Visualization of Water Distribution in Fuel Cell Microporous and Catalyst Layers with 3D Nanoscale X-Ray Imaging
Notice bibliographique
Résumé
As an efficient green energy technology, polymer electrolyte membrane fuel cells (PEMFCs) are rapidly growing in a wide range of applications. Development of high power-density PEMFCs is under investigation to further reduce the overall cost and footprint. However, in such operational conditions, excessive water production could restrict the transport of reactants due to flooding, and subsequently degrade the performance of the PEMFC. This makes the water management a major challenge and necessitates a good understanding of water distribution and transport within the porous layers of the PEMFC [1, 2]. 3D X-ray computed tomography (XCT) is a strong tool for visualizing the water distribution within porous media. Micro-scale XCT has shown great promise in understanding and visualizing the water behavior within the gas diffusion layer (GDL) substrate region possessing ~10-100 µm pores [3–6]. This resolution, however, is not sufficient to capture water distribution in microporous and catalyst layers (MPL and CL), which are predominantly composed of nano-scale pores. This work aims to explore a method of water capture and visualization within the MPL and CL of a PEMFC using a lab-based nano-resolution XCT (NXCT) with a spatial resolution as high as 50 nm. Benefitting from the non-invasive NXCT imaging technique, the same spot of a dry/wet material has been studied in Zernike phase contrast mode using a custom-built enclosed fixture with high X-ray throughput to maintain a constant humidity (100% RH) over the tomographic acquisition period. Water domains have been identified after alignment of wet and dry image data sets followed by subtraction of dry from wet data set. Figure 1a shows a 2D projection of the MPL on AvCarb GDL after vacuum-assisted water infiltration. A gold microsphere is fixed on the sample within a thin layer of epoxy to allow for accurate identification of the region of interest. Figure 1b shows a selected segmented 2D projection of wet MPL illustrating how liquid water clusters are distributed within the pores and the 3D volume rendering of structural features is displayed in Figure 1c. Using the above-described method, 18% and ~7% volume fraction of liquid water has been identified within the wetted MPL and CL, respectively. The methodology discussed here is a step toward deep understanding of water transport phenomena in CL and MPL materials dictating the strategies for materials design to prevent flooding under high current densities. Moreover, the set-up and methodology can be further optimized to visualize water domains under controlled temperature and relative humidity, to simulate the real operating conditions. Figure 1. XCT imaging of a wet MPL: (a) A radiograph of MPL on AvCarb GDL after vacuum-assisted water infiltration; (b) a selected 2D projection of MPL showing the segmented solid (green), liquid water (blue), and unoccupied pores (black) domains; and (c) 3D volume rendering of a subdomain in the MPL showing the distribution of liquid water within the pores of the structure (voxel size 32 nm in b and c). Acknowledgement Funding for this research was provided by the Natural Sciences and Engineering Research Council of Canada, Ballard Power Systems, Canada Foundation for Innovation, British Columbia Knowledge Development Fund, and Canada Research Chairs. References [1] Q. Liu, F. Lan, J. Chen, C. Zeng, and J. Wang, J. Power Sources, 517, 2021, 230723, 2022. [2] A. Bazylak, Int. J. Hydrogen Energy, 34, 9, 3845–3857, 2009. [3] R. T. White et al., Sci. Rep., 9, 1, 1–12, 2019. [4] M. Andisheh-Tadbir, F. P. Orfino, and E. Kjeang, J. Power Sources, 310, 61–69, 2016. [5] J. Eller, T. Rosén, F. Marone, M. Stampanoni, A. Wokaun, and F. N. Büchi, J. Electrochem. Soc., 158, 8, B963–B970, 2011. [6] P. Shrestha, C. Lee, K. F. Fahy, M. Balakrishnan, N. Ge, and A. Bazylak, J. Electrochem. Soc., 167, 5, 054516, 2020. 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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».