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Record W2132502040 · doi:10.1149/05002.0711ecst

MEA Design for Improved Cathode Durability under Startup Shutdown Automotive Conditions

2013· article· en· W2132502040 on OpenAlexaff
Joy Roberts, Francine Berretta, Herwig Haas, Amy Yang, Sima Ronasi, Sumit Kundu, Andrew Leow, Brian Lew, Chris Elvidge, Irwin Bellosillo, Georgeta Orha, Yvonne Hsieh, Nicolae Bârsan

Bibliographic record

VenueECS Transactions · 2013
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsAutomotive Fuel Cell Cooperation (Canada)
Fundersnot available
KeywordsDurabilityShutdownCathodeAnodeDegradation (telecommunications)Proton exchange membrane fuel cellCorrosionMaterials scienceAutomotive industryNuclear engineeringAutomotive engineeringLayer (electronics)Process engineeringEngineeringComposite materialFuel cellsElectrodeChemical engineeringElectrical engineeringChemistry

Abstract

fetched live from OpenAlex

A novel MEA design solution - an "intelligent switch" to provide protection of the cathode catalyst layer from degradation caused by startup/shutdown events is proposed (1). This unique approach adapts gas sensor-type materials (2) that exhibit several orders of magnitude change in resistance depending on its gas environment (1) in PEM fuel cell anodes. The high resistance of the new layer in air environments prevents corrosion loop currents. In hydrogen environments, the layer becomes conductive and fuel cell performance is minimally hindered. In-situ fuel cell test results demonstrate a successful reduction in cathode peak potentials during startup events leading to reduced performance degradation in startup/shutdown stress tests. This strategy of preventing corrosion loop currents is compatible with various catalyst types and with different catalyst loadings. The approach enables MEA design to minimize air/air start/stop degradation and provides a pathway for the use of alternative catalyst options.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.964
Threshold uncertainty score0.999

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)0.0020.000

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 head0.016
GPT teacher head0.219
Teacher spread0.202 · 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.

Study designSimulation or modeling
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

Citations1
Published2013
Admission routes1
Has abstractyes

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