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Record W2052182953 · doi:10.1149/1.2780992

Molecular Dynamics Simulations of Proton Diffusion in the Short-Side-Chain Perfluorosulfonic Acid Ionomer

2007· article· en· W2052182953 on OpenAlexafffund
Iordan I. Hristov, Stephen J. Paddison, Reginald Paul

Bibliographic record

VenueECS Transactions · 2007
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Calgary
FundersArmy Research OfficeNatural Sciences and Engineering Research Council of Canada
KeywordsIonomerMolecular dynamicsHydroniumNafionProtonSide chainDiffusionProton transportChemistryPolymerMembraneIonMaterials scienceChemical physicsThermodynamicsPhysical chemistryComputational chemistryElectrochemistryPhysicsOrganic chemistryNuclear physics

Abstract

fetched live from OpenAlex

This computational study seeks to understand why low equivalent weight short-side-chain (SSC) perfluorosulfonic acid membranes exhibit higher proton conductivities than Nafion when hydrated at similar water contents. The diffusion of protons in the SSC ionomer at water contents of 3, 6, and 13 H2O/SO3H were investigated through classical molecular dynamics simulations. We developed a unique force field set based on torsion profiles derived from extensive electronic structure calculations of a two side chain fragment of the SSC ionomer and simulated a system consisting of a single 40 repeat unit of the polymer (F3C-{[CF2-CF(OCF2CF2SO3H)]-(CF2CF2)3}40-CF3) corresponding to an equivalent weight of 578 at each of the three water contents. After an equilibration time of approximately 450 ps at 300 K where the density was fixed at 1.67 g/cm3, structural information was collected from 2 ns production runs at 315C. We calculated proton (as a hydronium ion) diffusion coefficients of 2.83×10-7, 1.37×10-6, and 3.87×10-6 cm2/s at the three water contents which show excellent agreement to experimentally measured diffusion coefficients at the lower water content only.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.349

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 head0.213
Teacher spread0.206 · 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 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

Citations0
Published2007
Admission routes2
Has abstractyes

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