Tailoring magnetism of graphene nanoflakes via tip-controlled dehydrogenation

Atomically precise graphene nanoflakes called nanographenes have emerged as a promising platform to realize carbon magnetism. Their ground state spin configuration can be anticipated by Ovchinnikov-Lieb rules based on the mismatch of π electrons from two sublattices. While rational geometrical design achieves specific spin configurations, further direct control over the π electrons offers a desirable extension for efficient spin manipulations and potential quantum device operations. To this end, in a recent publication, we applied a site-specific dehydrogenation using a scanning tunneling microscope tip to nanographenes deposited on a Au(111) substrate, which showed the capability of precisely tailoring the underlying π-electron system and therefore efficiently manipulating their magnetism. Through first-principles calculations and tight-binding meanfield-Hubbard modeling, we demonstrated that the dehydrogenation-induced Au—C bond formation along with the resulting hybridization between frontier π orbitals and Au substrate states effectively eliminate the unpaired π electron. Our results establish an efficient technique for controlling the magnetism of nanographenes. This record contains data that support the scientific results discussed in our manuscript.

Identifier
Source https://archive.materialscloud.org/record/2024.79
Metadata Access https://archive.materialscloud.org/xml?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:2193
Provenance
Creator Zhao, Chenxiao; Huang, Qiang; Valenta, Leoš; Eimre, Kristjan; Yang, Lin; V. Yakutovich, Aliaksandr; Xu, Wangwei; Feng, Xinliang; Juríček, Michal; Fasel, Roman; Ruffieux, Pascal; Pignedoli, Carlo A.
Publisher Materials Cloud
Publication Year 2024
Rights info:eu-repo/semantics/openAccess; Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode
OpenAccess true
Contact archive(at)materialscloud.org
Representation
Language English
Resource Type Dataset
Discipline Materials Science and Engineering