Clar's Theory, STM Images, and Geometry of Graphene Nanoribbons

TL;DR

This paper demonstrates that Clar's theory effectively predicts various properties of graphene nanoribbons, including stability, electronic structure, and STM images, aiding in their classification and edge termination identification.

Contribution

It introduces a Clar-based classification scheme for graphene nanoribbons and links STM and Raman spectra to edge terminations using DFT simulations.

Findings

Clar's theory predicts stability and electronic properties of nanoribbons.

STM images and Raman spectra can identify edge terminations.

Classification scheme groups configurations by bond length and spectral features.

Abstract

We show that Clar's theory of the aromatic sextet is a simple and powerful tool to predict the stability, the \pi-electron distribution, the geometry, the electronic/magnetic structure of graphene nanoribbons with different hydrogen edge terminations. We use density functional theory to obtain the equilibrium atomic positions, simulated scanning tunneling microscopy (STM) images, edge energies, band gaps, and edge-induced strains of graphene ribbons that we analyze in terms of Clar formulas. Based on their Clar representation, we propose a classification scheme for graphene ribbons that groups configurations with similar bond length alternations, STM patterns, and Raman spectra. Our simulations show how STM images and Raman spectra can be used to identify the type of edge termination.