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Record W2790995532 · doi:10.1149/ma2018-01/32/1992

Modeling of Coupled Mechanical and Chemical Degradation of the Ionomer Membrane in Polymer Electrolyte Fuel Cells

2018· article· en· W2790995532 on OpenAlexaff
Mohamed El Hannach

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsIonomerElectrolyteDegradation (telecommunications)PolymerMembraneFuel cellsChemical engineeringMaterials scienceChemistryComposite materialEngineeringCopolymerElectrical engineeringElectrode

Abstract

fetched live from OpenAlex

The hydrogen fuel cell is a promising technology that supports the development of sustainable energy systems and zero emission vehicles. One of the key technical challenges for the use of fuel cells in the transportation sector is the high durability requirements 1–3. One of the key components that control the overall life time of a hydrogen fuel cell is the ionomer membrane that conducts the protons and allows the separation between the anode and the cathode. During fuel cell operation, the membrane is subjected to two categories of degradation: mechanical and chemical. These degradations lead to reduction in the performance, crossover of reactants between anode and cathode and ultimately total failure of the fuel cell. The mechanical degradation occurs when the membrane swells and shrinks under the variation of the local hydration level. This leads to fatigue of the ionomer structure and ultimately irreversible damage. However, under pure mechanical degradation the damage takes a very long time to occur 4,5. Sadeghi et al. 5 observed failure of the membrane after 20,000 of accelerated mechanical stress testing. This translates into a longer lifetime in comparison to what is observed in field operation 6. The chemical degradation on the other hand is caused by the presence of harmful chemicals such as OH radicals that attack the side chains and the main chains of the ionomer 7,8. Such attacks weaken the structural integrity of the membrane and make it prone to severe mechanical damage. Hence understanding the effect of combining both categories of membrane degradation is the key to accurate prediction of the time to failure of the fuel cell. In this work we propose a novel model that represents accurately the structural properties of the membrane and couples the chemical and the mechanical degradations to estimate when the ultimate failure is initiated. The model is based on a network of agglomerated fibrils corresponding to the basic building block of the membrane structure 9–11. The mechanical and chemical properties are defined for each fibril and probability functions are used to evaluate the likelihood of a fibril to break under certain operating conditions. The description of the fundamentals behind the approach will be presented. Two set of simulations will be presented and discussed. The first one corresponding to standard testing scenarios that were used to validate the model. The second set of results will highlight the impact of coupling both degradation mechanisms on the estimation of the failure initiation time. The main strengths of the model and the future development will be discussed as well. T. Sinigaglia, F. Lewiski, M. E. Santos Martins, and J. C. Mairesse Siluk, Int. J. Hydrogen Energy, 42, 24597–24611 (2017). T. Jahnke et al., J. Power Sources, 304, 207–233 (2016). P. Ahmadi and E. Kjeang, Int. J. Energy Res., 714–727 (2016). X. Huang et al., J. Polym. Sci. Part B Polym. Phys., 44, 2346–2357 (2006). A. Sadeghi Alavijeh et al., J. Electrochem. Soc., 162, F1461–F1469 (2015). N. Macauley et al., J. Power Sources, 336, 240–250 (2016). K. H. Wong and E. Kjeang, J. Electrochem. Soc., 161, F823–F832 (2014). K. H. Wong and E. Kjeang, ChemSusChem, 8, 1072–1082 (2015). P.-É. A. Melchy and M. H. Eikerling, J. Phys. Condens. Matter, 27, 325103–6 (2015). J. A. Elliott et al., Soft Matter, 7, 6820 (2011). L. Rubatat, G. Gebel, and O. Diat, Macromolecules, 37, 7772–7783 (2004).

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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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0040.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.192
Teacher spread0.185 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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