Self‐powered sensor based on compressible ionic gel electrolyte for simultaneous determination of temperature and pressure

Author:

Zou Junjie1,Ma Yanan12,Liu Chenxu1,Xie Yimei1,Dai Xingyao1,Li Xinhui1,Li Shuxuan1,Peng Shaohui1,Yue Yang3ORCID,Wang Shuo1,Nan Ce‐Wen4,Zhang Xin1ORCID

Affiliation:

1. State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Center of Smart Materials and Devices & International School of Materials Science and Engineering Wuhan University of Technology Wuhan the People's Republic of China

2. Hubei Key Laboratory of Energy Storage and Power Battery, School of Mathematics, Physics and Optoelectronic Engineering Hubei University of Automotive Technology Shiyan the People's Republic of China

3. Information Materials and Intelligent Sensing Laboratory of Anhui Province, Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of Education, Institutes of Physical Science and Information Technology Anhui University Hefei the People's Republic of China

4. State Key Lab of New Ceramics and Fine Processing, School of Materials Science and Engineering Tsinghua University Beijing the People's Republic of China

Abstract

AbstractThe simultaneous detection of multiple stimuli, such as pressure and temperature, has long been a persistent challenge for developing electronic skin (e‐skin) to emulate the functionality of human skin. Meanwhile, the demand for integrated power supply units is an additional pressing concern to achieve its lightweightness and flexibility. Herein, we propose a self‐powered dual temperature–pressure (SPDM) sensor, which utilizes a compressible ionic gel electrolyte driven by the potential difference between MXene and Al electrodes. The SPDM sensor exhibits a rapid and timely response to changes in pressure‐induced deformation, while exhibiting a slow and hysteretic response to temperature variations. These distinct response characteristics enable the differentiation of current signals generated by different stimuli through machine learning, resulting in an impressive accuracy rate of 99.1%. Furthermore, the developed SPDM sensor exhibits a wide pressure detection range of 0–800 kPa and a broad temperature detection range of 5–75°C, encompassing the environmental conditions encountered in daily human life. The dual‐mode coupled strategy by machine learning provides an effective approach for temperature and pressure detection and discrimination, showcasing its potential applications in wearable electronics, intelligent robots, human–machine interactions, and so on.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Wiley

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