Antioxidant potential and increased photocatalytic efficiency of gallic acid-capped ZnO and NiO NPs for azo dye degradation: effect of heterojunction coupling and machine learning-assisted modeling

and ˙OH radicals. Parameters such as pH, catalyst dose, dye concentration, and radical scavengers were optimized, confirming the role of reactive oxygen species in degradation process. Total organic carbon (TOC) analysis indicated significant mineralization (84% and 80% of CV and CR, respectively), and reusability tests showed high stability with a meager decrease of activity (∼6%) over five cycles. Machine learning models, including Decision Tree, Random Forest, and ANN, accurately predicted the photocatalytic degradation process. The antioxidant assay results depicted the higher efficiency of g-ZnO-NiO NCs than pristine NPs and gallic acid, assessed by DPPH, TPC, and FRAP assays. Conclusively, it was emphasized that the g-ZnO-NiO heterojunction is a promising, sustainable photocatalyst for organic pollutant removal under solar irradiation and has better antioxidant potential than g-ZnO NPs, g-NiO NPs, and gallic acid.