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利用导波算法在布拉格相干衍射成像中识别缺陷。

Identifying Defects with Guided Algorithms in Bragg Coherent Diffractive Imaging.

机构信息

Materials Science Division, Argonne National Laboratory, Argonne, Illinois, 60439, USA.

Mathematics and Computer Science, Argonne National Laboratory, Argonne, Illinois, 60439, USA.

出版信息

Sci Rep. 2017 Aug 30;7(1):9920. doi: 10.1038/s41598-017-09582-7.

Abstract

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 priori knowledge.

摘要

晶体学缺陷,如位错,可以显著改变材料的性质和功能。然而,由于涉及的短长度尺度和感兴趣的反应环境,在操作过程中对这些缺陷进行成像仍然具有挑战性。布拉格相干衍射成象 (BCDI) 已经成为一种强大的工具,能够在反应环境中对纳米晶和晶粒内的位错、孪晶畴和其他缺陷以纳米级空间分辨率进行三维细节识别。然而,BCDI 依赖于相位恢复算法,这些算法可能无法准确地重建缺陷网络。在这里,我们使用数值模拟来探索使用 BCDI 对有缺陷晶体进行成像的不同引导相位恢复算法。我们探索了不同的缺陷类型、缺陷密度、布拉格峰和引导算法拟合度指标作为信噪比的函数。基于这些结果,我们提供了一种针对无先验知识的缺陷晶体定相的一般方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b170/5577107/e8d3cf6ed110/41598_2017_9582_Fig1_HTML.jpg

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