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Record W2008517639 · doi:10.1039/c3sm52247d

Change in morphology of fuel cell membranes under shearing

2013· article· en· W2008517639 on OpenAlexafffund
Noureddine Metatla, Samuel Palato, Armand Soldera

Bibliographic record

VenueSoft Matter · 2013
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesUniversité de Sherbrooke
KeywordsDissipative particle dynamicsShearing (physics)NafionMaterials scienceConductivityHumidityMorphology (biology)Composite materialChemical engineeringChemistryThermodynamicsPolymer

Abstract

fetched live from OpenAlex

The effect of shearing on Nafion/water systems was investigated using Dissipative Particle Dynamics (DPD). This simulation approach has been shown to accurately reveal the morphology of such systems in the steady state. We first confirmed that the length of the Nafion chain, 5 and 20 DPD units, has no influence on the overall morphology for four different concentrations, 10, 20, 30, and 40%, of water. Shearing effects with 0.05 and 0.2 rates were then studied according to the Nafion chain length, 5 and 20 DPD units, and water content, 10 and 30%. It was shown that low water contents and long chain length lead to the formation of water-rich tubes, aligned with the shear direction. The formation of such channels can ultimately lead to an increase in the proton conductivity at low humidity. The size of these water tubes thus results from a combined influence of shear strain, chain relaxation time, interfacial tension and water content.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.196
Teacher spread0.184 · 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 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

Citations19
Published2013
Admission routes2
Has abstractyes

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