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Record W2325740555 · doi:10.1017/s1727719100001325

A Study on Mass Transfer in the Cathode Gas Channel of a Proton Exchange Membrane Fuel Cell

2007· article· en· W2325740555 on OpenAlexaff
King-Tsai Jeng, Chih‐Yung Wen, Lê Đức Anh

Bibliographic record

VenueJournal of Mechanics · 2007
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMass transferProton exchange membrane fuel cellSherwood numberCathodeCurrent (fluid)MechanicsWater vaporMaterials scienceStoichiometryOxygenThermodynamicsFlow (mathematics)Limiting oxygen concentrationAnalytical Chemistry (journal)ChemistryMembraneChromatographyPhysicsTurbulenceNusselt number

Abstract

fetched live from OpenAlex

Abstract A two-dimensional, transient mathematical model for the mass transfer of a reactant gas in the cathode gas channel of a PEMFC is developed. This model accounts concurrently for gas flow and multicomponent species (oxygen, water vapor and nitrogen) transport in the gas channel at specified cell current densities. The governing equations along with the boundary and initial conditions are solved numerically by using finite-difference methods. The numerical results show that the oxygen and water vapor concentrations in the gas channel are strong functions of stoichiometry. However, at a fixed stoichiometry, the current density has only a slight influence on the concentration variations. The fully-developed Sherwood number for oxygen mass transfer in the gas channel was found to be 6.0, which agrees well with the Sherwood number estimated from the correlation between mass and heat transfer. The current mathematical model and numerical results are confirmed by the experimental verification of the location of first appearance of liquid water at the channel/GDL interface.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.231
Teacher spread0.212 · 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 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

Citations5
Published2007
Admission routes1
Has abstractyes

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