Triboelectric signal waveform feature enhanced by magnetic field-assisted strategy for human–machine interaction

Triboelectric nanogenerators (TENGs) represent a promising technology for next generation human–computer interaction. The effective enhancement of induced charges are critical factors that determine the recognition accuracy of TENG-based tactile sensors. Here, we propose a magnetic field-assisted TENG device utilizing waveform feature enrichment strategies to significantly enhance the tactile recognition accuracy in natural environments. An elastic micro-nano structure was fabricated on a polydimethylsiloxane (PDMS) film via a facile templating method. Leveraging the inherent hydrophobicity and microscale surface roughness of PDMS, our device demonstrates stable and distinct waveform characteristics under natural operating conditions. Importantly, the introduction of a magnetic field generates a Lorentz force, which effectively modulates induced charges within the electrode, yet minimally affects triboelectric charges at the PDMS interface. This selective modulation induces an asymmetric charge distribution inside the electrode, substantially increasing the induced charge density, consequently, subtle waveform features are markedly enhanced. These enriched signal features play a crucial role in elevating material recognition accuracy. As a result, the sensor achieves a remarkable recognition accuracy of 99% when distinguishing among ten different materials under magnetic field assistance. This work provides valuable guidelines for advancing the performance and accuracy of TENG-based tactile sensing systems.