Synthesis of CoFe 2 O 4 @ZnMOF/Graphene nanoflake for photocatalytic degradation of diazinon under visible light irradiation: Optimization and modeling using a fractional factorial method

doi:10.21203/rs.3.rs-2250621/v1

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Synthesis of CoFe 2 O 4 @ZnMOF/Graphene nanoflake for photocatalytic degradation of diazinon under visible light irradiation: Optimization and modeling using a fractional factorial method

https://doi.org/10.21203/rs.3.rs-2250621/v1

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One of the pesticides made of organophosphates is diazinon. If it persists in soil and water resources, it may endanger animal and human health. Here, Diazinon was photocatalytically degraded under visible light using CoFe₂O₄@ZnMOF/Graphene nanoflake (GNF) nanocomposite, which had not been used before for diazinon degradation. Initially, a CoFe₂O₄@ZnMOF /GNF with excellent optical characteristics was developed. This nanocomposite characterized with XRD, FESEM and TEM microscopy. Numerous factors influencing the photocatalytic degradation of diazinon were investigated using a fractional factorial design 2⁴⁻¹. The investigated factors were: the initial concentration of diazinon, the photocatalyst dosage, contact time and pH. According to the studies, these parameters have a significant impact on degradation efficiency. The highest experimental degradation efficiency achieved was 97.38%. The software determined that the optimum conditions were 10mg catalyst, 30 ppm Diazinon dosage, 90 min reaction time, and pH 9. The kinetics of diazinon photocatalytic degradation were investigated and reported to follow a pseudo-first-order kinetic model. Finally, after five runs, the reusability of the CoFe2O4@Zn MOF-GNF photocatalyst demonstrated excellent chemical stability during the photocatalytic degradation of diazinon.

Diazinon

Experimental design

fractional factorial

graphene nanoflake

metal-organic framework

There is no doubt that the use of pesticides in agriculture around the world, which can effectively prevent pest damage, has brought advantages, But because the pesticide is persistently toxic and complicated to biodegrade, it also has negative influences on both human health and the environment [1]. In groundwater and surface water, pesticides are frequently found to be a source of toxic substances in recent decades [2]. The science community has therefore become more curious about discovering effective ways to treat wastewater that usually contains pesticides disposed of the agricultural and industrial output [3, 4]. A wide range of domestic and agricultural bugs can be controlled using O-diethyl O-(2-isopropyl-6 methylpyrimidin- 4-yl) thiophosphate (Diazinon, C₁₂H₂₁N₂O₃PS), non-systemic, and connection organophosphorus pollutant, that easily metabolized [5]. The diazinon was consistently ranked a moderately hazardous compound for aquatic life, animals, and people by the World Health Organization (WHO) [6]. Due to the wide variety of pesticides with various chemical compositions and properties, the removal of pesticides from such industrial effluents is a very complicated situation [7]. There are different mechanisms for removing pesticides from wastewaters, and while one method may be deemed appropriate for several pollutants but not appropriate for other pesticides. However, these procedures can still be classified as physical treatment like adsorption [8], chemical methods [9], biodegradation [9], coagulation and flocculation [10], membrane separation [11], ozonation (oxidation) [12], and ultraviolet degradation process [13].

Except for some exceptions, experimental design methods in photocatalytic degradation have primarily concentrated on improving degradation processing conditions. However, to develop effective photocatalysts, a systematic study of optimal preparation conditions is critical. The use of experimental design approaches would be extremely beneficial in the design and development of high-efficiency photocatalysts for the removal of pollutants [14]. Using experimental design techniques, more information can be collected while able to perform some less experiments. Numerous techniques exist for designing experiments, including Central Composite Designs (CCD)[15], Box-Behnken Designs (BBD) [16], Doehlert Designs [17], Full factorial designs [18], Fractional Factorial Designs [19], Mixture Designs [20], Plackett-Burman Designs [21], Taguchi Designs [22], and so on [23]. Full factorial experiments are not always feasible due to financial, duration, or other limitations [24]. Fractional Factorial Design would be more efficient in the case of advanced materials, especially when considering the costs of production and characterization [25]. Furthermore, if there are only major effects and a small amount of low order interactions, Fractional Factorial Design provides sufficient information. When compared to full factorial designs, the fractional design seems to have the ability to decrease the number of experiments. When the entire two-level factorial strategies necessitate a significant number of N experiments (N = 2^k, where k is the number of factors to be investigated), this approach is frequently preferred [26]. A deeper understanding of the mechanisms controlling this dynamic process is thus made possible by the development of linear and straightforward targets for the development of kinetic models. This work is distinguished by its novel modeling approach. Additionally, this study's focus is to propose statistically sound relationships experimental measurements that describe how this process's rate tends to vary with the solution pH and with the initial concentrations of the pollutant or photocatalyst [27]. As a result, it was able to determine statistically trustworthy correlations between the percentage of diazinon degradation and a variety of experimental parameters, including the pH of the solution, the initial concentrations of diazinon, the dosage of the catalyst, and the contact time. Additionally, these regression equations, which were supported by the Analysis of Variance (ANOVA), helped to identify the individual effects of each parameter on the reaction as well as how those parameters interacted with one another. These designs were also used to calculate how each parameter's and their relationship with one another affected the chosen reactions [24].

Graphene nanoflake (GNF) has been considered an excellent candidate for improving photocatalytic performance due to its remarkable attributes including such good thermal conductivity, extremely good charge carrier mobility, high specific surface area, lofty mechanical stiffness, and wonderful stability in an aquatic system. GNF incorporation with other nanomaterials such as CoFe₂O₄@ZnMOF, because of synergic effects, has been shown to outperform pristine CoFe₂O₄@ZnMOF and GNF in pollutant degradation. Considering the facts and circumstances, we attempted to investigate the photocatalyst degradation of diazinon using CoFe2O4@ZnMOF/GNF as a stable and effective photocatalyst under visible light irradiation. In this study, the 2⁴⁻¹ fractional design was applied to determine the best condition with a high performance of diazinon photodegradation treatment. The kinetic of the diazinon photodegradation on the CoFe2O4@ZnMOF/GNF nanocomposite has been analyzed using pseudo-first-order models. Also, the reusability of diazinon adsorption was investigated in 5 cycles.

2.1 Materials and instruments

The preliminary substances used for preprating CoFe₂O₄ nanoparticles, CoFe₂O₄@ZnMOF and CoFe₂O₄@ZnMOF/GNF nanocomposite were as follows: Iron(III) chloride hexahydrate (FeCl₃.6H₂O, 97%), Iron (II) Chloride tetrahydrate (FeCl₃.6H₂O, 99%), Zinc chloride (ZnCl₂, 98%), Sodium hydroxide (NaOH, 99%), ethanol (C₂H₅OH, 99%), glacial aceticacid (CH₃COOH, 99%), graphite powder (99%), 1,3,5 benzenetricarboxylate (BTC) (C₉H₃O₆, 95%), Cetyltrimethylammonium bromide (CTAB, ≥ 98%) and Thioglycolic acid (HSCH₂COOH, 99%). All of the chemicals were purchased from Sigma-Aldrich and Merck, and they were not further purified before use.

2.2 Preparation Of Photocatalyst

2.2.1 Synthesis of CoFe₂O₄@ZnMOF

Cobalt ferrite nanoparticles were synthesized according to previous articles [28]. Next, the layer by layer method was used to prepare the CoFe₂O₄@ZnMOF core-shell structure. In this method, 300 mg of cobalt ferrite nanoparticles were added to 15 ml of ethanolic thioglycolic acid solution (0.6 mM) and then vigorously stirred for 5 hours at room temperature. Cobalt ferrite nanoparticles functionalized with thioglycolic acid and were collected by an external magnetic field. The precipitate obtained was filtered and washed with water and ethanol.

After that, 0.2 g of cobalt ferrite nanoparticles functionalized with thioglycolic acid were dispersed in 20 ml of ZnCl₂ ethanolic solution (10 mM) for 20 minutes under ultrasonication to obtain a uniform solution. Then the resulting precipitate was collected with an external magnetic field and washed three times with ethanol. In the following, the precipitate was dispersed in 25 ml of ethanoic 1,2,4,5 benzene tetracarboxylic acid (BTC) solution (10 mM) for 30 minutes under a temperature of 70°C, and was collected with an external magnetic field and was washed with ethanol. These steps were repeated 20 times until the CoFe₂O₄@ZnMOF core-shell nanocomposite was produced. Finally, the obtained precipitate was dried under vacuum at 70°C for 8 hours [29].

2.2.2 Synthesis Of Graphene Nanoflake

In 15 ml of glacial acetic acid, 200 mg of graphite powder was combined with 0.5 mM CTAB surfactant to make graphene nanoflake. The final product was heated to 100°C for 24 hours in a nitrogen environment after being ultrasonically treated for 4 hours at room temperature. Then the preparing solution was centrifuged and the resulting black sample was washed with distilled water and acetone. Finally, the preparing precipitate was dried at 40°C for a day and night to obtain the graphene nanoflake precipitate [30].

2.2.3 Synthesis Of Cofeo@znmof/gnf

To prepare CoFe₂O₄@Zn MOF-graphene nanoflake nanocomposite, first, 100 mg of CoFe₂O₄@Zn MOF was dispersed in 20 ml of distilled water for 15 minutes. Then 200 mg of graphene nanoflakes were dispersed in 30 ml of distilled water. Next, both the resulting solutions were mixed together and was subjected to ultrasonication for 2 hours. An external magnetic field was used to collect the precipitate, which was then washed many times with ethanol and deionized water. Finally, the obtained precipitate was dried in an oven at 70°C for 14 hours.

2.3 Photocatalyst Experiment

The photocatalytic activities of CoFe₂O₄@ZnMOF/GNF was evaluated for diazinon degradation. A total of 20 photodegradation experiments were carried out. The experimental conditions of each experiment were designed according to a 2⁴⁻¹ fractional factorial experimental design. This optimization technique entails designing the k factors with just three levels for each factor (-1, 0, and + 1). In this study and according to the 2⁴⁻¹ factorial design, the factors consisted of pH (A), photocatalyst dosage (B), diazinon concentration (C), and contact time (D). The coded and uncoded parameters of the experiments designed by the 2^k − 1 factorial was presented in Tables 1.

The mixtures were centrifuged in order to study the photodegradation of diazinon, and the concentration of diazinon was assessed using a UV-visible spectrophotometer with a maximum wavelength of 250 nm. The following was measured for the diazinon degradation amount:

$$\%D=\frac{{A}_{0}-{A}_{t}}{{A}_{0}}\times 100$$

Where A₀ and A_t, respectively, stand for the diazinon's adsorption at initial concentration and time t.

Table 1

levels for designing an effective parameter on the photocatalyst activity of CoFe₂O₄@ZnMOF/GNF, both coded and uncoded
Level	Coded level	Uncoded level
		pH (A)	Catalyst dosage (mg) (B)	Concentration of diazinon (ppm) (C)	Contact time (min) (D)
High	+ 1	3	2	10	30
Center	0	6	6	20	60
Low	-1	9	10	30	90

3.1 Characterization of CoFe₂O₄@ZnMOF/GNF

Images of a graphene nanoflake taken using field emission scanning electron microscopy (FESEM) are displayed in Fig. 1. The transparent flaky structure in these images is easily recognized as thin layers of GNF. On the other hand, sheets with different dimensions and sizes are visible in the scanning electron microscope images, and in some parts, several layers of graphene nanoflakes are stacked on top of each other. In GNFs, the abundance of edge plane locations may improve a number of properties and help with charge transfer at interfaces.

The XRD pattern of CoFe₂O₄@Zn MOF nanoparticles is shown in Fig. 2(a). In the figure, five characteristic peaks can be seen at 30.14, 35.46, 43.11, 56.97, and 62.65°, corresponding to plates (220), (311), (400), (511), and (440), respectively. Also, a peak is observed at position 18.55, which is related to Zn-MOF based on previous studies [31]. In addition, the diffraction pattern of CoFe₂O₄@Zn MOF shows that the position of the peaks did not change with the coating of Zn-MOF on cobalt ferrite. Figure 2(b) shows the XRD pattern of CoFe₂O₄@Zn MOF-graphene nanoflake nanocomposite. As it is clear in the figure, the peaks related to CoFe₂O₄@Zn MOF nanoparticles are present, which shows the placement of the CoFe₂O₄@Zn MOF core-shell structure on the graphene nanoflake does not destroy the desired core-shell structure. In addition, a sharp peak is observed at the 26.52º position, which is related to graphene nanoflake according to previous reports [30].

The CoFe₂O₄@Zn MOF's core-shell structure is illustrated in Fig. 3 using images taken with a transmission electron microscope (TEM). TEM images show that the synthesized nanoparticles have a core-shell structure, which means that the cobalt ferrite nanoparticles are well coated with the metal-organic framework of Zn-MOF.

The EDS analysis of the prepared CoFe₂O₄@Zn MOF-graphene nanoflake nanocomposite is shown in Fig. 4(a). The analysis of these images confirms the presence of carbon, oxygen, iron, cobalt and zinc elements in the nanocomposite structure, which indicates the high purity of the synthesized nanocomposite. The morphology of CoFe₂O₄@Zn MOF-graphene nanoflake nanocomposite was also investigated using FESEM (Fig. 4(b)). As shown in the figure, the prepared CoFe₂O₄@Zn MOF nanostructures are uniformly dispersed on the surface of graphene nanoflakes.

3.2 Investigating The Optical Properties Of Cofeo@zn Mof-graphene Nanoflake Nanocomposite

One of the crucial factors in selecting the kind of beam of light to excite electrons from the valence band to the conduction band is the photocatalyst's bandgap energy. After this, electron-hole pairs are produced, and eventually, pollutant degradation occurs. The UV-Vis spectrum of CoFe₂O₄@Zn MOF-graphene nanoflake nanocomposite is shown in Fig. 5 (a), in which the amount of photocatalyst absorption is plotted against the wavelength. The optical bandgap of CoFe₂O₄@Zn MOF-graphene nanoflake nanocomposite was obtained from Tauc's relation according to the following equation [32]:

$${\left(\alpha h\nu \right)}^{2}=A\left(h\nu -Eg\right)$$

Where, respectively, Eg, A, and α stand for the prepared materials' optical bandgap, constant, and absorption coefficient. Figure 5(b) shows the graph of (αhν)² versus photon energy. Using the extrapolation of the linear part of this graph, the bandgap value of the prepared nanocomposite can be obtained. Bandgap energy of CoFe₂O₄@Zn MOF-graphene nanoflake nanocomposite was calculated at 4.7 eV using line extrapolation the Fig. 5(b).

3.3 Experimental Design

Through the use of a 2^k − 1 factorial design, the variables influencing the photocatalytic properties of the CoFe₂O₄@Zn MOF-graphene nanoflake nanostructure in the degradation of diazinon were examined.. The degradation percentage of four parameters was investigated, which are given in Table 2. The residual plots for the CoFe₂O₄@Zn MOF-graphene nanoflake nanocomposite's photocatalytic activities are depicted in Figs. 6a and b in various modes (versus order, versus fit, Histogram, and Normal probability). The CoFe₂O₄@Zn MOF-graphene nanoflake nanocomposite sample has a randomly assigned distribution, according to the results, supporting the dispersion of the experimental studies.

Table 2

CoFe₂O₄@Zn MOF-graphene nanoflake nanocomposite's diazinon degradation experimental studies by a randomized complete 2^K − 1 factorial design
StdOrder	RunOrder	Center Pt	Blocks	pH	Catalyst dosage (mg)	Concentration of diazinon (ppm)	Contact time (min)	Degradation %
6	1	1	1	9	2	30	30	10.0
1	2	1	1	3	2	10	30	9.73
2	3	1	1	9	2	10	90	34.79
5	4	1	1	3	2	30	90	9.45
8	5	1	1	9	10	30	90	96.46
7	6	1	1	3	10	30	30	31.78
3	7	1	1	3	10	10	90	20.03
10	8	0	1	6	6	20	60	70.98
4	9	1	1	9	10	10	30	77.27
9	10	0	1	6	6	20	60	70.16
18	11	1	2	9	10	30	90	97.38
17	12	1	2	3	10	30	30	31.06
19	13	0	2	6	6	20	60	70.64
12	14	1	2	9	2	10	90	34.17
20	15	0	2	6	6	20	60	71.3
13	16	1	2	3	10	10	90	19.84
15	17	1	2	3	2	30	90	9.27
11	18	1	2	3	2	10	30	9.1
14	19	1	2	9	10	10	30	76.6
16	20	1	2	9	2	30	30	9.3

4.1. Influences Of Experimental Factors On The Diazinon Photocatalytic Degradation Process

Analysis of variance (ANOVA) was used to investigate the impact of experimental parameters on the photocatalytic degradation of diazinon using by CoFe₂O₄@Zn MOF-graphene nanoflakes sample (Table 3). According to the findings, photocatalyst dosage, contact time, diazinon concentration, and pH all have an important impacts on diazinon degradation. The Pareto plot (Fig. 7) shows the probability of experimental distribution in the degradation of diazinon and actually the effectiveness of the factors graphically. Also, these graphs agree with the results of the analysis of variance, which confirms the significance influence of experimental factors. According to the findings, all four factors, and also their interactions, can have a major impact on how quickly diazinon degrades. According to the Pareto plot, among these factors, pH has the greatest effect on the rate of diazinon degradation. It is worth mentioning that pH is an important influencing factor in pollutant removal efficiency due to its effect on the ionization state of pollutants and also the surface properties of nanoparticles in solution. In this study, the effects of acidic, neutral, and alkaline environments on diazinon degradation were also examined. The results showed that the rate of degradation of diazinon in alkaline environments is higher than in neutral and acidic environments. The surface of the photocatalyst will have a positive charge in acidic pHs (pH = 3), a neutral charge in neutral pHs (pH = 6) and a negative charge in alkaline pHs (pH = 9). On the other hand, diazinon has a pKa of about 2.6, which means that it becomes negatively charged at pHs above 2.6 [33]. Electrostatic attraction between photocatalyst and diazinon at alkaline pH can increase the rate of diazinon degradation. On the other hand, the highest amount of diazinon degradation occurs at pH equal to 9. The cause can be attributed to the production of hydroxyl radicals. As a result, the electrostatic attraction between diazinon and the photocatalyst and the generation of hydroxyl radicals are both responsible for the pH influence on the diazinon removal percentage. The following is the proposed mechanism for the photocatalytic behavior of CoFe2O4@Zn MOF-graphene nanoflakes:

$$1)CoFe2O4@Zn MOF-GNF \to \text{C}\text{o}\text{F}\text{e}2\text{O}4@\text{Z}\text{n} \text{M}\text{O}\text{F}-\text{G}\text{N}\text{F} \left({h}^{+}+{e}^{-}\right)$$

$$2) {h}^{+}+{H}_{2}O \to {OH}^{.}+{H}^{.}$$

$$3) {h}^{+}+{OH}^{-}\to {OH}^{.}$$

$$4) {e}^{-}+ {O}_{2}\to {O}_{2}^{-.}$$

$$5) {O}_{2}^{-.} + {H}_{2}O \to {OH}^{.}$$

$$6) {OH}^{.}+Diazinon \to Degraded products$$

Table 3

Analysis of Variance for diazinon degradation with CoFe₂O₄@Zn MOF-graphene nanoflakes sample
Source	DF	Adj SS	Adj MS	F-Value	P-Value
Model	9	19421.7	2157.97	10068.54	0.000
Blocks	1	0.2	0.22	1.01	0.339
Linear	4	12338.4	3084.59	14391.93	0.000
pH	1	5461.9	5461.95	25484.07	0.000
Catalyst dose	1	6589.4	6589.38	30744.38	0.000
Conc. of diazinon	1	11.0	10.99	51.27	0.000
time	1	276.1	276.06	1288.02	0.000
2-Way Interactions	3	3218.9	1072.98	5006.23	0.000
pH*catalyst dose	1	2357.1	2357.10	10997.64	0.000
pH*conc. of diazinon	1	66.6	66.59	310.67	0.000
pH*time	1	795.2	795.24	3710.39	0.000
4-Way Interactions	1	3864.2	3864.20	18029.38	0.000
pHcatalyst dose conc. of diazinon*time	1	3864.2	3864.20	18029.38	0.000
Lack-of-Fit	8	1.6	0.20	0.72	0.698
Pure Error	2	0.6	0.28
Total	19	19423.9

Also, according to this plot, it is clear that one of the influencing factors on the degradation rate of diazinon is the amount of CoFe₂O₄@Zn MOF-graphene nanoflake. According to the data from Table 2, with the increase of photocatalyst from 2 to 10 mg, the percentage of diazinon degradation also increases. The reason for this is considered to be the increase in total active surface, which can play an important role in increasing the degradation of diazinon when the dose of diazinon is changed. Of course, we should not ignore the discussion of more scattering and less penetration of UV irradiation when the CoFe₂O₄@Zn MOF-graphene nanoflake nanocomposite amount increases. Because increasing the photocatalyst amount too much can have the opposite effect and reduce the amount of pollutant degradation.

Another important factor affecting the rate of diazinon degradation is the initial concentration of the pollutant. As the concentration of diazinon increases, more molecules are adsorbed to the surface of the catalyst. As a result, the position of absorbed hydroxyl radicals is replaced by diazinon and increased the degradation efficiency of diazinon. In addition, the competitive consumption of produced hydroxyl radicals also reduces the degradation rate of highly concentrated diazinon. The length of time that the catalyst and pollutant are in contact can also affect the rate of diazinon degradation. Of course, the effect of the time factor in this experiment is less than the other three factors. Figure 8 shows the normal plot of diazinon degradation that the data obtained from this diagram are completely consistent with the data of the Pareto plot.

The 2⁴⁻¹ method was used to determine the polynomial regression equations. All experimental studies were carried out in accordance with Table 2. The purpose in summary is to improve the response variable so that the appropriate relationship between several independent variables and the dependent variables is created. The RSM techniqe was used to determine the following equation, which demonstrates the experimental relationship between the experimental variables and the performance percentage:

% Degradation = 70.770 + 18.476 A + 20.294 B + 0.829 C + 40154D + 12.137 A*B – 2.040A*C + 7.050 A*D – 34.750 A*B*C*D

Various surface plots have been designed using these equations (Figs. 9). By selecting different values for the A, B, C, and D factors, various percentages of degradation are theoretically estimated by considering these plots. The top of these plots contains the optimal values. The results of applying the theoretical equations to the experimental findings are displayed in Table 2. The results of the experimental investigation are consistent with the theoretical data.

The value of R², which ranges from 0 to 1, can be used to determine how well the approximate model is statistically significant. The regression equation's best fit to the test data is when the R² value is close to 1. In particular, R² represents the percentage of Y variation explained by the regression equation. According to R² values of 0.95 for diazinon degradation, the regression coefficients equations will be used to estimate the percent degradation of diazinon inside the observations. R² values of 0.95 also indicated that the independent variables accounted for 95% of the variations in degradation. However, including a variable in the model can still enhance R² regardless of whether the additional factors are statically important. As a result, several scientists favor using adjusted-R². When parameters are introduced into the model, the adjusted- R² does not always enhance. In actuality, the amount of adjusted- R² frequently decreases when extraneous variables are included in the model. Considerable variation between R2 and adjusted-R² indicates the inclusion of unimportant factors in the model. Adjusted- R² amount of 99.98 in this case were sufficiently close to R² values of 99.99 in terms of diazinon degradation (see Table 4) [34].

Table 4

The chosen model's analysis of variance (ANOVA)
Experiment	R-sq	R-sq(adj)	R-sq(pred)
Dwgradation of diazinon	99.99%	99.98%	99.96%

3.3. Possible Photodegradation Pathway Of Diazinon

Li et al. presented some theoretical molecular structures for the major diazinon degradation intermediate products. Figure 10 suggests 3 potential diazinon degradation pathways based on these intermediate products. The degradation pathway may be composed of path 1, path 2 and path 3. Path 1 of the diazinon degradation process results in the formation of R1 (1E, 2E)-3-((dimethoxyphosphoryl)oxy)-N-methylbut-2-enimidic acid), R2 (2-(phosphonothiooxy) acrylic acid) and R3 (2-isopropyl-6-methylpyrimidine-4-ol). In path 2, the diazinon molecules' sulfur phosphorus double bond (S = P) is changed to an oxygen phosphorus double bond (O = P), resulting in the formation of R4 (diazoxon). Following that, the C-O bond in R4 is broken, forming the R5 (triethyl phosphate) (diazoxon) and R3 (2-isopropyl-6-methylpyrimidine-4-ol). Path 3 produces R6 (hydroxydiazinon), which is identical to that suggested in reference, by oxidizing hydroxyl radicals. These intermediate products, which can only exist for a short time due to their molecular structures and compositions, may have minimal side effects and reduced activity. With more time, these intermediate products can finally be fully mineralized to PO₄³⁻, NO₃⁻, H₂O, SO₄²⁻, and CO₂ [3].

3.4. Kinetic Study

The Langmuir-Hinshelwood kinetic model was used to study the kinetics of diazinon photocatalytic degradation using a CoFe₂O₄@Zn MOF-graphene nanoflake catalyst. For this purpose, the following relationship was used [35]:

$$d\frac{{C}_{t}}{{d}_{t}}=-{k}_{app}\times {C}_{t}$$

$$\text{ln}\left(\frac{{C}_{t}}{{d}_{t}}\right)=\text{ln}\left(\frac{{A}_{t}}{{A}_{0}}\right)=-{k}_{app}\times t$$

In this equation, A_t represents the absorption of diazinon at time t, A₀ represents the absorption of diazinon at the initial time and k_app represents the apparent rate constant. The graph of A_t/A₀ ratio was drawn in terms of time (Fig. 11). The slope of this graph shows the apparent rate constant. As can be seen from the figure, the A_t/A₀ ratio changes linearly with time, and the value of R² = 9897 for the CoFe₂O₄@Zn MOF-GNF catalyst indicates that the reaction is pseudo-first-order.

3.5 Stability Of Cofeo@zn Mof-gnf Photocatalyst

The reusability of the CoFe₂O₄@Zn MOF-graphene nanoflake nanocomposite as a photocatalyst for successive cycles was examined. The results are shown in Fig. 12. As the findings demonstrate, the performance of the photocatalyst is effectively diminished after every use, but the percentage of diazinon degradation does not decrease significantly. So it can be concluded that it is possible to use the catalyst several times with almost the same performance for 5 consecutive cycles.

The degradation performance of the proposed photocatalyst for diazinon with various catalysts reported in the literature are compared in Table 5. As a result, the CoFe₂O₄@Zn MOF-GNF photocatalyst outperforms numerous different photocatalyst in diazinon degradation.

Table 5

Comparison of the diazinon pollutant's degradation rates with various photocatalysts
Photocatalyst	Time (min)	Reusability	%Degradation	References
CoO/Bi quantum dots/defective Bi₂MoO₆ hollow Z-scheme heterojunction	150	-	94.2	[36]
ZnO–TiO₂	120	5	87.26	[37]
TiO₂/Fe₂O₃	45		88.93	[38]
Ni-doped ZnO nanorods	120	5	99.96	[39]
CoFe₂O₄@Zn MOF-GNF	90	5	97.38	This work

The current research was conducted to determine the performance of photocatalytic diazinon degradation in aqueous phase using a CoFe₂O₄@Zn MOF-GNF nanocomposite. The synthesized nanoparticle were investigated by XRD, FESEM and TEM. the analysis shows that the synthesized CoFe₂O₄@Zn MOF have a core-shell structure, which means that the cobalt ferrite nanoparticles are well coated with the metal-organic framework of Zn-MOF, and the prepared core-shell nanostructures are uniformly dispersed on the surface of graphene nanoflakes. The photocatalytic degradation of diazinon was investigated using a 2^k − 1 fractional factorial design, with an assessment of the effect of catalyst dose, pH, contact time and initial diazinon concentrations. The photocatalysis process in the presence of CoFe₂O₄@Zn MOF-GNF nanocomposite demonstrated the highest efficiency degradation. The pseudo-first-order kinetic model was used to describe the photocatalytic degradations. Under optimized reaction conditions, the prepared composite's recovery and reusability were also investigated. After five runs, the results showed that the CoFe₂O₄@Zn MOF-GNF photocatalyst have an excellent chemical stability during the photodegradation of diazinon.

Competing interests statement

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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No competing interests reported.

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Synthesis of CoFe 2 O 4 @ZnMOF/Graphene nanoflake for photocatalytic degradation of diazinon under visible light irradiation: Optimization and modeling using a fractional factorial method

Status:

Version 1

Abstract

Figures

Introduction

Experimental Section

2.1 Materials and instruments

2.2 Preparation Of Photocatalyst

2.2.1 Synthesis of CoFe₂O₄@ZnMOF

2.2.2 Synthesis Of Graphene Nanoflake

2.2.3 Synthesis Of Cofeo@znmof/gnf

2.3 Photocatalyst Experiment

Result And Discussion

3.1 Characterization of CoFe₂O₄@ZnMOF/GNF

3.2 Investigating The Optical Properties Of Cofeo@zn Mof-graphene Nanoflake Nanocomposite

3.3 Experimental Design

4.1. Influences Of Experimental Factors On The Diazinon Photocatalytic Degradation Process

3.3. Possible Photodegradation Pathway Of Diazinon

3.4. Kinetic Study

3.5 Stability Of Cofeo@zn Mof-gnf Photocatalyst

Comparison Of Degradation Efficiencies Of Various Photocatalysts

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

Synthesis of CoFe 2 O 4 @ZnMOF/Graphene nanoflake for photocatalytic degradation of diazinon under visible light irradiation: Optimization and modeling using a fractional factorial method

Status:

Version 1

Abstract

Figures

Introduction

Experimental Section

2.1 Materials and instruments

2.2 Preparation Of Photocatalyst

2.2.1 Synthesis of CoFe2O4@ZnMOF

2.2.2 Synthesis Of Graphene Nanoflake

2.2.3 Synthesis Of Cofeo@znmof/gnf

2.3 Photocatalyst Experiment

Result And Discussion

3.1 Characterization of CoFe2O4@ZnMOF/GNF

3.2 Investigating The Optical Properties Of Cofeo@zn Mof-graphene Nanoflake Nanocomposite

3.3 Experimental Design

4.1. Influences Of Experimental Factors On The Diazinon Photocatalytic Degradation Process

3.3. Possible Photodegradation Pathway Of Diazinon

3.4. Kinetic Study

3.5 Stability Of Cofeo@zn Mof-gnf Photocatalyst

Comparison Of Degradation Efficiencies Of Various Photocatalysts

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

2.2.1 Synthesis of CoFe₂O₄@ZnMOF

3.1 Characterization of CoFe₂O₄@ZnMOF/GNF