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Record W2510204365 · doi:10.1021/acs.jpcc.5b04157

Gas Permeation through Nafion. Part 2: Resistor Network Model

2015· article· en· W2510204365 on OpenAlexaff
Maximilian Schalenbach, Michael A. Hoeh, Jeff T. Gostick, Wiebke Lueke, Detlef Stolten

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2015
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsMcGill University
FundersBundesministerium für Wirtschaft und Technologie
KeywordsNafionPermeationElectrolyteMaterials scienceChemical engineeringPermeability (electromagnetism)PolymerMembraneHydrogenOxygen permeabilityPlasticizerSofteningRelative humidityComposite materialChemistryPolymer chemistryOxygenThermodynamicsOrganic chemistryElectrochemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

In the first part of this study, the hydrogen and oxygen permeabilities of Nafion were measured. The aim of the second part of this study presented here is to physically characterize the influence of the aqueous phase, the solid phase, and the intermediate phase in Nafion on the macroscopic hydrogen and oxygen permeabilities. Hereto, a resistor network model morphologically representative for Nafion based on structural investigations reported in the literature is presented in which the different phases are described by individual permeabilities. As a result of the simulations, an enlarged permeability of the solid phase in comparison to that of dry Nafion had to be assumed in order to reproduce the measured influence of temperature and relative humidity on the permeability. This increase of the permeability of the solid phase toward greater water uptake was explained by the effect of water as a plasticizer and the resulting softening of the polymeric matrix. On the basis of the identified mechanisms, approaches to reduce the gas permeability of polymer electrolyte membranes are identified and discussed.

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 score0.199
Threshold uncertainty score0.228

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 head0.019
GPT teacher head0.217
Teacher spread0.199 · 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

Citations71
Published2015
Admission routes1
Has abstractyes

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