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Enregistrement W3114762970 · doi:10.1149/ma2020-02352257mtgabs

Exploring Component Interactions during Mechanical Degradation of Fuel Cell Membranes with 4D in Situ Visualization

2020· article· en· W3114762970 sur OpenAlexaffabout
Yadvinder Singh, Robin White, Dilip Ramani, Marina Najm, Alex Boswell, Francesco P. Orfino, Monica Dutta, Erik Kjeang

Notice bibliographique

RevueECS Meeting Abstracts · 2020
Typearticle
Langueen
DomaineEngineering
ThématiqueFuel Cells and Related Materials
Établissements canadiensSimon Fraser University
Organismes subventionnairesnon disponible
Mots-clésMembraneMaterials scienceIonomerElectrolyteMembrane electrode assemblyDelamination (geology)Proton exchange membrane fuel cellComposite materialPolymerElectrodeChemistry

Résumé

récupéré en direct d'OpenAlex

The ionomer membrane facilitates critical functions in a polymer electrolyte membrane fuel cell. Dynamic operational duty cycles can gradually introduce various damage features within the membrane, which could lead to overall performance failure of the fuel cell. Membrane durability is affected by chemical, mechanical, and thermal stressors that often act synergistically to degrade the ionomer membrane during fuel cell operation [1]. However, to understand the effect of each individual stressor, a useful approach is to isolate and study these degradation mechanisms separately. For instance, pure mechanical membrane degradation is typically affected in situ by actively cycling the membrane hydration levels under electrochemically inert conditions. These conditions generate cyclic mechanical stresses within the constrained membrane, leading to fatigue-induced fracture and electrode delamination under prolonged exposures [2]. When the cracks penetrate completely through the membrane thickness and spread across a substantial planar area, internal gas crossover is accelerated through convective transport, and ultimate fuel cell failure could result [3]. Furthermore, the mechanical membrane damage is strongly influenced by the microstructure of the adjoining membrane electrode assembly (MEA) components and cell design. Failure analysis investigations, using both 2D and 3D imaging, have consistently found significant proportion of membrane cracks to be co-located with the electrode cracks, which is typically attributed to the local stress concentration effects that could favour membrane crack initiation at these sites [2,4–6]. Recently, X-ray computed tomography (XCT) based 4D in situ visualization approach adopting a custom X-ray transparent fuel cell assembly [7] provided further insights into such interaction effects by demonstrating the temporal development of new membrane cracks underneath pre-existing electrode features [8]. Given the dominant role of electrode cracks in membrane fracture, the present work was undertaken to explore low crack density electrodes as a mitigation strategy against mechanical membrane degradation. This was achieved by subjecting two separate MEAs with different initial cathode catalyst layer (CCL) crack densities, i.e., high and low crack designs, to accelerated stress test (AST) involving humidity cycling for generating pure mechanical degradation. Periodic XCT-based in situ visualization was performed to elucidate differences in the magnitude and evolutionary processes of the membrane/MEA degradation between the two examined cases. Failure mechanisms responsible for mechanical membrane fracture were investigated in relation to the role of neighbouring components ― both electrodes and gas diffusion layers (GDLs). Membrane damage was found to be significantly curtailed through minimization of ab initio crack density in the CCL. While through-thickness membrane cracks developed within a similar timeframe in both high and low cathode crack density MEAs, the coverage area of fatigue-induced membrane fracture in the latter case was nearly six times lower after 2000 AST cycles, thereby establishing the adopted treatment of electrode morphology as an effective mitigation strategy in slowing the mechanical membrane degradation rate. Hydration-dehydration cycles, however, still altered this initial morphology by introducing electrode cracks at early stages of the AST. These electrode cracks, as an intermediate step, exclusively governed the subsequent initiation and propagation of membrane cracks. Two distinct membrane failure mechanisms were identified, each characterized by the presence and absence, respectively, of the MEA’s permanent buckling deformation into void spaces of the GDL (Figure 1). The buckling phenomenon was found to be strongly influenced by the non-uniform GDL microstructure and had a significant contribution towards the eventual frequency and coverage of membrane fracture. This finding suggests that critical component interactions during membrane failure extend beyond the immediately adjacent electrode layers. Additional comparative in situ visualizations of the fuel cell assembly held at varying hydration states showed that the size and geometry of the membrane fracture features that had developed in the low cathode crack MEA varied substantially between dry and wet environments. Acknowledgements Funding for this research was provided by the Natural Sciences and Engineering Research Council of Canada, Canada Foundation for Innovation, British Columbia Knowledge Development Fund, and Ballard Power Systems through an Automotive Partnership Canada grant. This research was undertaken, in part, thanks to funding from the Canada Research Chairs program. References [1] Rodgers, et al., Chem. Rev. 112 (2012) 6075–6103. [2] Sadeghi Alavijeh, et al., J. Electrochem. Soc. 162 (2015) F1461–F1469. [3] Lai, et al., J. Fuel Cell Sci. Technol. 6 (2009) 21002. [4] Singh, et al., J. Electrochem. Soc. 164 (2017) F1331–F1341. [5] Singh, et al., J. Power Sources. 345 (2017) 1–11. [6] Huang, et al., J. Polym. Sci. Part B 44 (2006) 2346–2357. [7] White, et al., J. Power Sources. 350 (2017) 94–102. [8] Singh, et al., J. Power Sources. 412 (2019) 224–237. Figure 1

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,034
Tête enseignante GPT0,221
Écart entre enseignants0,187 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission2
Résumé présentoui

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