Identifying Defects with Guided Algorithms in Bragg Coherent Diffractive Imaging
Abstract
In this study, crystallographic defects such as dislocations can significantly alter material properties and functionality. However, imaging these imperfections during operation remains challenging due to the short length scales involved and the reactive environments of interest. Bragg coherent diffractive imaging (BCDI) has emerged as a powerful tool capable of identifying dislocations, twin domains, and other defects in 3D detail with nanometer spatial resolution within nanocrystals and grains in reactive environments. However, BCDI relies on phase retrieval algorithms that can fail to accurately reconstruct the defect network. Here, we use numerical simulations to explore different guided phase retrieval algorithms for imaging defective crystals using BCDI. We explore different defect types, defect densities, Bragg peaks, and guided algorithm fitness metrics as a function of signal-to-noise ratio. Based on these results, we offer a general prescription for phasing of defective crystals with no a prior knowledge.
- Authors:
-
[1];
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Univ. Grenoble Alpes, Grenoble (France)
- Paul Scherrer Inst. (PSI), Villigen (Switzerland)
- Publication Date:
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22), Materials Sciences and Engineering Division
- OSTI Identifier:
- 1395865
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Scientific Reports
- Additional Journal Information:
- Journal Volume: 7; Journal Issue: 1; Journal ID: ISSN 2045-2322
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE
Citation Formats
Ulvestad, A., Nashed, Y., Beutier, G., Verdier, M., Hruszkewycz, S. O., and Dupraz, M. Identifying Defects with Guided Algorithms in Bragg Coherent Diffractive Imaging. United States: N. p., 2017.
Web. doi:10.1038/s41598-017-09582-7.
Ulvestad, A., Nashed, Y., Beutier, G., Verdier, M., Hruszkewycz, S. O., & Dupraz, M. Identifying Defects with Guided Algorithms in Bragg Coherent Diffractive Imaging. United States. https://doi.org/10.1038/s41598-017-09582-7
Ulvestad, A., Nashed, Y., Beutier, G., Verdier, M., Hruszkewycz, S. O., and Dupraz, M. Wed .
"Identifying Defects with Guided Algorithms in Bragg Coherent Diffractive Imaging". United States. https://doi.org/10.1038/s41598-017-09582-7. https://www.osti.gov/servlets/purl/1395865.
@article{osti_1395865,
title = {Identifying Defects with Guided Algorithms in Bragg Coherent Diffractive Imaging},
author = {Ulvestad, A. and Nashed, Y. and Beutier, G. and Verdier, M. and Hruszkewycz, S. O. and Dupraz, M.},
abstractNote = {In this study, crystallographic defects such as dislocations can significantly alter material properties and functionality. However, imaging these imperfections during operation remains challenging due to the short length scales involved and the reactive environments of interest. Bragg coherent diffractive imaging (BCDI) has emerged as a powerful tool capable of identifying dislocations, twin domains, and other defects in 3D detail with nanometer spatial resolution within nanocrystals and grains in reactive environments. However, BCDI relies on phase retrieval algorithms that can fail to accurately reconstruct the defect network. Here, we use numerical simulations to explore different guided phase retrieval algorithms for imaging defective crystals using BCDI. We explore different defect types, defect densities, Bragg peaks, and guided algorithm fitness metrics as a function of signal-to-noise ratio. Based on these results, we offer a general prescription for phasing of defective crystals with no a prior knowledge.},
doi = {10.1038/s41598-017-09582-7},
journal = {Scientific Reports},
number = 1,
volume = 7,
place = {United States},
year = {Wed Aug 30 00:00:00 EDT 2017},
month = {Wed Aug 30 00:00:00 EDT 2017}
}
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Works referencing / citing this record:
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