Research on High-Resolution Imaging Method of Precious Metal Particles Based on Contrast Difference

As one of the important research methods for nanomaterial analysis, transmission electron microscopy (TEM) uses high-energy electron penetration of the sample to achieve high-resolution imaging and intuitively analyze surface morphology, atomic-level lattice structure, element distribution, and other information. At present, supported precious metals, such as Pt and Pd, are widely used in the field of industrial catalysis, and in the atomic-scale characterization of supported precious metal catalysts, the difference in contrast resolution with different imaging contrasts makes it difficult to effectively distinguish between precious metal particles and catalyst supports. Based on this challenge, this paper systematically expounds the imaging principle, detection method system, and test optimization of transmission electron microscopy, providing ideas and theoretical support for solving the atomic-scale detection of supported precious metal catalysts. Taking platinum/carbon catalysts and diesel engine oxidation catalysts as examples, the results show that platinum-carbon catalysts are more suitable for bright-field imaging due to the large atomic number (Z) difference between carbon support and platinum particles and the influence of electron beam sensitivity, while diesel engine oxidation catalysts with moderate polymetallic Z difference and stable support are more suitable for high-angle annular dark-field images (HAADF). At the same time, the combined technology of X-ray spectroscopy and transmission electron microscopy has unique advantages in the elemental composition analysis of supported precious metal catalysts.

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Publication Details

Journal
Journal of Materials Science and Chemical Engineering
Published
2025-01-01
DOI
https://doi.org/10.4236/msce.2025.139010
Primary Topic
Machine Learning in Materials Science
Type
article
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Research on High-Resolution Imaging Method of Precious Metal Particles Based on Contrast Difference

Liqiang Dai, Zengbin Wang, Li Liu, Ning Liu et al.
Journal of Materials Science and Chemical Engineering
Machine Learning in Materials Science
article

Research on High-Resolution Imaging Method of Precious Metal Particles Based on Contrast Difference

Liqiang Dai, Zengbin Wang, Li Liu, Ning Liu, Zhengang Zhang, K. Lang
article en

Abstract

As one of the important research methods for nanomaterial analysis, transmission electron microscopy (TEM) uses high-energy electron penetration of the sample to achieve high-resolution imaging and intuitively analyze surface morphology, atomic-level lattice structure, element distribution, and other information. At present, supported precious metals, such as Pt and Pd, are widely used in the field of industrial catalysis, and in the atomic-scale characterization of supported precious metal catalysts, the difference in contrast resolution with different imaging contrasts makes it difficult to effectively distinguish between precious metal particles and catalyst supports. Based on this challenge, this paper systematically expounds the imaging principle, detection method system, and test optimization of transmission electron microscopy, providing ideas and theoretical support for solving the atomic-scale detection of supported precious metal catalysts. Taking platinum/carbon catalysts and diesel engine oxidation catalysts as examples, the results show that platinum-carbon catalysts are more suitable for bright-field imaging due to the large atomic number (Z) difference between carbon support and platinum particles and the influence of electron beam sensitivity, while diesel engine oxidation catalysts with moderate polymetallic Z difference and stable support are more suitable for high-angle annular dark-field images (HAADF). At the same time, the combined technology of X-ray spectroscopy and transmission electron microscopy has unique advantages in the elemental composition analysis of supported precious metal catalysts.

Journal of Materials Science and Chemical EngineeringVol. 13(09)
Openalex Percentile: Top 80%
Machine Learning in Materials Science
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