Ultra-conformal skin electrodes with synergistically enhanced conductivity for long-time and low-motion artifact epidermal electrophysiology

Yan Zhao, Song Zhang, Tianhao Yu, Yan Zhang, Guo Ye, Han Cui, Chengzhi He, Wenchao Jiang, Yu Zhai, Chunming Lu, Xiaodan Gu and Nan Liu ()
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Yan Zhao: Beijing Normal University
Song Zhang: The University of Southern Mississippi, Center for Optoelectronic Materials and Device
Tianhao Yu: Beijing Graphene Institute
Yan Zhang: Beijing Normal University
Guo Ye: Beijing Normal University
Han Cui: Shenzhen Traditional Chinese Medicine Hospital
Chengzhi He: Beijing University of Chemical Technology
Wenchao Jiang: Beijing Graphene Institute
Yu Zhai: Beijing Normal University
Chunming Lu: Beijing Normal University
Xiaodan Gu: The University of Southern Mississippi, Center for Optoelectronic Materials and Device
Nan Liu: Beijing Normal University

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract Accurate and imperceptible monitoring of electrophysiological signals is of primary importance for wearable healthcare. Stiff and bulky pregelled electrodes are now commonly used in clinical diagnosis, causing severe discomfort to users for long-time using as well as artifact signals in motion. Here, we report a ~100 nm ultra-thin dry epidermal electrode that is able to conformably adhere to skin and accurately measure electrophysiological signals. It showed low sheet resistance (~24 Ω/sq, 4142 S/cm), high transparency, and mechano-electrical stability. The enhanced optoelectronic performance was due to the synergistic effect between graphene and poly (3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS), which induced a high degree of molecular ordering on PEDOT and charge transfer on graphene by strong π-π interaction. Together with ultra-thin nature, this dry epidermal electrode is able to accurately monitor electrophysiological signals such as facial skin and brain activity with low-motion artifact, enabling human-machine interfacing and long-time mental/physical health monitoring.

Date: 2021
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DOI: 10.1038/s41467-021-25152-y

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