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Record W2109640152 · doi:10.1149/2.045202jes

Impedance Model of Oxygen Reduction in Water-Flooded Pores of Ionomer-Free PEFC Catalyst Layers

2011· article· en· W2109640152 on OpenAlexafffund
Karen Ka Wing Chan, Michael Eikerling

Bibliographic record

VenueJournal of The Electrochemical Society · 2011
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIonomerCatalysisOxygen reductionOxygenElectrical impedanceMaterials scienceReduction (mathematics)Chemical engineeringChemistryComposite materialElectrodeElectrochemistryElectrical engineeringOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

We present an impedance model of oxygen reduction in water-flooded pores of ionomer-free, ultrathin catalyst layers in polymer electrolyte fuel cells. This work expands on the continuum, single pore approach of a previously developed steady state model ( JES 158, B18, 2011), which postulated that protons are transported into water-flooded pores through their electrostatic interaction with the metal surface charge density. We derive approximate analytical expressions for the impedance response in various limiting cases. In contrast to the response of electrolyte-filled pores, the resistive and capacitive elements of the water-flooded pores are highly dependent on the surface charge density of the metal, which is determined by the applied bias potential and the potential of zero charge of the metal. We discuss the capabilities of the model to determine the kinetic, electrostatic, and transport contributions to the overall Faradaic current density, and to extract metal|solution interfacial parameters of electrocatalytic materials in ionomer-free catalyst layers from impedance data.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.181
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations20
Published2011
Admission routes2
Has abstractyes

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