Navigating the molecular landscape of environmental science and heavy metal removal: A simulation-based approach

Abstract

Heavy metals pose a significant threat to ecosystems and human health because of their toxic properties and their ability to bioaccumulate in living organisms. Traditional removal methods often fall short in terms of cost, energy efficiency, and minimizing secondary pollutant generation, especially in complex environmental settings. In contrast, molecular simulation methods offer a promising solution by providing in-depth insights into atomic and molecular interactions between heavy metals and potential adsorbents. This review highlights the potential of molecular simulation methods for removing types of pollutants in environmental science, specifically heavy metals. These methods offer a powerful tool for predicting and designing materials and processes for environmental remediation. We focus on removing specific heavy metals like lead, Cadmium, and mercury, utilizing cutting-edge simulation techniques such as Molecular Dynamics (MD), Monte Carlo (MC) simulations, Quantum Chemical Calculations (QCC), and Artificial Intelligence (AI). By leveraging these methods, we aim to develop highly efficient and selective materials and processes for environmental remediation. By unravelling the underlying mechanisms, these techniques pave the way for developing more efficient and selective removal technologies. This comprehensive review addresses a critical gap in the scientific literature, providing valuable insights for researchers in environmental protection and human health. Molecular modelling methods hold significant promise for revolutionizing the prediction and removal of heavy metals, ultimately contributing to sustainable solutions for a cleaner and healthier future.