Hydrogel-based flexible degradable triboelectric nanogenerators for human activity recognition

摩擦电效应 材料科学 纳米技术 工程类 电气工程 复合材料
作者
Zhenning Xie,Yanping Dai,Yuzhang Wen,Mengqi Zhang,Mingxi Tu,Fengxin Sun,Zida An,Tianming Zhao,Bing Liu,Yupeng Mao
出处
期刊:Sustainable Materials and Technologies [Elsevier]
卷期号:40: e00967-e00967 被引量:27
标识
DOI:10.1016/j.susmat.2024.e00967
摘要

The development of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has led to an increased demand for wearable electronic devices that can be used in a variety of contexts. Hydrogel sensors have been regarded as an ideal material for the fabrication of multifunctional flexible wearable devices. However, it is difficult to create hydrogel sensors with ideal electrical and mechanical properties. In this study, a conductive hydrogel prepared by a simple method has been introduced. The hydrogel was immersed in a binary solvent of glycerol, water and sodium citrate to form a transparent, long-term stable and highly conductive gelatin-NaCl organohydrogel (GNOH). A stretchable and durable gelatin Ecoflex triboelectric nanogenerator (GE-TENG) was created by combining an organic hydrogel with Ecoflex elastomer. It has high electrical and mechanical performance, with an open-circuit voltage of 142 V, current of 3.8 μA, power output of 19.36 μW and response time of 85 ms. Moreover, the single-electrode mode of the GE-TENG provides high electrical output for powering portable electronic devices and can capture biomechanical energy in temperatures as low as −20 °C. As a result of these capabilities, GE-TENG can effectively identify human movements with precision and transmit signals wirelessly, thereby broadening its potential applications. The GE-TENG-based self-powered intelligent sensing system (GSIS) for human-computer interaction demonstrates a wearable intelligent system for controlling the movement of a tank model and a manipulator by integrating the signal acquisition, signal processing and signal output components. The system achieves 100% signal recognition accuracy when combined with covariance matrix feature analysis and Convolutional Neural Network classification algorithms. This research demonstrates the potential of wearable hydrogel-based electronic devices for monitoring human motion, harvesting biomechanical energy and human-computer interaction.
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