Chemical gradients on graphene via direct mechanochemical cleavage of atoms from chemically functionalized graphene surfaces

Abstract

Manipulating the surface chemistry of graphene is critical to many applications that are achievable by chemical functionalization. Specifically, tailoring the spatial distribution of functional groups offers more opportunities to explore functionality using continuous changes in surface energy. To this end, careful consideration is required to demonstrate the chemical gradient on graphene surfaces, and it is necessary to develop a technique to pattern the spatial distribution of functional groups. Here, we demonstrate the tailoring of a chemical gradient through direct mechanochemical cleavage of atoms from chemically functionalized graphene surfaces via an atomic force microscope. Additionally, we define the surface characteristics of the fabricated sample by using lateral force microscopy revealing the materials' intrinsic properties at the nanoscale. Furthermore, we perform the cleaning process of the obtained lateral force images by using a machine learning method of truncated singular value decomposition. This work provides a useful technique for many applications utilizing continuous changes in the surface energy of graphene.