Fabric-based lamina emergent MXene-based electrode for electrophysiological monitoring

Sanghyun Lee, Dong Hae Ho, Janghwan Jekal, Soo Young Cho, Young Jin Choi, Saehyuck Oh, Yoon Young Choi, Taeyoon Lee, Kyung-In Jang () and Jeong Ho Cho ()
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Sanghyun Lee: Yonsei University
Dong Hae Ho: Daegu Gyeongbuk Institute of Science and Technology (DGIST)
Janghwan Jekal: Daegu Gyeongbuk Institute of Science and Technology (DGIST)
Soo Young Cho: Yonsei University
Young Jin Choi: Yonsei University
Saehyuck Oh: Daegu Gyeongbuk Institute of Science and Technology (DGIST)
Yoon Young Choi: University of Illinois at Urbana−Champaign
Taeyoon Lee: Yonsei University
Kyung-In Jang: Daegu Gyeongbuk Institute of Science and Technology (DGIST)
Jeong Ho Cho: Yonsei University

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract Commercial wearable biosignal sensing technologies encounter challenges associated with irritation or discomfort caused by unwanted objects in direct contact with the skin, which can discourage the widespread adoption of wearable devices. To address this issue, we propose a fabric-based lamina emergent MXene-based electrode, a lightweight and flexible shape-morphing wearable bioelectrode. This work offers an innovative approach to biosignal sensing by harnessing the high electrical conductivity and low skin-to-electrode contact impedance of MXene-based dry electrodes. Its design, inspired by Nesler’s pneumatic interference actuator, ensures stable skin-to-electrode contact, enabling robust biosignal detection in diverse situations. Extensive research is conducted on key design parameters, such as the width and number of multiple semicircular legs, the radius of the anchoring frame, and pneumatic pressure, to accommodate a wide range of applications. Furthermore, a real-time wireless electrophysiological monitoring system has been developed, with a signal-to-noise ratio and accuracy comparable to those of commercial bioelectrodes. This work excels in recognizing various hand gestures through a convolutional neural network, ultimately introducing a shape-morphing electrode that provides reliable, high-performance biosignal sensing for dynamic users.

Date: 2024
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DOI: 10.1038/s41467-024-49939-x

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