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Dominating impact of microporous layer thickness on gas diffusion layer oxygen transport resistance

2025· article· en· W4413143051 on OpenAlexafffund
Divya Parekh, Salvatore Ranieri, Tess Seip, Eric Alexander Chadwick, Beste Derebaşı, Nan Ge, Rainey Wang, Aimy Bazylak

Bibliographic record

VenueJournal of Power Sources · 2025
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsHydrogenics (Canada)University of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoGovernment of SaskatchewanCummins IncorporatedCanada Research ChairsNational Research Council CanadaCanada Foundation for InnovationCanadian Institutes of Health ResearchMitacsNational Research CouncilUniversity of Saskatchewan
KeywordsMicroporous materialLayer (electronics)OxygenDiffusionMaterials scienceChemical engineeringChemistryComposite materialThermodynamicsEngineeringPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

The thickness of the microporous layer (MPL) in polymer electrolyte membrane (PEM) fuel cells with gas diffusion layers (GDLs) has a dominating impact on oxygen transport resistance, particularly compared to the thickness of the GDL substrate. Specifically, GDLs with thinner MPLs showed superior oxygen transport resistance despite possessing relatively restrictive GDL substrates. Concurrent operando imaging revealed inconsequential liquid water accumulation at the catalyst layer interface and within the GDL substrate despite the high relative humidity conditions. The highly beneficial absence of liquid water that typically lowers oxygen transport resistance, enabled a closer examination of how GDL morphology impacts the oxygen transport resistance during operation. • Operando imaging reveals low liquid water content in GDL despite high RH. • MPL dominates oxygen transport resistance compared to GDL substrate. • MPL thickness has strong influence on oxygen transport 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 score0.173
Threshold uncertainty score0.442

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 head0.005
GPT teacher head0.232
Teacher spread0.227 · 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

Citations4
Published2025
Admission routes2
Has abstractyes

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