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Record W3032131663 · doi:10.1016/j.powera.2020.100010

A rapid mechanical durability test for reinforced fuel cell membranes

2020· article· en· W3032131663 on OpenAlexafffund
Alireza Sadeghi Alavijeh, Sandeep Bhattacharya, Owen Thomas, Carmen Chuy, Erik Kjeang

Bibliographic record

VenueJournal of Power Sources Advances · 2020
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsAutomotive Fuel Cell Cooperation (Canada)Simon Fraser University
FundersWestern Economic Diversification CanadaCanada Research ChairsSimon Fraser UniversityBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaMitacsCanada Foundation for Innovation
KeywordsDurabilityMembraneAnodeMaterials scienceComposite materialDegradation (telecommunications)PolycarbonateProton exchange membrane fuel cellCathodeRelative humidityElectrodeChemistryEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

An in situ accelerated mechanical stress test (ΔP-AMST) that applies relative humidity (RH) cycling combined with a pressure differential (ΔP) at a high temperature is proposed to accelerate mechanical degradation in all types of reinforced membranes used in fuel cells and obtain mechanical failure in a relatively short time. For validation, ePTFE reinforced membranes are mechanically degraded by RH cycling accelerated by means of a ΔP applied from cathode to anode using a custom designed polycarbonate spacer. Reinforced membrane failure detected by ΔP loss is reached within ∼10 to 10,000 RH cycles using this method, depending on the level of applied ΔP. The ΔP-AMST protocol is hence demonstrated as a fast, economical in situ alternative compared to existing methods for evaluating the mechanical fatigue durability of advanced fuel cell membranes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score0.498

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 head0.009
GPT teacher head0.203
Teacher spread0.195 · 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

Citations12
Published2020
Admission routes2
Has abstractyes

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