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Enregistrement W4391663463 · doi:10.1149/ma2023-02391919mtgabs

Chemo-Mechanical Durability of Reinforced Fuel Cell Membranes in the Presence of Metallic Foreign Particles

2023· article· en· W4391663463 sur OpenAlexaffabout
Nitish Kumar, Yixuan Chen, MohammadAmin Bahrami, Francesco P. Orfino, Monica Dutta, Michael Lauritzen, Erin Setzler, Alexander Agapov, Erik Kjeang

Notice bibliographique

RevueECS Meeting Abstracts · 2023
Typearticle
Langueen
DomaineEngineering
ThématiqueFuel Cells and Related Materials
Établissements canadiensSimon Fraser University
Organismes subventionnairesnon disponible
Mots-clésDurabilityMembraneMaterials scienceFuel cellsMetalComposite materialMetallurgyChemical engineeringEngineeringChemistry

Résumé

récupéré en direct d'OpenAlex

High-throughput production is vital to achieve cost-effective proton exchange membrane fuel cells (PEMFCs) at scale 1 . The high-volume mass production of PEMFCs using industrial manufacturing machinery may however introduce unwanted material in the final product due to the wear and tear of the machinery. As an example, roll-to-roll processes applied to the manufacture of catalyst coated membrane (CCM) generally use Fe alloys such as stainless steel 2 components that could release fine metallic particles or related debris that may affect the product quality 3,4 . Therefore, it is important to understand the fundamental impact of such non-uniformities on PEMFC performance and durability. This work focusses on the impact of slightly oxidized iron (Fe) and stainless steel 316L (SS316L) micro-particles entrapped between the cathode catalyst layer (CCL) and a GORE-SELECT® Membrane A. Fe cations are considered as Fenton’s catalyst/reagent which enhance harmful radical generation and ionomer attack, known to weaken its mechanical strength and ionic conductivity 5 . Therefore, membrane electrode assemblies (MEAs) containing solid Fe and SS316L particles were prepared to conduct in-situ PEMFC experiments and identify their impact on membrane degradation and durability. The chosen particles were placed carefully on the bare membrane at selected locations based on the flow field design of the graphite bipolar plates. Later, the assembled MEAs were tested using a small-scale fixture (SSF) fuel cell in a combined chemical and mechanical AST after beginning-of-life (BOL) conditioning and diagnostics 6 . X-ray computed tomography (XCT) has been shown to be a powerful and non-invasive 3D characterization tool for analyzing membrane degradation 7,8 . Thus, periodic same-location XCT visualization of the CCL-membrane interface was performed after every 10 AST cycles. According to the Fe Pourbaix diagram 9 , Fe oxidizes to Fe 2+ at 0.77 V and indeed, in this work, complete dissolution of the Fe-50µm particles was observed leaving cavities at the CCL-membrane interface. This dissolution manifested as a distinct black spot in CT imaging, as highlighted by the enclosed red circle in Figure 1. In contrast, corrosion of SS316L particles is usually prevented by a native oxide layer formation during fuel cell operational conditions 10 . The Fe particle laden AST reached membrane failure within 30 cycles and indicates an escalation of degradation by global membrane thinning. This is substantially lower than the baseline AST without particles. The XCT images for Fe-50µm AST seen in Figure 1 show that severe global membrane thinning occurred in the Fe-50µm MEA, which contributed to a high electrochemical leak detection (ELDT) signal exceeding test failure criteria. In contrast, the SS316L MEA did not reach the threshold failure criteria for the AST duration, however, the average OCV remained lower as compared to the baseline MEA AST. XCT imaging showed that the SS316L- 50µm particles did not dissolve throughout the AST duration and the membrane thinning was more pronounced near the particle rather than globally. Additionally, we have evaluated whether chemically and mechanically mitigated GORE-SELECT Membrane® B could enable more robustness and minimize impact of multiple metallic particles. Analysis of combined chemo-mechanical AST with a larger dimension SS316L-500µm present on mitigated GORE-SELECT® Membrane B suggest that chemical and mechanical mitigation can reduce impact of large particles and enable lifetime response at the level close to baseline. In conclusion, the membrane lifetime and failure mode were found to be strongly dependent on the particle chemical composition and membrane degradation mitigation strategies, which are therefore should be considered at PEM design stages to reduce risks and improve CCM and MEA production quality. Keywords: PEM durability, quality control, cost reduction, XCT Acknowledgments This research was supported by the Natural Sciences and Engineering Research Council of Canada, Canada Foundation for Innovation, British Columbia Knowledge Development Fund, Western Economic Diversification Canada, Ballard Power Systems, and W.L. Gore & Associates. This research was undertaken, in part, thanks to funding from the Canada Research Chairs program. References H. Tsuchiya and O. Kobayashi, Int. J. Hydrogen Energy , 29 , 985–990 (2004). J. Chen, H. Liu, Y. A. Huang, and Z. Yin, J. Manuf. Process. , 23 , 175–182 (2016). A. Phillips et al., Fuel Cells , 20 , 60–69 (2020). A. Phillips, M. Ulsh, K. C. Neyerlin, J. Porter, and G. Bender, Int. J. Hydrogen Energy , 43 , 6390–6399. J. G. Goodwin, K. Hongsirikarn, S. Greenway, and S. Creager, J. Power Sources , 195 , 7213–7220 (2010). Y. Chen et al., J. Power Sources , 520 , 230673 (2022). Y. Chen et al., J. Power Sources , 520 , 230674 (2022). Y. Singh et al., J. Power Sources , 412 , 224–237 (2019). R. Tolouei et al., Phys. Chem. Chem. Phys. , 18 , 19637–19646 (2016). N. Kumar et al., Int. J. Energy Res. , 44 , 6804–6818 (2020). 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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,382
Score d'incertitude au seuil0,326

Scores Codex et Gemma par catégorie

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

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 Opus0,017
Tête enseignante GPT0,219
Écart entre enseignants0,202 · 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 tête enseignante, 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
Publié2023
Routes d'admission2
Résumé présentoui

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