Full Metal Species Quantification of Metal Supported Catalysts Through Massive TEM Images Recognition

doi:10.1007/s40242-022-2218-3

Chemical Research in Chinese Universities ›› 2022, Vol. 38 ›› Issue (5): 1263-1267.doi: 10.1007/s40242-022-2218-3

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Full Metal Species Quantification of Metal Supported Catalysts Through Massive TEM Images Recognition

LIU Shuhui¹, ZHANG Fan², LIN Ronghe³, LIU Wei^2,4

1. School of Computer and Communication Engineering, Dalian Jiaotong University, Dalian 116028, P. R. China;
2. Division of Energy Research Resources, Dalian National Laboratory for Clean Energy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China;
3. Hangzhou Institute of Advanced Studies, Zhejiang Normal University, Hangzhou 311231, P. R. China;
4. University of Chinese Academy of Sciences, Beijing 100049, P. R. China

Received:2022-06-29 Online:2022-10-01 Published:2022-10-08
Contact: LIN Ronghe, LIU Wei E-mail:catalysis.lin@zjnu.edu.cn;weiliu@dicp.ac.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (Nos.22072150, 22172168), the CAS Project for Young Scientists in Basic Research, China(No.YSBR-022), the CAS Youth Innovation Promotion Association, China(No.2019190), and the Innovative Research Funds of Dalian Institute of Chemical Physics, China(No.DICPI202013).

Abstract

Abstract: For a practical high-loading single-atom catalyst, it is prone to forming diverse metal species owing to either the synthesis inhomogeneity or the reaction induced aggregation. The diversity of this metal species challenges the discerning about the contributions of specific metal species to the catalytic performance, and thus hampers the rational catalyst design. In this paper, a distinct solution of dispersion analysis based on transmission electron microscopy imaging specialized for metal-supported catalysts has been proposed in the capability of full-metal-species quantification(FMSQ) from single atoms to nanoparticles, including dispersion densities, shape geometry, and crystallographic surface exposure. This solution integrates two image-recognition algorithms including the electron microscopy-based atom recognition statistics (EMARS) for single atoms and U-Net type deep learning network for nanoparticles in different shapes. When applied to the C₃N₄- and nitrogen-doped carbon-supported catalysts, the FMSQ method successfully identifies the specific activity contributions of Au single atoms and particles in butadiene hydrogenation, which presents remarkable variation with the metal species constitution. This work demonstrates a promising value of our FMSQ strategy for identifying the activity origin of heterogeneous catalysis.

Key words: Single atom recognition algorithm, U-Net type network, Full metal species quantification, Transmission electron microscopy (TEM) image

LIU Shuhui, ZHANG Fan, LIN Ronghe, LIU Wei. Full Metal Species Quantification of Metal Supported Catalysts Through Massive TEM Images Recognition[J]. Chemical Research in Chinese Universities, 2022, 38(5): 1263-1267.

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Full Metal Species Quantification of Metal Supported Catalysts Through Massive TEM Images Recognition

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