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Record W2727240552 · doi:10.1109/ccece.2017.7970775

Design, fabrication, and test of flexible thin-film microelectrode arrays for neural interfaces

2017· article· en· W2727240552 on OpenAlexaff
Mohammad Sadegh Nahvi, Farhad Akbari Boroumand, Mohammad Hossein Maghami, Amir M. Sodagar, Amir Shojaei, Javad Mirnajafi‐Zadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsYork University
Fundersnot available
KeywordsMicroelectrodeFabricationComputer scienceMaterials scienceNeural ProsthesisOptoelectronicsEngineeringElectrodeBiomedical engineeringChemistry

Abstract

fetched live from OpenAlex

This paper describes the design, fabrication, characterization and application of PET/ITO and polyimidebased flexible microelectrode arrays for recording purposes and also stimulation of retinal bipolar and ganglion cells. Two different structures are designed in this work. The planar electrode arrays are fabricated on thin PET/ITO and polyimide substrates and encapsulated with SU-8 which is a biocompatible passivating material. Stimulation and recording sites are made by platinum in order to reduce the electrode/tissue interface impedance. The utilized substrates make the fabricated electrodes so flexible that they can easily shape to contoured surface of retina. Prototypes of 6×6 electrode arrays are fabricated for recording from and stimulation of target tissue. The exposed electrode surface has a diameter of 100μm with tracking path width of 40μm and 15μm spacing between interconnects. Fabricated microelectrodes were characterized by impedance spectroscopy and also were used to record ECoG signals from anesthetized rat. Experimental results show the electrode/tissue impedance of 6.4kΩ at the frequency of 1kHz.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.063
GPT teacher head0.297
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2017
Admission routes1
Has abstractyes

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