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Record W2007560790 · doi:10.1021/jp111400k

Microstructure-Based Modeling of Aging Mechanisms in Catalyst Layers of Polymer Electrolyte Fuel Cells

2011· article· en· W2007560790 on OpenAlexaff
Kourosh Malek, Alejandro A. Franco

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2011
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsBC Innovation CouncilSimon Fraser University
Fundersnot available
KeywordsMicrostructureMaterials scienceIonomerElectrolyteElectrochemistryChemical engineeringCarbon fibersWettingPolymerCatalysisMultiscale modelingCorrosionComposite materialNanotechnologyChemistryPhysical chemistryOrganic chemistryComputational chemistryCopolymerElectrode

Abstract

fetched live from OpenAlex

This work is comprised of a versatile multiscale modeling of carbon corrosion processes in catalyst layers (CLs) of polymer electrolyte fuel cells (PEFCs). Slow rates of electrocatalytic processes in CLs and materials aging are the main sources of voltage loss in PEFCs under realistic operating conditions. We combined microstructure data obtained from coarse-grained molecular dynamics (CGMD) simulations with a detailed description of the nanoscale elementary kinetic processes and electrochemical double-layer effects at the catalyst/electrolyte and carbon/electrolyte interfaces. We exclusively focused on morphology and microstructure changes in the catalyst layer of PEFCs as a result of carbon corrosion. By employing extensive CGMD simulations, we analyzed the microstructure of CLs as a function of carbon loss and in view of ionomer and water morphology, water and ionomer coverage, and overall changes in carbon surface. These ingredients are integrated into a kinetic model, which allows capture of the impact of the structural changes on the PEFC performance decay. In principle, such multiscale simulation studies allow a relation of the aging of CLs to the selection of carbon particles (sizes and wettability), the catalyst loading, and the level of ionomer structural changes during the CL degradation process.

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 score0.105
Threshold uncertainty score0.323

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 head0.006
GPT teacher head0.180
Teacher spread0.174 · 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

Citations85
Published2011
Admission routes1
Has abstractyes

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