Synthesis of nanoparticle and modelling of its photocatalytic dye degradation ability from colored wastewater

Accepted Manuscript Title: Synthesis of nanoparticle and modelling of its photocatalytic dye degradation ability from colored wastewater Authors: Niyaz Mohammad Mahmoodi, Samaneh Keshavarzi, Mina Ghezelbash PII: DOI: Reference:

S2213-3437(17)30314-7 http://dx.doi.org/doi:10.1016/j.jece.2017.07.010 JECE 1725

To appear in: Received date: Revised date: Accepted date:

3-5-2017 22-6-2017 3-7-2017

Please cite this article as: Niyaz Mohammad Mahmoodi, Samaneh Keshavarzi, Mina Ghezelbash, Synthesis of nanoparticle and modelling of its photocatalytic dye degradation ability from colored wastewater, Journal of Environmental Chemical Engineeringhttp://dx.doi.org/10.1016/j.jece.2017.07.010 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Synthesis of nanoparticle and modelling of its photocatalytic dye degradation ability from colored wastewater Niyaz Mohammad Mahmoodi, Samaneh Keshavarzi, Mina Ghezelbash Department of Environmental Research, Institute for Color Science and Technology, Tehran 1668814811, Iran

*

Corresponding author. Tel. +98 021 22969771, Fax. +98 021 22947537

E-mail addresses: [email protected] [email protected] [email protected]

1

Abstract The photocatalytic dye degradation ability of the green synthesized zinc oxide nanoparticle was evaluated using Basic Blue 41 (BB41) and Basic Red 46 (BR46). The characteristics of Nanoparticle were investigated using X-ray diffraction (XRD), Fourier transform infrared (FTIR) and scanning electron microscopy (SEM). The optimization of photocatalytic degradation of dyes was conducted using response surface methodology (RSM) and genetic algorithm (GA). Catalyst dosage (0- 0.02 g), pH (2.1- 8.5), dye concentration (20- 50 ppm) and reaction time (0-180 min) were the independent variables that have the significant impact on the dye degradation. Under the optimum conditions the photocatalytic degradation efficiencies of BB41 and BR46 based on RSM were 72.56% and 67.89% respectively, whereas the optimum dye degradation according to GA were 72.36% for BB41 and 68.34% for BR46. The predicted values using RSM and GA are close to the experimental values.

Keywords: Synthesis; Nanoparticle; Modelling; Dye removal; Colored wastewater

1. Introduction Attendance of dyes in water and wastewater treatment is a serious threat for human health and disturbs the ecosystem. The textile industry accounts for 1-20% of total world production of dyes. Among the several types of dyes used worldwide, the largest group is azo dyes that have one or more azo bonds (-N=N-) in their structure [1-9]. Among several treatment options for the degradation of dyes like biodegradation [10,11], adsorption [12-14] and chemical oxidation [15,16], advanced oxidation processes (AOPs) using strong oxidants such as hydroxyl radical (OH•) are a powerful technique [17-19]. Most of traditional treatment processes are ineffective, non-destructive and transfer pollutants from water 2

to another phase and causing secondary pollution. Advanced oxidation process based on heterogeneous photocatalysis appears as an efficient procedure because of a number of important features like mild operating temperature and pressure, complete mineralization of pollutants without secondary pollutants, low energy consumption and low cost [20-24]. Several major factors such as catalyst dosage, pH, dye concentration and reaction time can have obvious effect on the performance of photocatalytic degradation of dye. Response surface methodology (RSM) and genetic algorithm (GA) have an important application in the optimization of different industrial processes. The RSM is defined as a statistical technique that has been applied to optimize and predict effects of independent variables on process responses while it considers the interactions between process variables and reduces the number of essential experiments [25-27]. The use of genetic algorithm (GA) has also become a quickly field of research. The GA is an optimization and search technique based on the principles of genetics and natural selection. In order to apply genetic algorithm, some stages are initialization, reproduction, mutation and selection [28,29]. In this work, Historical Data RSM design was applied to evaluate data that already exists and determine an approximate function between dye degradation and catalyst dosage, pH, dye concentration and reaction time [30,31]. Response surface methodology is a traditional technique for experimental process optimization; however, a new optimization approach in the numerical field can be genetic algorithm. A literature review showed that photocatalytic degradation of Basic dyes was not investigated in details using response surface methodology and genetic algorithm. Thus, the objective of this study is to choose the optimum values of catalyst dosage, pH, dye concentration and reaction time on the photocatalytic degradation of Basic Blue 41 (BB41) and Basic Red 46

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(BR46) and compare these two techniques in optimization of the photocatalytic degradation of dyes.

2. Experimental 2.1. Chemicals Basic Blue 41 and Basic Red 46 were obtained from Alvan Co. (Iran) and used as cationic dyes (Figure 1). All other chemicals were of analytical grade and obtained from MERCK and used without purification. Figure 1. 2.2. Synthesis Sodium hydroxide (1 g) and tetraethyl orthosilicate (TEOS) were poured in 90 mL distilled water. Then ZnCl2 (1 g) was added to the solution and stirred for 30 min. The prepared mixture was heated in an autoclave for 24 h at 100 ºC. After that, the upper liquor was decanted, the product was washed with water four times and then heated to be dried.

2.3. Characterization The crystal structure of the synthesized nanoparticle was determined by X-ray diffraction (XRD) pattern using a D8 ADVANCE X-ray diffraction spectrometer (Bruker, German) with a CuK α target. The morphology of sample was characterized by scanning electron microscopy (SEM) using LEO 1455VP scanning microscope. The functional groups of the synthesized nonmaterial were studied by a

Fourier transform

infrared (FTIR) spectroscopy (Perkin-Elmer

Spectrophotometer Spectrum One).

4

2.4. Photocatalytic dye degradation In the photodegradation step, 800 mL aqueous solution with different concentration of dye (20, 30, 40 and 50 ppm) was prepared. Dye degradation was carried out in a cylindrical batch photoreactor (1 L) (Figure 2). Figure 2. To investigate the effect of catalyst dosage and determine the optimum amount of catalyst, different amounts of Nanoparticle (0-0.02 g) were added. The solution was then put under constant stirring in dark condition for 30 minutes to reach the dye adsorption and desorption equilibrium on the surface of catalyst. Then the stable solution was exposed to UV-radiation with magnetic stirring, to avoid the nanoparticles suspended in the solution, for 3 h. All the experiments were done in a batch photoreactor equipped with magnetic stirrer and a Philips UVlamp (9W) which was placed in the inner quartz tube and was hanged in the center of photoreactor to ferry total UV-irradiation in intended wave length. Then samples were collected in regular time intervals and centrifuged for 15 min at 3700 rpm for removing catalyst particles. The centrifuged samples were analyzed to determine the remaining dye by using Perkin Elmer UV-Vis spectrophotometer at 531 nm for BR46 and 609 nm for BB41. The effect of initial dye concentration on process was studied with different concentration of dye (20, 30, 40 and 50 ppm). To investigate the effect of pH (2.1-8.5), the acidity of solutions were adjusted by adding hydrochloric acid and sodium hydroxide solutions.

2.5. Experimental design and mathematical model fitting In the present study, Historical Data Design which is a form of RSM was applied to optimize the photocatalytic degradation of dyes [30-32]. The independent variables and their coded levels and real values are shown in Table1. The dye degradation was considered as the process response. 5

Table 1. For determination of the relationship between independent and dependent variables Design Expert software was employed to analyze the experimental data. Analysis of variance (ANOVA) fits the experimental data to a cubic polynomial equation that has the highest value of correlation coefficients (R2) in the form of following equation: 𝑛

𝑌 = 𝛽0 + ∑ 𝛽𝑖 𝑋𝑖 + 𝑖=1

𝑛

∑ 𝛽𝑖𝑖 𝑋𝑖2

𝑛

+

𝑖=1

∑ 𝛽𝑖𝑖𝑖 𝑋𝑖3 𝑖=1

𝑛

𝑛

𝑛

𝑛

𝑛

+ ∑ ∑ 𝛽𝑖𝑗 𝑋𝑖 𝑋𝑗 + ∑ ∑ ∑ 𝛽𝑖𝑗𝑘 𝑋𝑖 𝑋𝑗 𝑋𝑘 𝑖=1 𝑗=1

𝑖≠𝑗≠𝑘

𝑖=1 𝑗=1 𝑘=1

(1) In this equation, Y is the response model; 𝛽0 represents the constant coefficient, 𝛽𝑖 , 𝛽𝑖𝑖 and 𝛽𝑖𝑖𝑖 the linear, quadratic and cubic coefficients respectively;

𝛽𝑖𝑗 and 𝛽𝑖𝑗𝑘

are the interaction

coefficients; n is the number of independent variables; 𝑋𝑖 , 𝑋𝑗 and 𝑋𝑘 are the coded value of the independent variable.

3. Results and discussions 3.1. Characterization The XRD pattern of the synthesized nanoparticle is showed in Figure 3. Diffraction peaks are in good agreement with those of the standard patterns of hexagonal wurtzite ZnO [33]. Figure 3. The morphology of the prepared nanoparticle were investigated by SEM (Figure 4a). As indicated in Figure 4a, the powder consists of spherical nanoparticles. SEM image shows the synthesized nanoparticle has fine particles with uniform size. The FTIR spectrum of nanoparticle has two characteristic bands at 3430 cm−1 and 565 cm-1 due to the O-H stretching vibration and metal-oxygen vibration (Figure 4b). 6

Figure 4. 3.2. Application of RSM in fitting models Experimental data for BB41 and BR46 degradation in the photocatalytic system are evaluated using RSM. Historical data design was used to analyze the relationship between dye degradation and different factors affecting the process (catalyst dosage, pH, dye concentration and reaction time). Table 2 summarizes the analysis of variance (ANOVA) of the regression parameters of the predicted response models for BB41 and BR46 degradation. Probability values larger than 0.05 means that the term can be excluded from equations. Table 2. The value of correlation coefficient (R2, Adjusted R2 and predicted R2) presents the quality of the fit polynomial model and they are given in Table 3. Table 3. Application of RSM offers the following cubic equations for degradation of dyes in terms of coded values of independent variables (A: catalyst dosage, B: pH, C: dye concentration and D: reaction time): Cubic equation for BB41: Decolorization, % = +4.22+22.45 A-7.31B-7.31 C+9.49 D+13.89 AD+2.63 BD-8.60 CD +1.64A2-7.45 B2+4.56C2 +1.62 D2-2.72 AD2-5.69 B2D -3.21BD2+3.93C2D+3.63CD2-5.77A3+17.09B3-6.37C3 (2) Cubic equation for BR46: Decolorization, % = +24.41+9.37A-0.36B-16.14C+25.16D+4.66AD+3.68BD-10.01CD-7.72 A2 -1.65B2-2.79C2-1.29D2-5.75A2D-4.19AD2-2.12BD2-3.82C2D+5.41CD2 +5.80B3-1.21D3 (3) 7

3.3. Effect of the variables studied 3.3.1. Effect of nanoparticle dosage As shown in Figure 5, the influence of catalyst dose on the removal of dye was studied and results showed that the percent of dye degradation in the absence of nanoparticle is 17% and 36% for BB41 and BR46 respectively and at low dose of catalyst the increase of catalyst loading increases the degradation efficiency while for BR46 beyond 0.015 g doses of nanoparticle, the percentage of decolorization decreases. It can be because of turbidity of solution in high amount of catalyst [21,23,24]. Therefore, there is an optimum dosage for catalyst that gives the best efficiency. Dye degradation at optimum dosage of catalyst is 71% and 63% for BB41 and BR46, respectively. Figure 5. 3.3.2. Effect of initial dye concentration As can be seen from Figure 6, with increasing the initial concentration of dye, degradation percentage decreases. As the initial concentration of the dye increased, more dye molecules were adsorbed onto the surface of photocatalysts, thus, an increase in the number of substrate ions accommodating in the interlayer spacing inhibits the action of the catalyst, which thereby decreases the reactive free radicals attacking the dye molecules and photo-degradation efficiency. Moreover, by increasing the initial dye concentration, it is possible that the number of the light photons absorbed by the dye molecules, then initial power of illuminated light decreased [23,24]. Therefore, at high concentration of dye the observed decrease in dye degradation is not a surprising subject. Figure 6.

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3.3.3. Effect of pH on photocatalytic degradation The most important parameter influencing photocatalytic degradation is the solution pH [24]. The effect of pH on the photocatalytic degradation of BB41 and BR46 was given in Figure 7. The results showed that the photodegradibility in basic and neutral pH is higher than that of acidic pH. BB41 and BR46 are cationic dyes. With increasing pH, the negative charges on nanoparticle are expected to adsorb the cationic dyes and an increase in the efficiency of photodegradation with increasing pH is observed. Figure 7.

3.4. Optimization of experimental conditions for dye degradation Determination of the operating conditions to optimize the process response has special importance [26,30,31]. The RSM and GA were employed to determine the optimum values of the four independent variables given in Table1 to maximize the dyes degradation efficiencies. The derived equations models for BB41 and BR46 degradation (Eq.2 and Eq.3) were considered as objective functions. In RSM the goals were set as “in-range” for independent variables and “maximize” for process response. In addition to this, GA in MATLAB optimization toolbox was used to optimize the degradation of dyes. The constraints in GA to optimize the degradation of BB41 and BR46 were defined process efficiencies ≤ 100% and the range of independent variables between -1 (low level of coded variables) and +1 (high level of coded variables). The optimization of the photocatalytic dye degradation in GA was done by defining: Variables: catalyst dosage (A), pH (B), dye concentration (C) and reaction time (D) Variable range: -1≤ A ≤+1, -1≤ B ≤+1, 1≤ C ≤+1 and -1≤ D ≤+1 Maximize degradation process= f (A, B, C and D) Degradation process ≤ 100% 9

The GA optimization results of BB41 and BR46 degradation are shown in Table 4 and Figure 8. Table 4. Figure 8. To confirm the optimum results additional experiments were conducted and repeated for three times. The optimum values to achieve the maximum dyes degradation according to RSM, GA and the experimental study for BB41 and BR46 are presented in Table 5. Table 5. Table 5 shows that predicted results from optimization by RSM and GA at optimum operating conditions closely agree with the experimental results that confirm the accuracy of predicted models of BB41 and BR46 degradation.

4. Conclusion In this work, the degradation of BB41 and BR46 has been studied in a photocatalytic system using nanoparticle catalyst that was synthesized and characterized. Catalyst dosage, pH and dye concentration are the most important factors for decolorization. Optimizations of photocatalytic degradation of dyes were investigated using Historical Data Design combined with RSM and genetic algorithm. The optimum conditions to achieve the maximum BB41 and BR46 degradation were determined. This study showed that the synthesized nanoparticle could be used as a nanophotocatalyst for dye degradation in colored wastewater. In addition, RSM and GA could be the useful tools for optimization of the degradation process.

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[17] N.M. Mahmoodi, Photocatalytic degradation of dyes using carbon nanotube and titania nanoparticle. Water, Air, & Soil Pollution. 224 (2013) 1612. [18] A. Ajmal I. Majeed R.N. Malik M. Iqbal M. Arif Nadeem I. Hussain S. Yousuf Zeshan G. Mustafa M.I. Zafar M. Amtiaz Nadeem, Photocatalytic degradation of textile dyes on Cu2O-CuO/TiO2

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[26] N.M. Mahmoodi, S. Soltani-Gordefaramarzi, M. Sadeghi-Kiakhani, Dye removal using modified copper ferrite nanoparticle and RSM analysis. Environ. Monit. Assess. 185 (2013) 10235-10248. [27] G.E. Box, N.R. Draper, Empirical model-building and response surfaces. Wiley New York, 1987. [28] D. Goldberg, Genetic algorithms in search, optimization, and machine learning, addisonwesley, reading, MA, 1989. [29] T. Back, H.P. Schwefel, An overview of evolutionary algorithms for parameter optimization, Evolutionary Computation. 1 (1993) 1–23. [30] Z. Jeirani, B. Mohamed Jana, B. Si Alia, I.M. Noora, C.H. Seeb, W. Saphanuchart, Prediction of the optimum aqueous phase composition of a triglyceride microemulsion using response surface methodology. J. Ind. Eng. Chem. 19 (2013) 1304-1309. [31] K. Charoen, C. Prapainainar, P. Sureeyatanapas, T. Suwannaphisita, K. Wongamornpitak, P. Kongkachuichay, S.M. Holmesf, Application of response surface methodology to optimize direct alcohol fuel cell power density for greener energy production. J. Clean. Prod., 142 (2017) 1309–1320. [32] R.L., Mason, R.F. Gunst, J.L. Hess, Statistical design and analysis of experiments: with applications to engineering and science. John Wiley & Sons, New Jersey, 2003. [33] B. Li, Y. Wang, Facile synthesis and photocatalytic activity of ZnO–CuO nanocomposite, Superlattices Microstruct. 47 (2010) 615–623.

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Figure caption: Figure 1. The chemical structure of dyes (a) BB41 and (b) BR46. Figure 2. Photoreactor: (1) stirrer, (2) magnet, (3) quartz tube, (4) lamp and (5) sample cell. Figure 3. X-ray diffraction pattern of the synthesized nanoparticle. Figure 4. (a) SEM image and (b) FTIR spectrum of the synthesized nanoparticle. Figure 5. The effect of nanoparticle dosage on the photocatalytic degradation of dyes using UV/ nanoparticle (a) BB41 and (b) BR46. Figure 6. The effect of dye concentration on the photocatalytic degradation of dyes using UV/ nanoparticle (a) BB41 and (b) BR46. Figure 7. The effect of pH on the photocatalytic degradation of dyes using UV/nanoparticle (a) BB41 and (b) BR46. Figure 8. Plot of current best individuals vs. number of variables in GA Optimization for the photocatalytic degradation of (a) BB41 and (b) BR46.

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O2N

N N

N

CH2CH3 CH2CH2N (CH3) 3 + Cl

Cl

(a) MeO

S + N CH3

N N CH3OSO3-

N

CH2CH3 CH2CH2OH

(b) Figure 1. The chemical structure of dyes (a) BB41 and (b) BR46.

Figure 2. Photoreactor: (1) stirrer, (2) magnet, (3) quartz tube, (4) lamp and (5) sample cell.

16

Figure 3. X-ray diffraction pattern of the synthesized nanoparticle.

96

Trancemission (%)

95 94 93 92 91 90 450

1450

2450

3450 -1

Wavenumber (cm )

(a)

(b)

Figure 4. (a) SEM image and (b) FTIR spectrum of the synthesized nanoparticle.

17

100

Decolorization (%)

80 0g

60

0.005 g 0.01 g

40

0.015 g 20

0.02 g

0 0

50

100

150

200

Time (min)

(a)

100

Decolorization (%)

80 0g 0.0012 g

60

0.0025 g 0.005 g

40

0.01 g 20

0.015 g 0.02 g

0 0

50

100

150

200

Time (min)

(b) Figure 5. The effect of nanoparticle dosage on the photocatalytic degradation of dyes using UV/ nanoparticle (a) BB41 and (b) BR46.

18

100

Decolorization (%)

80

60

20 ppm 30 ppm

40

40 ppm 50 ppm

20

0 0

50

100

150

200

Time (min)

(a) 100

Decolorization (%)

80

60

20 ppm 30 ppm

40

40 ppm 50 ppm

20

0 0

50

100

150

200

Time (min)

(b) Figure 6. The effect of dye concentration on the photocatalytic degradation of dyes using UV/ nanoparticle (a) BB41 and (b) BR46.

19

100

Decolorization (%)

80

60 pH=2.1 PH=4

40

PH=8.5 20

0 0

50

100

150

200

Time (min)

(a)

100

Decolorization (%)

80

60 PH=2.1 40

PH=4 PH=8.5

20

0 0

50

100

150

200

Time (min)

(b) Figure 7. The effect of pH on the photocatalytic degradation of dyes using UV/ nanoparticle (a) BB41 and (b) BR46.

20

(a)

(b) Figure 8. Plot of current best individuals vs. number of variables in GA Optimization for the photocatalytic degradation of (a) BB41 and (b) BR46.

21

Table 1. Levels of the independent variables and their experimental range. Variables Catalyst dosage(g) pH Dye concentration (ppm) Reaction time (min)

Symbol

Real values of coded levels Low level(-1) High level(+1)

A B C D

0 2.1 20 0

22

0.02 8.5 50 180

Table 2. ANOVA results of the cubic polynomial model for photocatalytic degradation of BB41 and BR46. Source BB41 Model A B C D AD BD CD A2 B2 C2 D2 AD2 B2D BD2 C2D CD2 A3 B3 C3 BR46 Model A B C D AD BD CD A2 B2 C2 D2 A2D AD2 BD2 C2D CD2 B3

Sum of Squares

Degree of Freedom (Df)

Mean Square

F-Value

P-Value Prob > F

46232.76 1294.67 23.96 88.63 408.01 3011.10 25.14 997.73 28.18 328.79 180.70 10.90 45.81 65.72 19.50 62.28 74.14 79.55 128.14 66.09

19 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

2433.30 1294.67 23.96 88.63 408.01 3011.10 25.14 997.73 28.18 328.79 180.70 10.90 45.81 65.72 19.50 62.28 74.14 79.55 128.14 66.09

663.90 353.24 6.54 24.18 111.32 821.55 6.86 272.22 7.69 89.71 49.30 2.97 12.50 17.93 5.32 16.99 20.23 21.71 34.96 18.03

< 0.0001 < 0.0001 0.0121 < 0.0001 < 0.0001 < 0.0001 0.0102 < 0.0001 0.0066 < 0.0001 < 0.0001 0.0877 0.0006 < 0.0001 0.0231 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001

significant

49809.53 1284.90 0.068 4399.81 2306.67 301.66 116.17 1498.32 676.49 24.26

18 1 1 1 1 1 1 1 1 1

2767.20 1284.90 0.068 4399.81 2306.67 301.66 116.17 1498.32 676.49 24.26

1557.19 723.05 0.038 2475.91 1298.04 169.75 65.37 843.15 380.68 13.65

< 0.0001 < 0.0001 0.8448 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.0003

significant

70.81 12.71 223.80 85.14 12.72 61.14 197.30 17.79

1 1 1 1 1 1 1 1

70.81 12.71 223.80 85.14 12.72 61.14 197.30 17.79

39.85 7.15 125.94 47.91 7.16 34.41 111.03 10.01

< 0.0001 0.0085 < 0.0001 < 0.0001 0.0085 < 0.0001 < 0.0001 0.0020

23

D3

7.04

1

7.04

3.96

0.0488

Table 3. ANOVA results for response models. Dye BB41 BR46

R-Squared 0.9922 0.9958

Adjusted R-Squared 0.9906 0.9951

Predicted R-Squared 0.9890 0.9933

Table 4. Optimal conditions of the photocatalytic degradation of dye according to GA. Dye

BB41 BR46

A Coded

1.0000 0.3652

Real (g) 0.02 0.013

B Coded

Real

C Coded

4.6 8.5

-1.0000 -1.0000

-0.2078

1.0000

Real (ppm) 20 20

D Coded

1.0000 1.0000

Real (min) 180 180

Table 5. Optimal conditions of the photocatalytic degradation of BB41 and BR46. Method BB41 RSM GA Experimental study BR46 RSM GA Experimental study

A (g)

B

C (ppm)

D(min)

Decolorization (%)

0.02

5

20

180

72.56

0.02 0.02

4.6 5

20 20

180 180

72.36

0.013

8.45 8.5 8.5

20 20 20

179.53

0.013 0.01

24

180 180

71.53 67.89 68.34 67.87

Decolorization (%) 72.36

68.34