MétaCan
Menu
Back to cohort
Record W2512648889 · doi:10.1149/07514.0111ecst

Investigating the Equilibration Time of Catalyst Coated Membranes Using AC and DC Methods

2016· article· en· W2512648889 on OpenAlexafffund
Philippe J. Côté, Caroline R. Cloutier, Dzmitry Malevich, Jon G. Pharoah

Bibliographic record

VenueECS Transactions · 2016
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsRelative humidityMembraneCatalysisStack (abstract data type)Proton exchange membrane fuel cellHumidityDirect currentMaterials scienceAlternating currentAnalytical Chemistry (journal)Ionic bondingPlane (geometry)Current (fluid)ChemistryChemical engineeringChromatographyThermodynamicsIonVoltageElectrical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Methods to quantitatively measure the time for rolls of catalyst coated membrane to equilibrate to a controlled temperature and relative humidity were investigated. It is important to ensure that the water content of a catalyst coated membrane roll has equilibrated before it is used in the manufacturing of a proton exchange membrane fuel cell stack. Equilibration is achieved when the water content in the catalyst coated membrane reaches equilibrium with controlled relative humidity and temperature conditions. Alternating current and direct current methods were compared to measure the through-plane ionic resistance and in-plane electronic resistance as the catalyst coated membrane equilibrated. It was found that both alternating and direct current methods showed strong correlations as the CCM equilibrated in response to a relative humidity change. The average equilibration time estimated from the through-plane ionic measurements was 14.5 hours and the average equilibration time estimated from the in-plane electronic measurements was 13 hours.

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 score0.096
Threshold uncertainty score0.172

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 head0.020
GPT teacher head0.255
Teacher spread0.235 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueECS TransactionsSame topicFuel Cells and Related MaterialsFrench-language works237,207