Integrated deep eutectic system enrichment and AI-assisted high-throughput visual detection for Hg2+ in environmental samples

Mercury ion (Hg2+), a prevalent heavy metal, is commonly found in environmental soil and water. Its interaction with sulfhydryl groups in proteins or lipids can cause oxidative stress and disrupt calcium homeostasis. This leads to severe health issues, including digestive, nervous, and immune system damage. Conventional Hg2+ detection methods, such as ICP-MS and AAS, require complex procedures and bulky instruments, limiting their applicability for real-time, on-site analysis. AI-assisted detection methods have emerged as promising solutions, offering portability and rapid detection capabilities. Deep eutectic solvents (DESs), particularly hydrophobic DESs (HDESs), provide an environmentally friendly alternative for enriching and detecting metal ions. 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 Hg2+ enrichment and detection. AgNPs-HDES was synthesized using ethylene glycol containing silver nanoparticles (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 image acquisition combined with YOLOv8-based AI software enabled high-throughput colorimetric analysis for Hg2+ detection. A progressive color change from brownish-yellow to colorless was observed as Hg2+ concentration increased, eliminating hydrophilic interference and improving sensitivity. The AgNPs-HDES platform demonstrated a linear detection range of 1-40 μmol·L-1 (R2 = 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 %. The established platform enables portable, rapid, and highly accurate Hg2+ detection across multiple environmental samples simultaneously. This AI-assisted, high-throughput detection system presents a valuable tool for environmental monitoring and pollutant tracking.