Tailored perovskite nanomaterials for arsenic decontamination: a path to clean water

Arsenic contamination in water remains a critical global concern, demanding efficient and scalable treatment technologies. This review explores the integration of perovskite-based materials and machine learning (ML) approaches for enhanced arsenic removal. Perovskites, owing to their tunable bandgap, high surface area, and redox-active sites, show significant potential in photocatalysis and adsorption processes. Their performance is influenced by factors such as morphology, synthesis method, surface defects, and environmental conditions. However, challenges like limited solar activity, rapid electron–hole recombination, and poor reusability hinder their broader application. It critically analyses current advancements, limitations, and the synergistic potential of combining ML with perovskite-based systems. It also highlights the need for real-world testing, regeneration studies, and life-cycle assessments to ensure sustainable implementation. The findings aim to support future research toward scalable, data-driven arsenic remediation technologies.