Layer dependence of graphene-diamene phase transition in epitaxial and exfoliated few-layer graphene using machine learning
Filippo Cellini, Francesco Lavini, Claire Berger, Walt de Heer, and, Elisa Riedo

TL;DR
This paper uses machine learning to quickly identify graphene layer domains and investigates how the layer number influences the formation of ultra-hard diamene in epitaxial and exfoliated graphene, revealing it occurs only in monolayer epitaxial graphene on SiC.
Contribution
It introduces a machine learning method for rapid graphene domain identification and demonstrates the layer-dependent formation of diamene in graphene films.
Findings
Ultra-stiff diamene forms only in 1-layer epitaxial graphene on SiC.
No ultra-stiff phase observed in thicker epitaxial or exfoliated graphene.
Machine learning effectively identifies graphene layers from AFM and FFM data.
Abstract
The study of the nanomechanics of graphene and other 2D materials has led to the discovery of exciting new properties in 2D crystals, such as their remarkable in-plane stiffness and out of plane flexibility, as well as their unique frictional and wear properties at the nanoscale. Recently, nanomechanics of graphene has generated renovated interest for new findings on the pressure-induced chemical transformation of a few-layer thick epitaxial graphene into a new ultra-hard carbon phase, named diamene. In this work, by means of a machine learning technique, we provide a fast and efficient tool for identification of graphene domains (areas with a defined number of layers) in epitaxial and exfoliated films, by combining data from Atomic Force Microscopy (AFM) topography and friction force microscopy (FFM). Through the analysis of the number of graphene layers and detailed…
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