Highly transparent, antifreezing and stretchable conductive organohydrogels for strain and pressure sensors

Science China Technological Sciences - Tập 64 - Trang 2532-2540 - 2021
Rui Zhao1, Han Yang2, BaoQing Nie2, Liang Hu1
1State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, China
2Medical Image Processing, Analysis and Visualization (MIPAV), School of Electronic and Information Engineering, Soochow University, Suzhou, China

Tóm tắt

Conductive hydrogels are good candidates for flexible wearable sensors, which have received considerable attention for use in human-machine interfaces, human motion/health monitoring, and soft robots. However, these hydrogels often freeze at low temperatures and thus, exhibit low transparency, weak mechanical strength and stretchability, as well as poor adhesion strength. In this paper, conductive organohydrogels were prepared by thermal polymerization of acrylamide and N-(3-aminopropyl) methacrylamide in a glycerol-water binary solvent using NaCl as a conductive salt. Compared to other organohydrogels, our organohydrogels featured higher fracture stress (170 kPa) and greater stretchability (900%). The organohydrogels showed excellent antifreezing properties and high transparency (97%, at 400–800 nm wavelength) and presented outstanding adhesion strength to a variety of substrates. The conductive organohydrogels that were stored at −20°C for 24 h could still respond to both strain and pressure, showing a high sensitivity (gauge factor=2.73 under 100% strain), fast response time (0.4 s), and signal repeatability during multiple force cycles (∼100 cycles). Furthermore, the conductivity of cleaved antifreezing gels could be restored by contacting the broken surfaces together. Finally, we used our organohydrogels to monitor human tremors and bradykinesia in real-time within wired and wireless models, thus presenting a potential application for Parkinson’s disease diagnosis.

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