发表机构
electron Physical Science Imaging Centre, Diamond Light Source; Department of Materials, University of Oxford; Johnson Matthey（电子物理科学成像中心，钻石光源; 牛津大学材料系; 庄信万丰）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出交互式自动化4D-STEM数据采集与分析流程，实现Pt纳米颗粒的4D-STEM数据自动化采集及半自动化分析，可获取其取向、相分布等微观结构信息，将仪器通量转化为统计意义的原子尺度信息。
AI 中文摘要
现代透射电子显微镜是多功能仪器，已成为在纳米和原子尺度上理解结构与化学成分不可或缺的工具。在物理科学领域，这类仪器仍主要由人工控制，需要操作人员具备丰富专业知识，限制了通量，也无法对大型数据集进行统计分析。近期硬件和控制软件的技术进展，现在允许通过编程接口与显微镜的几乎所有功能交互，这能优化实验设计、实现数据采集自动化，同时减少操作人员采集偏差和所需专业知识。本研究提出一种具备机器驱动决策的自动化数据采集流程，可从大量尺寸选择性沉积的Pt纳米颗粒中采集数百个4D-STEM纳米束衍射和ptychography数据；还提出半自动化数据分析工作流，从海量采集数据中提取相关信息。对于纳米束衍射数据，将每个数据集简化为方位角方差轮廓，并结合自动化晶体取向图谱与逐颗粒形态描述符，可揭示整个集合的取向、形状和相分布，包括微弱的{110}织构；对于ptychography数据，自动化筛选流程可识别轴上颗粒，实现原子分辨率相位成像和单个晶粒的晶格应变映射。这些结果共同证明，自动化如何将仪器通量转化为具有统计意义的原子尺度微观结构信息。
英文摘要
Modern transmission electron microscopes are versatile instruments which have become indispensable tools for understanding structure and chemical composition at the nano- and atomic scale. In the physical sciences these instruments are still largely manually controlled, requiring significant operator expertise, limiting throughput, and precluding statistical analysis of large datasets. Recent technical advances in both hardware and in control software now allow for the interaction with almost every functionality of the microscope through a programming interface. This enables better experimental design and data collection automation while also reducing operator collection bias and required expertise. In this study, we present an automated data collection routine with machine-driven decision-making to enable the collection of hundreds of 4D-STEM nanobeam diffraction and ptychography data from a large distribution of size-selectively deposited Pt nanoparticles. We present a semi-automated data analysis workflow to extract pertinent information from the large volumes of collected data. For the nanobeam diffraction data, reducing each dataset to its azimuthal variance profile and combining automated crystal orientation mapping with per-particle morphology descriptors reveals the orientation, shape and phase distributions across the ensemble, including a weak {110} texture. For the ptychographic data, an automated screening pipeline identifies on-zone-axis particles and enables atomic-resolution phase imaging and lattice-strain mapping of individual grains. Together these demonstrate how automation turns instrument throughput into statistically meaningful, atomic-scale microstructural information.
Comments41 pages, 7 figures, 11 SI figures
