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Record W2515206912 · doi:10.1149/ma2016-02/38/2836

Correlation Between Crack Initiation and Chemical Decomposition in the Ionomer Membrane of Polymer Electrolyte Fuel Cells

2016· article· en· W2515206912 on OpenAlexaff
Mohamed El Hannach, Ka Hung Wong, Yadvinder Singh, Erik Kjeang

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMembraneIonomerMaterials sciencePolymerElectrolyteChemical decompositionDegradation (telecommunications)Composite materialChemical engineeringBrittlenessChemical stabilityChemical structureDecompositionChemistryCopolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Membrane stability is an important consideration for the overall durability and lifetime of polymer electrolyte fuel cells. During operating conditions, the ionomer membrane is subjected to chemical degradation due to the formation of radicals and their subsequent attack on the chemical bonds of the polymer [1,2]. The membrane is also subjected to mechanical degradation due to the hygrothermal cycles during operation, which causes the membrane to expand and contract repeatedly leading to the development of cyclic stresses [3,4]. Recent studies show that the mechanical and chemical degradation processes are connected and, once combined, cause accelerated degradation and reduced integrity of the membrane [3–6]. On one hand, the chemical degradation causes a change in the ionomer membrane mechanical properties, transforming it from a ductile material to a brittle material [3]. This transformation makes the membrane less resistant to crack formation and propagation and increases the mechanical damage that can be caused by typical hydrothermal cycles. On the other hand, it was observed that applying compression to the membrane increases the rate of chemical decomposition [7]. Understanding the interaction between the chemical and mechanical degradation processes is therefore an important step in developing more durable membranes and improving the performance and lifetime of the fuel cell. We propose a statistical model to establish a correlation between the chemical degradation at the level of the polymer backbone chains and the initiation of microcracks due to mechanical stress. The representation of the morphology of the ionomer is based on the fibrillary structure described in [6,8]. In this representation a bundle of backbone chains is considered as a basic building block of the structure. We generate a network of bundles representative of a microcrack initiation site. A crack initiation in the membrane structure is the tipping point that leads to an accelerated degradation in terms of hydrogen leaks and ultimate failure of the fuel cell under regular operating conditions. Once a crack is initiated, it increases the amount of reactant crossover leading to an increasing chemical decomposition [4]. The crack also constitutes a stress concentration site where the cyclic mechanical load participates further in its propagation. The combination of these processes accelerates the overall rate degradation leading to a complete failure of the ionomer. Clearly, the crack initiation process is a critical root cause for the accelerated cycle of degradation of the ionomer. Our model allows for the estimation of the time of the crack initiation under relevant mechanical stresses and follows a realistic chemical degradation pattern. The statistical analysis provides a comprehensive understanding of the interaction between the local amounts and distribution of chemical decomposition sites and the mechanical stressors causing the initiation of the physical damage in the ionomer structure. [1] K.H. Wong, E. Kjeang, J. Electrochem. Soc. 161 (2014) F823. [2] K.H. Wong, E. Kjeang, ChemSusChem 8 (2015) 1072. [3] A. Sadeghi Alavijeh, M.A. Goulet, R.M.H. Khorasany, J. Ghataurah, C. Lim, M. Lauritzen, E. Kjeang, G.G. Wang, R.K.N.D. Rajapakse, Fuel Cells 15 (2015) 204. [4] A. Kusoglu, A.Z. Weber, J. Phys. Chem. Lett. 6 (2015) 4547. [5] R.M.H. Khorasany, A. Sadeghi Alavijeh, E. Kjeang, G.G. Wang, R.K.N.D. Rajapakse, J. Power Sources 274 (2015) 1208. [6] P.-É.A. Melchy, M.H. Eikerling, J. Phys. Condens. Matter 27 (2015) 325103. [7] A. Kusoglu, M. Calabrese, A.Z. Weber, ECS Electrochem. Lett. 3 (2014) F33. [8] L. Rubatat, G. Gebel, O. Diat, Macromolecules 37 (2004) 7772.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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 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

Citations1
Published2016
Admission routes1
Has abstractyes

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