Next-Generation Water Quality Monitoring via Artificial Intelligence-Coupled Nanozymes

Abstract

Safe water management requires rapid and highly sensitive analytical platforms capable of detecting contaminants in complex environmental matrices. Nanozymes are nanomaterials with enzyme-mimicking catalytic activity that offer remarkable advantages for field-deployable sensing, including stability and compatibility with portable formats. However, conventional nanozyme sensors often exhibit nonlinear catalytic behavior, limited selectivity, and susceptibility to interference in natural waters. This Perspective evaluates how integrating artificial intelligence (AI) with nanozyme-based systems can resolve these constraints. We analyze mechanistic nanozyme responses to major pollutant classes, including heavy metals, dyes, pharmaceuticals, and pesticides, and show how machine learning and deep learning models enhance sensitivity, mitigate matrix effects, and enable multiplex classification via catalytic fingerprints. We further highlight emerging AI-enabled architectures, including smartphone analytics, Internet of Things (IoT)-based nanozyme devices, and cloud-connected autonomous monitoring systems. Finally, we discuss the potential of standardized datasets, digital-twin simulations, and AI-driven design of single-atom nanozymes to advance global water quality assessment.