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, Elisa Riedo

Research output: Contribution to journalArticle

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 Å-indentation experiments, we demonstrate that the formation of ultra-stiff diamene is exclusively found in 1-layer plus buffer layer epitaxial graphene on silicon carbide (SiC) and that an ultra-stiff phase is not observed in neither thicker epitaxial graphene (2-layer or more) nor exfoliated graphene films of any thickness on silicon oxide (SiO2).

Original languageEnglish (US)
Article number035043
Journal2D Materials
Volume6
Issue number3
DOIs
StatePublished - Jun 13 2019

Fingerprint

machine learning
Graphite
Graphene
Learning systems
graphene
Phase transitions
Nanomechanics
Silicon oxides
Buffer layers
silicon oxides
indentation
Indentation
Silicon carbide
silicon carbides
Topography
Atomic force microscopy
stiffness
Microscopic examination
flexibility
topography

Keywords

  • diamene
  • epitaxial grapheme
  • friction force microscopy
  • grapheme
  • machine learning
  • nanomechanics

ASJC Scopus subject areas

  • Chemistry(all)
  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering

Cite this

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

In: 2D Materials, Vol. 6, No. 3, 035043, 13.06.2019.

Research output: Contribution to journalArticle

Cellini, Filippo ; Lavini, Francesco ; Berger, Claire ; De Heer, Walt ; Riedo, Elisa. / Layer dependence of graphene-diamene phase transition in epitaxial and exfoliated few-layer graphene using machine learning. In: 2D Materials. 2019 ; Vol. 6, No. 3.
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