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Record W2317055176 · doi:10.1115/fuelcell2005-74174

An Analytical Model of the Membrane Electrode Assembly in a PEMFC

2005· article· en· W2317055176 on OpenAlexaff
Shawn Litster, Ned Djilali

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsProton exchange membrane fuel cellMembrane electrode assemblyElectrolyteMaterials scienceMembraneElectrodeVoid (composites)CatalysisCathodeLayer (electronics)PolymerRange (aeronautics)Chemical engineeringFuel cellsComposite materialEngineeringChemistryElectrical engineering

Abstract

fetched live from OpenAlex

An analytical model of the membrane electrode assembly (MEA) in a polymer electrolyte membrane fuel cell (PEM) has been developed for investigating the effect of catalyst layer specifications. Emphasis is placed on the cathode catalyst layer, which is modeled using a finite-thickness formulation with parameters obtained from a variable-width macrohomogeneous model. The variable-width formulation accounts for the effect of changing catalyst layer specifications on the dimensions of the catalyst layer by assuming a constant void fraction. Interest in low-humidity operation of micro-fuel cells that are passively fed ambient air has facilitated the present derivations and assumptions. The model is shown to agree well with experimental data over a substantial range of catalyst layer specifications. In addition, the model shows excellent promise as a tool for optimizing catalyst layers in micro-fuel cells with passive ambient air breathing.

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 score0.008
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 science0.0020.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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