Silicon Nanosheets: An Emerging 2D Photonic Material with a Large Transient Nonlinear Optical Response beyond Graphene.
Michalis StavrouAristeidis StathisIoannis PapadakisAlina Lyuleeva-HusemannEmmanouel KoudoumasStelios CourisPublished in: Nanomaterials (Basel, Switzerland) (2021)
The present work reports on the transient nonlinear optical (NLO) responses of two different types of 2D silicon nanosheets (SiNSs), namely hydride-terminated silicon nanosheets (SiNS-H) and 1-dodecene-functionalized silicon nanosheets (SiNS-dodecene). The main motivation of this study was to extend the knowledge regarding the NLO properties of these Si-based materials, for which very few published studies exist so far. For that purpose, the NLO responses of SiNS-H and SiNS-dodecene were investigated experimentally in the nanosecond regime at 532 and 1064 nm using the Z-scan technique, while the obtained results were compared to those of certain recently studied graphene nanosheets. SiNS-dodecene was found to exhibit the largest third-order susceptibility χ (3) values at both excitation wavelengths, most probably ascribed to the presence of point defects, indicating the importance of chemical functionalization for the efficient enhancement and tailoring of the NLO properties of these emerging 2D Si-based materials. Most importantly, the results demonstrated that the present silicon nanosheets revealed comparable and even larger NLO responses than graphene nanosheets. Undoubtedly, SiNSs could be strong competitors of graphene for applications in 2D-material-based photonics and optoelectronics.
Keyphrases
- reduced graphene oxide
- quantum dots
- metal organic framework
- highly efficient
- room temperature
- transition metal
- visible light
- gold nanoparticles
- healthcare
- computed tomography
- high resolution
- high speed
- emergency department
- carbon nanotubes
- randomized controlled trial
- photodynamic therapy
- walled carbon nanotubes
- single cell
- cerebral ischemia
- atomic force microscopy
- subarachnoid hemorrhage
- solid state
- meta analyses