Highly sensitive self-powered pressure sensor with high power output based on electrochemical reactions for rehabilitation monitoring.

Authors

Xiaohui Guo, Shangzhe Yin, Bing Hu, Haoyue Dong, Peng Wang, Zihan Xiong, Ci Song, Jinheng Wen, Wenxi Yi, Zhiyuan Liu, Zhangling Duan, Qi Hong, Ting-Jung Lin, Yunong Zhao

Published in

Talanta. Volume 312. Issue Pt A. Pages 130367. Jul 24, 2026. Epub Jul 24, 2026.

Abstract

Self-powered electrochemical pressure sensors have aroused considerable research interest owing to their capability of sensing both static and dynamic pressure without an auxiliary power supply. Nevertheless, developing self-powered pressure sensors that simultaneously possess high sensitivity, high output, and modular integration capability continues to represent a major obstacle. In this work, we fabricated a self-powered pressure sensor based on the principle of converting chemical energy into electrical energy via electrochemical reactions. Copper and zinc metals were employed as the cathode and anode respectively, while a conductive polyvinyl alcohol hydrogel served as the intermediate electrolyte to create a humid environment, thereby enhancing the sensor's output power. Furthermore, microstructures were fabricated on the hydrogel surface using 3D printing technology to improve the sensor's detection sensitivity. The results demonstrate that the sensor exhibits a high sensitivity of 1.126 mA/kPa within the range of 0.075 to 5 kPa. As a power supply component, it can output a voltage of 1.05 V and a high current of 16.1 mA at 100 kPa, with a maximum output power of 3.94 mW. The self-powered sensors we have developed can supply power to other electronic components to form a self-powered pressure detection platform. Moreover, the sensor can accurately monitor dynamic and static signals during human rehabilitation training and, in combination with deep learning, recognize five types of rehabilitation exercises with a high degree of accuracy. This study presents a novel solution for self-powered smart wearable devices.

PMID:
42503257
Bibliographic data and abstract were imported from PubMed on 27 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.