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Record W2749943324 · doi:10.11159/ffhmt17.136

Transient Changes in Liquid Water Distribution in Polymer Electrolyte Membrane Fuel Cells

2017· article· en· W2749943324 on OpenAlexaffvenue
Rupak Banerjee, Chuzhang Han, Nan Ge, Aimy Bazylak

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2017
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectrolyteTransient (computer programming)PolymerLiquid waterMembraneFuel cellsTransient analysisMaterials scienceProton exchange membrane fuel cellChemical engineeringTransient responseChemistryComputer scienceThermodynamicsComposite materialElectrical engineeringElectrodeEngineeringPhysics

Abstract

fetched live from OpenAlex

Water management is a critical factor in obtaining the highest performance and efficiency from polymer electrolyte membrane (PEM) fuel cells.The liquid water distribution in the individual layers of the PEM fuel cell has a strong impact on performance.The ionic conductivity of the membrane has a strong dependence on membrane hydration.The reactant gases in a PEM fuel cell are supplied through a humidification system to maintain appropriate levels of hydration in the membrane.However, the removal of the anode humidifier would significantly reduce the balance of plant costs and reduce the volume required for the fuel cell in an automotive setting.In this paper, the impact of lower anode humidification on the cell performance and the water distribution in the membrane and the cathode gas diffusion layer were studied.Synchrotron X-ray radiography was used to measure the changes in liquid water quantity in the individual layers.The impact of changing anode humidification on the water distribution is studied.The changes in membrane hydration levels have been measured by the radiographic technique and compared with the changes in membrane resistance.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.478

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 head0.011
GPT teacher head0.205
Teacher spread0.194 · 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 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

Citations0
Published2017
Admission routes2
Has abstractyes

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Same venueProceedings of the ... International Conference on Fluid Flow, Heat and Mass TransferSame topicFuel Cells and Related MaterialsFrench-language works237,207