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

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 effect