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Record W2000828899 · doi:10.1115/1.1538186

Modeling and Simulation of PEM Fuel Cells With CO Poisoning

2003· article· en· W2000828899 on OpenAlexafffund
J.J. Baschuk, Andrew Rowe, Xianguo Li

Bibliographic record

VenueJournal of Energy Resources Technology · 2003
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of VictoriaUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrolyteProton exchange membrane fuel cellTafel equationAnodeCathodeDesorptionPolymer electrolyte membrane electrolysisHydrogenCurrent densityElectrochemistryExchange current densityChemistryChemical engineeringAdsorptionThermodynamicsMaterials scienceAnalytical Chemistry (journal)ElectrodeCatalysisPhysical chemistryChromatographyElectrolysisOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

A polymer electrolyte membrane (PEM) fuel cell is analyzed by applying the conservation principle to the electrode backing, catalyst layers and polymer electrolyte. The conservation equations used are the conservation of species, momentum and energy, with the Nernst-Planck equation used for the electrolyte. Oxygen reduction at the cathode is modeled using the Butler-Volmer equation while the adsorption, desorption and electro-oxidation of hydrogen and CO at the anode are modeled by the Tafel-Volmer and “reactant-pair” mechanism, respectively. Temperature variations within the cell are minimized by decreasing current density or increasing temperature. An increase in pressure increases the cell voltage at low current density, but decreases the cell voltage at high current density. The electrochemical kinetics model used for the adsorption, desorption and electro-oxidation of hydrogen and CO is validated with published, experimental data.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.287

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 head0.004
GPT teacher head0.187
Teacher spread0.182 · 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 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

Citations24
Published2003
Admission routes2
Has abstractyes

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