Modeling Molecular Structural Properties of Magnetite (Fe3O4) and Mackinawite (FeS) Using Density Functional Theory (DFT)
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Abstract
Bare and sulfidized nanoscale zerovalent iron (bare NZVI or Fe0 and S-NZVI, respectively) has been widely utilized for environmental restoration. During the degradation and sequestration of contaminants of concern (COCs) such as chlorinated organics and toxic metals, interfacial detoxification reactions are governed by the physical chemistry of the iron oxide shell of bare NZVI and the iron sulfide shell of S-NZVI: magnetite (Fe3O4) and mackinawite (FeS), respectively. Because interfacial reactions generally cannot be directly and experimentally monitored, this study examines first-principles methods based on the use of the density functional theory (DFT) as a simulation tool to help understand interfacial phenomena. In this study, DFT approaches with and without long-range van der Waals interactions (so-called DFT and DFT-D2 approaches, respectively) were employed. The simulated unit cell parameters and electronic density of states (DOS) of bulk Fe3O4 and FeS were modeled using both DFT and DFT-D2 methods and compared with previous experimental results where these were available. We reveal that there was strong agreement between the simulated properties and previous experimental results. Nevertheless, for both Fe3O4 and FeS, the DFT-D2 method performed better than the DFT method in terms of the accuracy of simulated unit cell parameters. Furthermore, the DFT-D2 method simulated the DOS of both materials effectively. The DOS of Fe3O4 supports electron transfer from the central octahedral-FeB layer to the outer tetrahedral-FeA layer, while the DOS of FeS potentially explains the decrease of the NZVI aging effect and enhanced treatment for hydrophobic contaminants due to sulfidation reported in literature. This research projects that DFT-D2 can be used as a tool of choice for understanding the interaction between COCs and Fe3O4 and FeS surfaces at nanoscale in order to develop the environmental applications of nanomaterials. For this purpose, further modification of the model is required to properly downscale the computation from bulk to nanoscale materials
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