi T, Al-Makishah NH, Barakat MA. Hybrid chitosan/polyaniline-polypyrrole biomaterial for enhanced adsorption and antimicrobial activity. J Colloid Interface Sci. 2017;490:488–96.10.1016/j.jcis.2016.11.082Search in Google Scholar PubMed

18. Karthik R, Meenakshi S. Chemical modification of chitin with polypyrrole for the uptake of Pb(II) and Cd(II) ions. Int J Biol Macromolec. 2015;78:157–64.10.1016/j.ijbiomac.2015.03.041Search in Google Scholar PubMed

19. Omraei M, Esfandian H, Katal R, Ghorbani M. Study of the removal of Zn(II) from aqueous solution using polypyrrole nanocomposite. Desalination 2011;271:248–56.10.1016/j.desal.2010.12.038Search in Google Scholar

20. Ghorbani M, Eisazadeh H. Removal of COD, color, anions and heavy metals from cotton textile wastewater by using polyaniline and polypyrrole nanocomposites coated on rice husk ash. Composites Part B 2013;45:1–7.10.1016/j.compositesb.2012.09.035Search in Google Scholar

21. Rajeswari V, Janaki V, Shanthi K, Kamala-Kannan S. Adsorption and subsequent detoxification of hexavalent chromium in aqueous solution using polypyrrole-bacterial extracellular polysaccharide nanocomposite. Environ Prog Sustain Energy 2016;35:1293–7.10.1002/ep.12344Search in Google Scholar

22. Chávez-Guajardo AE, Medina-Llamas JC, Maqueira L, Andrade CAS, Alves KGB, de Melo CP. Efficient removal of Cr(VI) and Cu(II) ions from aqueous media by use of polypyrrole/maghemite and polyaniline/maghemite magnetic nanocomposites. Chem Eng J. 2015;281:826–36.10.1016/j.cej.2015.07.008Search in Google Scholar

23. Faruk O, Bledzki AK, Fink HP, Sain M. Biocomposites reinforced with natural fibers: 2000–2010. Prog Polym Sci. 2012;37:1552–96.10.1016/j.progpolymsci.2012.04.003Search in Google Scholar

24. Janaki V, Vijayaraghavan K, Oh BT, Ramasamy AK, Kamala-Kannan S. Synthesis, characterization and application of cellulose/polyaniline nanocomposite for the treatment of simulated textile effluent. Cellulose 2013;20:1153–66.10.1007/s10570-013-9910-xSearch in Google Scholar

25. Liu XY, Chang PR, Zheng PW, Anderson DP, Ma XF. Porous cellulose facilitated by ionic liquid [BMIM]Cl: fabrication, characterization, and modification. Cellulose 2015;22:709–15.10.1007/s10570-014-0467-0Search in Google Scholar

26. Husson E, Buchoux S, Avondo C, Cailleu D, Djellab K, Gosselin I, Wattraint O, Sarazin C. Enzymatic hydrolysis of ionic liquid-pretreated celluloses: contribution of CP-MAS C-13 NMR and SEM. Bioresour Technol. 2011;102:7335–42.10.1016/j.biortech.2011.04.097Search in Google Scholar PubMed

27. Hokkanen S, Bhatnagar A, Sillanpaa M. A review on modification methods to cellulose-based adsorbents to improve adsorption capacity. Water Res. 2016;91:156–73.10.1016/j.watres.2016.01.008Search in Google Scholar PubMed

28. Ma X, Liu C, Anderson DP, Chang PR. Porous cellulose spheres: preparation, modification and adsorption properties. Chemosphere 2016;165:399–408.10.1016/j.chemosphere.2016.09.033Search in Google Scholar PubMed

29. Ji F, Li C, Tang B, Xu J, Lu G, Liu P. Preparation of cellulose cetate/zeolite composite fiber and its adsorption behavior for heavy metal ions in aqueous solution. Chem Eng J. 2012;209:325–33.10.1016/j.cej.2012.08.014Search in Google Scholar

30. Ren H, Gao Z, Wu D, Jiang J, Sun Y, Luo C. Efficient Pb(II) removal using sodium alginate–carboxymethylcellulose gel beads: preparation, characterization, and adsorption mechanism. Carbohydr Polym. 2016;137:402–9.10.1016/j.carbpol.2015.11.002Search in Google Scholar PubMed

31. Stoica-Guzun A, Stroescu M, Jinga SI, Mihalache N, Botez A, Matei C, Berger D, Damian CM, Ionita V. Box-Behnken experimental design for chromium(VI) ions removal by bacterial cellulose-magnetite composites. Int J Biol Macromolec. 2016;91:1062–72.10.1016/j.ijbiomac.2016.06.070Search in Google Scholar PubMed

32. Sener M, Reddy DHK, Kayan B. Biosorption properties of pretreated sporopollenin biomass for lead(II) and copper(II): application of response surface methodology. Ecol Eng. 2014;68:200–8.10.1016/j.ecoleng.2014.03.024Search in Google Scholar

33. Myers RH, Montgomery DC. response surface methodology. 2nd ed. New York, USA: Wiley; 2001.Search in Google Scholar

34. Naumann D, Helm D, Labischinski H. Microbiological characterization by FT-IR spectroscopy. Nature 1991;351:81–2.10.1038/351081a0Search in Google Scholar PubMed

35. Zheng Y, Wang W, Huang D, Wang A. Kapok fiber oriented-polyaniline nanofibers for efficient Cr(VI) removal. Chem Eng J. 2012;191:154–61.10.1016/j.cej.2012.02.088Search in Google Scholar

36. Liu Y, Zhang Y, Ma G, Wang Z, Liu K, Liu H. Ethylene glycol reduced graphene oxide/polypyrrole composite for supercapacitor. Electrochim Acta. 2013;88:519–25.10.1016/j.electacta.2012.10.082Search in Google Scholar

37. Venkatachalam J, Seralathan K. Synthesis and application of benzenesulfonic acid doped polyaniline nanorods for decolorization of simulated remazol effluent. Environ Prog Sustain Energy 2017;36:1383–89.10.1002/ep.12582Search in Google Scholar

38. Ahmadzadeh S, Desobry S, Keramat J, Nasirpour A. Crystalline structure and morphological properties of porouscellulose/clay composites: the effect of water and ethanol as coagulants. Carbohydr Polym. 2016;141:211–9.10.1016/j.carbpol.2016.01.017Search in Google Scholar PubMed

39. Amini M, Younesi H, Bahramifar N, Lorestani AAZ, Ghorbani F, Daneshi A, Sharifzadeh M. Application of response surface methodology for optimization of lead biosorption in an aqueous solution by Aspergillus niger. J Hazard Mater. 2008;154:694–702.10.1016/j.jhazmat.2007.10.114Search in Google Scholar PubMed

40. El-Bahy SM, El-Bahy ZM. Synthesis and characterization of polyamidoxime chelating resin for adsorption of Cu(II), Mn(II) and Ni(II) by batch and column study. J Environ Chem Eng. 2016;4:276–86.10.1016/j.jece.2015.10.040Search in Google Scholar

41. Liao B, Sun W, Guo N, Ding S, Su S. Equilibriums and kinetics studies for adsorption of Ni(II) ion on chitosan and its triethylenetetramine derivative. Colloids Surf A Physicochem Eng Asp. 2016;501:32–41.10.1016/j.colsurfa.2016.04.043Search in Google Scholar

42. Zare-Dorabei R, Ferdowsi SM, Barzin A, Tadjarodi A. Highly efficient simultaneous ultrasonic-assisted adsorption of Pb(II), Cd(II), Ni(II) and Cu(II) ions from aqueous solutions by graphene oxide modified with 2,20-dipyridylamine: central composite design optimization. Ultrason Sonochem. 2016;32:265–76.10.1016/j.ultsonch.2016.03.020Search in Google Scholar PubMed

43. Srivastava V, Maydannik P, Sillanpaa M. Synthesis and characterization of PPy@NiO nano-particles and their use as adsorbent for the removal of Sr(II) from aqueous solution. J Mol Liq. 2016;223:395–406.10.1016/j.molliq.2016.08.034Search in Google Scholar

Received: 2017-10-18
Accepted: 2017-12-24
Published Online: 2018-02-15
Published in Print: 2018-07-26

©2018 Walter de Gruyter GmbH, Berlin/Boston

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Articles in the same Issue

  1. Frontmatter
  2. In this Issue
  3. Full length articles
  4. Synthesis, characterization and application of polypyrrole-cellulose nanocomposite for efficient Ni(II) removal from aqueous solution: Box-Behnken design optimization
  5. Biodegradable glucose and glucosamine grafted polyacrylamide/graphite composites for the removal of acid violet 17 from an aqueous solution
  6. Evaluation of activated composite membranes for the facilitated transport of phenol
  7. The effects of nanoparticles on morphology and thermal properties of erythritol/polyvinyl alcohol phase change composite fibers
  8. Rapid crystallization and mesophase formation of poly(L-lactic acid) during precipitation from a solution
  9. Structural deformation of PVDF nanoweb due to electrospinning behavior affected by solvent ratio
  10. Poly(vinyl amine) as a matrix for a new class of polymers
  11. Review
  12. Polymeric advanced delivery systems for antineoplasic drugs: doxorubicin and 5-fluorouracil
https://doi.org/10.1515/epoly-2017-0215
Keywords for this article
cellulose; metals; nanocomposites; pyrrole; response surface methodology
Creative Commons
BY-NC-ND 3.0

Articles in the same Issue

  1. Frontmatter
  2. In this Issue
  3. Full length articles
  4. Synthesis, characterization and application of polypyrrole-cellulose nanocomposite for efficient Ni(II) removal from aqueous solution: Box-Behnken design optimization
  5. Biodegradable glucose and glucosamine grafted polyacrylamide/graphite composites for the removal of acid violet 17 from an aqueous solution
  6. Evaluation of activated composite membranes for the facilitated transport of phenol
  7. The effects of nanoparticles on morphology and thermal properties of erythritol/polyvinyl alcohol phase change composite fibers
  8. Rapid crystallization and mesophase formation of poly(L-lactic acid) during precipitation from a solution
  9. Structural deformation of PVDF nanoweb due to electrospinning behavior affected by solvent ratio
  10. Poly(vinyl amine) as a matrix for a new class of polymers
  11. Review
  12. Polymeric advanced delivery systems for antineoplasic drugs: doxorubicin and 5-fluorouracil
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 21.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/epoly-2017-0215/html
Scroll to top button