原位透射/扫描透射电镜技术的展望与展望。

Renu Sharma, Wei-Chang David Yang
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摘要

在过去的几十年里,原位透射/扫描透射电子显微镜(TEM/STEM)测量已经成为建立结构-化学-性质关系的中心阶段。实现“间隙实验室”的挑战,即物镜杆片之间的间隙,或“芯片实验室”,用于进行实验,正在通过不断的仪器开发来满足。市售的TEM柱和样品支架经过了原位实验的修改,有助于揭示样品在受到外部刺激(如温度、压力、辐射(光子、离子、电子)、环境(气体、液体、磁场或电场)或其组合时发生的结构和化学变化。虽然使用TEM/STEM常规收集原子分辨率图像和光谱数据,但时间分辨率仅限于毫秒。另一方面,使用超快电子显微镜(UEM)或动态电子透射电子显微镜(DTEM)可以获得比飞秒更好的时间分辨率,但空间分辨率仅限于亚纳米级。在任何一种情况下,原位实验都会产生需要传输、存储和分析的大型数据集。事实证明,人工智能(AI),尤其是机器学习(ML)平台的出现对于处理这一大数据问题至关重要。为了充分利用我们理解、测量和控制化学和/或物理过程的能力,还需要进一步的发展。我们介绍了仪器和计算能力的现状,并讨论了未来的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Perspective and prospects of in situ transmission/scanning transmission electron microscopy.

In situ transmission/scanning transmission electron microscopy (TEM/STEM) measurements have taken a central stage for establishing structure-chemistry-property relationship over the past couple of decades. The challenges for realizing 'a lab-in-gap', i.e. gap between the objective lens pole pieces, or 'a lab-on-chip', to be used to carry out experiments are being met through continuous instrumental developments. Commercially available TEM columns and sample holder, that have been modified for in situ experimentation, have contributed to uncover structural and chemical changes occurring in the sample when subjected to external stimulus such as temperature, pressure, radiation (photon, ions and electrons), environment (gas, liquid and magnetic or electrical field) or a combination thereof. Whereas atomic resolution images and spectroscopy data are being collected routinely using TEM/STEM, temporal resolution is limited to millisecond. On the other hand, better than femtosecond temporal resolution can be achieved using an ultrafast electron microscopy or dynamic TEM, but the spatial resolution is limited to sub-nanometers. In either case, in situ experiments generate large datasets that need to be transferred, stored and analyzed. The advent of artificial intelligence, especially machine learning platforms, is proving crucial to deal with this big data problem. Further developments are still needed in order to fully exploit our capability to understand, measure and control chemical and/or physical processes. We present the current state of instrumental and computational capabilities and discuss future possibilities.

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