Graphene Detection System

Ever since the first isolation of graphene, the semimetal has grown appreciable and has been attracting increasing interest. This interest is reinforced by monolayer graphene's remarkable electronic properties and its usage in revolutionary device developments and applications. However, obtaining monolayer graphene which can be deployed for expansion of experiments in 2D physics comes with its own limitations like high human interventions that requires significant experience, is highly time consuming since it involves repetitive tasks and recognizing graphene crystallites from millions of thicker graphite flakes with other undesired particles is strenuous. Here, we report an approach to detect and discriminate monolayer graphene from other alternating layers of graphene and subsisting substrate impurities. We present a region of interest-based image segmentation process to extricate inapplicable information from the image and extract graphene particles. We, then apply an intensity-based detection model leveraging the characteristic color information to differentiate monolayer graphene from other particles and it is observed that the red color space of the monolayer graphene differs 1.8--6%, green 2.5--8% and blue differ 2.5% to 3% from the surrounding background pixels. We also describe an implementation of our algorithm in a semi-automatic system suitable with our needs.