Soft Neural Interfacing based on Implantable Graphene Fiber Microelectrode Arrays
Copyright: © Journal of Biomedical Physics and Engineering..
Microelectrode Arrays (MEAs) neural interfaces are considered implantable devices that interact with the nervous system to monitor and/or modulate brain activity. Graphene-based materials are utilized to address some of the current challenges in neural interface design due to their desirable features, such as high conductance, large surface-to-volume ratio, suitable electrochemical properties, biocompatibility, flexibility, and ease of production. In the current study, we fabricated and characterized a type of flexible, ultrasmall, and implantable neurostimulator based on graphene fibers. In this procedure, wet-spinning was employed to create graphene fibers with diameters of 10 to 50 µm. A 10-channel polyimide Printed Circuit Board (PCB) was then custom-designed and manufactured. The fibers were attached to each channel by conductive glue and also insulated by soaking them in a polyurethane solution. The tips were subsequently exposed using a blowtorch. Microstructural information on the fibers was obtained using Scanning Electron Microscopy (SEM), and the measurements of Electrochemical Impedance Spectroscopy (EIS) were conducted for each electrode. Flexible MEAs were created using graphene fibers with diameters ranging from 10 to 50 microns with a spacing of 150 microns. This method leads to producing electrode arrays with any size of fibers and a variety of channel numbers. The flexible neural prostheses can replace conventional electrodes in both neuroscience and biomedical research.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:13 |
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| Enthalten in: |
Journal of biomedical physics & engineering - 13(2023), 6 vom: 22. Dez., Seite 573-576 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Alsadat Hejazi, Maryam [VerfasserIn] |
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| Links: |
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| Themen: |
Biomedical Research |
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| Anmerkungen: |
Date Revised 28.12.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.31661/jbpe.v0i0.2306-1633 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM36639617X |
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| 520 | |a Microelectrode Arrays (MEAs) neural interfaces are considered implantable devices that interact with the nervous system to monitor and/or modulate brain activity. Graphene-based materials are utilized to address some of the current challenges in neural interface design due to their desirable features, such as high conductance, large surface-to-volume ratio, suitable electrochemical properties, biocompatibility, flexibility, and ease of production. In the current study, we fabricated and characterized a type of flexible, ultrasmall, and implantable neurostimulator based on graphene fibers. In this procedure, wet-spinning was employed to create graphene fibers with diameters of 10 to 50 µm. A 10-channel polyimide Printed Circuit Board (PCB) was then custom-designed and manufactured. The fibers were attached to each channel by conductive glue and also insulated by soaking them in a polyurethane solution. The tips were subsequently exposed using a blowtorch. Microstructural information on the fibers was obtained using Scanning Electron Microscopy (SEM), and the measurements of Electrochemical Impedance Spectroscopy (EIS) were conducted for each electrode. Flexible MEAs were created using graphene fibers with diameters ranging from 10 to 50 microns with a spacing of 150 microns. This method leads to producing electrode arrays with any size of fibers and a variety of channel numbers. The flexible neural prostheses can replace conventional electrodes in both neuroscience and biomedical research | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a Implantable Neurostimulators | |
| 650 | 4 | |a Microelectrodes | |
| 650 | 4 | |a Neural Prostheses | |
| 650 | 4 | |a Neurosciences | |
| 700 | 1 | |a Seyedi, Seyed Amir |e verfasserin |4 aut | |
| 700 | 1 | |a Mehdizadeh, Alireza |e verfasserin |4 aut | |
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