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Record W2171412439 · doi:10.1149/2.0921506jes

Characterization of PEMFC Gas Diffusion Layer Porosity

2015· article· en· W2171412439 on OpenAlexaff
Rinat Rashapov, Jonathan Unno, Jeff T. Gostick

Bibliographic record

VenueJournal of The Electrochemical Society · 2015
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsPorosityBuoyancyGas pycnometerVolume (thermodynamics)Gaseous diffusionMaterials scienceComposite materialGravimetric analysisWettingDiffusionThermogravimetric analysisEffective porosityMineralogyChemistryThermodynamics

Abstract

fetched live from OpenAlex

The porosity, thickness, skeletal and bulk density of a wide range of gas diffusion layers were measured using a buoyancy method. In this method, thin porous samples were weighed both dry and submerged in a wetting fluid, allowing their solid volume to be determined by application of Archimedes principle. The results showed that GDL porosity decreased as the amount of hydrophobic polymer additive was increased. In general, the observed decrease in porosity was in agreement with the theoretical pore volume reduction calculated for a given PTFE loading. A simple mass-based analysis was performed to estimate the amount of PTFE in a given sample, which revealed that materials generally do not possess the PTFE loading they are reported to have, a fact that was confirmed by thermogravimetric analysis. When the measured porosity values were plotted against these improved estimates of PTFE loading, the agreement with the theoretically expected porosity trend was excellent. Comparisons were also made to gas pycnometry. Finally, measurements were made on samples cut from various locations on a given sheet. It was found that although thickness and areal mass varied between locations, the porosity remained relatively constant.

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 score0.002
Threshold uncertainty score0.174

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 head0.008
GPT teacher head0.192
Teacher spread0.185 · 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

Citations104
Published2015
Admission routes1
Has abstractyes

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