Cu2O/Fe3O4/UiO-66 nanocomposite as an efficient fenton-like catalyst: Performance in organic pollutant degradation and influencing factors based machinelearning

Abstract

The persistent presence of organic pollutants like dyes in water environment necessitates innovative approaches for efficient degradation. In this research, we developed an advanced hybrid catalyst by combining metal oxides (Cu2O, Fe3O4) with UiO-66, serving as a heterogeneous Fenton catalyst for for efficient RB19 breakdown in water with H2O2. The control factors to the catalytic behavior were also quantified by machine learning. Experimental results show that the catalytic performance was much better than its individual components (P < 0.05 & non-zero 95% C.I). The improved catalytic efficiency was linked to the occurrence of active metal centers (Fe, Cu, and Zr), with Cu(I) from Cu2O playing a crucial role in promoting increased production of HO•. Also, UiO-66 served as a catalyst support, attracting pollutants to the reaction center, while magnetic Fe3O4 aids catalyst recovery. The optimal experimental parameters for best performance were pH at 7, catalyst loading of 1.6 g/L, H2O2 strength of 0.16 M, and reaction temperature of 25 °C. The catalyst can be magnetically separated and regenerated after five recycling times without significantly reducing catalytic activity. The reaction time and pH were ranked as the most influencing factors on catalytic efficiency via Random Forest and SHapley Additive exPlanations models. The findings show that developed catalyst is a suitable candidate to remove dyes in water by Fenton heterogeneous reaction.

Keywords

• Cu2O
• Fenton-like reaction
• Machine learning
• Reactive blue 19
• UiO-66
• Fe3O4