In Situ Transmission Electron Microscopy and Three-Dimensional Electron Tomography for Catalyst Studies

被引:0
|
作者
Sun, Chen [1 ,2 ]
Liu, Kuo [1 ,2 ]
Zhang, Jian [3 ]
Liu, Qian [4 ]
Liu, Xijun [5 ,6 ]
Han, Lili [2 ]
机构
[1] Fuzhou Univ, Coll Chem, Fuzhou 350116, Peoples R China
[2] Chinese Acad Sci, Fujian Inst Res Struct Matter, State Key Lab Struct Chem, Fuzhou 350002, Peoples R China
[3] Suzhou Univ, Key Lab Spin Electron & Nanomat Anhui Higher Educ, Suzhou 234000, Peoples R China
[4] Chengdu Univ, Inst Adv Study, Chengdu 610106, Peoples R China
[5] Guangxi Univ, MOE Key Lab New Proc Technol Nonferrous Met & Mat, Sch Resource Environm & Mat, Nanning 530004, Peoples R China
[6] Guangxi Univ, Guangxi Key Lab Proc Nonferrous Met & Featured Ma, Sch Resource Environm & Mat, Nanning 530004, Peoples R China
关键词
in situ TEM; catalyst; electron tomography; 3D reconstruction; artificial intelligence; machine learning; ALGEBRAIC RECONSTRUCTION TECHNIQUE; LITHIUM-ION BATTERIES; GALVANIC REPLACEMENT; HEATING HOLDER; IMAGE-RECONSTRUCTION; METAL NANOSTRUCTURES; HOLLOW NANOCRYSTALS; ATOMIC-STRUCTURE; MISSING WEDGE; TEM;
D O I
10.14102/j.cnki.0254-5861.2022-0187
中图分类号
O61 [无机化学];
学科分类号
070301 ; 081704 ;
摘要
An in-depth understanding of the catalytic reaction mechanism is the key to designing efficient and stable catalysts. In situ transmission electron microscope (TEM) is the most powerful tool to visualize and analyze the microstructures of catalysts during catalysis. In situ TEM combined with three-dimensional (3D) electron tomography (ET) reconstruction technique enables interrogations of catalysts' structural dynamics and chemical changes in high temporal and spatial dimensions. In this review, we discuss and summarize the recent advances in in situ TEM together with 3D ET for catalyst studies. Topics include the latest research progress of in situ TEM imaging as well as 3D visualization and quantitative analysis of catalysts. We also pay particular attention to artificial intelligence (AI)-enhanced smart 3D ET. These include deep learning (DL)-based data compression and storage for the analysis of large TEM data, recovery of wedge-shaped information lost in 3D ET reconstructions, and DL models for reducing residual artifacts in 3D reconstructed images. Finally, the challenges and development prospects of current in situ TEM and 3D ET re-search are discussed.
引用
收藏
页码:2210056 / 2210076
页数:21
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