Massive Dirac fermion behavior in a low bandgap graphene nanoribbon near a topological phase boundary


Graphene nanoribbons (GNRs) have attracted much interest due to their largely modifiable electronic properties. Manifestation of these properties requires atomically precise GNRs which can be achieved through a bottom–up synthesis approach. This has recently been applied to the synthesis of width‐modulated GNRs hosting topological electronic quantum phases, with valence electronic properties that are well captured by the Su–Schrieffer–Heeger (SSH) model describing a 1D chain of interacting dimers. In this record we provide data to support our recent publication where we demonstrate that ultralow bandgap GNRs with charge carriers behaving as massive Dirac fermions can be realized when their valence electrons represent an SSH chain close to the topological phase boundary, i.e., when the intra‐ and interdimer coupling become approximately equal. Such a system has been achieved via on‐surface synthesis based on readily available pyrene‐based precursors and the resulting GNRs are characterized by scanning probe methods. The pyrene‐based GNRs (pGNRs) can be processed under ambient conditions and incorporated as the active material in a field effect transistor. A quasi‐metallic transport behavior is observed at room temperature, whereas at low temperature, the pGNRs behave as quantum dots showing single‐electron tunneling and Coulomb blockade. This study may enable the realization of devices based on carbon nanomaterials with exotic quantum properties.

Metadata Access
Creator Sun, Qiang; Gröning, Oliver; Overbeck, Jan; Braun, Oliver; Perrin, Mickael L.; Borin Barin, Gabriela; El Abbassi, Maria; Eimre, Kristjan; Ditler, Edward; Daniels, Colin; Meunier, Vincent; Pignedoli, Carlo A.; Calame, Michel; Fasel, Roman; Ruffieux, Pascal
Publisher Materials Cloud
Publication Year 2020
Rights info:eu-repo/semantics/openAccess; Creative Commons Attribution 4.0 International
OpenAccess true
Contact archive(at)
Language English
Resource Type Dataset
Discipline Materials Science and Engineering