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Record W4250848503 · doi:10.1149/ma2017-01/31/1483

Improving FIB-SEM Reconstructions By Using Epoxy Resin Embedding

2017· article· en· W4250848503 on OpenAlexaff
Mayank Sabharwal, Andreas Pütz, Darija Susac, Jasna Janković, Marc Secanell

Bibliographic record

VenueECS Meeting Abstracts · 2017
Typearticle
Languageen
FieldEngineering
TopicIntegrated Circuits and Semiconductor Failure Analysis
Canadian institutionsAutomotive Fuel Cell Cooperation (Canada)University of Alberta
Fundersnot available
KeywordsFocused ion beamMaterials scienceScanning electron microscopeSample preparationNanotechnologyComposite materialIonChemistry

Abstract

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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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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.247
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2017
Admission routes1
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