In-Situ Capabilities of Lab-Based X-Ray Microscopy for Polymer Electrolyte Fuel Cells
Notice bibliographique
Résumé
X-ray Computed Tomography has proven to be a crucial method to properly characterize and understand a wide variety of materials applications. Due to the non-destructive 3D imaging capabilities, this imaging methodology provides unique insight into material properties with comparatively minimal sample processing. One limitation, however, has been resolution capabilities. Previously, X-ray tomography imaging has utilized the principle of geometric magnification to obtain resolution. This has several limitations, the main impact being the realistic resolution achievable for a given sample size. Recently, the application of using a photon-converting scintillator and objective lens magnification has enabled much higher resolution imaging [1]. A unique ability of this system architecture is to enable non-destructive, multi-length scale visualization for relatively large sample sizes; with an imaging field of view range from tens of millimeters down to tens of micrometers, and resolution capabilities reaching 500 nm in instruments such as the ZEISS Xradia Versa. This architecture provides capabilities perfectly suited for complex device visualization, such as polymer electrolyte fuel cell (PEFC) systems, as well as the ability to image in-situ . By combining 3-dimensional visualization through repeated identical location tomography scans at various temporal stages, powerful in-situ investigations of dynamic material properties can be obtained. This methodology is often termed as 4DCT (4-dimensional computed tomography) and is particularly well suited to study various dynamic and evolutionary processes in PEFCs due to their complex multi-layered and entangled system. Detailed images of the membrane electrode assembly (MEA) can be periodically obtained while the cell is still assembled in its operational housing as well as while it is producing current, to observe transient liquid water pathways [2, 3]. Extraction of quantitative information regarding material/geometrical properties, such as thickness, porosity, saturation, and local deformation, as well as temporal changes to these properties and morphology during degradation processes is facilitated by advanced image processing and visualization methodologies [3,4,5]. In this presentation, custom tools, workflows, and analysis methods are showcased that allow for insight into the lifetime changes of cathode catalyst layer morphology, water saturation, and crack propagation. It has been found through ageing that morphological interaction between different layers can have considerable impact on degradation mechanisms [5,6,7]. We present an overview of the 4DCT approach applied to various fuel cell degradation studies as well as GDL water distribution during in situ imaging. These visualization methods uncover unique evidence around the strongly interactive nature of material degradation within a fuel cell that has previously been unobserved. Figure 1: Overview of lab-based in-situ imaging methodology within the Zeiss Xradia Versa system. A model of the X-ray microscope is shown with an ‘opened’ in-situ fuel cell device. Resulting segmented liquid water and cathode catalyst layer before and after ageing are shown with GDL, ionomer membrane and anode catalyst layer removed for visualization. Acknowledgements The authors would like to acknowledge the support from Monica Dutta and Ballard Power Systems in providing samples and discussion of the presented work, as well as all members of the Fuel Cell Research Lab for their kind support. References [1] ZEISS Xradia Versa Product Information. https://www.zeiss.com/microscopy/us/products/x-ray-microscopy/zeiss-xradia-610-and-620-versa.html , accessed 24 April, 2020. [2] White, et al., J. Power Sources. 350 (2017) 94–102. [3] White, et al., Scientific Reports. 9 (2019) 1843. [4] White, et al., J. Electrochem. Soc. 166 (2019) F914-F925. [5] Ramani, et al., Int. J. Hydrogen Energ. 45 (2020) 10089-10103. [6] Singh, et al., J. Power Sources. 412 (2019) 224–237. [7] Singh, et al., J. Power Sources. 345 (2017) 1–11. Figure 1
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».