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Record W2139213936 · doi:10.1149/2.061304jes

A Semi-Empirical Two Step Carbon Corrosion Reaction Model in PEM Fuel Cells

2013· article· en· W2139213936 on OpenAlexaff
A. P. Young, Vesna Colbow, David J. Harvey, E. Rogers, Silvia Wessel

Bibliographic record

VenueJournal of The Electrochemical Society · 2013
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsBallard Power Systems (Canada)
FundersU.S. Department of Energy
KeywordsCorrosionProton exchange membrane fuel cellCarbon fibersChemistryPlatinumElectrolyteCatalysisCarbon monoxideInorganic chemistryDissolutionMaterials scienceChemical engineeringElectrodeComposite materialOrganic chemistryComposite numberPhysical chemistry

Abstract

fetched live from OpenAlex

The cathode CL of a polymer electrolyte membrane fuel cell (PEMFC) was exposed to high potentials, 1.0 to 1.4 V versus a reversible hydrogen electrode (RHE), that are typically encountered during start up/shut down operation. While both platinum dissolution and carbon corrosion occurred, the carbon corrosion effects were isolated and modeled. The presented model separates the carbon corrosion process into two reaction steps; (1) oxidation of the carbon surface to carbon-oxygen groups, and (2) further corrosion of the oxidized surface to carbon dioxide/monoxide. To oxidize and corrode the cathode catalyst carbon support, the CL was subjected to an accelerated stress test cycled the potential from 0.6 V RHE to an upper potential limit (UPL) ranging from 0.9 to 1.4 V RHE at varying dwell times. The reaction rate constants and specific capacitances of carbon and platinum were fitted by evaluating the double layer capacitance (Cdl) trends. Carbon surface oxidation increased the Cdl due to increased specific capacitance for carbon surfaces with carbon-oxygen groups, while the second corrosion reaction decreased the Cdl due to loss of the overall carbon surface area. The first oxidation step differed between carbon types, while both reaction rate constants were found to have a dependency on UPL, temperature, and gas relative humidity.

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)0.000
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 score0.363
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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

Citations63
Published2013
Admission routes1
Has abstractyes

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