A1 Journal article (refereed)
Real-space imaging with pattern recognition of a ligand-protected Ag374 nanocluster at sub-molecular resolution (2018)
Zhou, Q., Kaappa, S., Malola, S., Lu, H., Guan, D., Li, Y., Wang, H., Xie, Z., Ma, Z., Häkkinen, H., Zheng, N., Yang, X., & Zheng, L. (2018). Real-space imaging with pattern recognition of a ligand-protected Ag374 nanocluster at sub-molecular resolution. Nature Communications, 9, Article 2948. https://doi.org/10.1038/s41467-018-05372-5
JYU authors or editors
Publication details
All authors or editors: Zhou, Qin; Kaappa, Sami; Malola, Sami; Lu, Hui; Guan, Dawei; Li, Yajuan; Wang, Haochen; Xie, Zhaoxiong; Ma, Zhibo; Häkkinen, Hannu; et al.
Journal or series: Nature Communications
ISSN: 2041-1723
eISSN: 2041-1723
Publication year: 2018
Volume: 9
Issue number: 0
Article number: 2948
Publisher: Nature Publishing Group
Publication country: United Kingdom
Publication language: English
DOI: https://doi.org/10.1038/s41467-018-05372-5
Publication open access: Openly available
Publication channel open access: Open Access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/59110
Abstract
High-resolution real-space imaging of nanoparticle surfaces is desirable for better understanding of surface composition and morphology, molecular interactions at the surface, and nanoparticle chemical functionality in its environment. However, achieving molecular or sub-molecular resolution has proven to be very challenging, due to highly curved nanoparticle surfaces and often insufficient knowledge of the monolayer composition. Here, we demonstrate sub-molecular resolution in scanning tunneling microscopy imaging of thiol monolayer of a 5 nm nanoparticle Ag374 protected by tert-butyl benzene thiol. The experimental data is confirmed by comparisons through a pattern recognition algorithm to simulated topography images from density functional theory using the known total structure of the Ag374 nanocluster. Our work demonstrates a working methodology for investigations of structure and composition of organic monolayers on curved nanoparticle surfaces, which helps designing functionalities for nanoparticle-based applications.
Keywords: nanoparticles; imaging; microscopy
Free keywords: high-resolution real-space imaging; nanoparticle surfaces; surface composition; morphology
Contributing organizations
Related projects
- Metalli-molekyyli-rajapintojen nanorakenteet (NaMeMoInt)
- Häkkinen, Hannu
- Research Council of Finland
- Structure prediction of hybrid nanoparticles via artificial intelligence (HNP-AI)
- Häkkinen, Hannu
- Research Council of Finland
Ministry reporting: Yes
Reporting Year: 2018
JUFO rating: 3