Computational modelling of graphene/aluminum nitride (GP/AlN) hybrid materials for the detection of 2,4 dichlorophenoxyacetic acid (DCP) pollutant

Abstract

Despite their efficacy in eliminating undesired crops and increasing yield, a range of environmental issues and chronic ailments arise when hazardous chemicals are highly concentrated in wastewater and then deposited into rivers, lakes or the air. Hence, the detection of these chemicals has become a cause of concern for researchers and scientists because they contribute largely to serious health problems. Herein, the potential of newly tailored nanomaterials for the detection of 2,4 dichlorophenoxyacetic acid (DCP) in humans was examined. The theoretical approach adopted in this work is within the framework of density functional theory (DFT) using the DFT/B3LYP-D3/def2SVP computational method. The reduction in the energy gap upon adsorption is indicative of good adsorbing properties. A chemisorption phenomenon was observed for DCP-GP/AlN. However, in most cases, physisorption occurs. Interestingly, the noncovalent nature of the interactions was observed in all the cases, indicating that the material was good. The green colour of the 3D RDG maps implies a significant intermolecular interaction. Sensor mechanisms confirmed that the nanocomposite materials exhibit excellent detection potential for DCP through greater charge transfer, better sensitivity, conductivity, and enhanced adsorption capacity. The potential of nanocomposite materials as stable and promising detectors for DCP pollutants was confirmed in this study. Hence, the studied GP/AlN nanocomposite material can be used in the engineering of future sensor devices for detecting DCP.