A flexible and biocompatible trilayer coaxial heterogeneous structure microfiber electrode for long-term electrophysiological recordings in freely moving mice

Abstract

Neuroscience and neural engineering face the critical challenge of accurately capturing and interpreting electrophysiological signals for understanding brain function and developing neural prosthetics. Here, we develop a trilayer coaxial heterogeneous structure flexible neuronal electrode, rSF–Au–PC, which addresses these challenges through its innovative design and superior performance. Developed via a multi-step, large-scale fabrication process, the rSF–Au–PC electrode features an adjustable diameter and low specific impedance (7.67 MΩ at 1 kHz), ensuring precise signal capture. It also boasts high charge storage capacity (51.16 mC cm−2), high charge injection capacity (19.11 mC cm−2), and high signal-to-noise ratio (14.48 dB after three weeks), which are essential for reliable electrophysiological signal recording. The electrode's remarkable biocompatibility and robust electrochemical and mechanical stability make it suitable for long-term use, outperforming conventional tungsten wire electrodes in chronic in vivo applications. This advancement holds significant implications for neuroscience applications, particularly those requiring extended electrophysiological surveillance, and may pave the way for future innovations in neural prosthetics and diagnostic technologies.

Graphical abstract: A flexible and biocompatible trilayer coaxial heterogeneous structure microfiber electrode for long-term electrophysiological recordings in freely moving mice

Supplementary files

Article information

Article type
Paper
Submitted
12 Jul 2025
Accepted
03 Nov 2025
First published
09 Dec 2025

Nanoscale, 2026, Advance Article

A flexible and biocompatible trilayer coaxial heterogeneous structure microfiber electrode for long-term electrophysiological recordings in freely moving mice

J. Huang, X. Li, J. Jiang, J. Wang, S. Zhou, Y. Liang, Y. Liang, X. Chen, H. Chen, H. Wang, H. Qin and S. Lin, Nanoscale, 2026, Advance Article , DOI: 10.1039/D5NR02957K

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