Defect-Dependent Corrugation in Graphene

Abstract

Graphene's intrinsically corrugated and wrinkled topology fundamentally influences its electronic, mechanical, and chemical properties. Experimental techniques allow the manipulation of pristine graphene and the controlled production of defects which allows one to control the atomic out-of-plane fluctuations and thus tune graphene's properties. Here, we perform large scale machine learning-driven molecular dynamics simulations to understand the impact of defects on the structure of graphene. We find that defects cause significantly higher corrugation leading to a strongly wrinkled surface. The magnitude of this structural transformation strongly depends on the defect concentration and specific type of defect. Analyzing the atomic neighborhood of the defects reveals that the extent of these morphological changes depends on the preferred geometrical orientation and the interactions between defects. While our work highlights that defects can strongly affect graphene's morphology, it also emphasizes the differences between distinct types by linking the global structure to the local environment of the defects.

Atomic Insights into Fracture Characteristics of Twisted Tri-Layer Graphene

“…The amplitude of the wrinkles increase with strain as the crack propagates in the direction of the crack length until the material fractures at a strain of 0.022 (d). This trend of strain-correlated corrugation was also observed in monolayer and bilayer graphene, and has been demonstrated to influence their electronic and chemical properties [52][53][54][55]. The same fracture mechanism is observed in all the other samples tested consisting of different crack lengths, system sizes and strain rates.…”

Exploring the configurational space of amorphous graphene with machine-learned atomic energies

“…SW defects are created by a formal in-plane 90 • rotation of two atoms around the mid-point of the bond 28 and are the foundational example of (intrinsic) topological disorder in 2D carbon. [29][30][31][32] This approach has long been used to study aG. 33 Various implementations of the WWW algorithm exist: D'Ambrosio et al showed that the majority of bond transpositions are rejected during annealing, and that an early decision scheme can enhance computation speed by rejecting unfavorable transpositions; 34 Ormrod Morley et al constructed Metropolis criteria from topological metrics such as ring distributions (that is, energy changes were not considered in that case).…”

Observation of domain‐selective defect effects from a CVD‐grown graphene monolayer sandwiched at individual nanoparticle‐on‐mirror plasmonic junctions

“…This anticipated nanophysical phenomena can be correlated with the corresponding SERS spectral signatures from the center position such as I[RBLM], narrow FWHM [RBLM], and relatively compressive strain along the downward z-direction. [19] Specifically, considering the relative prevalence of sp 3 [27,28] The left position 6.6 24.9 Higher 147.5 (relatively compressive)…”