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Record W2513233833 · doi:10.3390/mi7090163

Direct Growth of Carbon Nanotubes on New High-Density 3D Pyramid-Shaped Microelectrode Arrays for Brain-Machine Interfaces

2016· article· en· W2513233833 on OpenAlexafffund
Bahareh Ghane Motlagh, M. Choueib, Alireza Hajhosseini Mesgar, M. Hasanuzzaman, Mohamad Sawan

Bibliographic record

VenueMicromachines · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaPolytechnique MontréalCMC Microsystems
KeywordsMicroelectrodeMaterials scienceElectrodeCarbon nanotubeMasking (illustration)SiliconNanotechnologyMultielectrode arrayCoatingOptoelectronicsElectrical impedanceMicroelectromechanical systemsBiomedical engineeringElectrical engineeringChemistry

Abstract

fetched live from OpenAlex

Silicon micromachined, high-density, pyramid-shaped neural microelectrode arrays (MEAs) have been designed and fabricated for intracortical 3D recording and stimulation. The novel architecture of this MEA has made it unique among the currently available micromachined electrode arrays, as it has provided higher density contacts between the electrodes and targeted neural tissue facilitating recording from different depths of the brain. Our novel masking technique enhances uniform tip-exposure for variable-height electrodes and improves process time and cost significantly. The tips of the electrodes have been coated with platinum (Pt). We have reported for the first time a selective direct growth of carbon nanotubes (CNTs) on the tips of 3D MEAs using the Pt coating as a catalyzer. The average impedance of the CNT-coated electrodes at 1 kHz is 14 kΩ. The CNT coating led to a 5-fold decrease of the impedance and a 600-fold increase in charge transfer compared with the Pt electrode.

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 score0.002
Threshold uncertainty score0.964

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 science0.0010.000
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 head0.016
GPT teacher head0.240
Teacher spread0.224 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

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