期刊
NANO LETTERS
卷 19, 期 1, 页码 477-481出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.nanolett.8b04303
关键词
in situ STEM; Pt nanoparticles; 3D imaging; catalysis; convolutional neural networks; quantitative ADF STEM
类别
资金
- European Research Council [335078 COLOURATOM, 770887 PICOMETRICS]
- European Commission [EUSMI 731019, MUMMERING 765604]
- Research Foundation Flanders (FWO, Belgium) [G.0368.15N, G.0369.15N, G.0267.18N]
- FWO [PEGASUS]2 Marie Sklodowska-Curie fellowship [12U4917N]
- Spanish Ministerio de Economia y Competitividad [MAT2017-86659-R]
Pt nanoparticles play an essential role in a wide variety of catalytic reactions. The activity of the particles strongly depends on their three-dimensional (3D) structure and exposed facets, as well as on the reactive environment. High-resolution electron microscopy has often been used to characterize nanoparticle catalysts but unfortunately most observations so far have been either performed in vacuum and/or using conventional (2D) in situ microscopy. The latter however does not provide direct 3D morphological information. We have implemented a quantitative methodology to measure variations of the 3D atomic structure of nanoparticles under the flow of a selected gas. We were thereby able to quantify refaceting of Pt nanoparticles with atomic resolution during various oxidation-reduction cycles. In a H-2 environment, a more faceted surface morphology of the particles was observed with {100} and {111} planes being dominant. On the other hand, in O-2 the percentage of {100} and {111} facets decreased and a significant increase of higher order facets was found, resulting in a more rounded morphology. This methodology opens up new opportunities toward in situ characterization of catalytic nanoparticles because for the first time it enables one to directly measure 3D morphology variations at the atomic scale in a specific gaseous reaction environment.
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