Machine learning-powered fluorescent sensor arrays for rapid detection of heavy metals and pesticides in complex environments

Abstract

The co-contamination of multiple pollutants in complex environmental matrices poses a significant threat to ecosystems and public health, necessitating advanced detection methods. In this study, we developed a machine learning-powered chemical sensor array capable of simultaneously identifying and discriminating nine heavy metal(loid)s (Cr[III], Cd[II], Hg[II], Pb[II], Co[II], Zn[II], Mn[II], As[III], and Se[VI]) and five pesticides (propiconazole, penconazole, cyproconazole, indoxacarb, and azoxystrobin). Using three distinct copper nanoclusters (Cu NCs) with unique ligand-based binding affinities, the system generated characteristic fluorescent “fingerprints”. By coupling with machine-learning algorithms (LDA and HCA), the sensor array achieved 100 % identification accuracy within 10 min, with exceptional sensitivity (limits of detection: ∼0.5 nM for heavy metal(loid)s and ∼7.1 ppb for pesticides). This approach was validated using real-world samples, including blood, urine, soil, tap water, vegetables, and fruits, demonstrating high selectivity, anti-interference capability, and practical applicability. This proposed nanosensor array provides a robust, rapid, and sensitive platform for multi-target detection, offering transformative solutions in food safety, environmental monitoring, and public health surveillance.