MétaCan
Menu
Back to cohort
Record W2043514294 · doi:10.1002/apj.194

Comparison of ethanol and methanol crossover through different MEA components and structures by cyclic voltammetry

2008· article· en· W2043514294 on OpenAlexaff
Jin Ling, Geoffrey Longtin, O. Savadogo

Bibliographic record

VenueAsia-Pacific Journal of Chemical Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMethanolCyclic voltammetryChemistryEthanolCrossoverElectrodeDiffusionAnalytical Chemistry (journal)Membrane electrode assemblyVoltammetryChromatographyOrganic chemistryElectrochemistryThermodynamicsPhysical chemistryElectrolyte

Abstract

fetched live from OpenAlex

Abstract The crossover rate of ethanol or methanol through membrane electrode assembly (MEA) and MEA components was studied quantitatively at 25 and 60 °C by cyclic voltammetry method. The results obtained in this work show that cyclic voltammetry is a powerful technique to assess the crossover phenomenon through MEA components and structures. In all cases, the ethanol crossover rates are lower than those of methanol. The ethanol and methanol crossover rates depend upon time and temperature. For an initial concentration of 1 M of ethanol or methanol, the crossover rate increases to a maximum after the first hour of the cell operation and then decreases gradually to a certain concentration after the third hour. At 60 °C, the maximum concentration of ethanol crossover rate is lower than that obtained at 25 °C. The crossover rate of ethanol or methanol through MEA is lower than through the components alone (pressed or nonpressed membranes or gas diffusion electrode). Copyright © 2008 Curtin University of Technology and John Wiley & Sons, Ltd.

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)0.001
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
Metaresearch0.0000.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.230
Teacher spread0.217 · 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

Citations10
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueAsia-Pacific Journal of Chemical EngineeringSame topicFuel Cells and Related MaterialsFrench-language works237,207