Unveiling the fate of metal leaching in bimetal-catalyzed Fenton-like systems: pivotal role of aqueous matrices and machine learning prediction.

ABSTRACT

Metal-based catalytic materials exhibit exceptional properties in degrading emerging pollutants within Fenton-like systems. However, the potential risk of metal leaching has become pressing environmental concern. This study addressed scientific issues pertaining to the leaching behavior and control strategies for metal-based catalytic materials. Innovative cobalt-aluminum hydrotalcite (CoAl-LDH) triggered peroxymonosulfate (PMS) activation system was constructed and achieved near-complete removal of Ciprofloxacin (CIP) across diverse water quality environments. Notably, it was found that the tunable ion exchange and Al3+ stabilization of CoAl-LDH occurred due to the particularity of neutral water quality, resulting in significantly lower Co2+ leaching levels (0.321 mg/L) compared to acidic conditions (5.103 mg/L). In light of this, machine learning technology was then employed for the first time to simulate the dynamic trend of Co2+ leaching and elucidated the critical regulatory roles and mechanisms of Al3+, aqueous matrix, and reaction rate. Furthermore, degradation systems based on different water quality and metal leaching levels regulated the generation levels of SO4.- and O2∙-, and the unique advantages of free radical attack paths were clarified through CIP degradation products and ecotoxicity analysis. These findings introduced novel insights and approaches for engineering application and pollution control in metal-based Fenton-like water treatment.