Investigation of mercury (II) adsorption from aqueous solution onto copper oxide nanoparticles: Optimization using response surface methodology

Process Safety and Environmental Protection 9 3 ( 2 0 1 5 ) 1–8

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Investigation of mercury (II) adsorption from aqueous solution onto copper oxide nanoparticles: Optimization using response surface methodology Ali Fakhri a,b,∗ a b

Department of Chemistry, Shahre-Qods Branch, Islamic Azad University, Tehran, Iran Young Researchers and Elite Club, Shahre-Qods Branch, Islamic Azad University, Tehran, Iran

a b s t r a c t Response surface methodology was practicable to optimize the mercury (II) removal using copper oxide nanoparticles in an aqueous matrice. The copper oxide nanoparticles structure was performed by TEM, SEM, XRD and BET. The experiment reactions were carried out based on a Box–Behnken design (BBD) and evaluated using RSM. Batch mode tests were conducted to prognosticate the adsorption equilibrium. The three parameters influence on the mercury removal was inquired by a response surface methodological approach. In study, influence of adsorbent dose, pH and temperature on the mercury removal unto copper oxide nanoparticles has been performed. The importance of the independent factors and their interactions were investigated by the ANOVA. The optimum pH, adsorbent dose and temperature were obtained to be 9.0, 0.05 g and 278 K, respectively. © 2014 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Keywords: Mercury; CuO nanoparticles; Optimization; Box–Behnken design; Response surface methodology

1.

Introduction

The heavy metal contamination demonstrates a major problem in environmental due to their toxicity influence. Heavy metals pollutions stand in aqueous wastage of many industries, such as mining operations, tanneries and metal plating facilities (Gogate and Pandit, 2004; Patterson, 1958; Srivastava et al., 1999). Amongst heavy metals, Hg(II) has high prior for removal from environments (Londrigan et al., 1990; Sari and Tuzen, 2009; Tuzen et al., 2009c). Mercury is a global environmental hazard. This is due, Hg has extreme toxicity at low levels, and its orientation to biomagnify in food chains and bioaccumulate in organisms (Mastrine et al., 1999; Hanish, 1998; Biester et al., 2002; Tuzen et al., 2009a, b). Municipal solid waste incineration, Worldwide coal burning, pharmaceutical industries, paper, electronic (Sznopek and Goonan, 2000; Tack et al., 2005) and scum of gold mines were recognize as the importance anthropogenic sources of Hg exportation. Furthermore, exportation

of Hg from paint, chlor-alkali, fertilizer industries, rubber processing, oil refining (Kadirvelu et al., 2004) and As well as can be used in barometers, thermometers, pumps, lamps (Davis et al., 2000; Chantawong et al., 2003). RSM is an empiric statistical technique that uses quantitative data found from suitable designed tests to determine operating conditions and regression model (Alam et al., 2007; Ricou-Hoeffer et al., 2001; Tan et al., 2008). Principal response surface methodologies that are applied in experimental design are Doehlert design, Box–Behnken, and central composite (Souza et al., 2005). Box–Behnken is a spherical rotational design requires an experiment number (N = k2 + k + cp). It has been used for the several physical and chemical processes optimization. Moreover, its application in analytical chemistry is much lower than the Doehlert matrix design and central composite (Souza et al., 2005). This project is mainly focused on the examination of combined effect of several parameters such as temperature, adsorbent dose, and pH on Hg(II) removal by CuO

∗ Correspondence to: Department of Chemistry, Shahre-Qods Branch, Islamic Azad University, Tehran, Iran. Tel.: +98 21 22873079; fax: +98 21 22873079. E-mail address: [email protected]

Available online 11 June 2014 http://dx.doi.org/10.1016/j.psep.2014.06.003 0957-5820/© 2014 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

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Process Safety and Environmental Protection 9 3 ( 2 0 1 5 ) 1–8

nanoparticles using BBD in Response Surface Methodology (RSM) by Design Expert Version 6.0.10 (Stat Ease, USA).

2.

Materials and methods

2.1.

Raw materials

Cupric chloride dihydrate (CuCl2 ·6H2 O) (molecular weight, 170.48 g/mol), glacial acetic acid (CH3 CO2 H) (molecular weight, 60.05 g/mol), sodium hydroxide (NaOH) (molecular weight, 40.00 g/mol) and mercury chloride (HgCl2 ) were supplied by Merck, Germany (maximum purity available).

2.2.

Purveyance of CuO nanoparticles

CuO nanoparticles were prepared by sol–gel method (Fakhri, 2014). The aqueous matrices of CuCl2 ·6H2 O (0.2 M) are collected in cleaned round bottom flask. 1 ml of glacial acetic acid is added to above aqueous matrices and warmed to 100 ◦ C with constant stirring. 8 M NaOH is added to above warmed solution till pH reaches to 7. It is centrifuged and washed 3–4 times with deionized water. The received precipitate was dried in air for 24 h. This powder is better used for the characterization of CuO nanoparticles. A scanning electron microscope (SEM); JEOL JSM-5600 Digital Scanning Electron Microscope, Japan and X-ray diffractometer (XRD) Philips X’Pert, USA were used to characterize the adsorbent for its morphological information. The particle size of the CuO nanoparticles was measured using Transmission Electron Microscope (TEM) (Zeiss EM900, Germany). The Brunauer–Emmett–Teller (BET) of the CuO nanoparticles was analyzed by nitrogen adsorption instrument in an ASAP2020 surface area (Micromeritics, USA).

2.2.1.

Adsorption experiment methods

The removal of Hg(II) using CuO nanoparticles was survey by batch methods. For this test 1000 mg/L solution of supply was confected by added 1 g of Hg(II) in 1000 mL water. Different concentrations (10, 30, 50 and 100 mg/L) of Hg(II) solutions were prepared by this stock solution. Various concentrations (10, 30, 50 and 100 mg/L) of Hg(II) solutions were confected. The between range for dosage of adsorbent 0.01–0.09 g was added after pH regulation made in between 2 and 12. A concentration of Hg(II) was determined by cold vapor AAS (Perkin Elmer, Kimia Shangarf Pars Research, Iran). The equilibrium adsorption capacity was calculated from the relationship qe =

(C0 − Ce )V W



(C0 − Ce ) C0

 × 100

(2)

where qe (mg/g) is the equilibrium adsorption capacity, Ce is the Hg(II) concentration at equilibrium (mg/l), V is the volume of solution (l) and w is the weight of adsorbent (g).

2.2.2.

is in use. Wash hands and face after handling mercury, before lunch or breaks, and at the end of each work period. Waste of mercury should be collected in sealable disposable containers and stored in the fume hood until they can be removed from the lab.

2.2.3. Experimental design for optimization of parameters using RSM Optimum condition for the Hg(II) adsorption by CuO nanoparticles was distinguished using of RSM and BBD. The RSM consists of a group of tentative techniques devoted to the appraisal of correlation existing among experimental factors and measured responses (Bayraktar, 2000; Kunamneni and Singh, 2005; Preetha and Viruthagiri, 2007). The selective non-aligned changeable applied in which work was coded conforming to Eq. (3):

xi =

xi − x0 x

(3)

where X0 is the quantity of Xi at the middle data, xi is the dimensionless coded quantity of the ith non-aligned changeable and X is the change of step quantity. The second-order polynomial model described to Eq. (4):

(1)

The removal efficiency of Hg(II), E (%), was calculated using the equation: E=

Fig. 1 – Cube plot for Y.

Working conditions of mercury in laboratory

Mercury is one of the most harmful pollutants. Mercury poisoning is a disease caused by exposure to mercury or its compounds. Do not eat, drink, or smoke; or store food, drinks, smoking materials, or cosmetics in any area where mercury

Y = ˇ0 +

k  i=1

ˇi xi +

k  i=1

ˇii xi2 +

k 

ˇij xi xj + ε

(4)

1≤i≤j

where Y is the predicted response, xi , xj ,. . ., xk are the input variables, ˇ0 is the intercept state, ˇi (i = 1, 2,. . ., k) is the influence of linear, ˇii (i = 1, 2,. . ., k) is the influence of squared, ˇij (i = 1, 2,. . ., k; j = 1, 2,. . ., k) is the influence of interaction and ε is a random transgression (Aksu and Gönen, 2006). The Box–Behnken design (BBD) is the most frequently used under RSM design. The study carried out involved the employment of Box–Behnken design to optimize the adsorption process due to it’s suitability to fit quadratic surface which usually works well for process optimization. The study performed afoul the BBD employment to optimize the process of removal.

Process Safety and Environmental Protection 9 3 ( 2 0 1 5 ) 1–8

3

Fig. 2 – SEM images (A), X-ray diffraction analysis (B), BET plot (C) and TEM image (D) of CuO nanoparticles.

3.

Results and discussions

3.1.

Characterizations of copper oxide nanoparticles

Fig. 2(A) shows the SEM image of as prepared CuO nanoparticles. It shows that the CuO nanoparticles are in oblong shape. Fig. 2(B) shows the XRD pattern of prepared CuO Nanoparticles. Reflection peaks at 2 = 68.0◦ , 65.7◦ , 58.3◦ , 38.7◦ , and 35.4◦ are cataloged as [2 2 0], [0 2 2], [2 0 2], [1 1 1], and [0 0 2] planes showed cubic symmetry of CuO NPs, respectively. Higher intensity at 2 = 35.4◦ and 38.7◦ indicates that phases has important segment and highly oriented crystalline of CuO NPs. Fig. 2(C) shows the nitrogen adsorption and desorption isotherm for the CuO nanoparticles. There are hysteresis loops that appear at high pressure in the isotherm of CuO nanoparticles, which is presumably due to interparticular spacing between agglomerated CuO nanoparticles. The values of total pore volume and specific surface area were 0.24 cm3 g−1 and 89.59 m2 g−1 , respectively. Fig. 2(D) shows TEM micrograph of CuO nanoparticles. The actual size of nanoparticles is estimated from TEM micrograph. Most of the nanoparticles have size around less than 100 nm and which is in relevance with the SEM image. The TEM graph is also showed that the copper oxide nanoparticles are consists of agglomerated particles with a regular morphology.

3.2.

Batch manner adsorption of Hg(II)

The data of batch manner test indicated that optimum qualifications for the adsorption of Hg(II) aboard aquatics matrices are as dosage of adsorbent = 0.05 g at 278 K temperature and pH 9. The results also illustrated that amount adsorption capacity

increased with enhance in pH and dose of adsorbent and decreased with enhance in temperature. The isotherm models such as Langmuir (Eq. (5)), Freundlich (Eq. (6)) and Fakhri (Eq. (7)) (Fakhri and Adami, 2014). were applied in this paper to purvey a goal frame to the begotten data of adsorption reaction. Ce 1 Ce = + qe KL qm qm ln qe = ln KF +

1 ln Ce n

ln() = BF ln(1 + KF Ce ) − ln(KF Ce )

(5)

(6) (7)

where qm , KL , KF , 1/n, , BF and KF are the maximum removal value (mg/g), constant of Langmuir (L/mg), constant of Freundlich, constant parameter, equilibrium binding constant (mg−1 ), degree of surface coverage, Fakhri isotherm exponent, equilibrium constant of adsorption (L g−1 ), respectively. The Langmuir isotherm model appropriated well to the data of removal with the high R2 value (0.9970). The quantities of qm and KL were obtained to be 825.21 mg/g and 6.9632 L/mg (Table 1). In order to investigate the controlling mechanism of adsorption processes such as mass transfer and chemical reaction, a suitable kinetic model is needed to analyze the data. Any kinetic or mass transfer representation is likely to be global. From a system design point of view, a lumped analysis of kinetic data is hence sufficient for practical operations (Fakhri, 2013b). The kinetic experiments were performed for various contact times (10, 15, 20, 25, 30, 35, 45, 65 and 95 min) with constant dose of adsorbent (0.05 g), concentration (30 mg/L) and pH (9). This kinetic study indicated that 40 min was adequate to attain equilibrium of Hg(II) onto CuO

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Process Safety and Environmental Protection 9 3 ( 2 0 1 5 ) 1–8

Table 1 – Isotherm and kinetic parameters for Hg(II) adsorption. CuO NPs Isotherm parameters Freundlich constants KF (mg/g) 1/n R2 Langmuir constants qm (mg/g) KL (L/mg) R2 Fakhri constants KF (L g−1 ) BF R2 Kinetic parameters Pseudo-first order qe (mg/g) (exp) qe (mg/g) (cal) k1 (min−1 ) R2 Pseudo-second order qe (mg/g) (exp) qe (mg/g) (cal) K2 (g mg−1 min−1 ) R2

19.354 0.5155 0.9902 825.21 6.9632 0.9970 15.258 0.9951 0.9900

28.20 18.38 0.0909 0.9886 28.20 31.05 0.0006 0.9973

nanoparticles. The correlation coefficients, R2 , showed that pseudo-second order model, an indication of a chemisorptions mechanism, fits the experimental data quite well with R2 = 0.9973.

3.3.

Fig. 4 – Linear curve of Y responses.

3.4.

Statistical analysis

The optimum quantities of the chosen changeable were obtained by regression equation and by distinguished the RSM contour plots (Garg et al., 2008; Sahu et al., 2009a,b, 2010; Acharya et al., 2009; Fakhri, 2013a). The results of each experiment are given in Table 2. The quadratic model has been expressed by Eq (8): Y = 3.964 − 0.212A − 2.337B + 2.100C − 0.050AB − 0.475AC − 0.075BC − 4.557A2 + 2.393B2 + 2.568C2

(8)

Removal efficiency of CuO nanoparticles

Removal efficiency (E) of mercury ions was calculated by measuring the mercury concentration before and after adsorption, respectively. This assessment was investigated in three states. As seen in Fig. 3, highest removal efficiency in state B occurred.

Fig. 4 shows that the points of data were close to an erect line (R2 = 0.9888), which infer a good correlation between the predicted and experimental quantity. The conclusions also display that the chosen quadratic model was proportional for the data of removal experimental.

3.5.

Disintegration of variance

The results from ANOVA (Table 3) display which the proportional of equation represented the concrete relevance between the principal changeable and the response. There is excellent relevance between the predicted and observed quantity as shown by nearly between R2 and adjusted R2 value. The pattern is regarded to be actuarial basic because the affiliated Prob. > F quantity for the pattern is lower than 0.05. The nonmain quantity of lack of fit (more than 0.05) demonstrated that the model of quadratic was valid for this work (Hamsaveni et al., 2001). Very low value of probability, for the variables temperature and pH and low values for CuO NPs dose–CuO NPs dose, temperature–temperature and pH–pH showing an excellent level of principal shows the importance of these variables in the removal process.

3.6. Effect of interactive variables and 3D response surface plot Fig. 3 – The plot of CuO NPs removal efficiency for adsorption of mercury ions (state A: CuO NPs dose: 0.05 g, Temp.: 318 K, pH: 9; state B: CuO NPs dose: 0.05 g, Temp.: 278 K, pH: 4 and state C: CuO NPs dose: 0.05 g, Temp.: 278 K, pH: 9).

3.6.1. Influence of temperature and adsorbent dosage on Hg(II) removal by CuO nanoparticles The composed influence of adsorbent dosage and temperature on the Hg(II) uptake by the CuO nanoparticles is shown in

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Process Safety and Environmental Protection 9 3 ( 2 0 1 5 ) 1–8

Table 2 – BBD and results for the study of three experimental variables in coded units. Runs 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

CuO NPs dose (g)

Temperature (K)

0.09 0.05 0.01 0.01 0.05 0.01 0.09 0.01 0.05 0.05 0.05 0.09 0.09 0.05 0.05 0.05 0.05

pH

298 298 278 318 278 298 298 298 298 298 318 278 318 298 298 318 278

YExp (mg/g)

4 7 7 7 9 4 4 9 7 7 4 7 7 7 7 9 4

33.50 34.50 34.30 31.50 46.10 31.50 31.80 35.10 35.05 35.10 33.90 34.20 31.20 35.08 35.09 39.50 40.20

Table 3 – ANOVA for quadratic model for Hg(II) adsorption (A: CuO NPs dose, B: temperature and C: pH). Source Model A B C AB AC BC A2 B2 C2 Residual Lack of fit Pure error Corr. total

Sum of squares 212.68 0.36 43.71 35.28 0.010 0.90 0.023 87.44 24.11 27.77 11.48 0.74 0.27 224.15

DF

Mean square

9 1 1 1 1 1 1 1 1 1 7 3 4 16

23.63 0.36 43.71 35.28 0.010 0.90 0.023 87.44 24.11 27.77 1.64 0.74 0.068

Fig. 5. It may be noted that the removal of the Hg(II) decreases with enhance in temperature as earlier. In a solution of constant Hg(II) concentration, an increasing in amount of removal with enhance in adsorbent dosage might be due to accessibility of rather active site at higher mass of adsorbent. A maximum Hg(II) removal (46.10 mg/g) was apperceived at fixed pH (9) and concentration of Hg(II) (50 mg/l).

F value 14.41 0.22 26.66 21.52 6.099 × 10−003 0.55 0.014 53.32 14.70 16.93 0.24

p-Value Prob. > F 0.0010 0.6531 0.0013 0.0024 0.9399 0.4823 0.9100 0.0002 0.0064 0.0045

Significant Significant Significant

Significant Significant Significant

0.4121

3.6.2. Influence of adsorbent dosage and pH on Hg(II) removal by CuO nanoparticles Adsorbent dose and pH are superlative major operation factors for evaluating the adsorption aptitude of CuO nanoparticles. The response surface plot for composed effect of the adsorbent dose and the pH of solution (Fig. 6) shows that at higher pH, the Hg(II) uptake by the CuO nanoparticles

Fig. 5 – Contour and 3D plots for the influence of CuO nanoparticles dose and temperature on amount adsorption of Hg(II).

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Process Safety and Environmental Protection 9 3 ( 2 0 1 5 ) 1–8

Fig. 6 – Contour and 3D plots for the influence of CuO nanoparticles dose and pH on amount adsorption of Hg(II).

Fig. 7 – Contour and 3D plots for the influence of pH and temperature on amount adsorption of Hg(II). adsorbent increases with the enhance dose, whereas, the trend reverses at lower (acidic) pH. The basic medium is favorable for the adsorption process of Hg(II). High adsorption of Hg(II) at high pH indicates that, the surface of seems to be alkaline which decrease the protonation at their surfaces due to neutralization of positive charges, resulting in easier diffusion. A maximum Hg(II) removal (>35 mg/g) was apperceived at fixed temperature (278 K) and initial concentration of Hg(II) (50 mg/l).

3.6.3. Influence of pH and temperature on Hg(II) removal by CuO nanoparticles In adsorption processes, the temperature has a very important role. Fig. 7 shows the 3D response surfaces representing the composed influence of pH and temperature. The amount of adsorption of Hg(II) slightly decreased with increasing temperature (Abdel Salam and Burk, 2010). This is due to most rapid diffusion to the MgO nanoparticles with lower temperatures. Further, the figure indicates that the rate of the Hg(II) adsorption enhances with enhanced in solution pH. A maximum adsorption of Hg(II) (43.48 mg/g) was apperceived at fixed concentration of Hg(II) (60 mg/l) and dose of adsorbent (0.05 g). As seen Fig. 8, the optimum temperature and pH values for the absorption of mercury by copper oxide nanoparticles are equal 278 K and 9, respectively . The pH value, at which the charge of the solid surface is zero is referred to as the point of zero charge. The pHPZC of CuO NPs was 9.2 (Fakhri, 2014).

Fig. 8 – The changes diagram of the two factors (Temp. and pH) for adsorption reaction of Hg(II) using CuO nanoparticle. Table 4 – Comparison of Hg(II) adsorption with different adsorbents. Adsorbents

qm (mg/g)

Activated carbon SWCNTs MWCNTs-SH TiO2 NPs SWCNT-SH ZnO NPs CuO NPs

33.22 40.16 84.66 101.1 131.58 714.00 825.21

Ref. Bandaru et al. (2013) Bandaru et al. (2013) Hadavifar et al. (2014) Doua et al. (2011) Bandaru et al. (2013) Sheela et al. (2010) This study

Process Safety and Environmental Protection 9 3 ( 2 0 1 5 ) 1–8

4.

Conclusion

The present work was performed to study the adsorption of mercury (II) from aqueous matrices using adsorption on CuO nanoparticles and to behavior mechanism optimization using RSM method. Based on RSM method using BBD for design of experimental and optimal conditions for mercury (II) adsorption were found to be pH 9, temperature 278 K and CuO nanoparticles dose 0.05 g. CuO nanoparticles were obtained convenient to detach mercury (II). The results of experimental demonstrated that under optimized status, CuO nanoparticles can be applied as a nano adsorbent for the adsorption of mercury (II) at basic medium. The Langmuir model provided the best fit to the test data for adsorption reaction, revealing the maximum adsorption capacity of 825.21 mg/g. The kinetics study demonstrated that the model of kinetics for removal of mercury (II) confirmed pseudo second-order equation.

Acknowledgment The authors gratefully acknowledge supporting of this research by the Islamic Azad University Shahre-Qods Branch.

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