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Enregistrement W3116032709 · doi:10.1149/ma2020-02332124mtgabs

Robust Edge Design Facilitated By 4D X-Ray Computed Tomography Visualization of Membrane Degradation in Fuel Cells

2020· article· en· W3116032709 sur OpenAlexaffabout
Yixuan Chen, Yadvinder Singh, Dilip Ramani, 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-clésMaterials scienceAdhesiveDurabilityComposite materialEnhanced Data Rates for GSM EvolutionMembraneTearingMembrane electrode assemblyElectrolyteProton exchange membrane fuel cellGasketPenetration (warfare)ElectrodeLayer (electronics)ChemistryComputer scienceEngineering

Résumé

récupéré en direct d'OpenAlex

Membrane electrode assembly (MEA) edges are sensitive regions that could strongly influence the durability of polymer electrolyte membrane fuel cells. Membrane failure in poorly designed edges can promote gas crossover and often lead to premature cell failures as well as reduced durability; hence, shortened fuel cell lifetimes. Currently, there is limited knowledge addressing the mechanisms of edge failure in the literature and this gap is explored in this work. Two different MEA edge designs for our small scale fixture and research scale MEA (<1 cm2), where edge effects are more pronounced, were implemented to study their robustness during a combined chemical/mechanical membrane degradation accelerated stress test (AST)[1]. Four-dimensional in-situ visualization[2], enabled by X-ray computed tomography, was performed to understand and thus mitigate the design issues responsible for edge failures. Interfacial interaction between the adhesive-containing polyimide (PI) gasket layer and the catalyst coated membrane (CCM) was identified as a key contributor to premature edge failures, which introduced significant voltage decay and fluctuations due to permanent membrane deformation subjected to the hygrothermally severe AST conditions. CCM slipping was observed at the edges likely due to adhesive melting, which led to excessive CCM cracks. This issue was mitigated in a subsequent design by using a non-adhesive polytetrafluoroethylene (PTFE) layer at the CCM interface along with changes in gasket coverage area, which led to: (i) delayed onset of edge failure; (ii) nearly five times reduction in edge crack size; (iii) elimination of membrane tearing; and (iv) minimal impact of edge failures on cell performance. This mitigation enabled a robust MEA edge wherein the performance-impacting failure was shifted from the edges to the active area regions where operational factors are expected to play a role in eventual degradation of performance. The active area is broadly composed of uncompressed channel regions and compressed land regions. Membrane cracks, observed exclusively in channel regions, were the predominant cause of performance loss by opening paths for internal gas crossover. All membrane cracks were driven by membrane buckling into macro pores in gas diffusion layer (GDL) through surface pores on microporous layer (MPL). Cracks were initiated from the catalyst layer (CL) surface, and gradually penetrated the entire membrane thickness. In-situ diagnostics data showed significant gas crossover through membrane cracks, which induced dramatic open circuit voltage loss. Formation of membrane buckling was highly dependent on preliminary non-uniformities in CL and GDL such as cracks and macro pores that cause uneven stress distribution. In land regions, membrane creep into macro GDL pores was observed instead of buckling, but creep did not lead to membrane crack. However, both creep and buckling were induced by similar mechanisms. The accumulation of ionomer and catalyst at creep spots led to local membrane thinning and loss in effective platinum surface area. Although mechanical stress was the main contributor to membrane failure, it is believed that chemical stress accelerated the degradation process since the number of AST cycles needed to achieve ultimate MEA failure was significantly reduced compared to our previously reported pure mechanical degradation tests[2]. Overall, a robust MEA edge design was the most significant outcome from this work. In addition, new knowledge was gained on membrane degradation under combined chemical/mechanical AST, which could contribute to enhanced fuel cell durability. Keywords: fuel cell; membrane durability; edge design; mechanical degradation; chemical degradation; X-ray computed tomography Acknowledgement 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. Reference [1] D. Ramani, et al., J. Electrochem. Soc. 165 (2018) F3200. [2] Y. Singh, et al., Journal of 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 seuil0,006

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 technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,000
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 Opus0,023
Tête enseignante GPT0,205
Écart entre enseignants0,182 · 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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