Heavy metal pollution, especially lead (Pb) and mercury (Hg), has always been a major concern in the fields of environment and food safety. Even trace levels of lead and mercury can cause serious damage to the human nervous system. Traditional detection methods, such as inductively coupled plasma mass spectrometry, although precise, are expensive in equipment, complex in operation, and have long detection cycles, making it difficult to meet the needs of rapid on-site detection.

Recently, a research team from the School of Mechanical and Electrical Engineering at Sichuan Agricultural University and Zhejiang University published a groundbreaking study in the Chemical Engineering Journal, developing an electrochemical sensor based on an active conductive layer stack structure, and combining deep learning algorithms and WeChat mini programs to build a real-time detection platform for heavy metal ions. In this study, MgFe layered double hydroxides were synthesized on the conductive layer of foam nickel loaded with SnO ₂ as the active layer. The results show that MgFe-LDH@SnO ₂/NF exhibits excellent sensitivity (Pb (II): 417 μ A/μ M; Hg(II)：986 μA/μM）、 A wider linear range (1.1-2.2 μ M) and lower detection limit (Pb (II): 1.21 nM); Hg(II)：3.7 nM）。 Density functional theory and electrochemical measurements confirm that the high performance of the sensor is attributed to the enhanced ability of oxygen vacancies to adsorb heavy metal ions by reducing binding energy, and SnO ₂ promoting rapid electron transfer by reducing charge transfer resistance. In particular, a platform for real-time detection of Pb (II) and Hg (II) pollution categories and concentrations was constructed by combining a newly proposed micro electrochemical detection network and WeChat mini program. The platform demonstrated high accuracy (0.98) and recovery rates (91.6% -106.8%) in actual water sample detection. This study innovatively achieved qualitative and quantitative calculations of electrochemistry through deep learning. In addition, smartphones can display detection results transmitted from cloud servers. Therefore, this work provides a new sensor platform for detecting typical heavy metal ions, improving the sensitivity, accuracy, and intelligence level of detection.

Source: Sensor Expert Network