Deep-learning-assisted low-cost flexible cotton yarn-based triboelectric nanogenerator for ultra-sensitive human-computer merging interfaces

Abstract

Currently, deep-learning-assisted triboelectric nanogenerators (TENGs) have shown great potential for human-computer interaction. The Ecoflex-based polyvinyl-alcohol layer (Ecoflex/PVA) and the Ecoflex-based graphitic carbon nitride layer (Ecoflex/g-C3N4) are sequential spin-coated on the substrate of flexible cotton yarn. The Ecoflex/g-C3N4 and Ecoflex/PVA based silicone composite layer (SCL@(g-C3N4/PVA)) is designed as a high-output artificial skin in TENG. The flexible silicone composite layer plays a crucial role in enhancing output of the TENG. The silicone composite layer-based triboelectric nanogenerators (SCL-TENG) with 10 wt% PVA and 1.6 wt% g-C3N4 yielded the optimal output (720 V, 134 μA, 0.255 mW/cm2) under the pressure of 5 kPa and frequency of 8 Hz. By applying the Internet of Things technology, the single electrode mode SCL-TENG can be integrated into the intelligent sensing system to control and monitor electronic and electrical systems. In addition, the single electrode mode SCL-TENG is capable of sensing and distinguishing the instantaneous mechanical contact generated by balls with different materials, which can be high-accuracy identified with the assistant of deep-learning method of convolutional neural network-gate recurrent unit (CNN-GRU). It shows that the flexible silicone composite layer has great potential in the next generation of AI and intelligent interactive applications.