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Record W2006743619 · doi:10.1149/06403.0839ecst

Investigation of Interfacial Water Transport at the Gas Diffusion Media by Neutron Radiography

2014· article· en· W2006743619 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueECS Transactions · 2014
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsFord Motor Company (Canada)
Fundersnot available
KeywordsDiffusionNeutron imagingMaterials scienceGaseous diffusionPorous mediumWater transportLiquid waterPorosityNeutronCatalysisNeutron transportChemical engineeringAnalytical Chemistry (journal)ChemistryFuel cellsChromatographyEnvironmental scienceComposite materialEnvironmental engineeringThermodynamicsNuclear physicsWater flowOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

For cost reduction of fuel cell, it is necessary to realize high current density operation, which requires a thorough understanding of mass transport. However, especially interfacial liquid water transport between carbon paper (CP), micro porous layer (MPL), and catalyst layer (CL) are not fully understood. Coupled cell performance evaluation, liquid water visualization by neutron radiography and numerical modeling were performed with three types of gas diffusion media (GDM): MPL free; CP with MPL; and CP free. It revealed that the presence of a gap at the CL-GDM interface has a significant effect on water accumulation and that the MPL could minimize this effect. At the same time, CP free case showed the best performance and the lowest liquid water content due to multiple impacts of interfacial liquid water transport both at CL-MPL and MPL-channel interfaces. These results confirm the importance of interfacial design between each component of fuel cell for further cost reduction.

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.

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 score0.024
Threshold uncertainty score0.505

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 head0.158
Teacher spread0.153 · 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