## Characterization of Ag2WO4

Microscopic approach was used to analyze the morphology of as-synthesized Ag2WO4. Fig. 2 (a) and (b) shows the FESEM micrographs, captured at x2,500 and x5,000 magnifications, respectively. It is evident that Ag2WO4 has attained flower-like structures. Further, TEM was used to examine the detailed physical entity of Ag2WO4. The TEM micrographs of Ag2WO4 are shown in Fig. 2 (c) and (d) confirming the flower-like structures.

EDX analysis was used to determine the material's elemental composition. Fig. 2 (e) along with the inset table presents the percent weight of each element in the material. Notably, silver (Ag), oxygen (O), and tungsten (W) are showing respective peaks that correspond to their respective K shell, L shell, and K shell transitions, respectively. The sample comprises 45.6% Ag, 17.8% O, and 36.6% W, with atomic percentages of 64.1%, 24.4%, and 11.5%, respectively. The EDX results highlight the successful synthesis of Ag2WO4 with no additional trace elements.

The crystallographic data were collected using XRD, with the Cu K-radiation assisting the 2θ angular measurements between 10° and 80° at a scan rate of 2°/min. The XRD pattern for as-synthesized Ag2WO4 nano-flowers is shown in Fig. 3 (a). The intense peaks at 29.28°, 31.49°, 35.61°, 44.48°, and 53.03° were similar to those in JCPDS file Number 861157 and earlier literature (Roca et al. 2017). The Ag2WO4 produced has high crystallinity, as evidenced by the narrow and sharp diffraction pattern.

Further, Ag2WO4 was studied by FTIR, and the spectrum is shown in Fig. 3(c). The broad peak at 3781.73 cm−1 may be assigned to the stretching vibration of the O-H bond due to surface hydration. The peak at 1604.69 cm−1 corresponds to W-O-H bending. Two peaks at 784.11 cm−1 and 1102.30 cm−1 were due to the bridging stretching modes of W-O and W-O-W, respectively. An intense peak at 471.08 cm−1 was observed, which was ascribed to the characteristic asymmetric stretching vibration of O-W-O bonds within the distorted [WO6] clusters. The peak at 784.11 cm−1 was attributed to the stretching modes of W-O in WO6 octahedra (Nubla and Sandhyarani 2020; Elgorban et al. 2021).

The UV–Vis spectra revealed the light scattering capability, which was used to determine the bandgap of as-prepared semiconductor material (Makuła et al. 2018). The UV–Vis spectra were obtained in the range of 280–800 nm as shown in Fig. 2 (c). The optical bandgap (Eg) was found to be 2.8 eV, which could be compared to previous studies (Shi et al. 2016; Andrade Neto et al. 2020). Eq (2) was used to compute Eg, where α is the absorption coefficient, *h* is the Planck constant, υ is the frequency, and *hυ* is the incident photon energy. The curve of αhυ versus hυ for Ag2WO4 is shown in Fig. 2 (c).

$${\left(\alpha h\upsilon \right)}^{2}=A(h\upsilon -{E}_{g})$$

2

## Response Surface Modeling For 2,4-dcp Degradation

RSM analysis was conducted to optimize degradation at each design level using four different variables and seven different tests. Over the course of each experiment, the phenol residual concentration was calculated at regular intervals, and The phenol degradation rate was calculated using the final data (percent). The response factor was 2,4-DCP degradation (percent), and the calculated data over multiple variable-level sequences were statistically behaved to create the model. The connection between predicted and actual data, as shown in Fig. 4, is used to determine if the model is significant or insignificant for phenol removal. The anticipated R² for phenol degradation is 0.98. The model's relevance is also checked in terms of F-value, P-value, and sufficient precision. The root mean square error is 6.7951, and the probability value is 0.0472.

Table 3

Analysis of variance report

Source | DF | Sum of Squares | Mean Square | F Ratio |

Model | 4 | 3773.0824 | 943.271 | 20.4290 |

Error | 2 | 92.3462 | 46.173 | **Prob > F** |

C. Total | 6 | 3865.4286 | | 0.0472* |

The total sum of squares is the sum of the squared differences between the response values and the sample mean. It refers to the entire spectrum of possible responses (3773.0824). The erroneous sum of squares is the sum of squared differences between the fitted and actual values. It illustrates the variability that the model is unable to account for (92.3462). The model sum of squares is the difference between the total sum of squares and also the error sum of squares, and it represents the variability explained by the model. In this case, the model's explained variability is 3773.0824, which is much higher than the 92.3462 that remains unexplained. The sum of squares divided by the relevant degree of freedom yields the mean square.

The F ratio is a statistical test that compares the error mean squares of the model with the error mean squares of the model. The F test has a p-value of Prob. > F. The p-value is used to determine the statistical significance of a finding under a null hypothesis. The p-value indicates the possibility of getting an F ratio as high as that observed. In other words, a low p-value suggests that if the null hypothesis were true, such an extreme observed outcome would be exceedingly implausible. The Prob. > F is less than 0.0001, indicating that the model has at least one significant effect.

The graphs of studentized residuals vs. run number and anticipated degradation, respectively, are shown in Fig. 5 (a) and (b). The residuals vs. run graph was quite helpful. lurking variables that may have influenced the response during experimental work were used to examine the acceptability of a constant variance plot of residuals vs. predicted values. As seen in Fig. 5 (b), they do not develop any evident pattern containing an abnormal structure. In addition, the expression of equal scatters down the x-axis demonstrates the sufficiency of the proposed model.

**Effects of independent operating parameters on pollutant removal.**

To visualise the influence of the independent operating factors on 2,4-DCP degradation, three-dimensional (3D) response surfaces and contour plots were developed. Overall, the effective degradation of 2,4-DCP was observed with the higher dosage of photocatalyst, lesser pollutant concentration in alkaline medium and the study carried out for the longer duration. In addition, 85% removal was observed with lesser contact time in acidic pH with higher dosage of catalyst. On the other hand, minimal removal efficiency was noted with the increased pollutant and photocatalyst dosage in the alkaline pH for lesser contact time. Fig. 6, 7, and 8 represents the 3D responses in the form of contour plots.

Figure 6 (a) is the 2,4-DCP removal as a function of catalyst dose and the pollutant concentration. It is seen that 91% degradation was achieved when the photocatalyst concentration was higher and the initial pollutant concentration was lesser. At a higher dosage of photocatalyst and pollutant concentration the degradation efficiency of 2,4-DCP was lesser. Figure 6 (b) shows the influence of pH and pollutant concentration on the photodegradation of 2,4-DCP. It is well established that pH between 5-8.5 and lower pollutant concentrations (10-45 ppm), there would be maximum mineralization of 2,4-DCP (>80%). Conversely, in alkaline medium with higher contaminant load, the degradation rate decreases obviously.

The response simulated for the variation in pH and contact time is shown in Fig. 7 (a). Uniquely, the percentage degradation of 2,4-DCP reaches above 85% at two distinct operating conditions. Firstly, at pH of 8.5-10.0 and longer contact time of 7-8 h, the degradation observed attains around 85%. Secondly, the degradation reaches 80-85% at lower pH (5-6) for shorter contact time, as well as at neutral pH (7.5) for average run time. Hence, there exist a positive effect of pH on both pollutant concentration and contact time. Fig. 7 (b) illustrates the 3D response as the function of contact time and catalyst dosage. The simulated response unveil that, maximum degradation of the target is with the higher catalyst load and extended reaction time (Nourieh et al. 2020).

Figure 8 (a) notify on the variation of 2,4-DCP removal as a function of pollutant concentration and contact time. It is straightforward that, for a broad range of pollutant concentration (10-65 ppm) and contact duration of 4-8 h, approximately 80% or higher rate of degradation would be accomplished. Hence, the present system driven by Ag2WO4 stands potential under sunlight to treat 2,4-DCP. Finally, Figure 8 (b) represents the probable degradation with the influence of catalyst dosage and pH of reaction solution. The degradation percentage would reach above 80% at moderate pH of 5-6 for higher catalyst loading (60-100 mg).

## Photocatalytic Mechanism

The 2.4-DCP undergoes systematic breakdown under the action of sunlight and the Ag2WO4 catalyst. The typical photo-activation of the catalyst takes place upon absorbing suitable photons (energy > Eg of Ag2WO4, 2.8 eV). The redox reactions at the conduction and valence bands of Ag2WO4 generates reactive oxidants, that act on the 2,4-DCP to disintegrate the molecule, and finally forming simple organic compounds like water and carbon dioxide (Ali et al. 2020). The specific chemical reactions are represented in Eq 3-6. The Fig. 9 shows the schematic of photodegradation of 2,4-DCP using Ag2WO4.

$${Ag}_{2}W{O}_{4}+h\nu \to {Ag}_{2}W{O}_{4}+{h}^{+}+{e}^{-}$$

3

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

4

$${e}^{-}+{O}_{2}\to {O}_{2}^{-}\to {OH}^{-}$$

5

$${e}^{-}or {h}^{+}or {O}_{2}^{-}+\text{2,4}-DCP\to Intermediate compounds+C{O}_{2}+{H}_{2}O$$

6

Further, the possible degradation pathway includes the dehalogenation and ring fragmentation, forming few intermediates and finally resulting in chlorine ions, water, and carbon dioxide (Ai et al. 2019). Specifically, the 2,4-DCP adsorbed on the surface of Ag2WO4, firstly undergoes dechlorination to form ortho- and para-chlorophenols, and, phenol. Subsequently, these are reduced to para-benzoquinone, which in turn decomposes into benign intermediates and ions (Rakibuddin and Ananthakrishnan 2016).