DFT modeling of the covalent functionalization of graphene: from ideal to realistic models

TL;DR

This paper reviews density functional theory (DFT) modeling of graphene functionalization, emphasizing the importance of realistic models that account for substrates, defects, and conditions to better match experimental results.

Contribution

It highlights the limitations of idealized models and discusses recent advances in DFT simulations that incorporate realistic factors affecting graphene chemistry.

Findings

Ideal uniform functionalization is rare in practice.

Substrates and defects lead to clustering of functional groups.

Stability of configurations depends on specific conditions.

Abstract

The production of multiple types of graphene, such as free standing, epitaxial graphene on silicon carbide and metals, graphene in solution, chemically grown graphene-like molecules, various graphene nanoribbons, and graphene oxide with different levels of reduction and various chemical composition, demonstrate the need for additional investigation beyond the basic principles of graphene functionalization for avoidance of occasionally contradictions between the predictions from first-principles simulations and experimental results. Herein, we discuss the current state of modeling of the different types of graphene using density functional theory (DFT) methods. We focus on the static (substrate, shape, curvature, strain and doping) and dynamic (starting point of functionalization, migration barriers and stability of configurations) aspects that provide a more correct and selective modeling of the chemisorption of various chemical species on the graphene scaffold. Based on the recent modeling of experimentally realized functionalization of different types of graphene we can conclude that the formation of uniform one- or two-sided functionalized graphene discussed in earlier studies is an exception to the typical scenarios of graphene chemistry. The presence of different substrates, defects and lattice distortions, such as ripples and strain, results in the formation of clusters or lines from the functional groups. Several configurations of the chemical species on the graphene substrate have been found to exist with ideal models but are only stable for graphene functionalized under special conditions. The role of the technical parameters, such as the functionals and computational methods employed, and the important guidelines for the modeling of the chemical properties of various types of graphene are also discussed.