Evidence map›Paper›PMID 38148964›Full record

ArticleJournal of biomedical physics & engineering2023

Soft Neural Interfacing based on Implantable Graphene Fiber Microelectrode Arrays.

Maryam Alsadat Hejazi, Seyed Amir Seyedi, Alireza Mehdizadeh

Abstract read
In one paragraph

Article in Journal of biomedical physics & engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Maryam Alsadat HejaziDepartment of Medical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
Seyed Amir SeyediDepartment of Medical Sciences, Faculty of Advanced Technology, Isfahan University, Isfahan, Iran.
Alireza MehdizadehResearch Center for Neuromodulation and Pain, Shiraz University of Medical Scineces, Shiraz, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Biomedical ResearchImplantable NeurostimulatorsMicroelectrodesNeural ProsthesesNeurosciences

Identifiers

PMID38148964
PMCPMC10749413

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.