DFT calculations of the structure and stability of copper clusters on MoS2

Layered materials, such as MoS2, are being intensely studied due to their interesting properties and wide variety of potential applications. These materials are also interesting as supports for low-dimensional metals for catalysis, while recent work has shown increased interest in using 2D materials in the electronics industry as a Cu diffusion barrier in semiconductor device interconnects. The interaction between different metal structures and MoS2 monolayers is therefore of significant importance and first-principles simulations can probe aspects of this interaction not easily accessible to experiment. Previous theoretical studies have focused particularly on the adsorption of a range of metallic elements, including first-row transition metals, as well as Ag and Au. However, most studies have examined single-atom adsorption or adsorbed nanoparticles of noble metals. This means there is a knowledge gap in terms of thin film nucleation on 2D materials. To begin addressing this issue, we present in this paper a first-principles density functional theory (DFT) study of the adsorption of small Cun (n = 1-4) structures on 2D M0S2 as a model system. We find on a perfect MoS2 monolayer that a single Cu atom prefers an adsorption site above the Mo atom. With increasing nanocluster size the nanocluster binds more strongly when Cu atoms adsorb atop the S atoms. Stability is driven by the number of Cu-Cu interactions and the distance between adsorption sites, with no obvious preference towards 2D or 3D structures. The introduction of a single S vacancy in the monolayer enhances the copper binding energy, although some Cun nanoclusters are actually unstable. The effect of the vacancy is localised around the vacancy site. Finally, on both the pristine and the defective MoS2 monolayer, the density-of-states analysis shows that the adsorption of Cu introduces new electronic states as a result of partial Cu oxidation, but the metallic character of Cu nanoclusters is preserved. © 2020 Nies and Nolan; licensee Beilstein-Institut. All Rights Reserved.