Employing α-Fe2O3 /Mn2P2O7 as a nano photocatalyst for degradation of toluene in aqueous environment
Sajjad Mafi
1
(
Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran
)
Kazem Mahanpoor
2
(
Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran
)
Keywords: Toluene, Photocatalyst, Manganese Pyrophosphate, α-Fe2O3 nanoparticles, Box-Behnken method,
Abstract :
The existence of different resistant organic pollutants especially toxic organic aromatics such as Toluene in aquatic environments has become a significant environmental matter in recent years. In this paper, α-Fe2O3 Nano-spheres are immobilized on the surface of Manganese Pyrophosphate (Mn2P2O7) support using Forced Hydrolysis and Reflux Condensation (FHRC) method. Products were characterized by FTIR, SEM, BET EDX, and XRD. The photocatalytic activity of α-Fe2O3/Mn2P2O7 was investigated for the removal of Toluene in aqueous solution by UV/H2O2 system. The chemical oxygen demand (COD) measurements were used to determine the amount of Toluene removal. The experiments were designed based on four affecting variables including pH, catalyst content, initial Toluene concentration and H2O2 at three levels using Box-Behnken experimental design. The results of this study showed that α-Fe2O3/Mn2P2O7 as a new photo catalyst has a higher photocatalytic activity than α-Fe2O3 nanoparticles. Based on the achieved results, the maximum degradation efficiency was 97.14% in optimal conditions.
Employing α-Fe2O3 /Mn2P2O7 as a nano photocatalyst for degradation of toluene in aqueous environment
ABSTRACT
In this study, α-Fe2O3 nano-spheres are immobilized on the surface of manganese pyrophosphate (Mn2P2O7) support using forced hydrolysis and reflux condensation (FHRC) method. The synthesized α-Fe2O3/Mn2P2O7 were characterized by FTIR, SEM, BET EDX, and XRD. The photocatalytic activity of α-Fe2O3/Mn2P2O7 was investigated for the removal of toluene in aqueous solution by UV/H2O2 system. The chemical oxygen demand (COD) measurements were used to determine the amount of toluene removal. The experiments were designed based on four affecting variables including pH, catalyst content, initial toluene concentration and H2O2 at three levels using Box-Behnken experimental design. The results of this study showed that α-Fe2O3/Mn2P2O7 as a new photocatalyst has a higher photocatalytic activity than α-Fe2O3 nanoparticles. Based on the achieved results, the maximum degradation efficiency was 97.14% in optimal conditions.
Keywords: Manganese Pyrophosphate; Photocatalyst; α-Fe2O3 nanoparticles; Box-Behnken method; Toluene.
1. INTRODUCTION
The presence of various organic pollutants especially volatile organic matter, including toluene in aquatic environments has become an important environmental issue in recent years [1-4]. The development of effective technologies to reduce and eliminate toxic and hazardous compounds in water and sewage is one of the issues that have been discussed globally [5-8]. One of the most important problems of water treatment methods is the inability to treat contaminants at low concentrations and toxic properties resistant to certain contaminants resistant to biological agents and cannot be easily decomposed [9-12].
Over the past decade, advanced oxidation processes (AOPs) proved to be the alternate method for removing recalcitrant, as well as nonbiodegradable compounds. One of the most important AOPs is heterogeneous photocatalytic oxidation process with semiconductors such as hematite (α-Fe2O3) due to its high stability, non-toxic, environmentally friendly and corrosion-resistant nature [13-16]. However, one of the important issues in using catalysts, especially in nanoscale dimensions, is recycling and reuse. Fixing the catalyst on a suitable basis is one of the ways to solve this problem. Manganese pyrophosphate (Mn2P2O7) has attracted much attention because of its versatile coordination modes, low toxicity, interesting magnetic property, catalyst in oxidative dehydrogenation of hydrocarbons and biocompatibility [17-18]. Therefore, here we used the manganese pyrophosphate in order to immobilization of the α-Fe2O3 nanoparticle. Forced Hydrolysis and Reflux Condensation (FHRC) techniques were used for the preparation of α-Fe2O3 / Mn2P2O7 [19]. Characterization of the α-Fe2O3 / Mn2P2O7 was carried out using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET), scanning electron microscopy (SEM) and energy dispersive X-ray (EDAX) analysis. The performance of pure and supported α-Fe2O3 NPs on the Toluene photocatalytic degradation was investigated using Box-Behnken designs [20-22]. In this study, chemical oxygen demands (COD) reduction was also monitored in order to explain the degradation of toluene in the UV/nanocomposite system, and the optimal conditions were presented.
2. MATERIALS AND METHODS
2-1 Materials and instrument
All reagents were of analytical grade were purchased from Merck (Germany) company and used without further purification. A Philips XL30 scanning electron microscope system was used for morphological and elemental analysis. The Fourier-transform infrared spectroscopy (Perkin-Elmer, Spectrum two) was accomplished in the range of 400–4000cm-1. XRD patterns were recorded by DX27-mini, X-ray diffractometer using Ni-filtered Cu Ka radiation (λ= 1.5406). The Chemical Oxygen Demands (COD) were measured by COD Meter and Multiparameter Photometer (Hanna-HI83214). The solutions pH values were measured on a Metrohm 780 pH meter. The textural property was measured using N2 adsorption-desorption isotherms method on the BET–BJH specific surface area measurement equipment (Micromeretics ASAP 2010 system).
2-2 Preparation of Mn2P2O7
The manganese metal powder and P2S5 as the reaction precursors under solvothermal condition were used in order to synthesize manganese pyrophosphate species. In a conventional method, 0.002 mol of Mn and 0.002 mol of P2S5 were blended together and with 30 mL of ethylene glycol in a stainless steel autoclave (volume: 50 ml). The autoclave was sealed and heated for a period of 24-40 hours at 220 °C, then allowed to naturally cool to room temperature. The final pale pink precipitates were separated using centrifugation, completely rinsed with double distilled water and ethanol, and then dried at 60 °C using a vacuum oven. The following Eq. 1 represents the overall reaction [18]:
2Mn + P2S5 + 7HOCH2CH2OH → Mn2P2O7 + 7CH3CHO + 5H2S + 2H2 (1)
2-3 Synthesis of α-Fe2O3 /Mn2P2O7 nano photocatalyst using FHRC
In the beginning, 50 ml of iron (III) chloride hexahydrate with a concentration of 0.25 M was entered into a beaker. Then, 4 g of synthesized Mn2P2O7 compound was added gently under intense agitation. The obtained mixture was stirred for 4-5 hours. Afterward, stirring was stopped until the obtained solids to be settled. The segregated solid transferred into a 50ml flask. Then 50 ml of urea with a concentration of 1 M was slowly added to the solution when it was stirred in the flask. The obtained mixture was kept at 90 °C for 12 hours under reflux condition. In order to remove the unreacted ions, the resulting separated precipitate was washed out using a 1:1 solution of ethanol/deionized water. The obtained product was stored at room temperature for 2 hours and then dried at 80 °C for 2 hours. Ultimately, the calcination of the product was done at 300 °C for 1 hour [19].
2-4 Box- Behnken design
A second-order polynomial Eq. 2 was used through nonlinear regression to fit the experimental data and to identify the relevant model terms. The quadratic response model can be as follows [23]:
(2)
where α0 is the constant, αi the slope or linear effect of the input factor xi, αij the linear-by-linear interaction effect between the input factors xi and xj, and αii is the quadratic effect of input factor xi and ε is the residual error.
The experimental range and variables levels have been listed in Table 1. pH varied from 5 to 9 (5, 7 and 9), the initial concentration of toluene from 100 to 200 ppm (100, 150 and 200 ppm), catalyst concentration from 60 to 120 ppm (60, 90 and 120 ppm) and H2O2 initial concentration from 0.1 to 0.5 ppm (0.1, 0.3 and 0.5 ppm). The 27 experiments related to this Box-Behnken design and their experimental conditions have been listed in Table 2. The experiments 25, 26, and 27 were repeated to increase the accuracy of the results. The removal efficiency of toluene was a dependent response. In order to do DoEs, Minitab 16 version 16.2.0 statistical software was utilized. In addition, an analysis of variance (ANOVA) was run to analyze the results.
Table 1. Experimental range and levels of the variables.
Independent variables | Range of levels | ||
pH | 5 | 7 | 9 |
H2O2 con. (ppm) | 0.1 | 0.3 | 0.5 |
Catalyst con. (ppm) | 60 | 90 | 120 |
Initial con. of toluene (ppm) | 100 | 150 | 200 |
2-5 General procedure for photocatalytic degradation of toluene.
In order to carry out each experiment (according to Table 2), firstly 250 ml toluene solutions were made as specified concentration and poured inside the reactor. The photocatalytic degradation of pollutants, according to the Eq. 3 was calculated as a function of time :
(3)
Where the COD of toluene solution to the start of the photocatalytic process (t = 0) is COD0 and after a time t is CODt. Fig. 1 shows one schematic picture of photocatalytic reactor used in the work. In an MDF box was placed a glass reactor with a magnetic stirrer, UV irradiation was done from the top of the reactor and sampling was done using a syringe.
Fig. 1. Schematic picture of the photocatalytic reactor.
3. RESULTS AND DISCUSSION
3-1 Catalyst characterization.
The SEM images of Mn2P2O7 and α-Fe2O3 / Mn2P2O7 are shown in Fig. 2. As shown in SEM image Fig.2 (B), nanoparticles of α-Fe2O3 were quasi-spherically supported on the surface of Mn2P2O7. Creation of spherical structures of separate and independent boundaries can be observed that can have a relatively high effective level. These structures have a fairly uniform size distribution. The quasi-spherical particles of α-Fe2O3 synthesized in the FHRC method are denser and are less distant from each other on the base of the catalyst.
Fig. 2. SEM image of the synthesized Mn2P2O7 (A), and α-Fe2O3 /Mn2P2O7 by FHRC method (B).
The FTIR spectra of pristine Mn2P2O7 and synthesized α-Fe2O3/Mn2P2O7, which were prepared using the FHRC method, are shown in Fig. 3. In the FTIR spectrum of synthesized Mn2P2O7, the strong vibrating band at 1094 cm-1 is related to asymmetric stretching of PO2 unit. The peaks at 736 cm-1 and 950 cm-1 are corresponded to stretching vibration of P-O bond and symmetric or asymmetric stretching mode of P-O-P tertiary group, respectively. The FT-IR spectrum of α-Fe2O3/Mn2P2O7 composite showed the bands at 1600 cm−1 and 3500 cm−1 due to bending and stretching vibrating modes of O-H bond, respectively. The peaks at around 482 cm-1 and 886 cm-1 are related to vibration of Fe-O bond. Other changes in the spectral range from 400 to 500 cm-1 represents the formation of new bonds between α-Fe2O3 and Mn2P2O7 [24- 25].
Fig. 3. The FTIR spectra of catalyst support of Mn2P2O7 and α-Fe2O3/ Mn2P2O7.
The PXRD patterns of the synthesized Mn2P2O7, α-Fe2O3 and α-Fe2O3/Mn2P2O7 compounds are shown in Fig. 4. In the pattern of α-Fe2O3/Mn2P2O7 composite, the Bragg reflections of (111), (-201), (130), (131) and (-311) can be observed at 2θ = 28.96°, 30.36°, 34.41°, 41.61° and 43.71°, respectively, which were in good agreement with powder diffraction data of the monoclinic structure of Mn2P2O7 [18]. Moreover, some extra peaks at 2θ = 24.11, 33.06°, 35.66°, 49.26° and 54.01° which were attributed to the planes of (012), (104), (110), (024) and (116) in the rhombohedral (hexagonal) structure of α-Fe2O3 (space group: R-3c), were observed in the XRD pattern of the synthesized α-Fe2O3/Mn2P2O7 composite [26].
Fig. 4. The XRD patterns of the synthesized compounds.
To determine the specific surface area of the α-Fe2O3/Mn2P2O7 composite and pristine Mn2P2O7, the BET method has been used. Fig. 5 shows the obtained isotherms of the aforementioned compounds. The observed hysteresis loop in the obtained adsorption-desorption isotherms of these materials classified as type IV by IUPAC. The measured specific surface area of the solid support was 125 m2/g. After the formation of the composite, the surface area was increased to 160 m2/g. The observed change in the surface area is arising from the presence of the α-Fe2O3 nanoparticles on the surface of Mn2P2O7 support.
Fig. 5. Adsorption-desorption isotherms of (A) synthesized Mn2P2O7 and (B) α-Fe2O3/Mn2P2O7 composite.
In this study, EDAX method was used to determine the presence and the elements amount in the synthesized composite. The obtained EDAX spectrum of the synthesized composite is shown in Fig.6. Based on the obtained results from the spectrum, the presence of oxygen, phosphorus, iron and manganese elements in the synthesized composite was confirmed. Based on the results, it can be concluded that α-Fe2O3/Mn2P2O7 composite was formed.
Fig. 6. The EDX spectrum of the α-Fe2O3/ Mn2P2O7.
In Table 2, experiments data were reported for the 100 minutes based on the performed the toluene removal percentages. In general, the comparison of the removal results showed that the amount of toluene degradation in pure nanoparticles was less than that of the nanoparticles immobilized on the support. These results show that the addition of support to the nanoparticles has increased their photocatalytic activity.
The photocatalytic process essentially relates to the absorption and excretion of molecules on the catalyst surface, after irradiation of the α-Fe2O3 /Mn2P2O7 with UV light, the electrons of the valence band (VB) of the catalyst are driven to the conductive band (CB) and the cavities are produced in the valence band.
Table 2. The toluene removal percentage under various experimental conditions.
Exp. no.
| Variables | toluene removal (X %) | ||||
PH | H2O2 con. (ppm) | Catalyst con. (ppm) | toluene initial con. (ppm) | Pure α-Fe2O3 | α-Fe2O3 / Mn2P2O7 | |
1 | 5 | 0.1 | 90 | 150 | 50.22 | 62.89 |
2 | 9 | 0.1 | 90 | 150 | 52.13 | 68.37 |
3 | 5 | 0.3 | 90 | 150 | 54.67 | 70.56 |
4 | 9 | 0.5 | 90 | 150 | 62.34 | 75.80 |
5 | 7 | 0.3 | 60 | 100 | 73.18 | 78.55 |
6 | 7 | 0.3 | 120 | 100 | 84.32 | 95.23* |
7 | 7 | 0.3 | 60 | 200 | 39.45 | 44.07 |
8 | 7 | 0.3 | 120 | 200 | 68.33 | 50.99 |
9 | 5 | 0.3 | 90 | 100 | 81.42 | 77.28 |
10 | 9 | 0.3 | 90 | 100 | 78.57 | 90.47 |
11 | 5 | 0.3 | 90 | 200 | 40.87 | 45.78 |
12 | 9 | 0.3 | 90 | 200 | 35.43 | 42.48 |
13 | 7 | 0.1 | 60 | 150 | 53.36 | 65.51 |
14 | 7 | 0.5 | 60 | 150 | 50.43 | 61.45 |
15 | 7 | 0.1 | 120 | 150 | 61.78 | 74.25 |
16 | 7 | 0.5 | 120 | 150 | 72.47 | 82.83 |
17 | 5 | 0.3 | 60 | 150 | 50.26 | 67.73 |
18 | 9 | 0.3 | 60 | 150 | 54.67 | 65.09 |
19 | 5 | 0.3 | 120 | 150 | 64.41 | 76.18 |
20 | 9 | 0.3 | 120 | 150 | 73.57 | 83.55 |
21 | 7 | 0.1 | 90 | 100 | 62.52 | 70.05 |
22 | 7 | 0.5 | 90 | 100 | 79.57 | 90.12 |
23 | 7 | 0.1 | 90 | 200 | 37.65 | 43.02 |
24 | 7 | 0.5 | 90 | 200 | 35.78 | 38.98 |
25 | 7 | 0.3 | 90 | 150 | 62.13 | 74.81 |
26 | 7 | 0.3 | 90 | 150 | 62.55 | 74.26 |
27 | 7 | 0.3 | 90 | 150 | 62.12 | 74.94 |
The produced cavities can react with adsorbed water on the surface of the α-Fe2O3 /Mn2P2O7 porous nanoparticles and product very active hydroxyl radicals. While O2 acts as an electron receptor to form radical anion superoxide. Furthermore, radical superoxide anion (˙O2−) can act as oxidizing agent or additional hydroxyl radical sources [27-29]. These active radicals are highly oxidizing and can destroy the toluene.
α-Fe2O3 + hv → α-Fe2O3 (e- CB + h+vb) (4)
h+vb + H2O (ads) → H+ + •OH- (ads) (5)
h+vb + OH- (ads) → •OH (ads) (6)
e- CB + O2 (ads) → •O2- (ads) (7)
H2O ⇌ H+ + OH- (8)
•O2- (ads) + H+ → •HO2 (9)
2•HO2 → H2O2 + O2 (10)
H2O2 + α-Fe2O3 (e-CB) → •OH + OH- + α-Fe2O3 (11)
•OH (ads) + toluene → Degradation of toluene (12)
h+vb + toluene → Oxidation of toluene (13)
With the immobilization of α-Fe2O3 nanoparticles on the support surface, its photocatalytic activity increases. By absorbing toluene to a larger area of support, it is more exposed to produce radicals during the photocatalytic process, thus, the degradation degree rises. The general mechanism of photocatalyst and the support effect on the photocatalytic enhancement of nanoparticles have been shown in Fig. 7 [30].
Fig. 7. The schematic of photocatalysis mechanism (A) and photocatalytic activity of α-Fe2O3/Mn2P2O7 (B).
The effect of independent variables, namely catalyst amounts, pH, initial concentration of toluene and H2O2 on toluene removal were investigated. Moreover, in order to achieve the highest removal rate, the levels of the aforementioned variables were optimized. Four independent variables (each in three levels) were used in the Box-Behnken method. Independent variables for pH 5 to 9 at three levels (5, 7 and 9), H2O2 concentration from 0.1 to 0.5 ppm at three levels (0.1, 0.3 and 0.5 ppm), catalyst concentration from 60 to 120 ppm at three levels (60, 90 and 120 ppm) and the initial concentration of toluene from 100 to 200 ppm at three levels (100, 150 and 200 ppm), were considered (Table 1). The total number of the experiments, taking into account the test conditions, was achieved in the Box-Behnken method by 27 (Table 2).
The percentage of toluene removal was also considered as a dependent variable. The next stage involves graphically presenting the relationship between model and determining optimal operating conditions, which was done by the response and balance curve. Optimum operating conditions were searched for access to the highest amount of pollutant removal using the numerical optimization technique.
The results of ANOVA are shown in table 3, and was used to approve the validity of models. To determine the significance of the predicted model parameters, the P-value with a 0.01 significance level is used; the smaller the p-value the more significant are the corresponding term. The effect of independent variables on toluene removal is of the order of the following: Initial concentration of toluene > catalyst concentration > H2O2 concentration > pH.
Table 3. ANOVA results
Source | Degree of Freedom | Adj SS | Adj MS | F | P-Value |
Model | 14 | 6219.85 | 444.28 | 132.82 | 0.000 |
pH | 1 | 53.51 | 53.51 | 16 | 0.002 |
H2O2 con. | 1 | 105.91 | 105.91 | 31.66 | 0.000 |
Catalyst con. | 1 | 541.77 | 541.77 | 161.97 | 0.000 |
Initial con. of toluene | 1 | 4656.29 | 4656.29 | 1392.03 | 0.000 |
pH × pH | 1 | 13.89 | 13.89 | 4.15 | 0.064 |
H2O2 con. × H2O2 con. | 1 | 103.61 | 103.61 | 30.97 | 0.000 |
Catalyst con. × Catalyst con. | 1 | 3.35 | 3.35 | 1 | 0.337 |
Initial con. of toluene × Initial con. of toluene | 1 | 432.84 | 432.84 | 129.4 | 0.000 |
pH × H2O2 con. | 1 | 0.01 | 0.01 | 0 | 0.949 |
pH × Catalyst con. | 1 | 25.05 | 25.05 | 7.49 | 0.018 |
pH × Initial con. of toluene | 1 | 67.98 | 67.98 | 20.32 | 0.001 |
H2O2 con. × Catalyst con. | 1 | 39.94 | 39.94 | 11.94 | 0.005 |
H2O2 con. × Initial con. of toluene | 1 | 145.32 | 145.32 | 43.45 | 0.000 |
Catalyst con. × Initial con. of toluene | 1 | 23.81 | 23.81 | 7.12 | 0.020 |
Error | 12 | 40.14 | 3.34 |
|
|
Lack-of-Fit | 10 | 39.88 | 3.99 | 30.61 | 0.032 |
Pure Error | 2 | 0.26 | 0.13 |
|
|
Total | 26 | 6259.99 |
|
|
|
The information of the model should be tolerable so must have high values of R2-adjusted and R2-predicted. The fitting power of the model increases and the description of the response changes as a function of the independent variables increases. The R2-adjusted for the model is 98.61% and the R2-Predicted of the predicted model is 96.32%, these amounts indicate that the model has a very good fit and can explain the overall changes in the range of the values studied (Table 4) [31-35].
Table 4. Compare different term effect on results.
Term | Coef | SE Coef | T (Coef/SE Coef) | P value | Result |
Constant | 74.67 | 1.06 | 70.71 | 0.000 | Significant |
pH | 2.11 | 0.53 | 4.00 | 0.002 | Significant |
H2O2 con. | 2.97 | 0.53 | 5.63 | 0.000 | Significant |
Catalyst con. | 6.72 | 0.53 | 12.73 | 0.000 | Significant |
Toluene initial con. | -19.70 | 0.53 | -37.31 | 0.000 | Significant |
pH × pH | -1.61 | 0.79 | -2.04 | 0.064 |
|
H2O2 con. × H2O2 con. | -4.41 | 0.79 | -5.57 | 0.000 | Significant |
Catalyst con. × Catalyst con. | 0.79 | 0.79 | 1.00 | 0.337 |
|
Toluene initial con. × Toluene initial con. | -9.01 | 0.79 | -11.38 | 0.000 | Significant |
pH × H2O2 con. | -0.06 | 0.91 | -0.07 | 0.949 |
|
pH × Catalyst con. | 2.50 | 0.91 | 2.74 | 0.018 |
|
pH × Toluene Initial con. | -4.12 | 0.91 | -4.51 | 0.001 | Significant |
H2O2 con. × Catalyst con. | 3.16 | 0.91 | 3.46 | 0.005 | Significant |
H2O2 con. × Toluene initial con. | -6.03 | 0.91 | -6.59 | 0.000 | Significant |
Catalyst con. × Toluene initial con. | -2.44 | 0.91 | -2.67 | 0.020 |
|
S=1.83, R2=99.36%, Pred R2=96.32%, Adj R2=98.61%
The results of the actual and predicted values are given in Table 5. As can be seen, the actual and predicted values are very close together. This indicates that empirical values are close to matching the predicted values of pollutant emissions and are highly desirable for the proposed model. Mathematical model representing toluene photocatalytic decomposition in the range studied can be expressed by the following equation:
(14)
Table 5. Residual values.
Exp. no. | Actual | Predict | Residual (X%-fit) | St resid |
1 | 62.89 | 63.51 | -0.62 | -0.52 |
2 | 68.37 | 67.85 | 0.52 | 0.44 |
3 | 70.56 | 69.57 | 0.99 | 0.84 |
4 | 75.80 | 73.67 | 2.13 | 1.80 |
5 | 78.55 | 76.99 | 1.56 | 1.32 |
6 | 95.23 | 95.31 | -0.08 | -0.07 |
7 | 44.07 | 42.48 | 1.59 | 1.35 |
8 | 50.99 | 51.03 | -0.04 | -0.04 |
9 | 77.28 | 77.51 | -0.23 | -0.20 |
10 | 90.47 | 89.98 | 0.49 | 0.42 |
11 | 45.78 | 46.36 | -0.58 | -0.49 |
12 | 42.48 | 42.34 | 0.14 | 0.12 |
13 | 65.51 | 64.53 | 0.98 | 0.83 |
14 | 61.45 | 62.15 | -0.70 | -2.28 |
15 | 74.25 | 73.64 | 0.61 | 2.21 |
16 | 82.83 | 83.91 | -1.08 | -0.91 |
17 | 67.73 | 67.52 | 0.21 | 0.18 |
18 | 65.09 | 66.74 | -1.65 | -1.40 |
19 | 76.18 | 75.95 | 0.23 | 0.19 |
20 | 83.55 | 85.18 | -1.63 | -1.38 |
21 | 70.05 | 71.95 | -1.90 | -1.61 |
22 | 90.12 | 89.95 | 0.17 | 0.14 |
23 | 43.02 | 44.61 | -1.59 | -1.35 |
24 | 38.98 | 38.50 | 0.48 | 0.41 |
25 | 74.81 | 74.67 | 0.14 | 0.09 |
26 | 74.26 | 74.67 | -0.41 | -0.27 |
27 | 74.94 | 74.67 | 0.27 | 0.18 |
According to Fig. 8, the normal probability plot (a) indicates the distribution of residual values of the straight line and shows the linear pattern of compatibility with a normal distribution. The plot of residuals versus fitted values (b) has non-uniformity and does not show any particular trend. The abundance diagram of residual values (c) also indicates the relatively normal distribution of the residuals. The plot of the values remaining in relation to the observed values (d) also does not follow a particular process. Therefore, there is no systematic pattern of the residues and the fitting model is appropriate.
Fig. 8. Different residual plots for the photocatalytic removal of toluene from aqueous solution by the α-Fe2O3/Mn2P2O7 catalyst.
The results of the main effect of independent variables on pollutant removal percentage indicate that three pH, H2O2 and Catalyst variables had a positive and significant effect, and toluene had a negative and significant effect on pollutant removal percentage (Fig. 9). The most effective variable was toluene concentration. The removal percentage will decrease by increase of toluene concentration. After toluene, the catalyst was most affected. As the catalyst increased from 60 ppm to 120 ppm, the removal rate of pollutants increased significantly. The effect of pH and H2O2 was also positive. As the variables increased, the amount of pollutant removal increased.
|
|
|
|
Fig. 9. The plot of variable effect on the removal percentage of toluene.
The contour graphs and response surface graphs can be useful to detect the optimum conditions of the experiment. In Fig. 10 are shown the contour graph and the response 3D graph of the resulting regression model with the assumption that the concentration of catalyst and toluene is kept constant at 90 ppm and 150 ppm, respectively. As can be seen, under the constant values of catalyst and toluene, the increase in the pH and the amount of H2O2 increases the efficiency of toluene removal. Therefore, if the pH ranges from 6.83 to 9 and H2O2 concentration ranges from 0.28 ppm to 0.46 ppm, then the amount of toluene removal would be higher than 75%, as observed.
Fig. 10. Contour plots (A) and 3D Response surface (B). The interaction between pH and H2O2 concentration for photocatalytic toluene degradation.
The contour graph and the response 3D graph of the resulting regression model are shown in Fig. 11 that assuming a constant H2O2 concentration and toluene concentration of 0.3 ppm and 150 ppm, respectively. Under the constant amounts of H2O2 and toluene, the efficiency of toluene removal increases with increasing pH and increasing concentration of catalyst. If the pH ranges from 6.2 to 9 and catalyst concentration ranges from 105 ppm to 120 ppm, then the amount of toluene removal would be higher than 85%.
Fig. 11. Contour plots (A) and 3D Response surface (B). The interaction between pH and Catalyst concentration for photocatalytic toluene degradation.
The contour graph and the 3D response surface graph of the resulting regression model are shown Fig. 12 that assuming a constant H2O2 concentration and catalyst concentration in the values of 0.3 and 90, respectively under the constant values of H2O2 and catalyst, the efficiency of toluene removal. If the pH ranges between 8 and 9 and the concentration of toluene range between 100 ppm and 113 ppm, then the amount of toluene removal would be higher than 88%.
Fig. 12. Contour plots (A) and 3D Response surface (B). The interaction between pH and initial concentration of toluene for photocatalytic toluene degradation.
As shown in Fig. 13 (A) and (B), the contour graph and the 3D response surface of the resulting regression model with the assumption that the concentration of pH and toluene is kept constant at values of 7 and 150 ppm, respectively. Under the constant values of pH and toluene, increasing the amount of H2O2 and increasing the amount of catalyst increases the efficiency of toluene removal, so that if the H2O2 range is between 0.24 ppm to 0.5 ppm and the catalyst concentration is in the range of 107 ppm to 120 ppm, the amount of toluene removal will be above 80%.
Fig. 13. Contour plots (A) and 3D Response surface (B). The interaction between H2O2 concentration and catalyst concentration for photocatalytic toluene degradation.
The contour graph is illustrated as Fig. 14 (A), and Fig. 14 (B) shows the 3D response of the resulting regression model with the assumption of constant pH and catalyst concentration at values of 7 and 90 ppm, respectively. Under the constant pH and catalyst concentration, increasing the amount of H2O2 and reducing the concentration of toluene increases the efficiency of toluene removal, so if the H2O2 concentration ranges from 0.37 ppm to 0.5 ppm and the catalyst concentration ranges from 107 ppm to 120 ppm, the amount of toluene removal would be higher than 88%.
Fig. 14. Contour plots (A) and 3D response surface (B). The interaction between H2O2 concentration and initial concentration of toluene for toluene photocatalytic degradation.
With the assumption of pH=7 and H2O2 =0.3 ppm, the contour graph and 3D response surface graph of the resulting regression model are shown in Fig. 15 (A) and (B), respectively. Under the constant pH and H2O2 values, the efficiency of toluene removal increases with increasing catalyst content and decreasing toluene concentration. Accordingly, when the catalyst concentration ranges between 105-120 ppm and the toluene concentration being less than 120 ppm, then the toluene removal rate observed higher than 90%.
Fig. 15. Contour plots (A) and 3D Response surface (B). The interaction between Catalyst concentration and initial concentration of toluene for photocatalytic toluene degradation.
The exact optimal values of the variables are summarized in Table 6. As predicted, verification experiments were carried out under optimum operating conditions. The three times repeated experiments showed an average maximum of decomposition efficiency of around 97.14%. The proximity between the predicted and actual results approved the validity of the model and the existence of an optimum point. This proves that the RSM methodology is a powerful tool for determining the exact optimal values of the independent parameters [36-39].
Table 6. Degradation analysis of toluene removal in optimum concentration and pH value.
Optimum conditions | Efficiency (%) | ||||
pH | H2O2 con. (ppm) | Catalyst con. (ppm) | Toluene con. (ppm) | Predicted | Actual |
8.9 | 0.23 | 120 | 100 | 98.00 | 97.14 |
4. CONCLUSION
In summary, the α-Fe2O3/Mn2P2O7 nano photocatalyst was synthesized via the FHRC method. The physical and chemical properties of nano photocatalysts were investigated. The photocatalytic properties of the iron oxide nanoparticles have improved after fixation on the base. The results of the independent variables on toluene removal indicate that pH, H2O2 and catalyst have a significant effect on removal capacity but with increased toluene initial concentration, the removal percentage was decreased. Also, at optimum conditions such as pH= 8.9, the initial concentration of toluene = 100 ppm, catalyst concentration = 120 ppm and H2O2 concentration = 0.23 ppm, we observed the highest percentage of toluene degradation (97.14%).
Acknowledgment:
The authors are thankful to Islamic Azad University, Arak Branch for the preparation of experimental setup.
REFERENCES
1. J. C. Sousa, A. R. Ribeiro, M. O. Barbosa, M. F. R. Pereira, A. M. Silva, J. Hazard. Mater. 344, 146 (2018).
2. M. A. Abdel-Aziz, S. A Younis, Y. M. Moustafa, M. M. H. Khalil, J. Environ. Manage. 233, 459 (2019).
3. D. Robati, S. Bagheriyan, M. Rajabi, Int. Nano Lett. 5(3), 179 (2015).
4. B. Enayatpour, M. Rajabi, O. Moradi, N. Asdolehzade, A. Nayak, S.A. garwal, V.K. Gupta, J. Mol. Liq. 254, 93 (2018).
5. A. Shokri, K. Mahanpoor, D. Soodbar, Desalination Water Treat. 1,16473(2015).
6. M. Rajabi, O. Moradi, M. Sillanpää, K. Zare, A.M. Asiri, S. Agarwal, V. K. Gupta, J .Mol. Liq. 111, 484(2019).
7. M. Rajabi, K. Mahanpoor, O. Moradi, Composites, Part B. 167, 544 (2019).
8. A. Pintar, Catal. Today. 77, 451 (2003).
9. N. Daneshvar, D. Salari, A. R. Khataee, J. Photochem. A. Photobiol. Chem. 162, 317 (2004).
10. B. Enayatpour, M. Rajabi, M. Yari, M.R. Mirkhan, F. Najafi, O. Moradi, A.K. Bharti, et al, 231, 566 (2017).
11. M. Rajabi, O. Moradi, A. Mazlomifar, Int. J. Nano Dimens. 6(3), 227 (2015).
12. N. Daneshvar, M. Rabbani, N. Modirshahla, M. A. Behnajady, J. Photochem. A. Photobiol. Chem. 168, 39 (2004).
13. S. Khodadoost, A. Hadi, J. Karimi-Sabet, M. Mehdipourghazi, A. Golzary, J. Environ. Chem. Eng. 5, 5369 (2017).
14. M. Yari, M. Norouzi, A.H Mahvi, M. Rajabi, A. Yari, O. Moradi, I. Tyagi, V.K. Gupta, Desalin. Water Treat. 57(24), 11195 (2016).
15. Y. I. Choi, K. H. Jeon, H. S. Kim, J. H. Lee, S. J. Park, J. E. Roh, Y. Sohn, Sep. Purif . Technol. 160, 28 (2016).
16. M. Mishra, D. M. Chun, Appl. Catal. 498, 126 (2015).
17. T. J. Greenfield, M. Julve, R. P. Doyle, Coord. Chem. Rev. 384, 37 (2019).
18. S. Wang, X. Jiang, G. Du, Z. Guo, J. Jang, S. J. Kim, Mater. Lett. 65, 3265 (2011).
19. M. Saghi, K. Mahanpoor, H. Shafiei, Iran. J. Chem. Chem. Eng. Research Article. 37,1 (2016).
20. N. Tafreshi, S. Sharifnia, S. Moradi Dehaghi, Process. Saf. Environ. Prot. 106, 203 (2017).
21. E. Ambrosio, D. L. Lucca, M. H. Garcia, R. P. de Souza, J. V. Visentainer, D.L. Lucca, , J. C. Garcia, et al, Sci. Total. Environ. 581, 1 (2017).
22. D. Robati, S. Bagheriyan, M. Rajabi, O. Moradi, A. Ahmadi Peyghan, Phys. E, 83, 1 (2016).
23. S. N. Nam, H. Cho, J. Han, N. Her, J. Yoon, Process. Saf. Environ. Prot. 113, 10 (2018).
24. B. Boonchom, R. Baitahe, Mater. Lett. 63, 2218 (2009).
25. M. Chen, J. Liu, D. Chao, J. Wang, J. Yin, J. Lin, Z. X. Shen, Nano. Energy. 9, 364 (2014).
26. A. Lassoued, B. Dkhil, A. Gadri, S. Ammar, Results. Phys. 7, 3007 (2017).
27. O. Mehraj, B. M. Pirzada, N. A. Mir, M. Z. Khan, S. Sabir, Appl. Surf. Sci. 387, 642 (2016).
28. M. Rajabi, B. Mirza, K. Mahanpoor, M. Mirjalili, F. Najafi, O. Moradi, H. Sadegh, et al, Ind. Eng. Chem. 34, 130 (2016).
29. D. Zhao, G. Sheng, C. Chen, X. Wang, Appl. Catal. B. Environ. 111, 303 (2012).
30. V. Augugliaro, M. Bellardita, V. Loddo, G. Palmisano, L. Palmisano, S. Yurdakal, J. Photochem. Photobiol. C. Photochem. Rev. 13, 224 (2012).
31. O. Mehraj, B. M. Pirzada, N.A. Mir, M.Z. Khan, S. Sabir, Appl. Surf. Sci. 387, 642 (2016).
32. A. Shokri, K. Mahanpoor, D. Soodbar, Fress Environ Bull. 25(2), 500 (2016).
33. D. Robati, M. Rajabi, O. Moradi, F. Najafi, I. Tyagi, S. Agarwal, V.K. Gupta, J. Mol. Liq. 214, 259 (2016).
34. A. Lassoued, B. Dkhil, A. Gadri, S. Ammar, Results. Phys. 7, 3007 (2017).
35. D. Robati, B. Mirza, M. Rajabi, O. Moradi, I. Tyagi, S. Agarwal, V.K. Gupta, Chem. Eng. J. 284, 687 (2016).
36. A. Shokri, F. Rabiee, K. Mahanpoor, Int. J. Environ. Sci. Technol. 14, 2485 (2017).
37. M. Rajabi, K. Mahanpoor, O. Moradi, J. Appl. Polym. Sci. 136(22), 47495 (2019).
38. A. Shokri, K. Mahanpoor, D. Soodbar, J. Environ. Chem. Eng. 4, 585 (2016).
39. M Rajabi, O Moradi, K Zare, Int. Nano Lett.7(1), 35 (2017).