Green Synthesis, Characterization, and Visible-light Photocatalytic Application of Mo–TiO₂ for Reactive Orange 16 Degradation: RSM Optimization and GPR Modeling
J. Environ. Nanotechnol., Volume 15, No 3 (September 2026) pp. 771-788
Abstract
The present study developed a green-synthesized molybdenum-doped titanium dioxide (Mo–TiO₂) photocatalyst using pomegranate peel extract and evaluated its performance for the degradation of Reactive Orange 16 (RO16) under visible-light irradiation. The effects of pH, catalyst dosage, initial RO16 concentration, and irradiation time were investigated using a four-factor Box–Behnken design comprising 27 experimental runs. The resulting quadratic model was statistically significant (p < 0.0001), with R² = 0.9996, adjusted R² = 0.9992, predicted R² = 0.9979, and a nonsignificant lack of fit (p = 0.3631). Numerical optimization predicted 96.27% RO16 decolourization at pH 7.07, a catalyst dosage of 50.40 mg L⁻¹, an initial RO16 concentration of 17.75 mg L⁻¹, and an irradiation time of 68.12 min. Experimental confirmation yielded 95.88% decolorization, corresponding to an RSM prediction error of approximately 0.40%. For exploratory machine-learning modelling, the 27-run experimental dataset was computationally augmented to 120 observations and evaluated using Gaussian Process Regression (GPR), Support Vector Regression, Gradient Boosting, multilayer perceptron artificial neural network, and Bagged Random Trees. Among the five evaluated models, GPR showed the best performance within the augmented dataset, achieving a test R² of 0.99390 and a five-fold cross-validated R² of 0.99098. These metrics represent internal computational validation and should not be interpreted as independent experimental validation. Under the optimized conditions, GPR predicted 96.31% RO16 decolourization, closely agreeing with the RSM prediction. Permutation analysis identified the initial RO16 concentration as the most influential predictor, followed by catalyst dosage, irradiation time, and pH. Overall, the integration of green synthesis, RSM optimization, and GPR modelling provides a promising framework for modelling and optimizing photocatalytic treatment of dye-contaminated water.
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