Abstract
Triboelectric sensing technology reinforced with deep learning algorithm has shown significant potentials in the fields of sophisticated wearable devices and Internet of Things (IoT) applications. Herein, we present a large-scale and printable nanocomposite film for high-performance microstructured BaTiO3/Ecoflex triboelectric nanogenerator (MBE-TENG) and demonstrate its applications in deep-learning assisted self-powered sensing and human-machine interaction. The MBE-TENG device achieves a short-circuit current of 1.46 μA, an open-circuit voltage of 144 V, and a transfer charge density of 6.62 nC/cm². Additionally, the MBE-TENG device can charge a 10 μF capacitor from 0 to 2 V within 200 seconds, illuminate over 70 commercial LEDs, and power a digital calculator. Based on the MBE-TENG, a sensory array keyboard is demonstrated to wirelessly control the movement of a smart car via Bluetooth communication. Furthermore, a smart tactile sensing glove is developed by combining the MEB-TENG, residual neural network and convolutional block attention module, realizing the facile recognition on six different objects with a high accuracy of 96.33 %. This work presents a scalable fabrication for microstructured triboelectric layer and highlights the versatility and efficiency of TENG sensing technology when combined with advanced neural networks and embedded systems, paving the way for innovative development in human-machine interaction, personalized healthcare, and intelligent control interfaces.
Published Version
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