# Electrochemical Deposition of Copper on Epitaxial Graphene
## 1 Introduction
However, there is another aspect of mutual interplay between graphene and copper that needs to be addressed: the detection and removal of Cu\({}^{2+}\) ions in/from potable water. Even though Cu is the third most abundant transition metal in the human body and it is vitally important for human health, an excess of copper ions can cause cirrhosis of the liver in children, [PERSON]'s disease, [PERSON]'s disease, etc. [57; 58]. For this reason, the development of ultra-sensitive and selective methods for the monitoring and determination of trace Cu\({}^{2+}\) ions in water is highly demanded. Since graphene enables the detection of single molecules accommodated on its surface [59], it attracts a lot of attention as a highly sensitive and biofriendly material for environmental sensors [60], including the fast and real-time detection of Copper ions [61]. Two strategies in such a direction have been implemented so far: (i) electrochemical sensing by electrodes covered with functionalized graphene-based materials (reduced graphene oxide, graphene oxide, graphene quantum dots) [62; 63; 64; 65] and (ii) optical sensing using colorimetric and fluorescent probes based on graphene derivatives [66; 67; 68; 69; 70; 71; 72; 73; 74]. Both approaches present some limitations, mainly linked to the instability and complicated preparation procedure of the working electrode or fluorescence probe, which typically involves surface functionalization and multistage chemical reactions. In most cases, such a functionalization can increase the toxicity of a sensitive component and is, therefore, less desired. Exploiting epitaxial graphene grown on SiC (epitaxial graphene (EG)/SiC) using the high-temperature sublimation technique is regarded as an alternative strategy for sensing platform development. Due to evident advantages over other graphene family materials (namely, its large surface area free of functional groups, high quality of monolayer graphene, thickness uniformity, wide potential window, high signal-to-noise ratio, transfer-free technology, and direct sublimation growth without precursors [75; 76; 77]), epitaxial graphene has been tested with promising results for the real-time detection of Pb\({}^{2+}\) ions in aqueous solutions (with a detection limit far below the WHO's permissible level for lead in drinking water) through performing square-wave anodic stripping voltammetry and response-recovery measurements [78; 79]. Inspired by this, we have recently extended our activities beyond lead detection to include the investigation of the electrochemical behavior of the Hg\({}^{2+}\)/Hg\({}^{0}\) redox couple at the epitaxial graphene working electrode [80]; now, we are aiming to elucidate the nature of the copper electroreduction at the epitaxial graphene surface. Since this issue is very poorly investigated in the literature, in-depth investigations at an atomistic level are still needed to deal with the problem. In this regard, complementary electrochemical and theoretical studies of the graphene-Cu system presented here may provide critical information on the Cu kinetics, diffusion, adsorption and sensing mechanisms, thereby facilitating the development of a real-time detection platform. Leaving aside the Cu\({}^{2+}\) detection by using graphene, it should be recalled that a holistic understanding of the interplay between epitaxial graphene and copper is essential not only for sensing applications, but also for catalytic and electrical applications, due to the mutually reinforcing character of the graphene-Cu pair. In this report, the electrodeposition of copper on epitaxial graphene has been investigated in acidic environment. The electrodeposition of copper occurs by instantaneous nucleation at a limited number of sites, via a bi-electronic reduction bypassing of Cu\({}^{1+}\) intermediates at a significant overpotential with respect to the process in graphitic materials. The computational methods showed coherence with the experimental observations.