Neural Electrode Based on 3D Microscale/Nanoscale Porous Graphene Structures for Neural Stimulation

Conventional neural electrodes, made from flat and smooth metals such as platinum and gold, face challenges due to their low charge storage capacity and charge injection capacity, high impedance, poor electrochemical stability, and limited mechanical flexibility. Here, we present high-performance neural electrodes based on 3D microscale/nanoscale porous graphene structures derived from transparent fluorinated polyimide for efficient neural stimulation. 3D micro/nanoscale porous graphene structure is formed using a scalable, low-cost, and single-step laser photothermal manufacturing technique, providing an enhanced specific surface area, a high charge storage capacity of 362.4 mC cm–2, and a charge injection capacity of 10.32 mC cm–2. The charge storage capacity of our neural electrode is more than 2 orders of magnitude higher than that of monolayer graphene and gold electrodes due to its integrated micropores and high specific surface area, facilitating efficient electrolyte ion penetration and subsequent charge accumulation. 3D micro/nanoscale porous graphene-based neural electrodes also demonstrate high endurance, exceeding 1 million stimulation cycles, and exhibit remarkable flexibility and conformability, ideal for close contact with nerves during stimulation. Furthermore, the combination of high charge storage capacity and the transparency of the fluorinated polyimide film makes it a suitable material for future applications that require simultaneous nerve stimulation and imaging-based nerve identification.

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Publication Details

Journal
ACS Applied Nano Materials
Published
2024-10-02
DOI
https://doi.org/10.1021/acsanm.4c03772
Citations
6
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
1.61
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article

Neural Electrode Based on 3D Microscale/Nanoscale Porous Graphene Structures for Neural Stimulation

Muhammad Ajmal Khan, Byoung Gak Kim, Shirin Movaghgharnezhad, Pilgyu Kang et al.
6 citations
ACS Applied Nano Materials
Neuroscience and Neural Engineering
1.61
article

Neural Electrode Based on 3D Microscale/Nanoscale Porous Graphene Structures for Neural Stimulation

Muhammad Ajmal Khan, Byoung Gak Kim, Shirin Movaghgharnezhad, Pilgyu Kang, Shani E. Ross, Hyojin Kim
article en
6 citations

Abstract

Conventional neural electrodes, made from flat and smooth metals such as platinum and gold, face challenges due to their low charge storage capacity and charge injection capacity, high impedance, poor electrochemical stability, and limited mechanical flexibility. Here, we present high-performance neural electrodes based on 3D microscale/nanoscale porous graphene structures derived from transparent fluorinated polyimide for efficient neural stimulation. 3D micro/nanoscale porous graphene structure is formed using a scalable, low-cost, and single-step laser photothermal manufacturing technique, providing an enhanced specific surface area, a high charge storage capacity of 362.4 mC cm–2, and a charge injection capacity of 10.32 mC cm–2. The charge storage capacity of our neural electrode is more than 2 orders of magnitude higher than that of monolayer graphene and gold electrodes due to its integrated micropores and high specific surface area, facilitating efficient electrolyte ion penetration and subsequent charge accumulation. 3D micro/nanoscale porous graphene-based neural electrodes also demonstrate high endurance, exceeding 1 million stimulation cycles, and exhibit remarkable flexibility and conformability, ideal for close contact with nerves during stimulation. Furthermore, the combination of high charge storage capacity and the transparency of the fluorinated polyimide film makes it a suitable material for future applications that require simultaneous nerve stimulation and imaging-based nerve identification.

ACS Applied Nano MaterialsVol. 7(20)
George Mason University (US), Korea Research Institute of Chemical Technology (KR)
Openalex Percentile: Top 17%
Neuroscience and Neural Engineering
1.61
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