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Record W2132544664 · doi:10.1109/sensor.2007.4300399

The Effect of Biodegradable Drug Release Coatings on the Electrical Characteristics of Neural Electrodes

2007· article· en· W2132544664 on OpenAlexfundno aff
André Mercanzini, Sai T. Reddy, Marc Boers, Diana Velluto, Arnaud Bertsch, Jeffrey A. Hubbell, Philippe Renaud

Bibliographic record

VenueTRANSDUCERS 2007 - 2007 International Solid-State Sensors, Actuators and Microsystems Conference · 2007
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaÉcole Polytechnique Fédérale de LausanneEuropean Commission
KeywordsMicroelectrodeMaterials scienceElectrodeBiomedical engineeringElectrical impedanceCoatingDielectric spectroscopyMultielectrode arrayPolymerNanoparticleOptoelectronicsNanotechnologyComposite materialElectrochemistryChemistryElectrical engineeringMedicine

Abstract

fetched live from OpenAlex

This study describes how drug eluting coatings affect the signal quality of implantable microelectrodes. Polyimide-Platinum neural probes were micro fabricated and coated with a biodegradable polymer loaded with drug eluting nanoparticles. Electrical impedance spectroscopy was used to study how the coating affects electrical recording and stimulation characteristics. The measurements were performed in vitro and it was found that the biodegradable film slightly modified the measured impedance phase by +3deg at 10 kHz but not the magnitude. This change in impedance is permanent but does not adversely affect the recording and stimulation characteristics of the device. This result demonstrates the compatibility of controlled release drug coatings with neural probes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.251
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueTRANSDUCERS 2007 - 2007 International Solid-State Sensors, Actuators and Microsystems ConferenceSame topicNeuroscience and Neural EngineeringFrench-language works237,207