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Flexible strain sensors with high sensitivity, wide detection range, and low detection limit have continuously attracted great interest due to their tremendous application potential in areas such as health/medical-care, human–machine interface, as well as safety and security. While both of a high sensitivity and a wide working range are desired key parameters for a strain sensor, they are usually contrary to each other to be achieved on the same sensor due to the tightly structure dependence of both of them. Here, a flexible strain sensor with both high sensitivity and wide strain detection range is prepared based on the design of an integrated membrane containing both of parallel aligned and randomly aligned carbon nanofibers (CNFs). The parallel aligned CNF membrane (p-CNF) exhibits a low strain detection limit and high sensitivity, while the random aligned CNF membrane (r-CNF) exhibits a large strain detection range. Taking the advantages of both p-CNF and r-CNF, the strain sensor with stacked p-CNF and r-CNF (p/r-CNF) exhibits both high sensitivity and wide working range. Its gauge factor (GF) is 1,272 for strains under 0.5% and 2,266 for strain from 70% to 100%. At the same time, it can work in a wide strain range of 0.005% to 100%, fulfilling the requirements for accurately detecting full-range human motions. We demonstrated its applications in the recognition of facial expressions and joint movements. Furtherly, we constructed an intelligent lip-language recognition system, which can accurately track phonetic symbols and may help people with language disabilities, proving the potential of this strain sensor in health management and medical assistance. Besides, we foresee that the dual-alignment structure design of the p/r-CNF strain sensor may also be applied in the design of other high performance sensors.

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Publication history

Received: 29 August 2022
Revised: 30 September 2022
Accepted: 07 October 2022
Published: 21 November 2022

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© Tsinghua University Press 2022

Acknowledgements

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 52125201 and 21975141) and the National Key Research and Development Program of China (No. 2020YFA0210702).

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Reprints and Permission requests may be sought directly from editorial office.
Email: nanores@tup.tsinghua.edu.cn

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