Login / Signup

Alternative Route to Silicene Synthesis via Surface Reconstruction on h-MoSi2 Crystallites.

Cameron VoldersEhsan MonazamiGopalakrishnan RamalingamPetra Reinke
Published in: Nano letters (2016)
Silicene is a two-dimensional material with a Dirac-type band structure and it is particularly attractive due to its potential for integration with Si-based technology. The primary focus has been to grow single silicene layers and understand how the electronic structure is affected by the substrate and the phase transition between low- and high-buckling configurations. Typically, silicene is synthesized by depositing monolayer amounts of silicon onto a heated Ag(111) surface; however, other growth substrates such as Ir(111) and ZrB2 have been studied recently. We present a novel route for silicene synthesis via a high-temperature surface reconstruction of hexagonal-MoSi2 nanocrystallites. The h-MoSi2 crystallites are formed by annealing of thin Mo-layers on Si(100)-(2 × 1) and their crystallographic orientation is controlled via an epitaxial relation with the Si-substrate. The (0001) plane of h-MoSi2 is comprised of Si-hexagons with a Mo atom residing in the center. Annealing above approximately 650 °C causes the (0001) plane to undergo a surface reconstruction process leaving a honeycomb pattern on the surface of these crystallites as shown by scanning tunneling microscopy. We define this surface layer as a silicene-like reconstruction (SLR), and a detailed geometric analysis of our structure yields a perfect match with the (√3 × √3)R30° silicene superstructure in a low-buckled configuration (ABA̅). Scanning tunneling spectroscopy data of the SLR, Si(001)-(2 × 1) and h-MoSi2 surfaces agree with this interpretation. The formation of this structure on a transition metal silicide opens up the opportunity for integration into Si-based devices without the necessity for a transfer scheme.
Keyphrases
  • room temperature
  • high resolution
  • single molecule
  • high temperature
  • mass spectrometry
  • high throughput
  • transcription factor
  • machine learning
  • transition metal
  • escherichia coli
  • data analysis
  • solar cells