Abstract：
Flexible tactile sensors show promise for artificial intelligence applications due to their biological adaptability and rapid signal perception. Triboelectric sensors enable active dynamic tactile sensing, while integrating static pressure sensing and real-time multichannel signal transmission is key for further development. Here, we propose an integrated structure combining a capacitive sensor for static spatiotemporal mapping and a triboelectric sensor for dynamic tactile recognition. A liquid metal-based flexible dual-mode triboelectric-capacitive-coupled tactile sensor (TCTS) array of 4 × 4 pixels achieves a spatial resolution of 7 mm, exhibiting a pressure detection limit of 0.8 Pa and a fast response of 6 ms. Furthermore, neuromorphic computing using the MXene-based synaptic transistor achieves 100% recognition accuracy of handwritten numbers/letters within 90 epochs based on dynamic triboelectric signals collected by the TCTS array, and cross-spatial information communication from the perceived multichannel tactile data is realized in the mixed reality space. The results illuminate considerable application possibilities of dual-mode tactile sensing technology in human-machine interfaces and advanced robotics.
摘要：
柔性触觉传感器由于其生物适应性和快速信号感知而在人工智能应用中显示出希望。摩擦电传感器可实现主动动态触觉传感，而集成静态压力传感和实时多通道信号传输是进一步发展的关键。这里,我们提出了一种集成结构，将用于静态时空映射的电容传感器和用于动态触觉识别的摩擦电传感器相结合。4×4像素的基于液态金属的柔性双模摩擦电-电容耦合触觉传感器（TCTS）阵列可实现7mm的空间分辨率，具有0.8Pa的压力检测极限和6ms的快速响应。此外，基于TCTS阵列收集的动态摩擦电信号，使用基于MXene的突触晶体管的神经形态计算可实现90个时期内手写数字/字母的100％识别精度，在混合现实空间中实现了从感知到的多通道触觉数据的跨空间信息通信。结果阐明了双模式触觉传感技术在人机界面和高级机器人技术中的应用可能性。
