Real-time detection for water pollutant based on triboelectric nanogenerators and machine learning

Water cleanliness and safety are fundamental to sustaining human activities and maintaining ecological stability. In this study, a self-powered water-quality sensing system is developed based on contact electrification and the distinct charge-transfer behaviors of different pollutants at the liquid-solid interface. When water samples containing heavy metal ions, microplastics, or rust flow through a conductive sponge, contact friction between the pollutants and the flexible porous structure generates differentiated triboelectric signals, which are continuously collected using an electrometer and a data acquisition card. By further integrating a Light Gradient Boosting Machine (Light GBM) model, a mapping relationship between signal features and pollutant types and concentrations is established for water-quality prediction. Experimental results demonstrate that the system can effectively identify heavy metal ions (Zn, Ba, and Al), polypropylene (PP) microplastics, and rust (FeO), achieving an average classification accuracy of 86.67%. Validation experiments using municipal water samples from Kunming supplemented with quantified rust further confirm the reliability of the system. Under varying temperature (4.36-42.75 °C), pH (3-11), and turbidity conditions, the system maintains stable and accurate pollutant recognition, with detection accuracy reaching up to 100%. This study integrates liquid-solid triboelectric sensing with machine learning, providing a promising strategy for intelligent water-quality monitoring.