Extending 4D-STEM to Defect and Short-range Ordering Analysis: Principles, Methodology and Applications
Extending 4D-STEM to Defect and Short-range Ordering Analysis: Principles, Methodology and Applications
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将 4D-STEM 扩展到缺陷和短程有序分析：原理、方法和应用
DOI：
10.1093/micmic/ozad067.112
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发表时间：
2023
影响因子：
2.8
通讯作者：
Zhang, Jiong
中科院分区：
文献类型：
--
作者：
Zuo, Jian-Min;Hsiao, Haw-Wen;Yin, Kaijun;Ni, Hsu-Chih;Ni, Haoyang;Busch, Robert;Yuan, Renliang;Zhang, Jiong
晶体中的缺陷，例如位错、堆垛层错、短程有序和点缺陷的聚集体，破坏相干布拉格散射。在合适的衍射条件下，这种破坏足够大，可以在由单个布拉格光束形成的TEM图像中产生“衍射对比度”，从而绘制出缺陷周围的应变场[1-3]。在高分辨率下，使用许多光束，可以直接观察到缺陷的原子排列。衍射衬度成像和高分辨电子显微镜共同为我们提供了有关真实的材料缺陷的知识[4]。然而，先进材料的最新发展，如多主元素合金或高熵合金（HEAs），混合相电池材料和三维纳米器件，对电子成像提出了重大挑战[5]。此外，对高缺陷材料的表征一直是电子显微镜的挑战。随着快速电子探测器和高效计算机算法的最新发展，现在可以收集前所未有的大数据集衍射图案（DP）[6，7]。DPs的数据挖掘为探索新的电子成像技术提供了一个丰富的领域。特别是，提取晶体学信息形成基于晶体结构特性的图像或断层图是定量分析材料微观结构的有力手段。本次演讲的重点是基于4D-STEM的电子纳米衍射技术的原理，用于使用电子漫散射进行缺陷，应变和短程有序分析[8，9]。我们回顾了扫描电子纳米衍射（SEND）数据收集，基于倒谱分析的新算法[8]和基于机器学习的电子DP分析[10]的最新进展。这些进展将突出使用缺陷检测，和短程有序分析作为应用实例。研究的材料是中熵合金CrCoNi，它具有特殊的低温机械强度和延展性[11]。我们将展示SEND如何帮助我们理解CrCoNi合金中的非随机化学混合，这是由短程有序（图1）引起的，背后是CrCoNi的机械强度，以及这些发展如何为高级合金的原子结构研究提供一般机会。
Defects in crystals, such as dislocations, stacking faults, short-range order, and aggregates of point defects, disrupt coherent Bragg scattering. Under suitable diffraction conditions, the disruption is large enough to give rise to “diffraction contrast” in TEM images formed from a single Bragg beam, which maps out the strain field around a defect [1–3]. At high resolution, using many beams, the atomic arrangement of defects can be observed directly. Together, diffraction contrast imaging and HREM had contributed much of our knowledge of defects in real materials [4]. However, recent developments in advanced materials, such as multi-principal element alloys or high entropy alloys (HEAs), mixed phases battery materials and three-dimensional nanodevices, have posed significant challenges for electron imaging [5]. Additionally, characterization of highly defective materials has always been a challenge for electron microscopy.With recent developments in fast electron detectors and efficient computer algorithms, it now becomes possible to collect unprecedently large datasets of diffraction patterns (DPs)[6, 7]. Data mining of DPs has provided a rich field to explore new electron imaging techniques. Especially, extracting crystallographic information to form images or tomograms based on crystal structural properties is a powerful approach for quantitative analysis of materials microstructures. This talk focuses on the principles of 4D-STEM based electron nanodiffraction techniques for defect, strain and short-range ordering analysis using electron diffuse scattering [8, 9]. We review recent progress made in scanning electron nanodiffraction (SEND) data collection, new algorithms based on cepstral analysis [8], and machine learning based electron DP analysis [10]. These progresses will be highlighted using defect detection, and short-range ordering analysis as application examples. The materials of the study are the medium entropy alloy, CrCoNi, which has exceptional low-temperature mechanical strength and ductility [11]. We will show how SEND helps our understanding of non-random chemical mixing in a CrCoNi alloy, resulting from short-range ordering (Fig. 1), behind the mechanical strength in CrCoNi and how these developments provide general opportunities for an atomistic-structure study in advanced alloys.