Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-26T21:36:41.505Z Has data issue: false hasContentIssue false
  • English
  • Français

Laboratory-based 3D X-ray microscopy of unirradiated U-10Zr fuel

Published online by Cambridge University Press:  30 July 2021

Nikolaus Cordes ,
Laura Sudderth ,
Moiz Butt ,
James Jewell ,
Mitchell Meyer  and
Sean McDeavitt
Nikolaus Cordes
Affiliation:
Idaho National Laboratory, Idaho Falls, Idaho, United States
Laura Sudderth
Affiliation:
Idaho National Laboratory, United States
Moiz Butt
Affiliation:
Texas A&M University, United States
James Jewell
Affiliation:
Idaho National Laboratory, United States
Mitchell Meyer
Affiliation:
Idaho National Laboratory, United States
Sean McDeavitt
Affiliation:
Texas A&M University, United States

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Evaluation of Materials for Nuclear Applications
Information
Microscopy and Microanalysis , Volume 27 , Supplement S1 , August 2021 , pp. 1938 - 1939
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Maire, E.; Withers, P. J., Quantitative X-ray tomography. International Materials Reviews 2014 , 59 (1), 1-43.Google Scholar
Stock, S. R., Recent advances in X-ray microtomography applied to materials. International Materials Reviews 2008 , 53 (3), 129-181.Google Scholar
Dunsmuir, J. H.; Bennett, S.; Fareria, L.; Mingino, A.; Sansone, M., X-ray microtomographic imaging and analysis for basic research. Powder Diffraction 2006 , 21 (2), 125-131.CrossRefGoogle Scholar
Cordes, N.; Kane, J.; Craft, A., 3D X-ray Microscopy of Nuclear Energy Materials. Microscopy and Microanalysis 2020 , 26 (S2), 872-872.CrossRefGoogle Scholar
Kanungo, T.; Mount, D. M.; Netanyahu, N. S.; Piatko, C. D.; Silverman, R.; Wu, A. Y., An efficient k-means clustering algorithm: analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002 , 24 (7), 881-892.CrossRefGoogle Scholar
Bezdek, J. C.; Ehrlich, R.; Full, W., FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences 1984 , 10 (2), 191-203.CrossRefGoogle Scholar
Schindelin, J.; Arganda-Carreras, I.; Frise, E.; Kaynig, V.; Longair, M.; Pietzsch, T.; Preibisch, S.; Rueden, C.; Saalfeld, S.; Schmid, B.; Tinevez, J.-Y.; White, D. J.; Hartenstein, V.; Eliceiri, K.; Tomancak, P.; Cardona, A., Fiji: an open-source platform for biological-image analysis. Nature Methods 2012 , 9 (7), 676-682.CrossRefGoogle ScholarPubMed
Haase, R.; Royer, L. A.; Steinbach, P.; Schmidt, D.; Dibrov, A.; Schmidt, U.; Weigert, M.; Maghelli, N.; Tomancak, P.; Jug, F.; Myers, E. W., CLIJ: GPU-accelerated image processing for everyone. Nature Methods 2020 , 17 (1), 5-6.CrossRefGoogle ScholarPubMed