MétaCan
Menu
Back to cohort
Record W6990472707

Development of iridium-bismuth-oxide coatings for use in neural stimulating electrodes

2019· dissertation· en· W6990472707 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2019
Typedissertation
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsnot available
FundersMcGill University
KeywordsElectrodeBismuthBiocompatibilityElectrical impedanceOxideTitanium
DOInot available

Abstract

fetched live from OpenAlex

Implantable neural prosthetics with stimulating electrodes is an increasingly-employed medical practice to treat neural disability.Further development of prosthetics to recover complex neuron function requires electrodes with higher capacity to delivery charge to neuron.Ir-oxide is currently considered as state-of-the-art stimulating electrode material.However, further improvement of its properties is needed.Consequently, in this work, addition of bismuth to Ir-oxide to produce IrxBi1-x-oxide coatings of various composition (x = 0, 0.2, 0.4, 0.6, 0.8 and 1.0) were fabricated by thermal deposition of their salts on a titanium substrate, and their charge-storage/delivery capacity, surface morphology, crystalline structure and biocompatibility was evaluated.The mixed metal oxides were characterized as consisting of multi-oxide states of Ir and Bi.It was found that only a 20mol.%addition of bismuth to Ir-oxide to produce Ir0.8Bi0.2-oixdeyielded superior properties to Ir-oxide.This electrode exhibited a five-fold increase in charge storage capacity over the Ir-oxide electrode, yielding 26.8 mC/cm 2 .At the same time, this electrode yielded the lowest impedance at 1 kHz.The superior performance of Ir0.8Bi0.2-oixdewas explained to originate from change in lattice structure upon introduction of Bi to Ir-oxide, which enables better access of H + and OH -ions deeper into the oxide structure, thus yielding a higher charge storage capacity.The Ir0.8Bi0.2oixdeelectrode also showed good stability and biocompatibility, which makes potentially a better candidate for neural stimulating electrodes than the current state-ofthe-art Ir-oxide. II Abr gLes prothses neurales implantables avec lectrodes de stimulation constituent une pratique mdicale de plus en plus employe dans le traitement d'incapacit neurales.Le dveloppement davantage de prothses pour rtablir une fonction neuronale complexe ncessite des lectrodes ayant une capacit plus leve pour dlivrer la charge au neurone.L'oxyde d'Ir est actuellement considr comme un matriau d'lectrode de stimulation suprieur.Cependant, une amlioration de ses proprits est ncessaire.Pour cette raison, dans ce travail, l'addition de bismuth l'oxyde de Ir pour produire des revtements d'IrxBi1-x-oxydes de compositions diverses (x = 0, 0,2, 0,4, 0,6, 0,8 et 1,0) ont t fabriques par le processus bien tabli dcomposition thermique de leurs sels sur des titane substrats, et leur capacit de stockage/livre de charge, leur morphologie de surface, leur structure cristalline et leur biocompatibilit ont t valus.Les oxydes mtalliques mixtes fabriques dans le projet ont constitues d'Ir et de Bi selon des technique de caractrisation.Il a t constat que seule une addition de 20% en moles de bismuth l'oxyde d'Ir pour produire de l'Ir0.8Bi0.2-oxydedonnait des proprits suprieures celles de l'oxyde d'Ir.Cette lectrode prsentait une capacit de stockage cinq fois plus lev que l'lectrode en oxyde d'Ir, ce qui donnait 26.8mC/cm 2 .Au mme temps, cette lectrode produisait la plus faible impdance 1 kHz.La performance suprieure d'Ir0.8Bi0.2-oixdeest expliqu par le changement de structure du rseau lors de l'introduction de Bi dans l'oxyde d'Ir, ce qui permet un meilleur accs approfondi des ions H + et OH- la structure de l'oxyde, produisant ainsi une capacit de stockage plus leve.L'lectrode Ir0.8Bi0.2-oxyde a galement montr une bonne stabilit et une bonne biocompatibilit, ce qui fait en sorte que ce-dernier a la potentielle d'tre un meilleur candidat pour les lectrodes de stimulation neurale que l'oxyde d'Ir. III AcknowledagementsFirst and foremost, I would like to thank my supervisor, Prof. Sasha Omanovic, for taking me as a master's student in his group and for the research opportunities that I have been given.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.017
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.277
Teacher spread0.230 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueeScholarship@McGill (McGill)Same topicNeuroscience and Neural EngineeringFrench-language works237,207