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:: Volume 10, Issue 1 (Winter 2021) ::
Arch Hyg Sci 2021, 10(1): 1-10 Back to browse issues page
Photocatalytic Degradation of Dye Pollutant in Synthetic Wastewater by Nano-Fe3O4 Based on Clinoptilolite Zeolite
Maryam Sabonian * , Kazem Mahanpoor
Young Researchers and Elite Club, Arak Branch, Islamic Azad University, Arak, Iran
Keywords: Alcohols, Box-behnken design, Clinoptilolite zeolite, Environmental pollutants, Fe3O4, Nanoparticles, Water decolorization
Full-Text [PDF 756 kb]   (80 Downloads)     |   Abstract (HTML)  (174 Views)
Type of Study: Original Article | Subject: General
Received: 2019/07/24 | Accepted: 2020/11/16 | Published: 2020/12/15
Full-Text:   (33 Views)
The remediation of wastewater polluted by nitro phenols through traditional methods is really complicated and costly, producing secondary pollution and taking a long reaction time. In addition, phenol derivatives are chemically resistant based on high solubility and constancy in water (1, 2). Therefore, it is vital to adopt new approaches for the treatment of the wastewater containing these pollutants without the above-mentioned problems.
Advanced oxidation processes (AOPs) are active and ecologically friendly approaches that can degrade the organic contaminants that are resilient to the conservative treatment systems into modest byproducts and lastly mineralize them into carbon dioxide and water (3, 4). The oversensitive and general oxidant and hydroxyl radicals with high electrochemical oxidation potential were formed by AOPs (5, 6).
Heterogeneous photocatalytic techniques are usually used for the treatment of wastewater containing refractory organic pollutants with the purpose of reusing due to its ability to attain the complete mineralization of the compounds under mild conditions, such as ambient temperature and pressure. Numerous solid semiconductor metal oxides (e.g., TiO2, CeO2, ZnO, ZrO2, V2O5, WO3, and Fe2O3) and sulfides (e.g., CdS and ZnS) have been employed for the degradation of chemical substances (7-9).
Environmental obstacles in beet molasses fermentation manufacturing are principally related to the production of large quantities of polluted and brown colored sewages known as vinasse. The ultimate products of the Maillard reaction, mainly melanoidins, are part of the vinasse combination. Melanoidins are brown nitrogenous polymers with a mainly unknown structure, mostly constructed from sugar decomposition products (10). Magnetite (Fe3O4) is an ideal applicant for biological usages, such as drug delivery, cell separation, and magnetic-resonance imaging, due to its specific magnetic virtues, low poisoning, and good bio adaptability (11). Among the nanoparticles of metal, iron nanoparticles have been more widely considered for frequent, inexpensive, non-toxic, and rapid reaction and high ability and efficiency in the adsorption of pollutants and removal of heavy metals from contaminated waters.
Natural zeolites are becoming more and more significant for the removal of pollutant substances, such as heavy metals, due to their capacity for ion exchange, adsorption, and selectivity, in addition to thermal and mechanical properties (12). Clinoptilolite, mordenite, and phillipsite are instances of natural zeolites. Natural clinoptilolite depends on the heulandite family, with a chemical formula of Na6[(AlO2)6(SiO2)30].24H2O (13).  The ratio of silicon to aluminum in the context of clinoptilolite is within the range of 4-5.3. However, the ion exchange capacity of clinoptilolite is lower than that of other zeolites.
Usually, clinoptilolite zeolite can be ion-exchanged with cations, such as Na, K, Ca, and Mg (13). Due to the physical and chemical properties of zeolites, they are relatively diverse compounds. Therefore, clinoptilolite zeolite has been widely considered due to its special spatial structure, chemical stability, low cost, natural, non-recyclable, and environmentally friendly features, and wide distribution in the world (14).
In order to optimize a process, such as the removal of dye pollutant process, it is essential to study all factors influencing the process. Nonetheless, perusing the effects of individual factors on the process is difficult and time-consuming, particularly if these factors are not independent and affect each other. Using an experimental design could remove these difficulties owing to the interaction effects of various factors that could be obtained using only the Design of Experiments.
The Box-Behnken design which is the most popular experimental design style was employed to optimize the process factors (15-18) due to fewer runs. The Box-Behnken design technique has demonstrated to be a very noteworthy instrument, allowing the precise optimum values of experimental factors to be specified and feasibility to appraise the interaction between variables with a reduced number of experiments (16, 17). The analysis of the experiment was accomplished using Minitab statistical software (version 18). Multiple regression analysis was utilized to analyze the experimental data and correlation coefficients (R2). In addition, interaction and quadratic terms were appraised through analysis of variance (ANOVA). Generally, a second-order model is used in response surface methodology (19, 20).
 
   (1)
 
In this model, y represents the dependent variable;  is a constant value; , , and  refer to the regression coefficients for the linear, second-order, and interactive effects, respectively;  and  are the independent variables;  is a random error. The  coefficients, which should be quantified in the second-order model, are obtained by the method of least squares. Generally, equation 1 can be written in matrix form as follows:
 
 =  X +                                                                      (2)
 
where  can be defined as a matrix of measured values, and X is a matrix of independent variables. In general, the  and  matrixes consist of coefficients and errors, respectively.
 
Aims of the study
The purpose of this study was to use a clinoptilolite zeolite as a base for the stabilization of Fe3O4 photocatalyst and identification. In this study, the Box-Behnken design was used to appraise the effect of process parameters on the removal of dye pollutants. Factors and responses were defined as experimental variables that can be changed self-sufficiently of each other and measured value of the results of trials, respectively.
 
 
Materials & Methods

 
Materials
Iron(II) chloride (FeCl2), Iron(III) chloride (FeCl3), urea, acetone, ethanol, clinoptilolite zeolite, and hydrogen peroxide were purchased from The Merck Group (Germany).
 
Procedure of catalyst production
At first, about 4.72 g of FeCl3 with 1.72 g of FeCl2 were mixed, and then 50 g of urea CO(NH2)2 and 100 ml of distilled water were added. The balloon was then filled with nitrogen and placed above the condenser. The solution was closed by the reflux system and placed on top of the hot water bath at 90°C for 2 h. The precipitate was composed at the bottom of the container and washed with distilled water to reach a neutral pH. Finally, it was washed with an organic solvent, such as acetone (C3H6O), and dried at 80°C for 2 h. Subsequently, 6 g of clinoptilolite zeolite powder with 2 g of synthesized iron oxide were add in a mortar and pestle in addition to some ethanol. Then, it was shed for 30 min after drying in a furnace at 300°C for 4 h.
 
General procedure
In this study, according to Figure 1, iron oxide magnetic nanoparticles were stabilized on clinoptilolite zeolite, and then a dye pollutant solution was added. The suspension of nanoparticles was organized after regulating the pH under ultrasonic waves. Then, the suspension was provided within three rotary photoreactors, including a quartz tube. The solution was passed over a quartz tube and condenser, and the temperature was controlled by a thermo bath. After equilibrium, the solution was subjected to hydrogen peroxide


Figure 1) Schematic of laboratory photoreactor for catalytic process
and ultraviolet light to remove the dye pollutant; afterward, it was sampled at a certain interval. The regulation of pH was performed through the least use of H2SO4 and NaOH solution. The concentration of dye pollutants in the samples was determined using an ultraviolet-visible (UV-Vis) spectrophotometer at λmax of 268 nm.
 
Box-Behnken experimental design
Table 1 shows the relations between the coded and original values. The effects of the reaction of pH, catalyst dosage, and H2O2 concentration on the percentage of dye pollutant removal were investigated. All the variables were evaluated at three levels, namely low, middle, and high. The low, middle, and high levels of each factor were chosen as −1, 0, and +1, respectively.
The number of experiments obtained using the Box-Behnken model was determined as follows:

N = 2K (K-1) + C0                                                         (3)
 
where N is the number of the experiments; K is the number of the factors; C0 is the number of the central points [21]. Table 2 tabulates the details of the performed Box-Behnken design of the experiment. The design presented 15 experimental runs, which were randomized to maximize the effects of unfamiliar variability in the apperceive responses owing to extraneous factors.
 
Effects of experimental parameters on specific surface area of samples:
The ANOVA is a collection of several statistical methods used in the analysis of the distinction between the mean of groups and related methods. The ANOVA is employed to test the significance of the mean of three or more of the three variables. In addition, this method is used for graphical data analysis to determine the interaction between process
 
 
Table 1) Factors and values of the levels for the experimental design
Design Level Catalyst amount (mg L-1) pH Initial concentration of H2O2 (ppm)
Box-Behnken -1 100 2 20
0 150 3 25
+1 200 4 30
 
Table 2) Experimental conditions for photocatalytic process
Run Catalyst amount
(mg L-1)
pH Initial concentration of H2O2 (ppm) Experimental responses (%) Predicted responses (%)
1 150 2 30 47.03 47.67
2 100 4 25 68.45 66.99
3 200 3 20 80.43 81.42
4 200 2 25 85.10 86.2
5 200 4 25 66.58 67.42
6 100 2 25 41.21 42.51
7 100 3 20 62.83 63.49
8 150 4 30 69.36 69.91
9 150 3 25 64.49 64.96
10 150 2 20 77.58 78.4
11 100 3 30 46.82 48.01
12 200 3 30 71.26 72.21
13 150 4 20 63.98 63.87
14 150 3 25 64.49 64.96
15 150 3 25 64.49 64.96
 
 
 

variables and response. The quality of the polynomial model is expressed through the coefficient of determination of R2, and the significance of the coefficients is determined using F-test (Fisher's test). The components of the model are evaluated through a p-value.
Table 3 tabulates the coefficient of each of the parameters and other characteristics in the mathematical model. The model equations were obtained for the percentage of dye pollutant removal in equation 1, respectively. Based on equation 4, it can be observed that a positive value represents an effect favoring the optimization; however, a negative value indicates an inverse relationship between the factor and response. Table 4 shows the ANOVA analysis of the Box-Behnken experimental design. The correlation coefficient (R2) is used to check the precision of a model.
The p-values were less than 0.05 (22).
 
R% = 64.967 + 10.533 [Fe3O4/CZ] + 1.926 [pH] - 6.174 [H2O2] + 1.318 [Fe3O4/CZ × Fe3O4/CZ] - 11.315 [Fe3O4/CZ × pH] + 1.570 [Fe3O4/CZ × H2O2] + 9.192 [pH × H2O2]                                                                  (4)
 
The value of R2 was an index for the measurement of the range of variability in the observed response. The obtained results indicated that this model had a correlation coefficient of R2 equal to 0.9989. The value of R2 showed that 99.89% of the changes happened in the efficiency of reduction by the independent variables. The model was ineffective to account for only 0.11% of the changes.
Further parity plot (Figure 2) illustrates a good correlation between the experimental and predicted values indicating that the model can predict the response with adequate precision.
 
 
Table 3) Coefficient of each parameter in mathematic model
Term Coefficient Standard error coefficient
Constant 64.967 0.213
Fe3O4/CZ 10.533 0.199
pH 1.926 0.199
H2O2 -6.174 0.199
Fe3O4/CZ × Fe3O4/CZ 1.318 0.291
Fe3O4/CZ × pH -11.315 0.281
Fe3O4/CZ × H2O2 1.570 0.281
pH × H2O2 9.192 0.281
            CZ: Clinoptilolite zeolite
 
Table 4) Analysis of variance results of three factorial Box-Behnken experimental designs for dye pollutant removal
Source DF Adjusted SS Adjusted MS F-value P-value
Model 7 2088.54 298.363 941.49 0.000
Linear 3 1222.07 407.353 1285.42 0.000
Fe3O4/CZ 1 887.47 887.468 2800.41 0.000
pH 1 29.68 29.684 93.67 0.000
H2O2 1 304.92 304.922 926.18 0.000
Square 1 6.48 6.484 20.46 0.003
Fe3O4/CZ × Fe3O4/CZ 1 6.48 6.484 20.46 0.003
2-way interaction 3 895.98 286.662 904.56 0.000
Fe3O4/CZ × pH 1 512.12 512.117 1615.99 0.000
Fe3O4/CZ × H2O2 1 9.86 9.860 31.11 0.001
pH × H2O2 1 338.01 338.008 1066.59 0.000
R-squared=99.89%       R-squared (adjusted)=99.79%    R-squared (predicted)=99.48%
CZ: Clinoptilolite zeolite
 
 

Figure 2) Correlation graph between predicted and experimental yield values
 
 
Results

 
X-ray diffraction analysis
X-ray diffraction (XRD) is one of the most significant characterization tools employed in solid-state chemistry and materials science. The crystallographic structure of the synthesized products was recognized by XRD measurement. Figure 3 depicts the XRD pattern of Fe3O4 and Fe3O4/clinoptilolite zeolite. No hematite peaks, metal hydroxides, or other impurities were indicated, thereby affirming the complete formation of Fe3O4. The strong and sharp peaks showed that Fe3O4 nanoparticles were of high purity and well crystalline. The average crystallite size of Fe3O4 nanoparticles was calculated by the Debye Scherrer formula (23).
 
                                                                       (5)
 
where  is the crystallite size;  is the X-ray wavelength;  is the Bragg diffraction angle;  is the full width at half maximum. The average crystallite size of Fe3O4 nanoparticles supported on the surface of clinoptilolite zeolite was reported as 34 nm.
 
Scanning electron microscopy studies:
Figure 4 displays the representative micrograph morphology and structure of Fe3O4-CZ. As shown in the scanning electron microscopy (SEM) image, clinoptilolite zeolite is formed as different sized plates on which Fe3O4 nanoparticles are distributed, and the distribution of iron oxide particles is non-uniform. Cavities are observed at the catalyst level, which can increase the level of the catalyst. These cavities do not have the same dimensions, and the accumulation of iron oxide nanoparticles in the cavity openings is higher. Furthermore, in addition to crystalline parts, sections are also observed as amorphous;


Figure 3) X-ray diffractograms of synthesized Fe3O4, clinoptilolite zeolite (CZ), and synthesized Fe3O4/CZ
 
 

Figure 4) Results of scanning electron microscopy image of A) clinoptilolite zeolite (CZ) and B) Fe3O4/CZ
 
 
however, at high temperatures, small parts appear to be observed at the catalyst level as hollow accumulation.
 
Optical properties by Diffuse Reflectance Spectroscopy studies
The optical possessions of Fe3O4 nanoparticles are inspected by UV-Vis Diffuse Reflectance Spectroscopy (DRS). The assessed bandgap of Fe3O4 is 2.12 eV for Fe3O4/CZ. The isothermal adsorption/desorption curve for Fe3O4/CZ appears as a hysteresis loop of H3 type with a typical two-dimensional-lamellar structure in accordance with the International :union: of Pure and Applied Chemistry type IV template. The sharp increases in N2 adsorption happened at relative pressures of 0.64-0.93 (Figure 5A). The hysteresis loops of the N2 adsorption/desorption isotherms for Fe3O4/CZ clearly propose delayed agglomeration and desorption. The results of the Brunauer-Emmett-Teller (BET) surface area, volume, and pore diameter for Fe3O4/CZ are shown in Figure 5B and Table 5. The information in Table 5 tabulates the N2 adsorption/desorption isotherm and pore size distribution of the nanoparticle.

Figure 5) A) N2 adsorption/desorption isotherms and B) Brunauer-Emmett-Teller of synthesized Fe3O4/clinoptilolite zeolite
Table 5) Brunauer-Emmett-Teller surface area, volume, and pore size for Fe3O4/clinoptilolite zeolite
Parameter Value
BET specific surface area (asBET) 617.1 m2 g-1
Monolayer volume (Vm) 135.4 cm3 (STP) g-1
Total pore volume (Vp) 0.6053 cm3 g-1
Mean pore diameter (dp) 68.27 nm
BET:  Brunauer-Emmett-Teller
 
Based on Barrett-Joyner-Halendaʼs theory, the
analysis of BET demonstrated that the mean surface area, total pore volume, and mean pore diameter of the present nanoparticle was 617.1 m2/g, 0.6053 cm3/g, and 68.27 nm, respectively.
 
Effects of influential variables on dye removal
Figure 6 illustrates the effect of the amount of photocatalyst and initial concentration of H2O2 of the solution on dye pollutant in the percentage of alcohol industrial wastewater. Figure 6 depicts the fact that with the increase for photocatalyst, the percentage of dye pollutant removal increases. The tests were performed in the darkness, and only a limited amount of dye pollutant was adsorbed on the surface of the catalyst; however, the main part of the reaction started while irritation. The reason for the increase in dye pollutant removal


Figure 6) Three-dimensional response surface plots illustrating effect of amount of photocatalyst and initial concentration of H2O2 on dye pollutant removal efficiency

Figure 7) Three-dimensional response surface plots showing the effect of initial concentration of H2O2 and pH on dye pollutant removal percentage
 
was related to increasing the amount of photocatalyst to increase the available active centers. Moreover, by increasing the amount of photocatalyst, there is an increase in the surface area of the catalyst for adsorption leading to the absorption of pollutants (24, 25).
 
 
Discussion

 
The term R1 in all figures represent the removal percent of pollutant. Figure 7 shows the effect of the concentration of H2O2 and pH of the solution on the percentage of dye pollutant removal. By an increase in the concentration of hydrogen peroxide, the conversion rate of the photocatalytic degradation reaction reduced due to the increase in the concentration of hydrogen peroxide. This is due to the fact that hydrogen peroxide acts as a destroyer of hydroxyl radicals and it is competitively used for dye substances, causing the production of less reactive radicals of prehydroxyl (HO2·). As shown in Figure 3, for dye pollutants, as the pH lowers, the effect of the photocatalytic process increases due to the following reactions in the acidic environment, which leads to the production of active radicals (26).
eCB + O2(ads) ® O2(ads)·                                                    (6)
O2(ads)· + H+ ® HO2·                                                                                   (7)
2HO2· ® O2 + H2O2                                                       (8)
H2O2 + O2(ads)· ® OH· + OH + O2                                 (9)
 
 
Conclusion

 
The present study investigated the practicability of organic and dye pollutants to be removed from the wastewater by Fe3O4/CZ nanoparticle as an efficient photocatalyst. Based on the results of XRD, SEM, and DRS, it was observed that Fe3O4 nanoparticles were decorated on the surface of clinoptilolite zeolite. The AOPs (e.g., Fe3O4/clinoptilolite zeolite and UV/H2O2) as solution-based methods were used to examine the removal of dye pollutants in the synthetic wastewater. The results of the statistical analysis indicated that the model used in this study was significantly reliable and valid. The optimal conditions were determined as the amount of photocatalyst equal to 200 mg L-1, pH equal to 2, and concentration of H2O2 equal to mg L-1. Removal efficiency in the optimal condition was reported as 85.10%. The results of the photocatalysis test showed that the removal efficiency appeared to reach 85.10%. The heterogeneity and recyclability of the photocatalyst system, along with the proper efficiency of the catalyst, are considered the most important features of the synthesized catalyst.
 
 
Footnotes

 
Acknowledgements
The authors would like to express their gratitude to the Young Researchers and Elite Club, Arak Branch, Islamic Azad University, Arak, Iran, for financial support.
 
Conflict of Interest
The authors declare that there is no conflict of interest.
 
 
References

 
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References
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2. Dhaka S, Kumar R, Lee SH, Kurade MB, Jeon BH. Degradation of ethyl paraben in aqueous medium using advanced oxidation processes: Efficiency evaluation of UV-C supported oxidants. J Clean Prod 2018;180: 505−513. [DOI:10.1016/j.jclepro.2018.01.197]
3. Shokri A. Investigation of UV/H2O2 process for removal of ortho-toluidine from industrial wastewater by response surface methodology based on the central composite design. Desalin Water Treat 2017;58:258-266. [DOI:10.5004/dwt.2017.0292]
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5. Shokri A, Mahanpoor K, Soodbar D. Degradation of Ortho-Toluidine in petrochemical wastewater by ozonation, UV/O3, O3/H2O2 and UV/O3/H2O2 processes. Desalin Water Treat 2015;57:16473-82. [DOI:10.1080/19443994.2015.1085454]
6. Shokri A. The treatment of spent caustic in the wastewater of olefin units by ozonation followed by electrocoagulation process. Desalin Water Treat 2018;111:173-182. [DOI:10.5004/dwt.2018.22248]
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9. Sabonian M, Mahanpoor K. Preparation of ZnO Nano catalyst supported on todorokite and photocatalytic efficiency in the reduction of chromium (VI) pollutant from aqueous solution. Iran J Catal 2019.
10. Shokri A. Degradation of 2-nitrophenol from petrochemical wastewater by ozone. Russ J Appl Chem 2015;88:2038−43. [DOI:10.1134/S10704272150120216]
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Sabonian M, Mahanpoor K. Photocatalytic Degradation of Dye Pollutant in Synthetic Wastewater by Nano-Fe3O4 Based on Clinoptilolite Zeolite. Arch Hyg Sci. 2021; 10 (1) :1-10
URL: http://jhygiene.muq.ac.ir/article-1-397-en.html


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