Computational Study of Graphene Quantum Dots (GQDs) Functionalized with Thiol and Amino Groups for the Selective Detection of Heavy Metals in Wastewater

Abstract

Given the growing interest in contaminant detection, research has addressed the functionalization behavior of graphene quantum dots (GQDs) with thiol (-SH) and amino (-NH2) groups to optimize and improve the selective detection of heavy metals in wastewater. Implementing Density Functional Theory (DFT), the interactions between the functionalized GQDs and hydrated metals such as Cr, Cd, and Pb were simulated. The results showed that GQDs with thiol groups exhibited a high affinity for metals such as Pb and Cd, with an energy gap (Eg) of 0.02175 eV in the interaction with Pb, showing optimized reactivity. On the other hand, amino-modified GQDs presented a higher Eg, indicating a lower reactivity and efficacy in contaminant identification. Furthermore, this study evaluated electronic properties such as the energy gap and total dipole moment (TDM), resulting in the -SH-functionalized GQDs showing a higher TDM, which presented a greater interaction capacity with these metals. Likewise, the electrostatic potential maps (MEPs) provided information on the charge distribution when adsorbing metals, an important parameter to understand electronic interactions. These results showed that the modification of GQDs improved the detection of heavy metals, although limitations in the DFT method used are recognized and the need for experimental studies is suggested to validate the results and investigate other functional modifications.