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Unraveling the Dynamic Structural Evolution of Phthalocyanine Catalysts during CO2 Electroreduction
Jianing Mao, Bingbao Mei*, Ji Li, Shuai Yang, Fanfei Sun, Siyu Lu, Wei Chen, Fei Song* and Zheng Jiang*

Phthalocyanine catalysts have well-defined active site structures that allow reaction-based mechanism exploration. In this regard, the actual behaviors of metal ions in the phthalocyanine catalysts have aroused considerable attention. Operando high-energy resolution fluorescence detected X-ray absorption (HERFD-XANES) can be employed in the practical situation of electrocatalysis to realize the interfacial interaction between metal ions and the reactants, offering a unique insight into the active site geometry and structural evolution during CO2 reduction. In this work, the CO2RR to CO dominates over the HER with Faradaic efficiency reaching the maximum value of 89% at 0.85 V versus RHE. The results demonstrate the atomically dispersed, low-valent Ni(I) centres with high intrinsic CO2 reduction activity.

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

Chen Sun, Kuo Liu, Jian Zhang, Qian Liu, Xijun Liu and Lili Han*

Chin. J. Struct. Chem. 2022, 41, 2210056-2210076  DOI: 10.14102/j.cnki.0254-5861.2022-0187

October 25, 2022

in situ TEM, catalyst, electron tomography, 3D reconstruction, artificial intelligence, machine learning

ABSTRACT

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 research are discussed.



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