Analysis of Photocatalytic Degradation of Phenol with Exfoliated Graphitic Carbon Nitride and Light-Emitting Diodes Using Response Surface Methodology

Abstract

Response surface methodology (RSM) involving a Box–Benkhen design (BBD) was employed to analyze the photocatalytic degradation of phenol using exfoliated graphitic carbon nitride (g-C₃N₄) and light-emitting diodes (wavelength = 430 nm). The interaction between three parameters, namely, catalyst concentration (0.25–0.75 g/L), pollutant concentration (20–100 ppm), and pH of the solution (3–10), was examined and modeled. An empirical regression quadratic model was developed to relate the phenol degradation efficiency with these three parameters. Analysis of variance (ANOVA) was then applied to examine the significance of the model; this showed that the model is significant with an insignificant lack of fit and an R² of 0.96. The statistical analysis demonstrated that, in the studied range, phenol concentration considerably affected phenol degradation. The RSM model shows a significant correlation between predicted and experimental values of photocatalytic degradation of phenol. The model’s accuracy was tested for 50 ppm of phenol under optimal conditions involving a catalyst concentration of 0.4 g/L catalysts and a solution pH of 6.5. The model predicted a degradation efficiency of 88.62%, whereas the experimentally achieved efficiency was 83.75%.

1. Introduction

For all living beings, water is considered to be the most important resource. Easy access to clean water is one of the biggest challenges for mankind. In the last few decades, advancements in science, technology, and industrialization have led to considerable benefits to mankind but at the cost of a more polluted environment, particularly water [1]. There are multiple categories of pollutants in water, such as heavy metals, dyes, pesticides, pharmaceuticals, and other organic pollutants. Amongst organic pollutants, phenolic compounds, with ~3 million tons of global production, are an emerging contaminant detected in water [1,2,3,4].

Phenols or phenolics are essential because of their wide range of applications in the processing and manufacturing industry. However, the ecosystem’s contamination by phenolics is concerning because of the adverse implications on human health such as their endocrine-disrupting abilities and carcinogenic behavior [1,5,6]. Moreover, these chemicals cause environmental issues such as water hardness, pH change, and a decrease in dissolved oxygen level. Furthermore, the Environmental Protection Agency (EPA) and the European Union (EU) have included a few phenols in their priority pollutants list. It is necessary to make this polluted water containing phenols and other pollutants suitable for human use and aquatic life using certain techniques to minimize the usage of these chemicals [5].

The removal of phenolic compounds from wastewater has attracted considerable attention from researchers [5]. Many biological, chemical, and physical techniques such as membrane filtration, coagulation–flocculation, adsorption [7,8], ion exchange, bacterial and fungal biosorption [9], aerobic and anaerobic processes [10] are used for phenol removal. In these processes, there are many constraints such as high cost, and low efficiency; furthermore, these methods do not completely remove phenol from wastewater [11,12]. Moreover, using these techniques, phenol is transferred from wastewater to a solid phase that requires treatment for safe disposal, which leads to additional cost for the whole process. Thus, it is necessary to develop an alternative effective and cost-efficient method for phenol removal from wastewater.

Advanced oxidative processes (AOP) are successful for achieving the complete removal of pollutants [13]. The degradation process using AOP can be performed in several ways, such as using only oxidizing agents, light irradiance in addition with oxidizing agents, and photocatalysis [14]. For all these processes, the degradation process is conducted using OH⁻ radicals that are generated during the oxidation reaction. Among these processes, photocatalysis has attracted considerable interest because it can harvest solar light with the help of semiconductor materials (catalysts). The catalysts can help solve environmental issues related to water contaminations; these semiconductor materials are nontoxic and efficient. Note that different semiconductor materials such as ZnO [15], TiO₂ [16], SiO₂, Al₂O₃ [8], and g-C₃N₄ [17,18], are used for environmental applications in photocatalysis; these have considerable advantages because of the large surface areas, adsorption capacities, and better absorption of light. Among these materials, g-C₃N₄ offers improved visible light absorption [17,19,20,21].

g-C₃N₄, a polymeric semiconductor, composed of C, N, and H, has gained considerable interest from researchers for novel generation of photocatalysts because of its widespread catalytic uses in oxidation and reduction processes, such as pollutant degradation, water splitting, and CO₂ reduction. These materials have been extensively used for environmental remediation because they are easy to synthesize, metal-free, inexpensive, and easily available [22,23,24]. Furthermore, g-C₃N₄ possesses higher thermal and chemical stability because of π-conjugated frameworks connecting the 2D layered structure of tri-s-triazine building blocks. g-C₃N₄ can be activated by visible light of 420–460 nm because of its low bandgap energy (2.7 eV) [25,26]. There are, however, certain challenges associated with the application of g-C₃N₄ in phenol removal such as low surface area, fast recombination rate, and low conductivity, thus resulting in lower efficiency. To overcome these limitations, multiple strategies have been used to improve the surface electronic structures and activity of the bulk g-C₃N₄ in visible light. To improve the activity of pristine g-C₃N₄, strategies such as metal and non-metal doping, exfoliation, hard and soft templating, and metal oxide heterojunctions have been used [27,28,29,30,31].

Factors affecting the removal efficiency can be tuned by the morphology and/or chemistry of the catalyst and by optimizing the operating parameters. Multiple operating parameters play an important role in the photocatalytic degradation process, thus making their optimization important for achieving good photocatalytic degradation of the target pollutant. Response surface methodology (RSM) is one of the most commonly applied optimization techniques; it is a powerful optimization tool for an experimental design that efficiently helps in systemic analysis [5,11,14]. RSM uses mathematics and statistics to analyze the relative significance of influencing factors on the response of the studied system. RSM is suitable for predicting the effect of individual experimental operating parameters, in addition to locating interactions between parameters and their impact on a response variable. RSM uses a systematic technique to simultaneously vary all parameters and evaluate the influence of these parameters on photocatalytic degradation [32,33]. The greatest advantage of RSM lies in the systematic approach for the experimental design, which mostly requires fewer experiments, thus reducing the time required and thereby being more economical. For designing these experiments, a central composite design (CCD) [3] and Box–Benkhen design (BBD) [11,12] are most commonly used. For the same number of parameters, BBD requires fewer experiments than CCD [3]; therefore, in this study, BBD is selected as a preferred design approach.

The objective of this study was to analyze the photocatalytic degradation of phenol with metal-free g-C₃N₄ and visible LED light and to model the process using RSM. In this study, the operating parameters considered were catalyst concentration, phenol concentration, and pH of the solution. BBD was used for the experimental design and RSM was applied to determine the mathematical relationship between operating parameters and phenol degradation. Finally, the correlation determined by RSM was experimentally validated.

2. Materials and Methods

2.1. Chemicals and Materials

Melamine (C₃H₆N₆, 99%) was purchased from Alfa Aesar. Phenol (C₆H₅OH, 99%) was purchased from Merck. Acetonitrile (C₂H₃N, 99.99%) and ultra-pure water for high-performance liquid chromatography (HPLC) were purchased from Sigma Aldrich. NaOH and HCl were purchased from VWR chemicals. All chemicals used were of analytical grade and used as-received without any further purification.

2.2. Photocatalyst Synthesis

Photocatalyst was prepared as per the procedure used in our previous study [18]; the synthesis process is briefly reported here. Melamine was placed in a muffle furnace (Carbolite Gero, GPC 1200, Derbyshire, UK) in a closed crucible to prepare bulk g-C₃N₄ using thermal decomposition. The synthesis process comprised two steps: A heating ramp rate of 2 °C min⁻¹ was programmed up to 450 °C; this temperature was maintained for 2 h. Then, the temperature was increased to 550 °C using a heating ramp rate of 2 °C min⁻¹ and then maintained for 4 h. The material synthesized was crushed in mortar after cooling, then rinsed with ultrapure water, and dried overnight at 80 °C. The exfoliation process was conducted in an open crucible at 500 °C for 2 h at a heating ramp rate of 2 °C min⁻¹ in a muffle furnace.

2.3. Characterization of the Photocatalyst

Fourier transform infrared (FTIR) measurements (4000–400 cm⁻¹) were performed on a Spectrum Two FT-IR Spectrometer (PerkinElmer, Switzerland) with a universal ATR (UATR Two) cell equipped with a ZnSe single crystal. The acquisition performed using 60 scans and the resolution was set to 4 cm⁻¹. Zetasizer Nano ZEN5600 (Malvern, UK) was used to measure the zeta potential of the synthesized material. SU8030 (Hitachi, Japan) SEM-type microscope operated at an acceleration voltage of 10 kV and a probe current of 15 pA was used to examine the morphology of the material with scanning electron microscopy (SEM).

2.4. RSM with Box–Behnken Experimental Design

The influence of three independent operating parameters, i.e., catalyst concentration (A), phenol initial concentration (B), and pH of the solution (C), was considered in RSM. The remaining reaction conditions, namely, the airflow rate (50 mL/min) and reaction time (3 h), was kept constant in the experiment based on previous study [18]. The degradation efficiency of phenol (Equation (1)) was set as a response variable. Note that a previous study [18] was conducted to obtain the upper and lower limits of the parameters.

Table 1. Independent parameters and their ranges and levels.

Independent Parameters	Symbol	Range and Level
		Low (−1)	Middle (0)	High (+1)
Catalyst concentration (g/L)	A	0.25	0.5	0.75
Phenol initial concentration (ppm)	B	20	60	100
pH	C	3	6.5	10

shows the ranges and levels of independent parameters A, B, and C. BBD was used to examine the combined effect of these three variables. Section 3.3 lists the set of experiments in table; it includes a replication of experiments at the central point. Regression analysis was the performed using OriginPro 2021 9.8.0.200 (OriginLab Corporation, Northampton, MA, USA) software. The suggested model’s data were analyzed for significance and suitability using analysis for variance (ANOVA).

2.5. Photocatalytic Experiments

Figure 1 shows the photocatalytic experiments that were conducted in a jacketed glass reactor (working volume 225 mL) (Peschl Ultraviolet GmbH, Mainz, Germany) with a safety cabinet. The reactor was irradiated from inside using a custom-made LED immersion lamp; the LED has maximum emission at 430 nm. Glass reactor was then sonicated with a reaction mixture for uniform dispersion, followed by stirring with continuous airflow to maintain adsorption–desorption equilibrium for 30 min. Subsequently, lights were turned on, which is considered as zero time (t_o). Nine to ten samples (1 mL) were periodically collected from the reaction mixture. After centrifugation and filtration, the samples were analyzed using HPLC. For acidic and basic reaction conditions, the pH of the mixture was adjusted using 0.1 M HCl and NaOH. The phenol degradation efficiency was determined using the following Equation:

$$Degradation efficiency (\%)=\frac{C_o-C}{C_o}\times 100$$

(1)

where $C_o$ is the initial phenol concentration and $C$ is the residual phenol concentration in the solution at an irradiation time t.

The reduction of the reaction mixture volume due to the sampling was less than 5% at the end of the experiments and was therefore not considered in the calculation of the phenol degradation efficiency.

2.6. Analytical Techniques

A prominence HPLC system from Shimadzu (Kyoto, Japan) was used for analyzing the samples obtained from the reactor. The system is equipped with a binary pump (Model LC-20AB), an autosampler (Model SIL-20A), a degasser (Model DGU-20A3,) and a diode-array detector (Model SPD-M20A). Phenomenex (C18, 150 × 4.6 mm, 3 µm) column was used with a fixed flow rate of 0.8 mL/min, with the mobile phase gradient of water (A) and acetonitrile (B): starts with 15% B, followed by 60% B in 7 min and back to 15% B in 8 min; injection of 5 μL; UV light of 254 nm. Phenol was analyzed at a maximum absorption wavelength (λmax) of 270 nm.

3. Results and Discussion

3.1. Photocatalyst Characterization

The metal-free g-C₃N₄ used in this study was synthesized and characterized in our previous study [18] using transmission electron microscopy (TEM), Brunauer–Emmett–Teller isotherms (BET), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), photoluminescence (PL), and UV-Vis spectroscopy. In this study, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and zeta potential analyses were performed.

Table 2. Summary of characterization results [18].

Characterization	Bulk g-C3N4		Exfoliated g-C3N4
BET	Surface area	Pore size	Surface area	Pore size
	11 m2/g	1.91 Å	170 m2/g	1.96 Å
XRD	Weak peaks (2θ)	Strong peaks (2θ)	Weak peaks (2θ)	Strong peaks (2θ)
	13.0°	27.2°	13.1°	27.4°
PL/UV-Vis	Max. absorption	Bandgap	Max. absorption	Bandgap
	458 nm	2.58 eV	436 nm	2.68 eV
XPS	C1s peaks	N1s peaks	C1s peaks	N1s peaks
	288.2, 284.6, 286.2 and 292.9 eV	398.5, 399.8, 400.8, 404.1 eV	287.8, 284.7, 286.2 and 293.5 eV	397.8, 399.1, 400.1, 403.5 eV

lists the physical properties of metal-free g-C₃N₄ before and after its exfoliation.

The exfoliated material has a significantly higher surface area than the bulk material, while the average pore size of both materials is almost the same (Table 2 and Figure S1). Using XRD, the material shows two characteristic peaks of g-C₃N₄ (Figure S4) [34,35]. The strong and weak peaks of N1s and C1s observed in XPS confirm the chemical state of g-C₃N₄ (Figure S3) [17,36,37,38,39,40,41]. Table 2 lists the maximum absorption wavelength and bandgap of the material, which are presented in Figure S2 [42,43].

In Figure 2, the selected SEM images of bulk and exfoliated g-C₃N₄ are presented. The thermal exfoliation transformed the stacked and aggregated structure of bulk g-C₃N₄ in a porous nanosheet structure. The reduction in layer thickness (Figure 2b) leads to an increase in the specific surface area of g-C₃N₄ [17,44,45,46].

Figure 3 shows the catalysts’ FTIR spectra. A broad peak is observed between 3200 and 3000 cm⁻¹, which can be attributed to the stretching vibrations of N–H bonds from residual amino groups and adsorbed H₂O. The sharp peak that appears at 806 cm⁻¹ can be attributed to the breathing mode of triazine units [47,48], whereas the strong bands between 1636 and 1242 cm⁻¹ belong to the C=N and C–N bonds of heterocyclic rings. Because the spectra of both materials show the same absorption bands, the chemical structure remained unaltered after treatment.

Figure 4 shows the effect of pH on the zeta potential of the exfoliated g-C₃N₄. The catalyst surface is positively charged at acidic pH (3) and negatively charged at natural (6) and basic pH (10).

The optical properties (PL/UV-Vis) and surface area (BET) of the material have changed with exfoliation; however, the chemical state (XPS), phase (XRD), and the chemical structure (FTIR) remained the same after exfoliation.

3.2. Photodegradation Studies

The photodegradation efficiency of exfoliated g-C₃N₄ photocatalyst was evaluated under visible light irradiation using 430 nm wavelength LEDs. The influence of individual operation parameters, catalyst concentration, phenol concentration, and pH of the solution, in their preselected ranges (Table 1), was examined. For all experiments, an adsorption time of 30 min was used before the light irradiation was started. Moreover, the photolysis experiment was performed to verify the removal of phenol in the absence of the catalyst. Phenol removal with adsorption in the dark and photolysis is insignificant compared to the removal of phenol obtained in the presence of light (Figure 5a). Figure 5a shows the effect of g-C₃N₄ photocatalyst concentration in the range of 0.1–0.75 g/L on phenol degradation, which increased with the increase in catalyst concentration up to 0.75 g/L because of an increased number of active sites available for the reaction to occur. However, there is no significant increase at >0.5 g/L because an additional increase of the catalyst concentration might cause light scattering and hindrance in light absorption. The effect of phenol concentration on the performance of the catalyst on phenol degradation was examined for three concentrations between 20 and 100 ppm and is shown in Figure 5b. The phenol degradation efficiency decreased as the concentration increased because of the higher number of molecules for adsorption on the available active sites, which hinders the absorption of light. Figure 5c shows the effect of different pH on phenol degradation. Increasing the pH decreases the degradation efficiency of exfoliated g-C₃N₄. Note that acidic pH is most favorable for phenol degradation because as per the zeta potential (Figure 3) and the surface charge of the catalyst is positive at an acidic pH, which helps attract OH– ions produced in the solution due to dissociation of H₂O₂ to the surface and improves the degradation efficiency.

3.3. Response Surface Methodology

3.3.1. Model Equation

To analyze the combined effect of three variables: catalyst concentration (A), phenol concentration (B), and pH of the solution (C) on the degradation efficiency of phenol (Equation (1)), a three-variable BBD was used in the experimental design for RSM.

Table 3. Box–Behnken design with experimental and predicted phenol degradation efficiency values with Equation (2).

Run	Experimental Conditions			Phenol Degradation Efficiency (%)
	Catalyst Concentration (g/L)	Phenol Initial Concentration (ppm)	pH	Experimental	Predicted
1	0.25	100	6.5	43.49	44.23
2	0.50	60	6.5	82.25	85.72
3	0.25	20	6.5	100	93.95
4	0.75	60	3.0	94.09	86.18
5	0.75	20	6.5	100.00	100.00
6	0.50	20	10.0	79.18	74.07
7	0.50	20	3.0	100.00	100.00
8	0.50	60	6.5	84.93	85.72
9	0.25	60	10.0	40.77	43.02
10	0.50	60	6.5	85.94	85.72
11	0.50	100	10.0	24.09	24.35
12	0.50	100	3.0	54.43	54.79
13	0.75	60	10.0	53.15	55.74
14	0.25	60	3.0	70.39	73.46
15	0.50	60	6.5	88.37	85.72
16	0.75	100	6.5	58.31	56.95
17	0.50	60	6.5	87.12	85.72

lists the set of performed experiments and the obtained phenol degradation (in 3 h and under an airflow of 50 mL/min).

Experimental data were fitted with four different models: two-factor interaction (2FI), linear, quadratic, and cubic model to obtain regression equations. Three different tests, namely, the sequential model sum of squares, lack of fit, and model summary statistics, were conducted to determine the adequacy of various models; the results are presented in

Table 4. Adequacy of the models tested.

Source	Sum of Squares	Degree of Freedom	Mean Square	F Value	p-Value	Remark
Sequential model sum of squares
Linear	7118.98	3	2372.99	18.77	<0.0001	-
2FI	109.60	3	36.53	0.238	0.8678	-
Quadratic	1407.83	3	469.27	26.07	0.0004	Suggested
Cubic	104.30	3	34.76	6.41	0.0523	Aliased
Lack of fit tests
Linear	1621.73	9	180.19	33.22	<0.0021	-
2FI	1512.13	6	252.02	46.46	<0.0012	-
Quadratic	104.29	3	34.76	6.41	0.0523	Suggested
Cubic	0	0	-	-	-	Aliased
Source	Standard deviation	R2	Adjusted R2	Predicted R2	PRESS	-
Model summary statistic
Linear	11.24	0.8124	0.769	0.694	2678.06	-
2FI	12.38	0.8250	0.720	0.462	4712.53	-
Quadratic	4.24	0.9856	0.967	0.805	1702.70	Suggested
Cubic	2.33	0.9975	0.999	-	-	Aliased

. The response surface model is then used to select the best model based on the following criterion: the highest-order polynomial with additional significant terms and the model is not aliased (Table 4). The cubic model has the highest polynomial model because there are no sufficient unique design points to independently estimate all terms for that model. The aliased model results in unstable and inaccurate coefficients and graphs. Thus, the aliased model cannot be selected [49,50]. The criteria used in the lack of fit test is the non-significant lack of fit (p-value > 0.05) based on which a quadratic model is selected. Moreover, multiple summary statistics are calculated to compare models or to confirm the adequacy of the model. These statistics include adjusted R², predicted R², and prediction error sum of squares (PRESS). A good model will have a largely predicted r², and a low PRESS. According to the aforementioned criteria, adjusted R² (0.967) and predicted R² (0.805) are in reasonable agreement with each other and have a low PRESS. Thus, the quadratic model is finally selected to build the response surface.

Based on regression coefficients from

Table 5. Coefficients of the second-order polynomial (quadratic) equation.

Factor	Coefficient Estimate	Degree of Freedom	Standard Error	95% Confidence Interval Low	95% Confidence Interval Low	F Value	p-Value
Intercept	85.72	1	1.90	81.24	90.21	-	-
A	6.36	1	1.50	2.82	9.91	17.99	0.0038
B	−24.86	1	1.50	−28.40	−21.31	274.63	<0.0001
C	−15.22	1	1.50	−18.76	−11.67	102.89	<0.0001
AB	3.71	1	2.12	−1.31	8.72	3.05	0.1242
AC	−2.83	1	2.12	−7.85	2.19	1.78	0.2239
BC	−2.38	1	2.12	−7.40	2.64	1.26	0.2989
A2	−5.05	1	2.07	−9.94	−0.16	5.96	0.0446
B2	−5.22	1	2.07	−10.11	−0.33	6.38	0.0394
C2	−16.07	1	2.07	−20.96	−11.18	60.44	0.0001

, the following empirical second-order polynomial equation was obtained:

$$\begin{array}{l}\mathrm{Degradation} \mathrm{Efficiency} (\%)\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}=85.72+6.36 \mathrm{A}-24.86 \mathrm{B}-15.22 \mathrm{C}+3.71 \mathrm{AB}-2.83 \mathrm{AC}-2.38 \mathrm{BC}-5.05{ \mathrm{A}}^2-5.22 {\mathrm{B}}^2\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}-16.07 {\mathrm{C}}^2\end{array}$$

(2)

where, A, B, and C are the catalyst concentration, phenol concentration, and pH of the solution, respectively.

The influence of model terms on the degradation of phenol as per p-values (Table 5) is in the following order B < C < C² < A < B² < A² < AB < AC < BC. The mixed interaction terms AB, AC, and BC are not significant because their p–value is > 0.05 and may be removed from Equation (2).

An ANOVA of the second-order polynomial (Equation (2)) for phenol degradation was conducted; the results are shown in

Table 6. Analysis of variance ANOVA of the second-order polynomial (Equation (2)).

Source	Sum of Squares	Degree of Freedom	Mean Square	F Value	p-Value	Remark
Model	8636.42	9	959.60	53.31	<0.0001	Significant
Residual	126.00	7	18.00	-	-	-
Lack of fit	104.30	3	34.77	6.41	0.0523	Not Significant
Pure error	21.70	4	5.42	-	-	-
-	Adjusted R2 = 0.967	Predicted R2 = 0.810	Model precision = 24.89		-	-
-	Std. dev. = 4.24	Mean = 73.32	C.V. % = 5.79	-	-	-

. In statistics, the significance of the model can be confirmed by a large F-value (53.31) and a small p-value (<0.0001). Furthermore, the significance of the model can be confirmed by the lack of fit test. In this study, the lack of fit is not significant because its p-value is >0.05. The accuracy of the model is confirmed by the low coefficient of variation (CV) value of 5.79%. The results showed that the signal-to-noise ratio of 24.89 is adequate.

Furthermore, the coefficient of determination R² confirmed the fit of the model. For the used model, the value of the predicted R² = 0.810 (Table 6) is in agreement with adjusted R² = 0.967, which indicates that the obtained model is significant.

Equation (2) provides a suitable relationship (R² = 0.810) between the response (degradation efficiency) and the parameters, which can be seen in Figure 6. In this figure, the experimental values of phenol degradation are plotted against the predicted values obtained from the RSM model; these values of the percentage phenol degradation fit well.

3.3.2. Interaction Effects of Independent Operating Parameters

Three dimensional (3D) response surface and contour plots were generated using the regression model (Equation (2)) to visualize the influence of the independent operating parameters on phenol degradation; they are presented in Figure 7, Figure 8 and Figure 9. In surface and contour plots, one parameter is maintained constant at its zero levels, whereas the other two are varied in the studied range reported in Table 1.

Figure 7 shows the influence of pH and catalyst concentration on the degradation efficiency of phenol at a constant phenol concentration of 60 ppm. The contour lines show a decrease in the degradation efficiency with an increase in pH; there is no considerable increase in efficiency, even at higher catalyst concentrations. However, an increase in degradation efficiency with a decrease in pH is observed. These results demonstrate that pH has a significant effect on phenol degradation and a low pH favors the degradation process. This phenomenon is linked with the zeta potential of the catalyst surface [18]. There is a positive charge at the surface of the catalyst at an acidic pH (Figure 2), which attracts the OH⁻ ions produced in the solution due to dissociation of H₂O₂ and significantly increases the degradation process. However, at a basic pH, the surface charge is negative and there could be electrostatic repulsion that reduces the efficiency of the degradation process.

Figure 8 shows the influence of pH and pollutant concentration on phenol degradation at a constant catalyst concentration of 0.5 g/L. For selecting the catalyst concentration, the effect of initial pollutant concentration is important. The contour lines demonstrate that simultaneously increasing both parameters (pH and phenol concentration) considerably decreases the degradation efficiency of phenol (33%), which is 62% at a low pH. As shown in Figure 5b, at low pH and low pollutant concentration, 100% degradation is achieved in a considered reaction time of 3 h. An increase in degradation efficiency from high to low pH can then be associated with catalyst surface charge. However, a decrease in efficiency at low pH from low to high phenol concentration is attributed to the increased number of pollutant molecules compared with the available active sites.

Figure 9 shows the effect of catalyst concentration and pollutant concentration at a constant pH of 6.5. The contour lines demonstrate that both parameters independently affect the degradation efficiency. By increasing the catalyst concentration at a lower pollutant concentration, phenol degradation increases; however, at a higher pollutant concentration, the degradation efficiency decreases. This can be attributed to the availability of active sites on the catalyst surface for OH⁻ radicals, as well as phenol molecules. The electron–hole pair generated from the catalyst surface improves the degradation rate.

3.3.3. Experimental Validation of RSM Model

To demonstrate the applicability of the model, a hypothetical case study for water with a phenol concentration of 50 ppm was considered. The model equation was used to identify the optimum catalyst concentration and pH, leading to maximal phenol degradation in 3 h under an airflow rate of 50 mL/min. According to the model prediction, maximal phenol degradation of 88.62% is achievable using 0.4 g/L of catalyst concentration and operating at a pH of 6.5. To examine the accuracy of the model prediction, an experiment was conducted under these conditions. The experimentally obtained phenol degradation was 83.75%, which is less than a 5% deviation from the predicted value. Thus, the optimum operating point obtained by RSM was successfully confirmed; this suggests that RSM can be a useful tool for optimizing photocatalytic processes. Similarly, the model developed can be used for minimizing the catalyst amount or for maximizing the degradation efficiency of phenols for any set of parameters in range.

4. Conclusions

Metal-free g-C₃N₄ was used for the photocatalytic degradation of phenol from an aqueous solution. The morphology of the catalyst was confirmed by SEM, and the surface charge was confirmed using zeta potential. Based on zeta potential, the catalyst surface was confirmed to have a positive surface charge under acidic conditions and a negative surface charge under basic conditions; therefore, acidic pH favors the degradation process. A RSM based on the BBD was used to analyze the degradation efficiency of phenol. The influence of experimental parameters, namely, catalyst concentration, pollutant concentration, and pH of the solution, and their interaction at a different level was examined for phenol degradation. An empirical regression quadratic model was developed for the response variable. Analysis of variance (ANOVA) demonstrated that the model is significant with an insignificant lack of fit and a high coefficient of determination (R²) of 0.96, which can be helpful to navigate the design space. Furthermore, an optimized degradation efficiency of 83.75% was achieved for phenol concentration of 50 ppm, catalyst concentration of 0.4 g/L, and a solution pH of 6.5 pH (in 3 h and under an airflow of 50 mL/min). Thus, the results suggest that the RSM can be used for the optimization of parameters for maximizing the photocatalytic degradation of phenol using g-C₃N₄ and LEDs.