Electrochemical self-powered strain sensor for static and dynamic strain detections

被引:48
|
作者
Huang, Qi [1 ]
Jiang, Yadong [1 ]
Duan, Zaihua [1 ]
Wu, Yuanming [1 ]
Yuan, Zhen [1 ]
Zhang, Mingxiang [1 ]
Zhao, Qiuni [1 ]
Zhang, Yajie [1 ]
Liu, Bohao [1 ]
Tai, Huiling [1 ]
机构
[1] Univ Elect Sci & Technol China UESTC, Sch Optoelect Sci & Engn, State Key Lab Elect Thin Films & Integrated Device, Chengdu 610054, Peoples R China
关键词
Strain sensor; Electrochemical reactions; Self-powered; Static strain detection; Respiratory monitoring;
D O I
10.1016/j.nanoen.2023.108997
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
The self-powered strain sensors based on piezoelectric and triboelectric principles have been widely reported in flexible electronics, but they cannot achieve static strain detection. Inspired by electrochemical reactions, we propose and construct an electrochemical self-powered strain sensor for static and dynamic strain detections. Specifically, the sensor is composed of Cu/Al electrodes, elastic core-spun yarn coated with LiCl-carbon nanotubes (CNTs), and latex tube encapsulation. Among them, Cu and Al electrodes are used for electrochemical reactions; Elastic core-spun yarn endows the sensor with excellent tensile performance; LiCl provides conductive ions in electrochemical reactions; CNTs with good conductivity not only reduce the resistance between Cu and Al electrodes, but also facilitate good resistance strain effect; Latex tube encapsulation inhibits the evaporation of water molecules in the electrolyte. The strain sensing performance of the sensor is evaluated based on the current response. The results show that the sensor has wide strain detection range (2-100 %) and good repeatability (1000 times). By analyzing the strain voltage and current responses, as well as the morphology characterization of the sensor, the strain response mechanism of the sensor has been clarified, which is controlled by electrochemical reactions and resistance strain effect. The static strain monitoring function of the sensor is verified by monitoring finger bending. Combined with machine learning, the sensor can be used for respiratory behavior recognition. This work fundamentally contributes to developing self-powered strain sensor with static and dynamic strain detections.
引用
收藏
页数:10
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