Nano-Scale X-Ray Computed Tomography of Micro-Porous Layers
Notice bibliographique
Résumé
Recent advances in the development of polymer electrolyte fuel cells have proven that application of a thin micro-porous layer (MPL) on top of the macro-porous fibrous gas diffusion layer (GDL) can significantly enhance the fuel cell performance. Therefore, specific information on the structure of this layer could assist the understanding of the underlying mechanisms. Porosity and pore size distribution (PSD) are the main quantities that reveal important information about the structure. Mercury intrusion porosimetry (MIP) technique has been used previously to characterize the pore phase of the MPL [1]. Focused ion beam integrated with scanning electron microscopy (FIB-SEM) technique is another method of determining the MPL structure, in terms of both solid and pore phases [1,2]. However, these two approaches are generally destructive in nature, and may therefore skew the results by altering the MPL structure either during imaging or during post-processing. In addition, neither method can distinguish between carbon and PTFE inside the MPL [3]. The recent emergence of lab scale micro- and nano-scale X-ray computed tomography facilities provides a new opportunity for obtaining the 3D structure of porous materials by means of a non-destructive approach that can provide high quality images. This approach was used to study the cathode catalyst layer by Nano-scale X-ray computed tomography (NXCT) [4]. The reliability of the approach was assessed by comparing the obtained images by NXCT with transmission electron microscopy (TEM) images [4]. To our knowledge, there is only one published investigation with the focus on MPL imaging using NXCT [5]. In [5], an MPL was used as the sample for comparison of NXCT and FIB-SEM imaging capabilities, and some properties were calculated based on the obtained structures. By increasing the application of NXCT in the field of fuel cell materials, there will be a need for standardized imaging techniques. Hence, the objective of the present work is to develop suitable NXCT imaging, reconstruction, and post-processing protocols for MPLs. For this purpose, commercially available Sigracet SGL 24BC is used as the sample GDL material in this analysis. Two different sample preparation techniques are compared. Furthermore, various algorithms for automatic thresholding of the images are discussed and the most appropriate one for MPL segmentation is recommended. Fig. 1 shows the obtained raw versus segmented image on a cross section of the MPL, for which the porosity is obtained to be 50-52%. Shown in Fig. 2 is also the pore size distribution for the same MPL.MPL thermo-physical properties are also calculated based on the reconstructed images and compared to literature data. Moreover, for the first time, we explore the viability of determining the nanoscale PTFE distribution inside the MPL using the low energy X-ray beam of the Zeiss Xradia 810 Ultra facility. This instrument is considered state-of-the-art for laboratory NXCT and generates a monochromatic X-ray beam with 5.4 keV of energy. The low energy of the X-ray beam provides a good image contrast even for chemical compounds with low effective atomic numbers. Aknowledgment 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. References [1]A. Nanjundappa, A.S. Alavijeh, M. El Hannach, D. Harvey, E. Kjeang, Electrochimica Acta 110 (2013) 349. [2]H. Ostadi, P. Rama, Y. Liu, R. Chen, X.X. Zhang, K. Jiang, Journal of Membrane Science 351 (2010) 69. [3]M. El Hannach, R. Singh, N. Djilali, E. Kjeang, Journal of Power Sources 282 (2015) 58. [4]W.K. Epting, J. Gelb, S. Litster, Advanced Functional Materials 22 (2012) 555. [5]E. a. Wargo, T. Kotaka, Y. Tabuchi, E.C. Kumbur, Journal of Power Sources 241 (2013) 608. 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 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 ».