Computational Insights into Graphene-Based Materials for Arsenic Removal from Wastewater: A Hybrid Quantum Mechanical Study

The discharge of industrial wastewater, particularly from chemical and mining industries, poses significant threats to the environment, public health, and safety due to high concentrations of pollutants leading to serious illnesses and the loss of aquatic life. It is therefore essential and urgent to devise measures for mitigating these threats. To advance the understanding of graphene membranes for arsenic removal from wastewater, we investigated the arsenic adsorption mechanism and relative selectivity on graphene-based materials using computational approaches. Our study employed hybrid quantum mechanical calculations for energy and geometry optimization to explore arsenic adsorption on pristine graphene membrane surfaces in vacuum and aqueous environments. We assessed the effect of different adsorption sites on the surface, including top (T), bridge (B), and hollow (H) across both edge (E) and center (C) regions, to identify the optimal site. Our results identified edge sites as the most effective for adsorption, with strong adsorption energies in both vacuum (-1.98 eV) and aqueous environments (-1.97 eV), which are generally stronger than those for water adsorption (-0.25 to -0.26 eV) on the surface. Geometrical analyses confirmed the bridge edge sites as the most preferred adsorption configuration. Our findings advance computational methodologies for designing efficient adsorbents and offer valuable insights for developing graphene-based materials. By elucidating adsorption mechanisms and optimizing membrane properties, this study contributes to the novel design of adsorbents for arsenic removal, addressing critical challenges in environmental remediation.