Estimation of Number of Graphene Layers Using Different Methods: A Focused Review

Graphene, a two-dimensional nanosheet, is composed of carbon species (sp(2) hybridized carbon atoms) and is the center of attention for researchers due to its extraordinary physicochemical (e.g., optical transparency, electrical, thermal conductivity, and mechanical) properties. Graphene can be synthesized using top-down or bottom-up approaches and is used in the electronics and medical (e.g., drug delivery, tissue engineering, biosensors) fields as well as in photovoltaic systems. However, the mass production of graphene and the means of transferring monolayer graphene for commercial purposes are still under investigation. When graphene layers are stacked as flakes, they have substantial impacts on the properties of graphene-based materials, and the layering of graphene obtained using different approaches varies. The determination of number of graphene layers is very important since the properties exhibited by monolayer graphene decrease as the number of graphene layer per flake increases to 5 as few-layer graphene, 10 as multilayer graphene, and more than 10 layers, when it behaves like bulk graphite. Thus, this review summarizes graphene developments and production. In addition, the efficacies of determining the number of graphene layers using various characterization methods (e.g., transmission electron microscopy (TEM), atomic force microscopy (AFM), scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectra and mapping, and spin hall effect-based methods) are compared. Among these methods, TEM and Raman spectra were found to be most promising to determine number of graphene layers and their stacking order.

Automated summary

Graphene, a two-dimensional nanosheet composed of carbon species, possesses extraordinary physicochemical properties and is widely used in electronics, medical, and photovoltaic systems. Determining the number of graphene layers and their stacking order is crucial, with TEM and Raman spectra being the most promising methods for characterization.