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Scalable Fabrication of 3D-Protruded Neural Microelectrodes Using Flexible PCB Technology for High-Fidelity Neural Interfaces

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Title
Scalable Fabrication of 3D-Protruded Neural Microelectrodes Using Flexible PCB Technology for High-Fidelity Neural Interfaces
Issued Date
2025-11
Citation
ACS APPLIED ELECTRONIC MATERIALS, v.7, no.22, pp.10478 - 10488
Type
Article
Author Keywords
3D electrodeflexiblePCBelectrophysiologyelectrodepositionflexible neural electrode
Abstract

Flexible neural electrodes hold great promise for monitoring electrophysiological activity in the brain, but achieving both fabrication scalability and high signal fidelity remains a significant challenge. Conventional high-performance neural interfaces typically require complex and costly microfabrication processes, while simpler approaches often fail to optimize the electrode-tissue interface. Here, we introduce a scalable, cost-effective platform for fabricating high-performance three-dimensional (3D) neural electrodes using an industrial flexible printed circuit board (fPCB) process. We systematically compare two electrode architectures: (i) recessed microelectrodes with gold (Au) electroless plating, serving as a benchmark, and (ii) 3D protruded microelectrodes formed by tin (Sn) electroplating. To isolate the role of geometry, both electrode types were further functionalized with high-surface-area platinum black (Pt black) and poly(3,4-ethylenedioxythiophene) (PEDOT) coatings. Electrochemical analysis revealed that the Sn-protruded electrodes exhibited lower impedance at 1 kHz and a higher charge storage capacity (CSC) than Au-recessed electrodes. In vivo recordings from the mouse hippocampus further demonstrated that Sn-protruded electrodes achieved a significantly higher signal-to-noise ratio (SNR) and stronger spike amplitudes compared to both bare and coated Au-recessed electrodes. These findings establish 3D protruded electrode geometry as a key determinant of recording fidelity, primarily by reducing the electrode-neuron distance. More broadly, our results demonstrate that the fPCB-based approach provides a rapid, accessible, and scalable route to high-performance neural interfaces, highlighting its potential for widespread adoption in neural engineering.

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URI
https://scholar.dgist.ac.kr/handle/20.500.11750/60398
DOI
10.1021/acsaelm.5c02055
Publisher
AMER CHEMICAL SOC
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