Solar-driven photocatalytic removal of cefuroxime from water: process optimization via machine learning and nature-inspired algorithms

Abstract

The extensive presence of antibiotics in aquatic environments has raised significant concerns regarding their ecological impact and the potential development of antibiotic-resistant bacteria. This study investigates the photocatalytic degradation of Cefuroxime (CFX) using silver-doped zinc oxide (Ag-ZnO) nanoparticles under solar irradiation. Ag-ZnO nanoparticles were synthesized with varying Ag doping percentages (1, 2, 2.5, and 3 wt%) via a sol–gel method, followed by structural and optical characterizations using XRD, SEM-EDS, ATR-FTIR, and UV-Vis spectroscopy. Photocatalytic experiments have revealed that 2 wt% Ag-ZnO exhibited the highest degradation efficiency, attributed to reduced electron–hole recombination and enhanced light absorption in the visible spectrum. The characterization results provided valuable information on the morphological, structural, and compositional features of the prepared catalysts, emphasizing the influence of different silver loadings on their properties. The optical study revealed a decrease in the band gap value from 3.15 eV (ZnO) to 3.01 eV (Ag:ZnO, 2%). Furthermore, the photodegradation kinetics were analyzed, and scavenger tests were performed to examine the role of reactive species, providing a comprehensive understanding of the photocatalytic mechanism. According to the kinetic study, CFX degradation followed pseudo-first-order kinetics, and Hydroxyl radicals (˙OH) were identified as the dominant reactive species driving the photodegradation process. To optimize the degradation process, a Decision Tree coupled with the Least Squares Boosting (DT_LSBOOST) algorithm was employed to model and predict CFX photodegradation efficiency based on key operational parameters: reaction time, catalyst dosage, initial CFX concentration, pH, and Ag doping percentage. The optimized DT_LSBOOST model demonstrated high predictive accuracy (R > 0.9996) with minimal root mean square error (RMSE < 0.88). Furthermore, the Dragonfly Algorithm (DA) was implemented to determine the optimal reaction conditions, achieving an experimentally validated degradation rate of 84.25% under optimized conditions (pH = 6.11, catalyst dose = 0.1 g L⁻¹, initial CFX = 50 mg L⁻¹, 180 min reaction time). The integration of machine learning-based modeling and nature-inspired optimization highlights an effective approach for enhancing photocatalytic processes. The results provide a robust framework for optimizing semiconductor-based water treatment technologies, contributing to sustainable environmental remediation strategies.