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Method for 3D atomic structure determination of multi-element nanoparticles with graphene liquid-cell TEM

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Title
Method for 3D atomic structure determination of multi-element nanoparticles with graphene liquid-cell TEM
Author(s)
Junyoung Heo; Kim, D.; Choi, H.; Sungin Kim; Chun, H.; Reboul, C.F.; Van, C.T.S.; Elmlund, D.; Choi, S.; Kim, K.; Park, Y.; Elmlund, H.; Han, B.; Jungwon Park
Publication Date
2023-02
Journal
Scientific Reports, v.13, no.1
Publisher
Nature Research
Abstract
Determining the 3D atomic structures of multi-element nanoparticles in their native liquid environment is crucial to understanding their physicochemical properties. Graphene liquid cell (GLC) TEM offers a platform to directly investigate nanoparticles in their solution phase. Moreover, exploiting high-resolution TEM images of single rotating nanoparticles in GLCs, 3D atomic structures of nanoparticles are reconstructed by a method called “Brownian one-particle reconstruction”. We here introduce a 3D atomic structure determination method for multi-element nanoparticle systems. The method, which is based on low-pass filtration and initial 3D model generation customized for different types of multi-element systems, enables reconstruction of high-resolution 3D Coulomb density maps for ordered and disordered multi-element systems and classification of the heteroatom type. Using high-resolution image datasets obtained from TEM simulations of PbSe, CdSe, and FePt nanoparticles that are structurally relaxed with first-principles calculations in the graphene liquid cell, we show that the types and positions of the constituent atoms are precisely determined with root mean square displacement values less than 24 pm. Our study suggests that it is possible to investigate the 3D atomic structures of synthesized multi-element nanoparticles in liquid phase. © 2023, The Author(s).
URI
https://pr.ibs.re.kr/handle/8788114/14383
DOI
10.1038/s41598-023-28492-5
ISSN
2045-2322
Appears in Collections:
Center for Nanoparticle Research(나노입자 연구단) > 1. Journal Papers (저널논문)
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