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ACS Nano

ACS Nano

IF: 15.8
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Ultrathin Bioelectrode Array with Improved Electrochemical Performance for Electrophysiological Sensing and Modulation

Published:12 December 2024 DOI: 10.1021/acsnano.4c13325
Xiaojia Du, Leyi Yang, Xiaohu Shi, Chujie Ye, Yunfei Wang, Dekui Song, Wei Xiong, Xiaodan Gu, Chunming Lu, Nan Liu

Abstract

To achieve high accuracy and effectiveness in sensing and modulating neural activity, efficient charge-transfer biointerfaces and a high spatiotemporal resolution are required. Ultrathin bioelectrode arrays exhibiting mechanical compliance with biological tissues offer such biointerfaces. However, their thinness often leads to a lack of mechano-electrical stability or sufficiently high electrochemical capacitance, thus deteriorating their overall performance. Here, we report ultrathin (~115 nm) bioelectrode arrays that simultaneously enable ultraconformability, mechano-electrical stability and high electrochemical performance. These arrays show high opto-electrical conductivity (2060 S cm–1@88% transparency), mechanical stretchability (110% strain), and excellent electrochemical properties (24.5 mC cm–2 charge storage capacity and 3.5 times lower interfacial impedance than commercial electrodes). The improved mechano-electrical and electrochemical performance is attributed to the synergistic interactions within the poly(3,4-ethylenedioxythiophene) sulfonate (PEDOT:PSS)/graphene oxide (GO) interpenetrating network (PGIN), where π–π and hydrogen bonding interactions improve conductive pathways between PEDOT chains and enhance the charge-transfer mobility. This ultrathin bioelectrode is compatible with photolithography processing and provides spatiotemporally precise signal mapping capabilities for sensing and modulating neuromuscular activity. By capturing weak multichannel facial electromyography signals and applying machine learning algorithms, we achieve high accuracy in silent speech recognition. Moreover, the high transparency of the bioelectrode allows simultaneous recording of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals, facilitating dual-mode brain activity analysis with both high temporal and high spatial resolution.

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