Ab-initio transport fingerprints for resonant scattering in graphene

TL;DR

This paper extends a scaling approach to calculate energy-dependent scattering cross sections for various adsorbates on graphene, enabling prediction of transport properties and identifying unique transmission fingerprints for each defect type.

Contribution

It introduces a method to derive defect-specific transmission fingerprints from ab-initio calculations, facilitating accurate transport predictions in graphene with adsorbates.

Findings

Resonant scattering fingerprints for different adsorbates are identified.

The approach allows reliable prediction of elastic mean free paths.

Tight-binding parameters are provided to match DFT scattering data.

Abstract

We have recently shown that by using a scaling approach for randomly distributed topological defects in graphene, reliable estimates for transmission properties of macroscopic samples can be calculated based even on single-defect calculations [A. Uppstu et al., Phys. Rev. B 85, 041401 (2012)]. We now extend this approach of energy-dependent scattering cross sections to the case of adsorbates on graphene by studying hydrogen and carbon adatoms as well as epoxide and hydroxyl groups. We show that a qualitative understanding of resonant scattering can be gained through density functional theory results for a single-defect system, providing a transmission "fingerprint" characterizing each adsorbate type. This information can be used to reliably predict the elastic mean free path for moderate defect densities directly using ab-initio methods. We present tight-binding parameters for carbon and epoxide adsorbates, obtained to match the density-functional theory based scattering cross sections.