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Record W1983317433 · doi:10.1039/c1sm05273j

Poroelectroelastic theory of water sorption and swelling in polymer electrolyte membranes

2011· article· en· W1983317433 on OpenAlexaff
Michael Eikerling, Peter Berg

Bibliographic record

VenueSoft Matter · 2011
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Ontario Institute of TechnologySimon Fraser University
Fundersnot available
KeywordsSwellingSorptionElectrolyteMembranePolymerWater transportThermodynamicsMaterials scienceOsmotic pressureChemistryChemical engineeringComposite materialPhysical chemistryAdsorptionGeotechnical engineeringWater flowElectrodeGeology

Abstract

fetched live from OpenAlex

This paper presents a novel poroelectroelastic model of equilibrium water sorption and swelling in polymer electrolyte membranes. These phenomena determine transport properties, electrochemical performance, durability and lifetime of the membrane in a polymer electrolyte fuel cell. Based on a consistent treatment of thermodynamic equilibrium conditions, involving capillary, osmotic and elastic effects, we establish the equation of state of water in a single membrane pore. It relates the charge density at the pore walls to a microscopic swelling parameter. Extended to the water sorption equilibrium in a pore ensemble, the model reconciles microscopic swelling in a single pore with macroscopic swelling of the membrane. Theoretical relations are developed that rationalize the impact of external conditions, statistical distribution of anionic head groups and elastic properties of the polymer on water sorption and swelling. The model resolves Schröder's paradox and other unexplained phenomena related to water sorption equilibria, pressure distribution, and transport properties in PEM.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.006
GPT teacher head0.157
Teacher spread0.151 · 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 designTheoretical or conceptual
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

Citations97
Published2011
Admission routes1
Has abstractyes

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