VISIBLE- LIGHT DRIVEN PHOTOCATALYTIC DEGRADATION OF ORGANIC POLLUTANTS OVER CuWO4@TiO2 NANOCOMPOSITE: A COMBINED EXPERIMENTAL AND THEORITICAL STUDIES

Abstract

With the growing presence of organic pollutants like methylene blue (MB) in wastewater, there are significant concerns for both the environment and public health, highlighting the urgent need for the development of effective remediation strategies. This study addressed the urgent need for effective remediation strategies for organic pollutants, specifically methylene blue (MB), in wastewater by synthesizing and evaluating a CuWO₄@TiO₂ nanocomposite photocatalyst. A comprehensive characterization using UV-Vis diffuse reflectance spectroscopy (DRS), field emission scanning electron microscopy (FESEM), X ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Brunauer-Emmett-Teller (BET) surface area analysis, electrochemical impedance spectroscopy (EIS), and cyclic voltammetry (CV) was conducted to assess the nanocomposite's structural, morphological, and electrochemical properties. Density functional theory (DFT) calculations provided insights into the photocatalytic mechanism, particularly the most stable Ti/Cu@CuWO₄ interface configuration. To enhance predictive modeling for MB degradation, ten machine learning (ML) algorithms, including AdaBoost, Bagging, CatBoost, Decision Tree, Extra Trees, Gradient Boosting, HistGradientBoosting, LightGBM, Random Forest, and XGBoost, were evaluated based on coefficient of determination (R²), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and median absolute error (MedAE). Among this HistGradientBoosting model demonstrating exceptional performance, achieving an R² of 0.9998 in training and 0.9915 in testing, along with low error metrics. Experimental validation under optimal conditions (200 mg/L catalyst, 150 mW/cm² light intensity, 88.6 min reaction time) confirmed a 98.5% MB degradation efficiency, closely aligning with the ML-predicted 98.99%. The nanocomposite exhibited significantly higher catalytic activity as compared to individual CuWO₄ and TiO₂, with rate constants of 0.0302 min⁻¹ for the nanocomposite, 0.0197 min⁻¹ for CuWO₄, and 0.0167 min⁻¹ for TiO₂. Scavenger experiments revealed the order of active species in the degradation process as O₂•⁻ > HO• > ¹O₂ > e⁻ > h⁺, this research emphasizing the combined effectiveness of experimental, computational, and machine learning methods in optimizing photocatalytic water treatment processes.