Band Gap Opening Induced by the Structural Periodicity in Epitaxial Graphene Buffer Layer.
Maya N NairIrene PalacioArlensiú CelisAlberto ZobelliAlexandre GloterStefan KubskyJean-Philippe TurmaudMatthew ConradClaire BergerWalter de HeerEdward H ConradAmina Taleb-IbrahimiAntonio TejedaPublished in: Nano letters (2017)
The epitaxial graphene buffer layer on the Si face of hexagonal SiC shows a promising band gap, of which the precise origin remains to be understood. In this work, we correlate the electronic to the atomic structure of the buffer layer by combining angle resolved photoemission spectroscopy (ARPES), scanning tunneling microscopy (STM), and high-resolution scanning transmission electron microscopy (HR-STEM). We show that the band structure in the buffer has an electronic periodicity related to the structural periodicity observed in STM images and published X-ray diffraction. Our HR-STEM measurements show the bonding of the buffer layer to the SiC at specific locations separated by 1.5 nm. This is consistent with the quasi 6 × 6 periodic corrugation observed in the STM images. The distance between buffer C and SiC is 1.9 Å in the bonded regions and up to 2.8 Å in the decoupled regions, corresponding to a 0.9 Å corrugation of the buffer layer. The decoupled regions are sp2 hybridized. Density functional tight binding (DFTB) calculations demonstrate the presence of a gap at the Dirac point everywhere in the buffer layer, even in the decoupled regions where the buffer layer has an atomic structure close to that of graphene. The surface periodicity also promotes band in the superperiodic Brillouin zone edges as seen by photoemission and confirmed by our calculations.
Keyphrases
- electron microscopy
- high resolution
- deep learning
- mass spectrometry
- single molecule
- density functional theory
- room temperature
- optical coherence tomography
- molecular dynamics
- photodynamic therapy
- molecular dynamics simulations
- machine learning
- computed tomography
- drug induced
- transcription factor
- dna binding
- meta analyses