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Record W1990759819 · doi:10.1149/1.3210666

Water Sorption and Transport Properties of Polymer Electrolyte Membranes: New Insights from Theory

2009· article· en· W1990759819 on OpenAlexafffund
Ata Roudgar, Charles W. Monroe, Michael Eikerling

Bibliographic record

VenueECS Transactions · 2009
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrolyteMembraneSorptionProtonPolymerProton transportWater transportSwellingConductivityProton exchange membrane fuel cellChemical physicsMaterials scienceThermodynamicsChemical engineeringChemistryPhysical chemistryPhysicsWater flowComposite materialEnvironmental scienceEnvironmental engineeringElectrodeEngineering

Abstract

fetched live from OpenAlex

Pertinent polymer electrolyte membranes (PEM) channel protons through random networks of water-filled pores. Their operation hinges on sufficient amounts of water for proton conduction. This requirement affects structure and properties on > 6 scales. Theoretical research on PEM employs a hierarchy of physical models to address these multiscale challenges. The first part of this contribution focuses on recent progress in ab initio calculations of structural correlations and proton dynamics at dense interfacial arrays of protogenic surface groups. These results have vital implications for the design of PEM that could attain high proton conductivity at minimal hydration. The second part explores the realms of equilibrium and dynamic water sorption and swelling of PEM. The outlined approaches in modeling provide capabilities to predict the membrane response to changing external conditions and to extract parameters of bulk water transport in PEM and vaporization exchange at the membrane surfaces from measured water fluxes through the membrane.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.304

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 head0.159
Teacher spread0.154 · 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

Citations3
Published2009
Admission routes2
Has abstractyes

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