Nanocrystal segmentation in scanning precession electron diffraction data.
Nanocrystal segmentation in scanning precession electron diffraction data.
复制标题
扫描进动电子衍射数据中的纳米晶体分割。
DOI：
10.1111/jmi.12850
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发表时间：
2020
影响因子：
2
通讯作者：
Bergh T
中科院分区：
文献类型：
--
作者：
Bergh T
扫描进动电子衍射（SPED）通过在样品上扫描动态摇摆电子束时，在每个探针位置记录二维进动电子衍射（PED）模式，从而能够在纳米尺度上探测材料的局部晶体学。来自纳米晶体材料的SPED数据通常包含一些PED模式，其中衍射是由多个晶体测量的。为了分析这些数据，重要的是要进行纳米晶体分割，以分离每个晶体的位置和相应的代表性衍射信号。这也显著降低了数据维度。本文提出了两种纳米晶体分割的方法，第一种是基于虚拟暗场成像，第二种是基于非负矩阵分解。比较了部分重叠纳米粒子在高速衍射数据中应用的优点和局限性，并强调了在相同衍射条件下晶体激发的特殊挑战。研究表明，这两种方法都可以用于纳米晶体分割，而不需要事先了解晶体结构，但也可以产生分割伪影，必须仔细考虑。与这项工作相关的分析工作流是开源的。扫描进动电子衍射是一种电子显微镜技术，可以在纳米尺度上研究广泛选择的材料的局部晶体学。该技术包括获取样品区域内每个探针位置的二维衍射图。通过这种技术收集的四维数据集通常可以包含多达50万个衍射图样。对于纳米晶材料来说，单个衍射图通常包含重叠晶体的信号。为了处理这些数据，我们使用纳米晶体分割，其中为每个单独的晶体构建具有代表性的衍射图案，以及显示其在数据中的形态和位置的真实空间图像。这降低了数据的维度，并允许从重叠晶体中分离信号。在这项工作中，我们展示了两种纳米晶体分割方法，一种基于创建虚拟暗场图像，另一种基于无监督机器学习。部分重叠纳米颗粒的模型系统用于演示分割，并强调了分割的苛刻情况，其中一些晶体根据其衍射模式无法识别。为了获得更完整的纳米晶体分割，我们在这两种方法中添加了图像分割例程，并讨论了两种方法的优点和局限性。演示数据和使用的代码都是开源的，因此每个人都可以使用它来分析纳米晶体材料，或者作为扫描进动电子衍射数据中纳米晶体分割的进一步开发的起点。
Scanning precession electron diffraction (SPED) enables the local crystallography of materials to be probed on the nanoscale by recording a two‐dimensional precession electron diffraction (PED) pattern at every probe position as a dynamically rocking electron beam is scanned across the specimen. SPED data from nanocrystalline materials commonly contain some PED patterns in which diffraction is measured from multiple crystals. To analyse such data, it is important to performnanocrystal segmentationto isolate both the location of each crystal and a corresponding representative diffraction signal. This also reduces data dimensionality significantly. Here, two approaches to nanocrystal segmentation are presented, the first based on virtual dark‐field imaging and the second on non‐negative matrix factorization. Relative merits and limitations are compared in application to SPED data obtained from partly overlapping nanoparticles, and particular challenges are highlighted associated with crystals exciting the same diffraction conditions. It is demonstrated that both strategies can be used for nanocrystal segmentation without prior knowledge of the crystal structures present, but also that segmentation artefacts can arise and must be considered carefully. The analysis workflows associated with this work are provided open‐source.Lay DescriptionScanning precession electron diffraction is an electron microscopy technique that enables studies of the local crystallography of a broad selection of materials on the nanoscale. The technique involves the acquisition of a two‐dimensional diffraction pattern for every probe position in an area of the sample. The four‐dimensional dataset collected by this technique can typically comprise up to 500 000 diffraction patterns. For nanocrystalline materials, it is common that single diffraction patterns contain signals from overlapping crystals. To process such data, we use nanocrystal segmentation, where a representative diffraction pattern is constructed for each individual crystal, together with a real space image showing its morphology and location in the data. This reduces the dimensionality of the data and allows unmixing of signals from overlapping crystals. In this work, we demonstrate two methods for nanocrystal segmentation, one based on creating virtual dark‐field images, and one based on unsupervised machine learning. A model system of partly overlapping nanoparticles is used to demonstrate the segmentation, and a demanding case for segmentation is highlighted, where some crystals are not discernible based on their diffraction patterns. To obtain a more complete nanocrystal segmentation, we add an image segmentation routine to both methods, and we discuss benefits and limitations of the two methods. The demonstration data and the used code are provided open‐source, so that it can be used by everyone for analysis of nanocrystalline materials or as a starting point for further development of nanocrystal segmentation in scanning precession electron diffraction data.
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影响因子：
2.2
作者：
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影响因子：
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影响因子：
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DOI：
--
发表时间：
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期刊：
影响因子：
--
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影响因子：
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