Abstract
INTRODUCTION: Mercury ion (Hg(2+)), a prevalent heavy metal, is commonly found in environmental soils and waters. Its interaction with sulfhydryl groups in proteins and lipids can cause oxidative stress and disruption of calcium homeostasis. These lead to severe health issues, including digestive, nervous, and immune system damage. Conventional Hg(2+) detection methods, such as ICP-MS and AAS, require complex procedures and bulky instruments, limiting their applicability for real-time, on-site analysis. Recently, AI-assisted detection methods have emerged as promising solutions, offering portability and rapid detection capabilities. Deep eutectic solvents (DESs), and in particularly hydrophobic DESs (HDESs), provide an environmentally friendly alternative for the enrichment and detection metal ions. OBJECTIVES: This study aims to develop a portable, cost-effective, and environmentally friendly colorimetric sensing platform based on a silver nanoparticles hydrophobic deep eutectic system (AgNPs-HDES) for Hg(2+) enrichment and detection. METHODS: AgNPs-HDES was synthesized using silver nanoparticle-containing ethylene glycol (AgNPs-EG) as the hydrogen bond donor. Electrostatic potential maps (ESP) and density functional theory (DFT) were employed to elucidate its synthesis and enrichment mechanisms. Smartphone-based image acquisition combined with YOLOv8-based AI software enabled high-throughput colorimetric analysis for Hg(2+) detection. RESULTS: A progressive color change from brownish-yellow to colorless was observed with increasing Hg(2+) concentration, thereby eliminating hydrophilic interference and improving sensitivity. The AgNPs-HDES platform demonstrated a linear detection range of 1-40 μmol·L(-1) (R(2) = 0.9889) and a detection limit of 0.23 μmol·L(-1). Recovery rates in real samples, including lake water, soil, seawater and industrial sewage, ranged from 90.3% to 123%. CONCLUSION: The established platform enables portable, rapid, and highly accurate Hg(2+) detection across multiple environmental samples simultaneously. This AI-assisted, high-throughput detection system presents a valuable tool for environmental monitoring and pollutant tracking.