Quality Implications of Foreign Metallic Particles in the Membrane Electrode Assembly of a Fuel Cell
Notice bibliographique
Résumé
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
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Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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