Abstract

Lead-ion (Pb2+) contamination in drinking water poses a serious threat to public health, but conventional laboratory-based methods rely on bulky equipment and are unsuitable for on-site monitoring. Here, we develop a wireless monitoring system based on a dual-channel liquid–solid triboelectric nanogenerator probe (TENG probe) for detecting Pb2+ in drinking water. Based on the dynamic contact electrification at the liquid–solid interface, the sliding of a water droplet containing Pb2+ on the FEP surface is converted into an electrical signal for Pb2+ detection. A wireless acquisition circuit transmits the electrical signals via Wi-Fi to a computer, enabling remote and wireless detection. By integrating a one-dimensional convolutional neural network (1D CNN) deep learning model, the TENG probe achieved a detection accuracy of 99.62% and was capable of detecting Pb2+ in drinking water at the ppb level, exceeding the national standard. This work opens a way for safeguarding drinking-water quality.

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