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.
