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Record W2319274795 · doi:10.1149/1.3502343

Modeling the Effect of Low Carbon Conductivity of the Cathode Catalyst Layer on PEM Fuel Cell Performance

2010· article· en· W2319274795 on OpenAlex
Morteza Baghalha, Jürgen Stumper, David J. Harvey, Michael Eikerling

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueECS Transactions · 2010
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsAutomotive Fuel Cell Cooperation (Canada)Simon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConductivityCarbon fibersProton exchange membrane fuel cellMaterials scienceCathodeOhmic contactCorrosionCatalysisChemical engineeringIonomerLayer (electronics)ChemistryComposite materialOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Carbon corrosion is a major degradation mechanism in cathode catalyst layers (CCL) of PEM fuel cells. Carbon corrosion causes carbon particles to decrease in size and the ionomer to carbon ratio to increase. One of the consequences of these changes is the reduced electronic conductivity of the carbon phase in CCL. In the present study, the effect of reduced carbon conductivity on the cell performance was investigated through modeling of two experimental case studies of a fresh and a severely degraded CCL. The critical carbon conductivity of CCL, below which the electronic potential loss in a degraded CCL becomes an important contributing loss, is defined. The inter-relations between low carbon conductivity, electronic ohmic loss, and EPSA loss (through Pt/C particles detachment mechanism) are also discussed.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.235

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 integrity0.0000.000
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 head0.006
GPT teacher head0.186
Teacher spread0.180 · 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