Machine learning-assisted Au@UiO-66-NH₂-based electrochemical sensor for detection of ubiquitous heavy metal ions

Electrochemical detection is widely used for monitoring heavy metal ions. However, the complex solution environment hinders accurate prediction of optimal detection conditions, resulting in low selectivity. To address this issue, we integrated machine learning for accurate current prediction. Comprehensive analysis shows that Au@UiO-66-NH₂ nanomaterials can achieve high sensitivity detection, with an R² of 0.994 for current prediction. This material exhibits excellent performance for Cd²⁺, Pb²⁺, Cu²⁺, Hg²⁺, with a sensitivity of 11.5 μA μM^-1, a linear range of 10 nM - 5 μM, a limit of detection (LOD) of 12.7 nM. The system evaluation covers various heavy metals, including interface modification, signal amplification, and microstructure modulation of gold doped UiO-66. Analyze the heavy metal ion content in the actual samples of Tarim River and Hu Yang River in the local area, and come up with a solution. This work has promoted the development of electrochemical sensing platforms, strengthened environmental monitoring and public health protection.