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<p>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<sup>2+</sup>, Ba<sup>2+</sup>, and Al<sup>3+</sup>), polypropylene (PP) microplastics, and rust (Fe<sub>2</sub>O<sub>3</sub>), 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&#xa0;°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.</p>

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

  • 期刊论文

  • 发表日期：

    2026年3月10日

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  • Zhijie Zhang,
  • Wei Long,
  • Peiyin Liu

摘要

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 (Zn2+, Ba2+, and Al3+), polypropylene (PP) microplastics, and rust (Fe2O3), 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.