Self-assembly of vertically oriented graphene nanostructures: multivariate characterisation by Minkowski functionals and fractal geometry

Abstract The enormous self-assembly potential that graphene and its derived layered materials offer for responding to the contemporary environmental challenges has made it one of the most investigated materials. Hence, tuning its extraordinary properties and understanding the effect at all scales is crucial to tailoring highly customised electrodes. Vertically oriented graphene nanostructures, also known as carbon nanowalls (CNWs), due to the large surface area and unique maze-like morphology, have attracted attention as a platform for advanced sensing applications. In this work, a holistic investigation approach has been developed to disrupt the synthesis-composition-structure-property paradigm and to dig out the hidden materials relationships. To achieve that, autonomous advanced image-analysis methods (Minkowski Functionals, Fractal Analysis) have been applied to SEM micrographs and successfully classified them. Morphological, electrical, and electrochemical characterisation has been performed for all of the samples. Multivariate data analysis has been employed to mine the relationships between the material features, specifically as it relates to the understanding of the intrinsic properties. As a result, this study is intended to both shed light on CNWs as a promising transparent hybrid electrochemical substrate for perfectly assembled electrochemical devices and to provide a new flexible method for nanomaterial design, characterisation and exploitation.