Real-space imaging with pattern recognition of a ligand-protected Ag374 nanocluster at sub-molecular resolution
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
Published inNature Communications
Lu, Hui |
© the Authors, 2018.
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. ...
PublisherNature Publishing Group
ISSN Search the Publication Forum2041-1723
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Academy Programme, AoF; Research costs of Academy Professor, AoF
Additional information about fundingThe experimental work done in Dalian Institute of Chemical Physics (DICP), Chinese Academy of Sciences, was supported both by Xiamen University (The National Key R&D Program of China grant 2017YFA0207302, National Natural Science Foundation of China, grant 21731005, 21420102001 and 21721001 the National Key R&D Program of China grant 2017YFA0207302) and DICP (National Natural Science Foundation of China grant 21688102, the Strategic Priority Research Program of Chinese Academy of Science, grant XDB17000000, the National Key Research and Development Program of the MOST of China, grant 2016YFA0200603 and the open fund of the state key laboratory of molecular reaction dynamics in DICP, CAS, grant SKLMRD-K201707). Q.Z. thanks Dr. Huayan Yang for providing the samples for STM imaging. S.M. and H.H. thank T. Kärkkäinen and P. Nieminen for discussions on pattern recognition algorithms. The theoretical and computational work in the University of Jyväskylä was funded by the Academy of Finland (grants 294217, 315549, AIPSE program, and H.H.’s Academy Professorship). H.H. acknowledges the support from China’s National Innovation and Intelligence Introduction Base visitor program. S.K. thanks the Vilho, Yrjö, and Kalle Väisälä Foundation for the grant for doctoral studies. The DFT simulations were done at the Finnish national supercomputing center CSC and at the Barcelona Supercomputing Center (PRACE project “NANOMETALS”). ...
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