Improving FIB-SEM Reconstructions By Using Epoxy Resin Embedding
Notice bibliographique
Résumé
Focus ion beam-scanning electron microscopy (FIB-SEM) is a viable technique to visualize and reconstruct the morphology of polymer electrolyte membrane fuel cell (PEMFC) porous media, such as catalyst layers (CLs) (1,2) and micro-porous layers (MPLs) (3,4), with a high resolution of few nanometers. FIB-SEM provides morphological information about the solid and pore phase from which several statistical descriptors and effective transport properties (2) can be extracted to characterize the random heterogeneous structure. A major challenge in the analysis of the FIB-SEM images is the binarization of the grey scale images into the solid and pore phases (5). This problem arises because the SEM detects the solid particles inside the pores which are not in the plane of the image. This adds to the complexity of the segmentation by introducing an additional dependency to not only distinguish between the pore and solid phase but also, distinguish between solid phase in the plane of the image and solid phase in the background. In order to tackle this problem, advanced segmentation algorithms have been developed (5,6). However, these algorithms lack consistency across datasets and often require manual intervention which limits their robustness. Binarization can be improved by enhancing the sample preparation for the image acquisition. This has been done by penetrating the pores with filling material by embedding the sample in an epoxy based resin (7,8) or silicon based resin (9), platinum vapor deposition (10) and atomic layer deposition (ALD) of zinc oxide (11). ALD and platinum vapor deposition result in partial filling of the pores but provide a higher contrast from the carbon rich backbone. However, due to the partial filling manual corrections and additional image filtering operations are required post segmentation of the images. Epoxy and silicon based resin embedding results in complete filling of the pores but provides a very low contrast for the images. Ghosh et al. (8) recently showed that images obtained using the backscattered electrons (BSE) provide a better contrast than the secondary electron (SE) images (8,9) for a PEMFC CL embedded with an epoxy based resin. However, Ghosh et al. (8) only examined and compared 2D images of the CL. Comparison of a full 3D reconstruction of the PEMFC CL using FIB-SEM with and without epoxy embedding has yet to be performed. In the present article, 3D reconstructions of a conventional high surface area CL are performed using FIB-SEM images of the same sample treated with and without epoxy embedding to analyze the differences in image analysis and structure for both the modes of sample preparation. Figure 1 below shows the raw images obtained using SE mode for the sample without epoxy embedding and BSE mode for the sample with epoxy embedding. Multiple stacks of images are processed from both the datasets to ensure global validity of the results. Image processing operations are performed to further enhance the raw images before segmentation. Image analysis reveals that a simple thresholding algorithm, such as Otsu, is sufficient to accurately segment the images for the sample with epoxy embedding due to the lack of background features. Comparison of the reconstructions for the two modes suggests an increase in porosity and chord length function (12) for the sample imaged using epoxy embedding due to a more accurate segmentation. Transport simulations are being carried out to compute the effective transport properties. References 1. S. Thiele et al., Nano Research, 4(9), 849 (2011). 2. M. Sabharwal et al., Fuel Cells (2016). 3. X. Zhang et al., Int. J. Hydrogen Energy, 39(30), 17222 (2014). 4. H. Ostadi et al., Journal of Membrane Science, 351(1), 69 (2010). 5. M. Salzer et al., Materials Characterization, 95, 36 (2014). 6. M. Salzer et al., Materials Characterization, 69, 115 (2012). 7. H. Iwai et al., Journal of Power Sources, 195(4), 955 (2010). 8. S. Ghosh et al., International Journal of Hydrogen Energy, 40(45), 15663 (2015). 9. M. Ender et al., Electrochemistry Communications, 13(2), 166 (2011). 10. S. K. Eswara-Moorthy, P. Balasubramanian, W. van Mierlo, J. Bernhard, M. Marinaro, M. Wohlfahrt-Mehrens, L. Jörissen and U. Kaiser. Microscopy and Microanalysis, 20(05), 1576 (2014). 11. S. Vierrath, F. Güder, A. Menzel, M. Hagner, R. Zengerle, M. Zacharias and S. Thiele. Journal of Power Sources, 285, 413 (2015). 12. L. M. Pant, M. Sabharwal, S. Mitra and M. Secanell. ECS Transactions, 69(17), 105 (2015). Figure 1
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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 | 0,001 | 0,000 |
| Communication savante | 0,001 | 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 ».