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Record W2517189638 · doi:10.1149/07514.0251ecst

Determining the Impact of Dynamic Load Conditions on Interfacial Liquid Water Accumulation in Polymer Electrolyte Membrane Fuel Cell Gas Diffusion Layers Using Synchrotron X-Ray Radiography

2016· article· en· W2517189638 on OpenAlexafffund
Rupak Banerjee, Nan Ge, Jongmin Lee, Michael G. George, Hang Liu, Daniel Muirhead, Pranay Shrestha, Stéphane Chevalier, James Hinebaugh, Aimy Bazylak

Bibliographic record

VenueECS Transactions · 2016
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Toronto
FundersNational Research Council CanadaOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsElectrolyteSaturation (graph theory)Proton exchange membrane fuel cellSynchrotronPower densityCurrent densityMaterials scienceDiffusionAnalytical Chemistry (journal)PolymerCurrent (fluid)MembraneChemistryComposite materialElectrodeThermodynamicsChromatographyPower (physics)Optics

Abstract

fetched live from OpenAlex

Automotive power demand is dynamic, so the transient multiphase transport behavior must be understood and considered in the design of next generation polymer electrolyte membrane (PEM) fuel cell materials. In-operando synchrotron X-ray radiography was used to measure the changes in liquid water saturation of the gas diffusion layer (GDL) during changes in operational current density. Through in-operando visualizations at high temporal and spatial resolutions, we observed that the liquid water saturation of the GDL increases with increasing current density, but a threshold saturation in the GDL is eventually reached, despite further increases in current density. A time lag between the change in current density and the onset of increasing GDL saturation was also observed.

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 score0.106
Threshold uncertainty score0.454

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 head0.009
GPT teacher head0.245
Teacher spread0.236 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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