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Record W2132286467 · doi:10.1149/1.3502346

Investigation of Proton Transport in the Catalyst Layer of PEM Fuel Cells by Electrochemical Impedance Spectroscopy

2010· article· en· W2132286467 on OpenAlexaff
Massimiliano Cimenti, Dmitri Bessarabov, Mickey Tam, Jürgen Stumper

Bibliographic record

VenueECS Transactions · 2010
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsAutomotive Fuel Cell Cooperation (Canada)
Fundersnot available
KeywordsDielectric spectroscopyProton exchange membrane fuel cellProtonOhmic contactMaterials scienceElectrical impedanceCatalysisConductivityElectrochemistryProton transportCathodeLayer (electronics)Analytical Chemistry (journal)Chemical engineeringChemistryComposite materialElectrodeChromatographyElectrical engineeringPhysical chemistryEngineering

Abstract

fetched live from OpenAlex

The proton conductivity of the catalyst layer is an essential parameter for the design and optimization of PEM fuel cells cathodes. Few methods have been proposed to measure this quantity. In this study we tested two methods based on electrochemical impedance spectroscopy (EIS) measurements performed in H2/N2 and in H2/O2, respectively. The EIS H2/N2 method allows the quantification of the CL proton resistance at equilibrium, and in principle could be used to obtain catalyst layer microstructural information. The EIS H2/O2 method allows the quantification of both ohmic and CL proton resistances under load. According to our results, the EIS H2/N2 method is preferable because it is faster to perform, the data analysis is simpler, and provides the CL resistance at equilibrium.

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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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

Citations55
Published2010
Admission routes1
Has abstractyes

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