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Enregistrement W4309816487 · doi:10.1149/ma2022-02391402mtgabs

Quality Implications of Foreign Metallic Particles in the Membrane Electrode Assembly of a Fuel Cell

2022· article· en· W4309816487 sur OpenAlexaffabout
Nitish Kumar, Yixuan Chen, Amin Bahrami, Francesco P. Orfino, Monica Dutta, Erin Setzler, Alexander Agapov, Erik Kjeang

Notice bibliographique

RevueECS Meeting Abstracts · 2022
Typearticle
Langueen
DomaineEngineering
ThématiqueFuel Cells and Related Materials
Établissements canadiensSimon Fraser University
Organismes subventionnairesnon disponible
Mots-clésMembraneProton exchange membrane fuel cellMembrane electrode assemblyMaterials scienceElectrolyteChemical engineeringLeaching (pedology)MetalElectrodeComposite materialChemistryMetallurgyEnvironmental science

Résumé

récupéré en direct d'OpenAlex

Hydrogen-based polymer electrolyte membrane fuel cells (PEMFCs) are advantageous in terms of high efficiency, zero emissions, and low-temperature operation in both stationary and transportation sectors. Recently, efforts to reduce the cost by developing novel materials used in the membrane electrode assembly (MEA) and its design are underway. Besides the materials, the other factor that could contribute to cost reduction is the high volume production of MEA components with a high yield 1 . Additionally, during MEA assembly, certain abnormal process conditions or external contaminants may negatively affect the quality of the MEA and its compatibility with scalable high-volume manufacturing. The durability of the perfluorosulfonic acid (PFSA) ion exchange membrane is a major concern for the fuel cell industry, especially under Fenton’s cations such as Fe 2+ 2–5 . Stainless steel instruments used during the production of catalyst coated membranes (CCMs) may be considered as a possible source contributor of contamination. Micron sized metallic particles of SS316L (or Fe) could attach themselves on to the membrane during fabrication; thus, potentially cause MEA structural damage by perforation, rupture, or electrode delamination. Additionally to structural damage, there is also a possibility that Fe 2+ cations leaching from metal corrosion would reduce the membrane proton conductivity, cause backbone chain scission, etc 2,6 . Whereas the presence of cations has been studied extensively in ex-situ Fenton’s reagent testing of ion-exchanged membranes, controlling, and understanding the role of such cations in the in-situ fuel cell environment is much more challenging. In such an instance, membrane thinning and degradation are anticipated due to the presence of cations which act as a catalyst for radical generation during fuel cell operation 6 ; therefore, it is essential to understand the effect of such metallic particles on the membrane and by extension, improving the fundamental understanding of the effects of various irregular features and foreign contaminants is desirable 7,8 . The present work focuses on understanding the effect of unintended solid metallic particles in the MEA during and after the conditioning phase of a fuel cell. Stainless steel 316L (SS316L) and pure iron (Fe) particles are located conveniently at the interface of the cathode catalyst layer (CCL) and membrane (M) inside an MEA using a robust and controlled method. During the conditioning phase, the MEA is subjected to a sequential order of air starves, cyclic voltammetry, and constant current hold procedures. After each conditioning procedure, cell imaging is performed using the X-ray computed tomography (XCT) method, which has been proven to provide relevant information without damaging the integrity of the fuel cell 9,10 . Figure 1(a) &(c) shows the position of SS316L and Fe 50µm particles, respectively located at the CCL/M interface before conditioning, as originally planned. Initializing the air starve cycles, the MEAs containing particles indicates that the SS316L-50µm remains intact, as seen in Figure 1(c), and the Fe-50µm tends to dissolve and leave behind a void directly exposing the membrane as shown in Figure 1(d). The dissolution of Fe particles also leaves behind an ion concentration of more than 50 ppm in the active area. It is believed that Fe 2+ leaches from SS316L corrosion as well; however, the process is delayed by the native oxide layer formation on its surface and the concentration of Fe 2+ is lower. Therefore, it is important to know the response of such particles when conditioning an MEA so that better quality control processes can be identified prior to fuel cell assembly. 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 G. Bender, W. Felt, and M. Ulsh, J. Power Sources , 253 , 224–229 (2014) http://dx.doi.org/10.1016/j.jpowsour.2013.12.045. J. G. Goodwin, K. Hongsirikarn, S. Greenway, and S. Creager, J. Power Sources , 195 , 7213–7220 (2010) http://dx.doi.org/10.1016/j.jpowsour.2010.05.005. J. Qi et al., J. Power Sources , 286 , 18–24 (2015) http://dx.doi.org/10.1016/j.jpowsour.2015.03.142. A. Tavassoli et al., J. Power Sources , 322 , 17–25 (2016) http://dx.doi.org/10.1016/j.jpowsour.2016.05.016. N. Kumar et al., Int. J. Energy Res. , 44 , 6804–6818 (2020). S. Kundu, L. C. Simon, and M. W. Fowler, Polym. Degrad. Stab. , 93 , 214–224 (2008). S. Komini Babu et al., J. Electrochem. Soc. , 168 , 024501 (2021). A. Phillips, M. Ulsh, K. C. Neyerlin, J. Porter, and G. Bender, Int. J. Hydrogen Energy , 43 , 6390–6399 (2018) https://doi.org/10.1016/j.ijhydene.2018.02.050. D. Ramani et al., Int. J. Hydrogen Energy , 45 , 10089–10103 (2020) https://doi.org/10.1016/j.ijhydene.2020.02.013. Y. Singh et al., J. Power Sources , 412 , 224–237 (2019). 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 enseignants0,369
Score d'incertitude au seuil0,290

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 Opus0,020
Tête enseignante GPT0,245
Écart entre enseignants0,225 · 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
Publié2022
Routes d'admission2
Résumé présentoui

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