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

被引：42

作者：

Mao, Ruiyuan ^{[1
]}

Zhang, Dongzhi ^{[1
]}

Wang, Zihu ^{[1
]}

Zhang, Hao ^{[1
]}

Wang, Dongyue ^{[1
]}

Tang, Mingcong ^{[1
]}

Zhou, Lina ^{[1
]}

Cai, Haolin ^{[1
]}

Xia, Hui ^{[1
]}

机构：

[1] China Univ Petr East China, Coll Control Sci & Engn, Qingdao 266580, Peoples R China

来源：

NANO ENERGY | 2023年 / 111卷

基金：

中国国家自然科学基金;

关键词：

Triboelectric nanogenerators; Flexible silicone composite layer; Convolutional neural network; Human-computer interaction; NATURAL-RUBBER COMPOSITES; CONDUCTIVE CARBON-BLACK; TACTILE SENSOR; PERFORMANCE; NANOTUBES; DESIGN; ENERGY;

D O I：

10.1016/j.nanoen.2023.108418

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

Currently, deep-learning-assisted triboelectric nanogenerators (TENGs) have shown great potential for humancomputer 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 mu A, 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.

引用

页数：12