Degradation of Methyl Orange from Aqueous Solution Using Fe-Ni-Co-Based Trimetallic Nanocomposites: Optimization by Response Surface Methodology

Abstract

Effluent-containing dye molecules is a significant environmental hazard. An economical and energy-saving solution is needed to combat this issue for the purpose of environmental sustainability. In this study, Fe-Ni-Co-based trimetallic nanocomposite was synthesized using the coprecipitation method. Scanning Electron Microscopy (SEM), X-ray diffraction (XRD), and Fourier Transform Infra-Red spectroscopy were conducted to explore the physical morphology, phase structure and functional groups of the synthesized catalyst. Among dyes, methyl orange is considered as a major contaminant in textile effluent. The current study focused on the degradation of methyl orange using a trimetallic Fe-Ni-Co-based nanocomposite. A central composite design in response surface methodology was employed to analyze the independent variables including dye concentration, catalyst dose, temperature, hydrogen peroxide, irradiation time, and pH. Dye degradation has been achieved up to 81% in 20 min at the lowest initial concentration (5 mg/L) in optimized conditions. Based on ANOVA, the predicted values were in great agreement with the actual values, signifying the applicability of response surface methodology in the photocatalytic decolorization of dyeing effluents. The results gained from this research demonstrated that the synthesis method of trimetallic nanocomposite (Iron Triad) is a cost-effective and energy efficient method that can be scaled up to a higher level for industrial application.

1. Introduction

The advancement in urbanization, overpopulation, and industrialization has been a major source of water pollution and a great threat to water scarcity all over the world. Pakistan has both surface and underground sources of water. However, underground water supplies are being drained because of exponential population growth and industrialization. The fourth biggest city in Pakistan, Rawalpindi, is also struggling with dwindling water supplies due to rapid increase in its population [1]. Pakistan has a diverse industrial sector that includes leather, textiles, pharmaceuticals, petrochemicals, chemicals, fertilizers, paper and pulp, electrical goods, cement, agriculture, automobile engineering, etc. These industries generate significant amounts of wastewater, including organic, inorganic, and biological contaminants, due to their water-intensive processes. For instance, agricultural industry accounts for 70% of global freshwater use. Pesticides and fertilizers can contaminate surface and groundwater, affecting 40% of global rivers [2]. In addition, chemical industry emits 35% of the worldwide industrial effluent, which contains heavy metals and solvents [3], while the mining industry releases heavy metals and other pollutants into waterways, affecting 20% of the global watersheds [4]. Among these industries, the textile industrial processes require large quantities of organic and inorganic dyes (more than 10,000) for various purposes. They consume a significant amount of water and release 72% of their wastewater into surface waters [5], hence generating 20% of global water pollution, with 10 tons of wastewater per day per mill [6,7]. These pollutants eventually result in polluted drinking water, noxious meat (due to toxin bioaccumulation), contaminated river and lake ecosystems that are no longer capable of maintaining biodiversity, forest loss owing to acid rain, and a variety of other negative repercussions. These factors impacting both human health and the environment, however, are particular to the type of pollutants [8]. The toxic impacts due to the presence of these dyes in water and soil for decades deteriorate the quality of water and result in many water-born infectious diseases, leading to jaundice, cardiac arrest, and hepatitis. A groundbreaking study revealed that by the year 2050, the higher level of nitrogen and phosphorous will cause up to 1 in 3 people to be at higher risk of exposure to water contamination [9]. Currently, 2.2 billion people are living in water-stressed areas without access to safe drinking water, and 771 million are among those who lack the basic amenity of drinking water. These harmful effects on natural resources and ecosystems have encouraged the researchers to study the most appropriate, cost-effective, and eco-friendly approach for handling the textile effluent efficiently [10].

The traditional water treatment methods were used to treat suspended bacteria and colloidal particles in wastewater. Only 20–30% of the organic matter can be removed from contaminated water by conventional treatments, according to domestic and international experimental research and real production outcomes. These methods are not considered efficient for the treatment of dissolved organic contaminants in effluents. Advance water treatment technologies need to be used to overcome the limitations of traditional methods [11]. The prevalent treatments used for the removal of dyes include precipitation, incineration, ozonation [12], disinfection, flocculation, absorption electrodialysis, and electrochemical oxidation [13]. However, these techniques are less efficient for the treatment of complex and chemically stable dyes. Moreover, toxic and volatile gasses are released during the combustion process. Plastics, nuclear waste, and Dichlorodiphenyltrichloroethane (DDT) cannot be removed by biological treatment. Chemical oxidation is another method which uses oxidants, but this method is quite expensive and time consuming, as the half-life of ozone is only 20 min [14].

Interestingly, nanotechnology offers opportunities to improve water treatment systems in terms of cost, size, and pollutant selectivity, rather than simply replacing existing methods. Nanomaterials have a significantly higher surface-to-volume ratio than bulk materials due to their incredibly small size and high activity, which has attracted researchers [15,16]. For the efficient removal of dyes, nanocomposites which contain the mixed metal oxides are currently used [17]. For example, in a recent study, Ni (II)-, Cd (II)- and Mn (II)-based coordination polymers were used as a catalyst for the photodecomposition of rhodamine and antibiotic dinitrazole (DTZ). It was observed that among them, the Mn (II)-based catalyst had a significant 95 and 91% degradation of RhB and DTZ, respectively, in 45 min [18]. In another study, Cu/C hierarchical nanocomposites were synthesized at different temperatures for the photocatalytic degradation of methyl violet under UV irradiation. The results demonstrated that the Cu/C-1000-4 composite achieved 96% efficiency even after four cycles, using a simple regeneration procedure [19]. In a similar study, Fe-doped NiO nanoparticles were prepared by a co-precipitation method for the photodegradation of rhodamine B under visible light. The results showed a 99% degradation of rhodamine B within 40 min with 8% Fe-doped to NiO [20].

Despite the fact that nanotechnology has made the treatment of contaminant facile, there still are some challenges related to the use of nickel oxide nanoparticles, including a lower surface to volume ratio, a larger band gap and the recombination of an electron–hole pair. Furthermore, the use of this catalyst on a large scale is impeded due to a small percentage (4–5%) of UV regions in the solar spectrum [21]. By considering these limitations, a compelling methodology is employed to synthesize an effective light-driven photocatalyst nanocomposite and the doping of transition metal oxides (Co, Cd, Cu, Fe, Zn, Mg, Mn) [20]. Among them, iron oxide nanoparticles are nontoxic, with an efficient photocatalytic efficiency in visible light by reducing the electron–hole recombination and band gap energy of NiO [9]. On the other hand, cobalt oxide is a cost effective, unique and environmentally friendly single component, with an effective band gap of ~2.00 ev for visible light absorption [15,16]. The p-type semiconductor has fascinating electrical and magnetic properties, with vast applications in the degradation of organic pollutants from wastewater. Furthermore, CoO displays an absorption edge in the visible range, demonstrating its potential use in photocatalysis [22].

The trimetallic nanocomposite, due to its high catalytic selectivity/activity, has shown greater efficiency in many applications compared to monometallic nanoparticles [23,24]. This strategy enhances the nanoparticle’s properties by reducing their size and increasing the surface area. In addition, the tuning of the band gap results in a unique photocatalyst with enhanced properties to better absorb light in the solar spectrum. However, the photocatalytic degradation of pollutants is influenced by other factors, such as temperature, catalytic dose, pH, dye concentration, irradiation time, etc. A multi-variable optimization technique is required to optimize these factors for efficient degradation.

In the present work, a novel nanocomposite, containing trimetallic oxides such as iron, nickel, and cobalt have been used for photocatalytic degradation. These three elements are also known as the “Iron Triad” due to their physical and chemical characteristics being similar to each other. The objectives of the present study include (1) the synthesis of a trimetallic nanocomposite of FeO₂-NiO₂-CoO₂ using co-precipitation with improved photocatalytic properties; (2) the efficient degradation of methyl orange (MO) dye using the trimetallic nanocomposite; (3) the optimization of selected parameters using response surface methodology (RSM), which affects the degradation efficiency. In this context, central composite design (CCD) was employed to minimize the runs and optimize the operational parameters. It is an advanced technique for avoiding data redundancy in experimental design [25]. The six operational parameters comprising catalytic dose, dye concentration, irradiation time, pH, hydrogen peroxide concentration and temperature have been optimized using RSM, while the validity of the model has been checked by the ANOVA response.

2. Materials and Methods

2.1. Reagents

All the reagents used in this project were utilized as received, unless specified. The chlorides of Co, Fe and Ni (98% purity), sodium hydroxide (NaOH, >98% purity), ethanol, (99% purity), and methyl orange (MO, 85% purity) were purchased from Sigma Aldrich (St. Louis, MO, USA). All the solutions were prepared in distilled water.

2.2. Synthesis of Fe-Ni-Co Nanocomposite

The nanocomposite was synthesized by the co-precipitation method, as shown in Figure 1. The co-precipitation method is better than other methods due to its cost effectiveness and high yield. The other advantages include desirable morphology and particle size [20]. The solutions were prepared by dissolving FeCl₃ (1.62 g), NiCl₂ (1.295 g) and CoCl₂ (2.3 g) in 100 mL of distilled water. To maintain the pH up to 10, a 0.1 M solution of NaOH was added to the above mixture and it was stirred overnight at 50 °C. The precipitates that formed were then centrifuged and washed with ethanol and water mixture (2:1). The synthesized nanocomposite was then dried in the muffle furnace, ground with a pestle and mortar for further use, and stored in sealed crucibles.

2.3. Material Characterization

Scanning Electron Microscopy (JEOL, JSM 6490A, Tokyo, Japan) was carried out to observe the surface morphology of the prepared composite. Prior to analysis, the sputtering technique was used to remove the volatile substances and moisture content present in the sample. Furthermore, the elemental configuration was confirmed by Energy Dispersive X-ray Spectroscopy (JEOL, EDS 2300). The X-ray diffraction (XRD, Theta/Theta STOE & Cie Gmbh, Darmstadt, Germany) pattern was recorded with a powder diffractometer 186 (D5000, Siemens; Cu-Ka1, 2 radiations, Dresden, Germany) in the 2θ range of 10–80° with a step size of 0.025° (0.4 s/step). Fourier Transform Infrared Spectroscopy was performed in the range of 400–4000 cm⁻¹ to study the functional groups.

2.4. Optimization of Experimental Parameters Using Response Surface Methodology

In this study, the response surface methodology (RSM) was utilized for the optimization of various parameters using the Design Expert software, version 7.0 [26,27]. Central composite design (CCD) has been used in the current study. In CCD, N = 2k + 2k + cp, in which K is the factor number while cp indicates the central point replica. The effects of six independent variables, including pH, temperature, catalytic dose, dye concentration, hydrogen peroxide concentration, and irradiation time, have been studied for MO degradation. A set of 30 runs were carried out and the obtained response was analyzed for regression and other statistical analysis. The experimental design and ranges are given in

Table 1. Experimental ranges and level of independent variables.

Variables	Ranges and Levels
	−α (−2)	−1	0	1	+α (+2)
Dye concentration (mg/L)	2.17	5	10	15	17.82
pH	2.0872	3.5	6	8.5	9.91
Irradiation time (min)	0.22	20	55	90	109.77
Catalytic dose (g/L)	0.03	0.2	0.5	0.8	0.96
Hydrogen peroxide (mg/L)	−0.29	0.1	0.8	1.5	1.89
Temperature (°C)	17.93	25	37.5	50	57.06

.

2.5. Photocatalytic Degradation

The efficiency of the prepared nanocomposite was studied for the degradation of methyl orange (MO) using Fe-Ni-Co (0.2–0.8 g/L) at optimized values obtained from the RSM-based experimental design. The pH of the solution was varied from 3.5 to 8.5. To adjust the pH of the solution, 0.1 M HCl and NaOH solutions were used. Prior to the photocatalytic reaction, the dye solution (5–15 mg/L) was stirred in the dark for 20 min to obtain an absorption–desorption equilibrium and then exposed to visible light. The change in the color of the mixture was analyzed using a UV–Visible spectrophotometer (T-60U PG Instruments, Alma Park, Leicestershire, UK), with the maximum absorbance value at 460 nm. To avoid errors, experiments were performed three times, and the percentage degradation was calculated using the formula given below:

$$\%degradation={\displaystyle \frac{C_0-C_t}{C_0}}\times 100$$

(1)

where C₀ and C_t are the initial and final absorbance value, respectively.

3. Results and Discussion

3.1. Material Characterization

3.1.1. Morphological and Elemental Characterization

For the morphological studies of the synthesized Fe-Ni-Co-based trimetallic nanocomposite, SEM was carried out. SEM images were taken at 200 and 500× magnification, as shown Figure 2a,b. The agglomerated particles can also be seen in high resolution images (10 kx and 50 kx) in Figure 2c,d. A high surface energy can cause nanoparticle agglomeration, as particles try to reduce their total energy by coming in to contact with one another, which leads to lower catalytic activity. Figure 2d illustrated the average particle size, ranging from 39 to 189 nm.

The synthesized nanomaterials were characterized for chemical analysis by EDS. In Figure 3a, the EDS-layered image shows the uniform distribution of the metals Fe, Ni and Co in nanocomposites. The separate diagram for each element including Fe, Ni, and Co is shown in Figure 3b–d, respectively. The homogeneous distribution of Fe, Ni, and Co in a trimetallic composite can result in synergistic effects, in which the presence of three metals improves catalytic activity in comparison to the sum of the individual metal components. The spectra of Figure 3e confirm the presence of all constituting elements (Fe, Ni, and Co) in the nanocomposite. The atomic and weight percentage of the synthesized nanocomposite are given in Figure 3f.

3.1.2. XRD and FTIR Analysis

The X-ray diffraction (XRD) pattern was recorded with a powder diffractometer (D5000, Siemens; Cu-Ka1, 2 radiations) in the 2θ range of 10–80° with a step size of 0.025° (0.4 s/step), as shown in Figure 4. The assignment of the peaks has been carried out with reference to XRD patterns of Fe, Ni and Co nanoparticles [28,29,30]. The diffraction peaks at 44° and 50.62° correspond to the (111) and (200) planes of FeNi_4.34, respectively, which correspond with FeNi JCPDS # 03-1109 [31]. All the observed XRD peaks positions were compared with JCPDS data (NiFe₂O₄, JCPDS no. 54-0964; CoFe₂O₄, JCPDS no. 22-1086; γ-Fe₂O₃, JCPDS no. 39-1346) [32]. The observed XRD pattern showed the amorphous nature of the synthesized trimetallic composite.

In the FTIR analysis (Figure 5a,b), several absorption bands were observed that provide valuable insights into the composition of the synthesized nanoparticles. Initially, a broad absorption band at 3332.5 was observed, which may be attributed to stretching vibrations in the –OH group. This peak may arise due to the absorbed moisture on the catalyst surface when FTIR analysis was carried out [33,34]. The absorption peak at 1630 corresponds to the stretching vibration peak in C=O. Some additional peaks were also observed at around 500 to 400 cm⁻¹, which are related to the metal oxide bond which belongs to the Co-O, Ni-O and Fe-O vibrations. A lower frequency band at 415 cm⁻¹ is assigned to the stretching mode of the M–O bond in the octahedral sites. Peaks at 482, 424 cm⁻¹ are assigned to Ni-O and peaks at 446 and 439 cm⁻¹ are assigned to the Fe-O bond [34,35].

3.2. Mechanism of Photocatalytic Degradtion

When light falls on the nanocomposite, the photons that have energy greater than the band gap get absorbed into the valence band, which causes the excitation of the electrons from the valance band to the conduction band, leaving behind holes in the valence band. The holes cause the splitting of water molecules and generate hydroxyl radical HO•; the electrons in the conduction band reduce the oxygen into superoxide anion $(O_2^{\bullet -}$). These photogenerated electron–hole pairs move to the catalytic surface of the nanocomposite, where they cause the degradation of the adsorbed dye into the non-toxic gasses [36]. The schematic representation of photocatalysis is illustrated in Figure 6.

The mechanism of photocatalytic degradation is shown below:

$$\mathrm{Fe}-\mathrm{Ni}-\mathrm{Co}\mathrm{composite}+\mathrm{hv}\to \mathrm{nanocomposite}(h^{+}+e^{-})$$

(2)

The positively charged holes and electrons caused the respective oxidative and reductive reactions.

Oxidative reactions

$$h^{+}+H_{2}O\to H^{+}+OH·$$

(3)

$$h^{+}+{OH}^{-}\to OH·$$

(4)

$$h^{+}+dye\to {dye}^{\dot{}+}(\mathrm{degradation} \mathrm{of} \mathrm{the} \mathrm{dye})$$

(5)

Reductive reaction

$$e^{-}+O_2\to {O^{\bullet -}}_2$$

(6)

2e⁻ + O₂ + 2H⁺→H₂O₂

(7)

e⁻ + H₂O₂→OH⁻ + OH∙

(8)

The OH· (hydroxyl radical), being a very strong oxidant, caused the complete or partial degradation of organic pollutants.

$$\mathrm{OH}·+dye\to dyeintermediates\to C{O}_2+H_{2}O$$

(9)

Hence, the efficient photocatalytic degradation of organic dyes occurs without causing any harm to the environment.

3.3. Photocatalytic Degradation

The central composite design has been employed to study the effect of the six independent variables on dye degradation at optimum conditions. The quadratic model second-order polynomial equation has been utilized to show the relationship between the independent variable and response. The central composite design (CCD) matrix generates 33 runs for the above experimental conditions; MO degradation was in correspondence with the degradation predicted by the model given below in

Table 2. Experimental results of photocatalytic degradation.

Std	Run	Dye Conc. (A)	Time (B)	pH (C)	Dose (D)	Peroxide (E)	Temp. (F)	Response Degradation (%)	Predicted%
1	12	15.00	90.00	8.50	0.80	0.10	25.00	75	75.62
2	1	15.00	90.00	8.50	0.20	1.50	25.00	50	50.62
3	8	15.00	90.00	3.50	0.80	0.10	50.00	76	76.62
4	31	15.00	20.00	8.50	0.20	1.50	50.00	68	68.62
5	26	5.00	90.00	3.50	0.80	1.50	50.00	70	70.62
6	13	15.00	20.00	8.50	0.80	0.10	50.00	45	45.62
7	6	5.00	90.00	8.50	0.20	0.10	25.00	44	44.62
8	23	15.00	90.00	3.50	0.20	1.50	50.00	54	54.62
9	16	15.00	20.00	3.50	0.80	0.10	25.00	80	80.62
10	33	5.00	20.00	8.50	0.20	0.10	50.00	81	81.62
11	5	5.00	90.00	3.50	0.20	0.10	50.00	65	65.62
12	29	15.00	20.00	3.50	0.20	1.50	25.00	53	53.62
13	25	5.00	20.00	3.50	0.80	1.50	25.00	35	35.62
14	14	5.00	20.00	8.50	0.80	1.50	50.00	76	76.62
15	28	5.00	90.00	8.50	0.80	1.50	25.00	74	74.62
16	22	5.00	20.00	3.50	0.20	0.10	25.00	54	54.62
17	17	12.17	55.00	6.00	0.50	0.80	37.50	80	77.97
18	11	17.83	55.00	6.00	0.50	0.80	37.50	81	78.97
19	7	10.00	0.20	6.00	0.50	0.80	37.50	78	75. 97
20	27	10.00	109.8	6.00	0.50	0.80	37.50	30	27.97
21	9	10.00	55.00	2.09	0.50	0.80	37.50	45	42.97
22	4	10.00	55.00	9.91	0.50	0.80	37.50	56	53.97
23	18	10.00	55.00	6.00	0.03	0.80	37.50	23	20.97
24	32	10.00	55.00	6.00	0.97	0.80	37.50	82	79.97
25	3	10.00	55.00	6.00	0.50	0.30	37.50	80	77.97
26	2	10.00	55.00	6.00	0.50	1.90	37.50	65	62.97
27	24	10.00	55.00	6.00	0.50	0.80	17.94	81	78.97
28	21	10.00	55.00	6.00	0.50	0.80	57.06	59	69.97
29	10	10.00	55.00	6.00	0.50	0.80	37.50	59	69.88
30	15	10.00	55.00	6.00	0.50	0.80	37.50	59	61.88
31	30	10.00	55.00	6.00	0.50	0.80	37.50	59	61.88
32	19	10.00	55.00	6.00	0.50	0.80	37.50	59	61.88
33	20	10.00	55.00	6.00	0.50	0.80	37.50	59	61.88

.

The effect of the initial concentration of MO on its percentage degradation was studied using the Fe-Ni-Co-based nanocomposite. The results were plotted in the form of 3D graphs by RSM. The effect of the initial concentration was studied against each parameter (time, pH, dose, peroxide concentration, and temperature), keeping other parameters constant, as shown in Figure 7a–e. The degradation rate decreased with time by increasing the initial dye concentration from 5 to 15 mg/L, as shown in Figure 7a. This might be attributed to the lower availability of active sites, as the catalyst dose also varies in this case [37]. This inverse relation can also be explained in this context by highlighting that the number of dye molecules increased by increase in concentration, in contrast to the generation of hydroxyl radicals. Thus, the limited hydroxyl radicals would not participate in further degradation. Also, more dye molecules would cause hindrance to the light responsible for initiating the reaction on the catalyst surface [38]. The significant degradation at the higher initial concentration might be attributed to the other factors involved in degradation. Figure 7b shows the effect of pH with increasing initial dye concentration. MO, being an anionic dye, achieves maximum degradation in acidic conditions where a positive charge takes up position on the surface of the catalyst [39]. However, the increase in degradation up to pH 6 is associated with the catalytic surface charge. Significant degradation is observed when increasing the catalyst dose and initial dye concentration, as shown in Figure 7c. The availability of the catalyst dose is responsible for the adsorption of more dye molecules on available active sites. Figure 7d shows the effect of the initial dye concentration and hydroxyl radicals on MO degradation. In contrast to the phenomenon (a decrease in dye degradation with an increase in initial concentration), the presence of hydroxyl radicals significantly increased the degradation rate, as expected. These results are rational enough to confirm the positive influence of hydroxyl radicals on photocatalytic activity [40]. The increase in temperature favors the significant degradation, even at a lower dye concentration, as shown in Figure 7e, which might be attributed to the intensified molecular movement followed by the chemical oxidation of dye molecules at higher temperatures [41].

Reaction time has a significant impact on MO degradation in response to other factors such as solution pH, dye concentration, catalyst dose, and the amount of hydrogen peroxide. Figure 7f shows significant degradation even in shorter irradiation times. However, the degradation increases with an increase in pH and irradiation time. These findings are supported by previous studies, where a shift in pH might affect the characteristics of a photocatalyst both in terms of adsorption behavior and surface charge. Gherbi et al., 2022 synthesized ZnO nanoparticles at different pHs, and an increase in size was observed towards acidic pH that lead to poor adsorption behavior [42]. The surface of the nanocomposite is positively charged at pH < 6.5 in acidic conditions due to the absorption of H^+ions, and at high pH > 7 due to the absorption of OH⁻ions on the catalyst surface. Two reaction mechanisms, such as spontaneous oxidation by positive holes and direct reduction by electrons, influence the photodegradation efficiency of the MO dye, which is hindered by its anionic or cationic form [43]. Similarly, MO degradation is reported in the literature to increase as the pH increases from 4 to 7, as observed by [44]. Figure 7g–i shows the effect of irradiation time along with catalyst dose, hydrogen peroxide and temperature on MO degradation. The increase in catalyst dose and the presence of hydrogen peroxide concentration dramatically increases the degradation; however, we could not observe a drastic change in degradation with temperature. This is due to the fact that increasing the temperature typically increases the kinetic energy of the reactant molecules, which can speed up the reaction process. This effect, however, only applies up to a certain temperature, after which the rate of reaction can slow, due to nanoparticle deactivation at high temperatures because of thermal structural instability or an increased agglomeration at high temperature [45]. Figure 7j shows the simultaneous effects of dose and pH on the photocatalytic degradation of MO. The catalyst dose in the range of 0.2–0.8 g/L has been used to find the optimum dose. It is determined that the rate of degradation increases with the increase in catalyst dose, irrespective of pH, because the increase in catalyst loading is responsible for the availability of more active sites for dye degradation. The increase in degradation is facilitated by the availability of hydrogen peroxide.

One of the most popular electron acceptors, hydrogen peroxide, enhances the photocatalytic process by raising the level of hydroxyl radicals in the surrounding environment. When added to the solution, it engages in electron-capture competition with other species. However, since it is a potent substance, it is more likely that electrons will be attracted to this substance. As a result, the efficiency of the process will be improved, and the electron–hole recombination will be decreased [46]. H₂O₂ captures electrons and prevents electron–hole recombination through the following reactions:

$$H_2{O}_2+e_{CB}^{-}\to O{H}^{\bullet }+O{H}^{-}$$

(10)

$$H_2{O}_2+hv\to 2O{H}^{\bullet }$$

(11)

$$H_2{O}_2+O_2^{-\bullet }\to O{H}^{\bullet }+O{H}^{-}+O_2$$

(12)

Figure 7k,l shows the positive effect of peroxide concentration and temperature on MO degradation due to the charge carrier mobility and interfacial charge transfer for the effective adsorption of dye molecules on the catalyst surface [47]. The elevated temperature improves this phenomenon due to increases in the kinetic energy of the reactant molecules. Also, phase composition and crystallite size are temperature dependent. At different temperatures, nanoparticles with different phase compositions can be obtained with varying photocatalytic efficiency. Furthermore, high temperature leads to nanoparticles with high surface area, which result in agglomeration and thus decrease photocatalytic efficacy [48].

Figure 7m,n demonstrates the maximum significant degradation (+82%) of MO, where catalyst dose has a positive effect on the presence of temperature and peroxide concentration. These results are in accordance with previous reasoning, where the addition of H₂O₂ improved process effectiveness in less time, and an increase in temperature also increased degradation activity. These results have a significant effect due to the presence of a catalyst dose (more available active sites). Figure 7o shows the positive effect of temperature with respect to the peroxide concentration responsible for the hydroxyl radicals, a strong oxidant which reduces electron–hole recombination and significantly increases the percentage degradation [49].

In this study, six independent variables are optimized using RSM. As per model validation, the maximum 81% degradation in 20 min was observed at the lowest initial concentration (5 mg/L) with 0.20 g/L catalyst dose, and 0.10 mg/L H₂O₂ concentration at 50 °C and pH 8. Similar removal efficiency was observed when we performed experiments under these optimal conditions, demonstrating the CCD model’s applicability and success in process optimization. Based on the experimental results and software validation, the optimal conditions are defined in terms of efficiency, time, and cost effectiveness. A comparison of the current work with the literature studies is given in

Table 3. Comparison of current work with literature.

Photocatalyst	Experimental Conditions				Degradation Efficiency	References
	Light Source	Catalyst Dose	Target Pollutant Concentration	Time
Ag2O/MgO/GO nanocomposites	UV light lamp (P = 25 W, λ = 365 nm) and visible light lamp (P = 400 W Xe lamp, λ > 410.0 nm).	0.1 g/L	Rhodamine B (Rh-B) 30 ppm, 50 mL)	60 min	UV (98.8%), visible light (64.8%)	[50]
Fe-NiO nanoparticles	Visible light produced by Xenon lamp, λ = 400 nm)	10 mg/100 mL	Rhodamine B (Rh-B) 40 ppm, 100 mL)	40 min	99% degaradtion with 8% of Fe-doped NiO	[20]
Co-CeO2 nanoparticles	Visible light (300 W)	1, 4, 8, and 12% cobalt-doped CeO2 (5 mg)	Methylene Blue (10 ppm, 50 mL), 4-nitrophenol (10 ppm, 50 mL)	5 h	>40% removal for both pollutants for 12% doped CeO2	[51]
Fe/Cu/Ag nanoparticles	In an open-batch system at room temperature	4, 7, 10 mg	Methyl orange (10, 25, 50, 100, 200 ppm, 50 mL)	1, 2, 3, 4, 5, 10, 15, 20, and 30 min	100% degradation in 1 min at 10 ppm dye, 10 mg catalyst and pH 3	[52]
Cu-Co-Ni trimetallic nanoparticles	UV light Xenon (Xe) arc lamp (150 W) with irradiation intensity of 50 mW/cm2	30 mg	Methylene blue (100 ppm, 30 mL)	100 min	92.67% degradation	[53]
Cd(II)-based coordination polymer with the formula [Cd(bpyp)(nba)2] denoted as CP 1	Mercury lamp (100 W, λ = 365 nm)	0.01 mmol	Methyl blue, Methyl orange, Rhodamine B and methyl violet (20 ppm, 50 mL)	9 min	RhB (95.52%) MV (58.92%) MO (35.44%), and MB (29.24%)	[54]
Fe-Ni-Co-based trimetallic nanocomposites	In an open-batch system at room temperature	0.2–0.8 g/L	Methyl orange (5–15 ppm)	20 min	81% degradation	Present work

.

3.4. RSM Analysis and ANOVA

The RSM was used to analyze the effect of operational parameters on the degradation efficiency of the nanocomposite in optimum conditions. According to ANOVA, the highest F-value was found for the catalytic dose, i.e., 89.18 and its p value < 0.0001, which shows that the catalyst amount has a great influence on the degradation of the dye. Similarly, the reaction time, with an F-value of 59.32, is also a major influencing variable. The best fitted model was the quadratic model which optimized the parameters. Based on the results, the model equation was derived as follows:

% degradation = −65.98367 − 2.01802A − 0.085802B + 37.80808C +139.79147D − 93.44843E + 1.50483F
−7.14286 × 10⁻⁴ AB − 0.38000AC − 1.52805AB + 4.27823AE − 0.10000AF − 0.082860BC + 0.60714BD +
0.040816BE + 4.31814 × 10⁻³ BF − 0.66667CD + 3.07143CE − 0.52394CF − 0.92606DE − 1.06667DF + 0.30000EF
+ 0.27070A² − 3.28694 × 10⁻³ B² − 0.87678C² − 51.81528D² + 7.14610E² + 0.032861F².

Through analysis of variance (ANOVA test), the RSM model suitability was checked [55]. The model’s F-value of 15.44 implies the model is significant. Values of “Prob > F” less than 0.0500 indicate the model terms are significant. In this case, B, D, AC, AE, AF, BC, BD, CE, CF, DF, A², B², C², D², E², F² are significant model terms.

Values greater than 0.1000 indicate the model terms are not significant. So, the lack of fit is non-significant. The recommendation is a minimum of 3 lack of fit df and 4 df for pure error. This ensures a valid lack of fit test. The value of the coefficient of variation % is 7.05. If its value is below 10, this means that the result of our experiment and our experimental design work efficiently. If its value is higher than 10, then it indicates that our model is not fit for the experiment.

The fit of the model was qualified by the estimated higher determination coefficient (R² = 0.9881) which indicated that the model could be used as a predictor of the response. The predicted responses were in good agreement with the experimentally obtained response (R² = 0.9881 and Adj R² = 0.9241). The ANOVA result has been shown in the following table,

Table 4. ANOVA and lack of fit for response surface quadratic model.

Source	Sum of Square	df	Mean Square	F Value	p-Value Prob > F
Model	8135.45	27	301.31	15.44	0.0032
A-dye concentration	0.50	1	0.50	0.026	0.8791
B-time	1157.77	1	1157.77	59.32	0.0006
C-pH	60.50	1	60.50	3.10	0.1386
D-dose	1740.50	1	1740.50	89.18	0.0002
E-per oxide	112.50	1	112.50	5.76	0.0616
F-temp	40.50	1	40.50	2.08	0.2093
AB	0.25	1	0.25	0.013	0.9143
AC	361.00	1	361.00	18.50	0.0077
AD	19.70	1	19.70	1.01	0.3611
AE	840.94	1	840.94	43.09	0.0012
AF	625.00	1	625.00	32.02	0.0024
BC	197.15	1	197.15	10.10	0.0246
BD	650.25	1	650.25	33.32	0.0022
BE	16.00	1	16.00	0.82	0.4067
BF	13.39	1	13.39	0.69	0.4453
CD	4.00	1	4.00	0.20	0.6697
CE	462.25	1	462.25	23.69	0.0046
CF	1005.44	1	1005.44	51.52	0.0008
DE	0.14	1	0.14	7.268 × 10−3	0.9354
DF	256.00	1	256.00	13.12	0.0152
EF	110.25	1	110.25	5.65	0.0634
A2	608.44	1	608.44	31.18	0.0025
B2	215.38	1	215.38	11.04	0.0210
C2	398.93	1	398.93	20.44	0.0063
D2	288.90	1	288.90	14.80	0.0120
E2	162.89	1	162.89	8.35	0.0342
F2	350.24	1	350.24	17.95	0.0082
Residual	97.58	5	19.52
Lack of Fit	97.58	1	97.58
Pure Error	0.000	4	0.000
Cor Total	8233.03	32
R2 = 0.9881			Adj R2 = 0.9241

.

3.5. Adequacy of Regression Model

A normal probability plot is a statistical test to see if the data points fall within the 95% confidence interval [26]. The predicted and the experimental values of degradation lie close to each other. Figure 8 shows strong correlation and the accuracy of the model. Since all the data points lie on the central line it shows the validity of the CCD.

4. Conclusions

Overall, this research on nanocomposite photocatalysts for the degradation of pollutants like MO contributes to the advancement of sustainable wastewater treatment technologies by offering efficient, cost effective, and environmentally friendly solutions. The Fe Ni-Co-based nanocomposite of trimetallic oxides was effectively used for the degradation of MO. RSM has been employed to optimize all the parameters using CCD. Six operational parameters, i.e., temp, time, dose, dye concentration, peroxide concentration, and pH, have been optimized by expert design software. The quadratic model was the best fit model to check the significance of the experimental design. Dye degradation has been achieved up to 81% in 20 min at the lowest initial concentration (5 mg/L) in optimized conditions (0.20 g/L catalyst dose, 0.10 mg/L H₂O₂ concentration at 50 °C and pH 8). Based on ANOVA, the predicted values were in great agreement with the actual values, signifying the applicability of RSM in the photocatalytic decolorization of dyeing effluents. A similar removal efficiency was observed when we performed experiments under these optimal conditions, demonstrating the CCD model’s applicability and success in process optimization. Based on the experimental results and software validation, the optimal conditions are defined in terms of efficiency, time, and cost effectiveness. Among the six experimental parameters, catalytic dose has a great influence over the degradation rate, and the increased amount of catalytic dose provides more active sites for the MO adsorption; however, a higher amount beyond the optimum level would decrease the efficiency. Nanotechnology, being an innovative approach for the degradation or removal of environmental pollutants from wastewater, is still being developed. Based on the results gained from this research, this synthesis method of a trimetallic nanocomposite (Iron Triad) is a cost-effective and energy efficient method that can be scaled up to a higher level for industrial application.