Laser-induced graphene based triboelectric nanogenerator for accurate wireless control and tactile pattern recognition

摩擦电效应 纳米发生器 触觉传感器 材料科学 接口(物质) 机器人 无线 石墨烯 电极 计算机科学 光电子学 纳米技术 人工智能 压电 电信 复合材料 化学 物理化学 毛细管数 毛细管作用
作者
Wenyu Guo,Yifan Xia,Yan Zhu,Shilei Han,Qingqing Li,Xin Wang
出处
期刊:Nano Energy [Elsevier BV]
卷期号:108: 108229-108229 被引量:76
标识
DOI:10.1016/j.nanoen.2023.108229
摘要

Accurate control interfaces and high-sensitive tactile sensing are essential for normal and efficient operation of intelligent robots. Despite most research focus on human-machine interface (HMI), little attention is paid to autonomous control interface of the robots. Simultaneous achievement of both accurate control interface and tactile array sensing in one device has been a fundamental challenge in developing intelligent robots. Herein, one device integrated by two different working-mode laser-induced graphene (LIG)-based triboelectric nanogenerators (TENGs) is developed to achieve simultaneously its accurate wireless control and tactile pattern recognition capability. Based on the same triboelectric effect, both the rolling of nylon pellet on the poly(vinylidene fluoride-co-hexafluoro propylene) (PVDF-HFP) concave surface and the touching on the tactile pattern recognition array can excite the LIG electrodes to generate voltage outputs, which can be further employed for accurate wireless control and real-time tactile sensing (pressure sensitivity of 2.2 V/kPa within 0–2.8 kPa), respectively. When combining with a microprocessor, the accurate control interface layer based on eight parallel freestanding interdigital electrode-based TENGs on a concave cavity of silicone rubber can wirelessly operate motion direction of a miniature car as a proof-of-concept. Meanwhile, the tactile imaging of an 8 × 8 sensing array based on single-electrode TENG can successfully achieve tactile pattern recognition. Consequently, the multifunctional device integrated by two different LIG-based TENGs has achieved simultaneously accurate wireless control and sensitive tactile pattern recognition, which would demonstrate a promising application in metaverse, unmanned vehicles, and intelligent robots.
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