论文标题
具有超模型数据融合的传输电子显微镜中结构，光学和化学特性的特定特异性相关性
Feature-specific correlation of structural, optical, and chemical properties in the transmission electron microscope with hypermodal data fusion
论文作者
论文摘要
现代TEM仪器可以探测具有前所未有的分辨率的各种结构，光学和化学性质。但是，这些属性中的每一个都必须使用不同的检测器模式记录在独立的数据集中，而目前无统一的框架可用于定量将其关系映射到化学不同的特征上，尤其是在复杂的形态中。在这里，我们通过提出一个称为“超模式数据融合”的数据采集和分析工作流来应对这一挑战，并描述了如何直接将任意数量的高度分散的检测器模式（包括光谱和扫描衍射）进行，并共同分析它们以进行相关性。我们在随机集合的解剖酶和金红石纳米颗粒的随机集合上证明了这一概念，首先详细介绍了如何使用核心损坏的鳗鱼沿梁方向构成不同的多晶型物，然后将其用于提取多晶型物特异性组成，带镜和晶体结构。最后，我们讨论了此工作流程在各种材料系统中的适用性。
Modern TEM instrumentation can probe a wide range of structural, optical, and chemical properties with unprecedented resolution. However, each of these properties must be recorded in independent datasets using different detector modes with no unifying framework currently available for quantitatively mapping their relationships onto chemically distinct features, particularly in complex morphologies. Here, we tackle this challenge by proposing a data acquisition and analysis workflow called "hypermodal data fusion," describing how to directly couple an arbitrary number of highly disparate detector modes including spectroscopy and scanning diffraction and jointly analyze them for correlations. We demonstrate this concept on a random collection of anatase and rutile nanoparticles, first detailing how to use core-loss EELS to unmix the different polymorphs despite three-dimensional overlap along the beam direction and then showing how this can be used to extract polymorph-specific composition, bandgaps, and crystal structure. We conclude with a discussion on the applicability of this workflow for a broad range of materials systems.
