(Invited) 3D Visualization of Membrane Failure
Notice bibliographique
Résumé
Membrane failure is an important factor for the overall durability of polymer electrolyte fuel cells. Lifetime limiting failure occurs when the membrane loses its ability to reliably separate the reactant gases such that potentially combustive conditions may arise due to mixing of hydrogen and oxygen. While diffusive gas crossover is regularly present at benign rates, critical leak rates require significant convective fluxes that can only occur in the presence of large holes and cracks that span the full thickness of the membrane. Membrane degradation and damage development during fuel cell operation takes place through complex interplay between chemical degradation due to radical attack of the ionomer and mechanical degradation due to hygrothermal variations under mechanically constrained conditions. The combined action of chemical and mechanical degradation, which is difficult to avoid during dynamic fuel cell operation, is known to drastically accelerate the accumulation of membrane damage and be the primary cause of ultimate failure [1]. However, the process through which failure occurs is only partially understood. Membrane failure analysis has historically been performed by 2D imaging techniques such as optical and electron microscopy, which is limited to surface views of the electrodes and cross-sectional snapshots of the internal MEA structure. These techniques are also destructive in nature, demand tedious sample preparation with risk of artifacts, operate under vacuum, and/or are suitable only for electrically conductive samples. Our group recently proposed the use of X-ray computed tomography (XCT) to overcome these limitations and open up a novel non-destructive 3D characterization method for imaging of membranes inside an MEA [2]. This approach leverages recent advances in laboratory-based XCT technology to non-destructively acquire 3D images of membrane damage features and to develop a unique 3D failure analysis framework for fuel cell membranes [3]. This methodology is systematically applied to fuel cells subjected to pure chemical, pure mechanical, and combined chemical and mechanical membrane degradation in order to reveal the different types of membrane failures that may occur during fuel cell operation and assign their presence to specific degradation mechanisms. Novel discoveries made using this technique include distinct identification of I, Y, and X branched cracks (Figure 1), formation of exclusive membrane cracks, interaction of membrane and catalyst layers cracks and delamination sites, and electrode shorts due to excessive chemical membrane degradation. However, regular 3D visualization of membrane failures cannot reveal the root cause of each damage feature. Therefore, our recent efforts have focused on further exploiting the non-destructive nature of XCT scans to perform 4D membrane visualization by means of 3D scans at different points in time [4]. This allows us to track the propagation of membrane damage during the degradation process and also enables back-tracking of failure modes to determine the actual root cause by inspecting same-location scans performed at earlier times. Using this methodology, we have determined under which circumstances catalyst layer cracks may propagate into membrane cracks and vice versa. Overall, 3D visualization of membrane degradation and failure by XCT is shown to be a highly promising method to help understand complex failure modes and degradation mechanisms in fuel cells. 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. Lim, C., Ghassemzadeh, L., Van Hove, F., Lauritzen, M., Kolodziej, J., Wang, G.G., Holdcroft, S. and Kjeang, E., J. Power Sources 257 (2014) 102-110 2. White, R.T., Najm, M., Dutta, M., Orfino, F.P. and Kjeang, E., J. Electrochem. Soc. 163 (2016) F1206-F1208 3. Singh, Y., Orfino, F.P., Dutta, M. and Kjeang, E., J. Power Sources 345 (2017) 1-11 4. White, R.T., Wu, A., Najm, M., Orfino, F.P., Dutta, M. and Kjeang, E., J. Power Sources 350 (2017) 94-102 Figure 1. Representative reconstructed false color tomogram of an MEA, showing planar, cross-sectional, and 3D virtual views of an X-shaped membrane crack and adjacent catalyst layer cracks (M = membrane; ACL = anode catalyst layer; CCL = cathode catalyst layer). Figure 1
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,097 | 0,025 |
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 source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».