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Record W1771593714 · doi:10.1149/1.2777011

3D Modeling of Catalyst Layers in PEM Fuel Cells

2007· article· en· W1771593714 on OpenAlexafffund
D Schwarz, Ned Djilali

Bibliographic record

VenueJournal of The Electrochemical Society · 2007
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Victoria
KeywordsProton exchange membrane fuel cellElectrolyteAgglomerateCatalysisPolymerMaterials scienceChemical engineeringComputational fluid dynamicsMembraneFuel cellsChemistryComposite materialElectrodeOrganic chemistryMechanicsEngineering

Abstract

fetched live from OpenAlex

Three-dimensional, multicomponent and multiphase transport computations are performed using a computational fluid dynamics (CFD) code with a polymer electrolyte membrane (PEM) fuel cell module, which has been further improved by taking into account the detailed composition and structure of the catalyst layers using a multiple thin-film agglomerate model. In this model, reactant concentrations are dependent on transport through liquid water (caused by flooding in the catalyst layer) and polymer electrolyte films which surround the catalyst sites. The results of CFD computations show that transport limitations associated with the liquid water and polymer electrolyte have substantial negative effects on PEM fuel cell performance.

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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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

Citations59
Published2007
Admission routes2
Has abstractyes

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