MétaCan
Menu
Back to cohort
Record W2095374428 · doi:10.1149/1.2716557

Modeling of Ultrathin Two-Phase Catalyst Layers in PEFCs

2007· article· en· W2095374428 on OpenAlexaff
Qianpu Wang, Michael Eikerling, Datong Song, Zhongsheng Liu

Bibliographic record

VenueJournal of The Electrochemical Society · 2007
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser UniversityBC Innovation Council
Fundersnot available
KeywordsCatalysisElectrolyteElectrochemistryProton conductorMaterials scienceCathodePhase (matter)Chemical engineeringProtonAnodeElectrodeIonomerChemistryAnalytical Chemistry (journal)Inorganic chemistryPolymerComposite materialPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

We present a model of transport and reaction kinetics in ultrathin cathode catalyst layers for polymer electrolyte fuel cells (PEFCs). In contrast to conventional catalyst layers, ultrathin catalyst layers neither contain a separate electronic conductor, other than the catalyst itself, nor are they impregnated with perfluorinated sulfonic acid ionomer as an intrinsic proton conductor. They can thus be regarded as two-phase composites. The model utilizes Poisson-Nernst-Planck theory for proton transport in the layer. It relates calculated spatial distributions of oxygen and proton concentrations, electrode potential, and electrochemical reaction rates to catalyst utilization and current-voltage performance. By comparison with experimental data from literature for current-voltage relations at low and intermediate current densities the transfer coefficient of the cathodic reaction was estimated. Catalyst layer thickness, composition, and volume fraction of water-filled pore were systematically varied to determine the values that maximize Pt utilization and voltage efficiency of the layer. The significance of these results for the optimization of catalyst layers in view of operation conditions and synthesis methods is discussed.

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)0.001
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 score0.043
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.234
Teacher spread0.228 · 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

Citations37
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueJournal of The Electrochemical SocietySame topicFuel Cells and Related MaterialsFrench-language works237,207