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Identifying Defects with Guided Algorithms in Bragg Coherent Diffractive Imaging

机译:在布拉格相干衍射成像中使用导引算法识别缺陷

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摘要

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.
机译:诸如位错的晶体学缺陷会大大改变材料的性能和功能。然而,由于所涉及的短尺度和感兴趣的反应环境,在操作期间对这些缺陷进行成像仍然具有挑战性。布拉格相干衍射成像(BCDI)已经成为一种功能强大的工具,能够在反应环境中的纳米晶体和晶粒内以纳米空间分辨率识别3D细节中的位错,孪晶域和其他缺陷。但是,BCDI依赖于相位检索算法,该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成像缺陷晶体的不同导引相位检索算法。我们探索了不同的缺陷类型,缺陷密度,布拉格峰和引导的算法适应度指标,作为信噪比的函数。基于这些结果,我们提供了在没有先验知识的情况下对有缺陷的晶体进行定相的一般处方。

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