Estudo da adsorção de glifosato em grafeno e algumas de suas funcionalizações via teoria do funcional da densidade

Abstract

The growing demand in the agricultural sector, coupled with an increase in per capita income, global population, and food consumption, makes the use of techniques, machinery, and substances that enhance production efficiency crucial. In this context, agricultural pesticides such as glyphosate have emerged as an excellent alternative. Glyphosate is a non-selective herbicide with a broad spectrum of action, eliminating weeds that compete for nutrients with the target crop. Introduced in the 1970s, glyphosate currently represents approximately 70% of pesticide use in Brazil. Its excessive use may lead to environmental issues in both flora and fauna, with recent studies suggesting a potential link to cancer in humans. Being a polar and highly water-soluble molecule, glyphosate poses a significant risk of contamination to aquatic biota. Therefore, there is considerable interest in techniques for detecting and/or removing glyphosate from water. This study aims to elucidate a possible route for the detection and removal of glyphosate using graphene-based substrates. The Density Functional Theory, employing the PBE exchange-correlation functional along with van der Waals D3 corrections from the projector-augmented wave method implemented in the VASP computational package, was utilized. Six substrates were studied: pristine graphene, graphene with monovacancy, nitrogen-doped graphene, oxygen-doped graphene, nitrogen-adsorbed graphene, and oxygen-adsorbed graphene. Glyphosate was adsorbed onto these substrates through tentative configurations, and the most promising ones were subjected to Ab-Initio Molecular Dynamics (AIMD) in two processes: thermalization (performed at a constant temperature of 300 K) and simulated annealing (initiated at 300 K and decreasing to 0 K). Subsequently, we selected snapshots of configurations with the lowest non-equivalent energies and conducted a more precise structural optimization to extract properties. The results of the adsorptions can be classified into three categories: (i) systems with weak adsorption characterized by physisorption, observed in adsorptions on pristine and N- and O-doped substrates; (ii) a system characterized by strong chemisorption with an adsorption energy greater than 2 eV, found in the adsorption on monovacancy graphene; (iii) systems with strong chemisorption, resulting in the formation of new compounds, observed in adsorptions on adsorbed graphene (N and O). We also conducted an analysis of charge density differences, revealing the molecule’s tendency to become anionic and the substrate cationic. Our results indicate the suitability of each graphenebased substrate for the removal and/or detection of glyphosate in water, serving as a guide for the development of graphene-based nanomaterials for these purposes.