Tough and Robust Mechanically Interlocked Gel-Elastomer Hybrid Electrode for Soft Strain Gauge

被引:16
|
作者
Huang, Jianren [1 ]
Chen, Anbang [1 ]
Han, Songjiu [1 ]
Wu, Qirui [1 ]
Zhu, Jundong [1 ]
Zhang, Jiayu [1 ]
Chen, Yujia [1 ]
Liu, Jiantao [2 ]
Guan, Lunhui [1 ]
机构
[1] Chinese Acad Sci, Fujian Inst Res Struct Matter, CAS Key Lab Design & Assembly Funct Nanostruct, Fujian Key Lab Nanomat, Fuzhou 350108, Peoples R China
[2] Xiamen Port Holding Grp co Ltd, Xiamen 361012, Peoples R China
基金
中国国家自然科学基金;
关键词
gel-elastomer; motion classification; robustness; soft strain gauge; FATIGUE; SKIN; SENSORS; RESILIENT;
D O I
10.1002/advs.202301116
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Soft strain gauges provide a flexible and versatile alternative to traditional rigid and inextensible gauges, overcoming issues such as impedance mismatch, the limited sensing range, and fatigue/fracture. Although several materials and structural designs are used to fabricate soft strain gauges, achieving multi-functionality for applications remains a significant challenge. Herein, a mechanically interlocked gel-elastomer hybrid material is exploited for soft strain gauge. Such a material design provides exceptional fracture energy of 59.6 kJ m(-2) and a fatigue threshold of 3300 J m(-2), along with impressive strength and stretchability. The hybrid material electrode possesses excellent sensing performances under both static and dynamic loading conditions. It boasts a tiny detection limit of 0.05% strain, ultrafast time resolution of 0.495 ms, and high linearity. This hybrid material electrode can accurately detect full-range human-related frequency vibrations ranging from 0.5 to 1000 Hz, enabling the measurement of physiological parameters. Additionally, the patterned soft strain gauge, created through lithography, demonstrates superior signal-noise rate and electromechanical robustness against deformation. By integrating a multiple-channel device, an intelligent motion detection system is developed, which can classify six typical human body movements with the assistance of machine learning. This innovation is expected to drive advancements in wearable device technology.
引用
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页数:13
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