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Record W2800680240 · doi:10.1149/2.0391806jes

Experimental Observations of Microstructure Changes in the Catalyst Layers of Proton Exchange Membrane Fuel Cells under Wet-Dry Cycles

2018· article· en· W2800680240 on OpenAlexafffund
Jian Zhao, Samaneh Shahgaldi, Xianguo Li, Zhongsheng Liu

Bibliographic record

VenueJournal of The Electrochemical Society · 2018
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsNational Research Council CanadaUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaSimon Fraser University
KeywordsAgglomerateMicrostructureProton exchange membrane fuel cellEvaporationDehydrationCatalysisChemical engineeringDurabilityMaterials scienceDegradation (telecommunications)Composite materialChemistryMeteorology

Abstract

fetched live from OpenAlex

Microstructure changes of the catalyst layers in proton exchange membrane fuel cells (PEMFCs) lead to significant performance degradation and durability limitations, especially under dynamic loading condition; and they are investigated experimentally in this study simulating the effect of wet-dry cycles through water intrusion-evaporation and water flow-through-dehydration experiments, respectively. It is found that the cycling of water intrusion-evaporation processes significantly contributes to the growth of agglomerates as well as the formation of pinholes and cracks, causing irreversible losses of active surface areas and catalytic activity; in contrast, the cycling of water flow-through-dehydration experiments enlarges the large pores but changes very little the agglomerate sizes. This is because water tends to flow through the path of least resistance, that is, the large pores in the catalyst layers, leaving the other parts of the catalyst layers with relatively small pores less affected. These observations provide experimental evidence of microstructure changes and their forms for a better understanding of degradation in PEMFCs, especially under dynamic operating conditions.

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 score0.003
Threshold uncertainty score0.258

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 head0.011
GPT teacher head0.223
Teacher spread0.211 · 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

Citations81
Published2018
Admission routes2
Has abstractyes

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