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Record W2606857126 · doi:10.1063/1.4979121

Modelling oxide formation and growth on platinum

2017· article· en· W2606857126 on OpenAlexaff
Heather Baroody, Gregory Jerkiewicz, Michael Eikerling

Bibliographic record

VenueThe Journal of Chemical Physics · 2017
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsQueen's UniversitySimon Fraser University
FundersNissan Motor Company
KeywordsPlatinumOxideDissolutionElectrolyteInorganic chemistryChemistryCatalysisMetalKineticsDiffusionMaterials scienceChemical engineeringElectrodePhysical chemistryThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

We present a mathematical model of oxide formation and growth on platinum. The motivation stems from the necessity to understand platinum dissolution in the cathode catalyst layer of polymer electrolyte fuel cells. As is known, platinum oxide formation and reduction are strongly linked to platinum dissolution processes. However, a consistent model of the oxidation processes on platinum does not exist. Our oxide growth model links interfacial exchange processes between platinum and oxygen ions with the transport of oxygen ion vacancies via diffusion and migration. A parametric analysis is performed to rationalize vital trends in oxide growth kinetics. The rate determining step of oxide formation and growth is identified as the extraction of platinum atoms at the metal-oxide interface. A kinetic effect is observed while adjusting the potential when growing the oxide layer, and the solution indicates that a structural change occurs at high potentials, around 1.5 VRHE. The model compares well to experimental data for various materials from various sources.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.224
Teacher spread0.207 · 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 designSimulation or modeling
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

Citations33
Published2017
Admission routes1
Has abstractyes

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