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Record W2972062250 · doi:10.1002/fuce.201800173

Characterization of Thermal and Electronic Conductivities of Catalyst Layers of Polymer Electrolyte Membrane Fuel Cells

2019· article· en· W2972062250 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFuel Cells · 2019
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsAutomotive Fuel Cell Cooperation (Canada)Simon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsProton exchange membrane fuel cellMaterials scienceElectrolyteCLs upper limitsCharacterization (materials science)Thermal conductivityPolymerConductivityMembraneChemical engineeringFuel cellsWork (physics)Composite numberPorosityMicrostructureComposite materialNanotechnologyElectrodeChemistryMechanical engineering

Abstract

fetched live from OpenAlex

Abstract This work proposes new and accurate systematic methodologies for ex situ measurements of through‐plane thermal and in‐plane electronic conductivities of catalyst layers (CLs) of polymer electrolyte membrane fuel cells (PEMFC). The developed methods are based on measurements of different thicknesses/lengths of a CL on different substrates. Suitability of the proposed methods is confirmed through a set of microstructural properties measurements on a typical CL design to ensure the measured CLs would be representative of CLs in a real fuel cell product. Conductivity measurements of two CL designs with different compositions and microstructures confirm capability of the developed procedures to track structural changes in CLs. The present characterization platform is not limited to CLs and may be used for other composite porous materials with similar structures.

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 score0.005
Threshold uncertainty score0.661

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 head0.003
GPT teacher head0.162
Teacher spread0.159 · 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