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

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<sup>-1</sup>@88% transparency), mechanical stretchability (110% strain), and excellent electrochemical properties (24.5 mC cm<sup>-2</sup> 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.

Authors

Institutions

Publication Details

Journal
ACS Nano
Published
2024-12-12
DOI
https://doi.org/10.1021/acsnano.4c13325
Citations
12
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
3.22

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Dekui Song, Leyi Yang, Chunming Lu, Xiaojia Du et al.
12 citations
ACS Nano
Neuroscience and Neural Engineering
3.22
article

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

Dekui Song, Leyi Yang, Chunming Lu, Xiaojia Du, Xiaohu Shi, Wei Xiong, C Ye, Xiaodan Gu, Nan Liu, Yunfei Wang
article en
12 citations

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<sup>-1</sup>@88% transparency), mechanical stretchability (110% strain), and excellent electrochemical properties (24.5 mC cm<sup>-2</sup> 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.

ACS NanoVol. 18(51)
Beijing Normal University (CN), Chinese Institute for Brain Research (CN), University of Southern Mississippi (US)
National Natural Science Foundation of China, Beijing Municipal Natural Science Foundation, Fundamental Research Funds for the Central Universities
Affordable and clean energy
Openalex Percentile: Top 7%
Neuroscience and Neural Engineering
3.22
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.