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Graphene on Rh(111): A template for growing ordered arrays of metal nanoparticles with different periodicities
The development of approaches to provide ordered networks of metal nanoparticles is a topic of great interest due to its potential contribution to the advent of a more efficient technology. The template-assisted formation of ordered nanoparticle arrays on graphene- or h-BN-metal surfaces has profile...
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Published in: | Carbon (New York) 2021-03, Vol.173, p.1073-1081 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | The development of approaches to provide ordered networks of metal nanoparticles is a topic of great interest due to its potential contribution to the advent of a more efficient technology. The template-assisted formation of ordered nanoparticle arrays on graphene- or h-BN-metal surfaces has profiled as a promising approach because of the periodic interfacial modulations associated to the moiré patterns. Nevertheless, a complete understanding of the requirements that a moiré must meet to act as a good template is still challenging. In order to shed light on this issue, here, the graphene/Rh(111) surface is investigated as model template for the growth of metal nanoparticles due to its large variety of moiré patterns. Scanning tunneling microscopy experiments have revealed different Ir nanoparticle spatial distributions depending on the underlying moiré periodicity ranging from randomly scattered nanoparticles to well-ordered lattices with multiple periodicities. To unveil the origin of this behavior, density functional theory calculations have been performed on representative moiré patterns for the adsorption of both Ir individual adatoms and nanoparticles. We conclude that the presence of distinctive chemical environments throughout the moiré, which controls the size and the adsorption configuration of the nanoparticles, is crucial for the formation of ordered arrays.
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ISSN: | 0008-6223 1873-3891 |
DOI: | 10.1016/j.carbon.2020.11.086 |