Graphene Fiber‐Based Bio‐Microelectrode for Precise Neural Signal Recording

ABSTRACT Accurate extraction of electrophysiological signals is crucial for advancing research into the pathogenesis of mental illnesses. Nevertheless, current physiological electrodes still face significant challenges, including modulus mismatch, poor biocompatibility, and suboptimal signal‐to‐noise ratio. It is well established that pore structure regulation is the key point to the preparation of high‐performance physiological electrodes. Herein, non‐liquid‐crystal spinning integrated with a shearing‐induced orientation process is employed for the fabrication of graphene fibers with an oriented porous structure, which exhibit high electrical conductivity (109.73 S cm −1 ), suitable interfacial impedance (14.23 Ω mm 2 ), and high charge storage capacity (82.89 mC cm −2 ). After coating with SiO 2 and hydrogel layers, the graphene fiber achieves electrical insulation of the side surface, preventing charge leakage, and establishes an adaptive interface that effectively reduces the interfacial modulus, respectively. Thus, it can serve as a bio‐microelectrode for the precise detection of neural activity in the targeted brain region, and the extracted electrophysiological signals possess a high signal‐to‐noise ratio of 12.83 dB. This work presents a scalable and synergistic strategy for constructing next‐generation bio‐microelectrodes, enabling high‐quality extraction of electrophysiological signals with relatively minor invasive injury.

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Publication Details

Journal
Advanced Functional Materials
Published
2026-04-03
DOI
https://doi.org/10.1002/adfm.202528803
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
0.00

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article

Graphene Fiber‐Based Bio‐Microelectrode for Precise Neural Signal Recording

GuoYin Chen, Zeqi Zhang, Yong Zhang, Hongyu Pan et al.
Advanced Functional Materials
Neuroscience and Neural Engineering
article

Graphene Fiber‐Based Bio‐Microelectrode for Precise Neural Signal Recording

GuoYin Chen, Zeqi Zhang, Yong Zhang, Hongyu Pan, Meifang Zhu, Kai Hou, Jiayuan Liu, Tianyu Wu
article en

Abstract

ABSTRACT Accurate extraction of electrophysiological signals is crucial for advancing research into the pathogenesis of mental illnesses. Nevertheless, current physiological electrodes still face significant challenges, including modulus mismatch, poor biocompatibility, and suboptimal signal‐to‐noise ratio. It is well established that pore structure regulation is the key point to the preparation of high‐performance physiological electrodes. Herein, non‐liquid‐crystal spinning integrated with a shearing‐induced orientation process is employed for the fabrication of graphene fibers with an oriented porous structure, which exhibit high electrical conductivity (109.73 S cm −1 ), suitable interfacial impedance (14.23 Ω mm 2 ), and high charge storage capacity (82.89 mC cm −2 ). After coating with SiO 2 and hydrogel layers, the graphene fiber achieves electrical insulation of the side surface, preventing charge leakage, and establishes an adaptive interface that effectively reduces the interfacial modulus, respectively. Thus, it can serve as a bio‐microelectrode for the precise detection of neural activity in the targeted brain region, and the extracted electrophysiological signals possess a high signal‐to‐noise ratio of 12.83 dB. This work presents a scalable and synergistic strategy for constructing next‐generation bio‐microelectrodes, enabling high‐quality extraction of electrophysiological signals with relatively minor invasive injury.

Advanced Functional Materials
Zhejiang Sci-Tech University (CN), Donghua University (CN)
National Natural Science Foundation of China, Shanghai Municipal Education Commission, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 25%
Neuroscience and Neural Engineering
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Graphene Fiber‐Based Bio‐Microelectrode for Precise Neural Signal Recording — GuoYin Chen, Zeqi Zhang, et al. · Advanced Functional Materials (2026) | TGRS Research Map | TGRS