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Record W2320341173 · doi:10.1021/jp305464r

Molecular Simulation of Gas Transport in Hydrated Nafion Membranes: Influence of Aqueous Nanostructure

2012· article· en· W2320341173 on OpenAlexaff
Shuai Ban, Cheng Huang, Xiao‐Zi Yuan, Haijiang Wang

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2012
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsNafionNanoporousMembraneNanostructureAqueous solutionChemical engineeringMaterials scienceDiffusionAdsorptionGaseous diffusionMoleculeSorptionPorosityMolecular dynamicsChemical physicsChemistryNanotechnologyPhysical chemistryOrganic chemistryComposite materialElectrochemistryFuel cellsComputational chemistryThermodynamicsElectrode

Abstract

fetched live from OpenAlex

Molecular simulation was used to investigate the nanoporous structure of hydrated Nafion membrane and its impact on gas transport. The structural changes of Nafion induced by water uptakes were characterized in terms of the density and the pore size distribution. It was found that membrane hydration leads to a growth of separated water domains, which are gradually interconnected via newly formed water channels. The sorption and diffusion of H 2 and O 2 were studied at different temperatures and water contents of Nafion. Simulation results show that the water loading reduces gas solubilities as the adsorption site of gas molecules transforms from small cavities to large surfaces due to the enlargement of aqueous domains. However, such an opening of Nafion porous nanostructure effectively reduces gas diffusion barriers, and results in a 2-fold increase of O 2 diffusivities. Molecular configurations of gases inside Nafion nanoporous frameworks were examined on the basis of calculated radial distribution functions.

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)0.001
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 score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.003
GPT teacher head0.197
Teacher spread0.194 · 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

Citations31
Published2012
Admission routes1
Has abstractyes

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