Micro-photoluminescence of Carbon Dots Deposited on Twisted Double-Layer Graphene Grown by Chemical Vapor Deposition

Carbon-based nanomaterials, such as carbon dots (CDs) and graphene (Gr), feature outstanding optical and electronic properties. Hence, their integration in optoelectronic and photonic devices is easier thanks to their low dimensionality and offers the possibility to reach high-quality performances. In this context, the combination of CDs and Gr into new nanocomposite materials CDs/Gr can further improve their optoelectronic properties and eventually create new ones, paving the way for the development of advanced carbon nanotechnology. In this work, we have thoroughly investigated the structural and emission properties of CDs deposited on single-layer and bilayer graphene lying on a SiO2/Si substrate. A systematic Raman analysis points out that bilayer (BL) graphene grown by chemical vapor deposition does not always respect the Bernal (AB) stacking, but it is rather a mixture of twisted bilayer (t-BL) featuring domains with different twist angles. Moreover, in-depth micro-photoluminescence measurements, combined with atomic force microscopy (AFM) morphological analysis, show that CD emission efficiency is strongly depleted by the presence of graphene and in particular is dependent on the number of layers as well as on the twist angle of BL graphene. Finally, we propose a model which explains these results on the basis of photoinduced charge-transfer processes, taking into account the energy levels of the hybrid nanosystem formed by coupling CDs with t-BL/SiO2.

Automated summary

Carbon-based nanomaterials such as carbon dots (CDs) and graphene (Gr) exhibit excellent optical and electronic properties. Integrating CDs and Gr into a nanocomposite material, CDs/Gr, can enhance their optoelectronic properties and pave the way for advanced carbon nanotechnology. Research on the structural and emission properties of CDs deposited on single-layer and bilayer graphene reveals insights into their interaction and potential for further optimization.