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Record W2325793767 · doi:10.1021/jp506333p

Radically Coarse-Grained Approach to the Modeling of Chemical Degradation in Fuel Cell Ionomers

2014· article· en· W2325793767 on OpenAlexafffund
Mahdi Ghelichi, Pierre-Éric Alix Melchy, Michael Eikerling

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2014
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
FundersGovernment of CanadaBallard Power Systems
KeywordsIonomerMembraneDegradation (telecommunications)NafionChemistryHydrogen peroxideRadicalChemical decompositionPolymer chemistryChemical engineeringMaterials sciencePolymerOrganic chemistryPhysical chemistryDecompositionComputer scienceElectrochemistry

Abstract

fetched live from OpenAlex

We present a kinetic model of chemical degradation in perfluorosulfonic acid ionomer membranes. It accounts for pathways of radical formation along with mechanisms of ionomer degradation through radical attack. Simplifications in the set of model equations leads to analytical expressions for the concentration of hydroxyl radicals as a function of initial concentrations of iron ions and hydrogen peroxide. The coarse-grained ionomer degradation model distinguishes units that correspond to ionomer head groups, trunk segments of ionomer side chains, and backbone segments between two side chains. A set of differential equations is formulated to describe changes in concentrations of these units. The model is used to study the impact of different degradation mechanisms and ionomer chemistries on fluorine loss and change in ion exchange capacity. Comparison of the model with experimental degradation data for Nafion and Aquivion membranes allows rate constants of degradation processes to be determined. Results of these analyses are discussed in view of strategies to mitigate chemical degradation of ionomer 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.224

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 head0.180
Teacher spread0.173 · 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

Citations41
Published2014
Admission routes2
Has abstractyes

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