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Record W3008714854 · doi:10.1002/fuce.201900171

New CCL|MPL Architecture Reducing Interfacial Gaps and Enhancing PEM Fuel Cell Performance

2020· article· en· W3008714854 on OpenAlexafffund
Lius Daniel, Arman Bonakdarpour, Jonathan Sharman, David P. Wilkinson

Bibliographic record

VenueFuel Cells · 2020
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicroporous materialProton exchange membrane fuel cellCathodeMaterials scienceChemical engineeringSinteringCatalysisLayer (electronics)Membrane electrode assemblyElectrodeFuel cellsNanotechnologyComposite materialAnodeChemistry

Abstract

fetched live from OpenAlex

Abstract We demonstrate an enhanced new architecture for fuel cell membrane electrode assemblies (MEAs), by deposition of the microporous layer (MPL) directly on the catalyst coated membrane (CCM), in order to reduce any gaps at the cathode catalyst layer (CCL) surface. A low bonding temperature Teflon was used to allow low temperature sintering of the CCM with the MPL. This modified structure enhances PEMFC performance by improving the electronic contact and minimizing water pooling at the CCL|MPL interface. The improvement in water management at the CCL|MPL is beneficial especially for thinner CCLs to fulfill the performance demand with low cathode catalyst loading MEAs (≤0.125 mgPt cm−2 as targeted by the US Department of Energy).

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.170
Teacher spread0.164 · 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

Citations16
Published2020
Admission routes2
Has abstractyes

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