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<p>Quantitatively determining a material’s tendency to gain or lose electrons is crucial for triboelectric devices but remains challenging. Here, we introduce a dual-reference triboelectric sensor integrated with deep learning to rapidly estimate surface potential. An unknown material is contacted with two reference surfaces of opposite triboelectric polarity, producing paired electrical signals that act as internal calibration. A deep neural network maps these dual signals to the material’s effective surface potential, capturing interaction patterns that conventional analytical models cannot resolve. The system reliably quantifies surface-potential differences across diverse materials, achieving prediction errors below 8% and clearly distinguishing materials across the triboelectric series. The dual-reference design enhances robustness by compensating for environmental and measurement variations, yielding ~85% improved accuracy over single-reference methods. Overall, our results show that combining nanogenerator-based sensing with data-driven analysis enables accurate, quantitative interpretation of triboelectric responses and significantly broadens the functional capabilities of triboelectric sensors.</p>

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Deep learning-based dual-reference triboelectric sensor for direct surface potential prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月26日

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  • Van Quan Phan,
  • Viet Anh Cao,
  • Minji Kim,
  • Pangun Park,
  • Junghyo Nah

摘要

Quantitatively determining a material’s tendency to gain or lose electrons is crucial for triboelectric devices but remains challenging. Here, we introduce a dual-reference triboelectric sensor integrated with deep learning to rapidly estimate surface potential. An unknown material is contacted with two reference surfaces of opposite triboelectric polarity, producing paired electrical signals that act as internal calibration. A deep neural network maps these dual signals to the material’s effective surface potential, capturing interaction patterns that conventional analytical models cannot resolve. The system reliably quantifies surface-potential differences across diverse materials, achieving prediction errors below 8% and clearly distinguishing materials across the triboelectric series. The dual-reference design enhances robustness by compensating for environmental and measurement variations, yielding ~85% improved accuracy over single-reference methods. Overall, our results show that combining nanogenerator-based sensing with data-driven analysis enables accurate, quantitative interpretation of triboelectric responses and significantly broadens the functional capabilities of triboelectric sensors.