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Record W4411743876 · doi:10.1002/jbm.a.37956

Iridium‐Bismuth‐Oxide Coatings for Use in Neural Stimulating Electrodes: The Influence of Ir/Bi Ratio

2025· article· en· W4411743876 on OpenAlexafffund
Xingge Xu, Sandra Minotti, Heather D. Durham, Sasha Omanovic

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceIridiumElectrodeBismuthAmorphous solidOxideNanocrystalline materialElectrochemistryIntercalation (chemistry)OptoelectronicsNanotechnologyInorganic chemistryCatalysisMetallurgy

Abstract

fetched live from OpenAlex

ABSTRACT Implantable neural prosthetics with stimulating electrodes are increasingly employed in medical practices to treat neural disabilities. The electrode material is expected to provide high charge storage and injection capacity (CSC/CIC) and low impedance for safe, efficient, and precise neural stimulation, while at the same time, being small. To improve the current state‐of‐the‐art neural‐electrode material, iridium oxide (IrOx), Ir m Bi 1‐m Ox coatings of various compositions ( m = 0, 0.2, 0.4, 0.6, 0.8, and 1.0) produced by thermal deposition were evaluated. The Ir 0.8 Bi 0.2 Ox yielded a CSC of 17.7 ± 1.1 mC/cm 2 , which is four‐fold higher than that of IrOx. At the same time, the impedance of Ir 0.8 Bi 0.2 Ox at 1 kHz was measured to be half of that of IrOx. The superior performance of Ir 0.8 Bi 0.2 Ox was explained by forming amorphous structures that facilitate the intercalation of H + and OH − ions into deeper oxide structures that contribute to faradaic charge storage. The Ir 0.8 Bi 0.2 Ox electrode also showed good stability and biocompatibility, which makes it potentially a good candidate for neural stimulating electrodes.

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)0.003
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.015
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.099
GPT teacher head0.401
Teacher spread0.302 · 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.

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

Citations1
Published2025
Admission routes2
Has abstractyes

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