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Record W3116652910 · doi:10.1149/ma2020-02332101mtgabs

Molecular Dynamics Study of the Nanoscale Proton Density Distribution at the Ionomer-Catalyst Interface

2020· article· en· W3116652910 on OpenAlexaff
Jacob Spooner, Mohammad J. Eslamibidgoli, Kourosh Malek, Michael Eikerling

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser UniversityUniversity of the Fraser Valley
Fundersnot available
KeywordsMolecular dynamicsChemical physicsMaterials scienceProtonIonomerOxidePlatinumElectrolyteProton transportAdsorptionMetalPolymerCatalysisDensity functional theoryChemical engineeringComposite materialComputational chemistryPhysical chemistryChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The proton density in the cathode catalyst layer is a crucial variable to define the local reaction conditions in and thereby determine the performance and lifetime of polymer electrolyte fuel cells. We present a molecular modeling study to examine the proton density distribution in a water-filled, slab-like pore. The slab is confined by two distinct boundaries, the first consisting of a platinum single-crystalline surface and the second being a thin and dense skin layer of proton-conducting ionomer. Classical molecular dynamics simulations serve as a valuable tool to rationalize the impact of the molecular structure and properties of the ionomer film as well as the adsorption and charging state of the metal surface on structure and properties of interfacial water and the spatial distribution of protons. Boundary conditions at the metal surface are obtained from explicit quantum mechanical simulations at the DFT level. The oxide coverage at the metal surface and the water layer thickness are considered as crucial parameters. Results of detailed structural analyses elucidate the impact of a structured layer of near-surface water molecules at the platinum surface on accumulating protons at the interface.

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 score0.755
Threshold uncertainty score0.305

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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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