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Record W3016083355 · doi:10.1103/physreve.101.042603

Fracture dynamics of correlated percolation on ionomer networks

2020· article· en· W3016083355 on OpenAlexafffund
Yule Wang, Michael Eikerling

Bibliographic record

VenuePhysical review. E · 2020
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBreakagePercolation (cognitive psychology)Materials scienceFracture (geology)MechanicsStress (linguistics)BundlePower lawMonte Carlo methodStatistical physicsComposite materialForensic engineeringPhysicsMathematicsStatistics

Abstract

fetched live from OpenAlex

This article presents a random network model to the study fracture dynamics on a scaffold of charged and elastic ionomer bundles that constitute the stable skeleton of a polymer electrolyte membrane. The swelling pressure upon water uptake by this system creates the internal stress under which ionomer bundles undergo breakage. Depending on the local stress and the strength of bundle-to-bundle correlations, different fracture regimes can be observed. We use kinetic Monte Carlo simulations to study these dynamics. The breakage of individual bundles is described with an exponential breakdown rule and the stress transfer from failed to intact bundles is assumed to exhibit a power-law-type spatial decay. A central property considered in the analysis is the frequency distribution of percolation thresholds, which is employed to analyze fracture regimes as a function of the stress and the effective range of stress transfer. Based on this distribution, we introduce an order parameter for the transition from random breakage to crack growth regimes. Moreover, as a practically important outcome, the time to fracture is analyzed as a descriptor for the lifetime of polymer electrolyte membranes.

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 score0.342
Threshold uncertainty score0.321

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 head0.007
GPT teacher head0.234
Teacher spread0.227 · 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

Citations3
Published2020
Admission routes2
Has abstractyes

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