We show that a simple first-principles correction based on the difference between the singlettriplet CO excitation energy values obtained by DFT and high-level quantum chemistry methods yields accurate CO adsorption properties on a variety of metal surfaces. We demonstrate a linear relationship between the CO adsorption energy and the CO singlet-triplet splitting, similar to the linear dependence of CO adsorption energy on the energy of the CO 2π* orbital found recently [Kresse et al., Physical Review B 68, 073401 (2003)]. Converged DFT calculations underestimate the CO singlet-triplet excitation energy ∆ES−T, whereas coupled-cluster and CI calculations reproduce the experimental ∆ES−T. The dependence of E chem on ∆ES−T is used to extrapolate E chem for the top, bridge and hollow sites for the (100) and (111) surfaces of Pt, Rh, Pd and Cu to the values that correspond to the coupled-cluster and CI ∆ES−T value. The correction reproduces experimental adsorption site preference for all cases and obtains E chem in excellent agreement with experimental results.
The persistence of lead (Pb) in contaminated topsoil is ranked as one of the most serious environmental issues in the U.S. and other countries. Adsorption of Pb at the aqueous interface of nanoscale metal oxide and metal (oxy)hydroxide particles is perhaps the most significant process responsible for controlling contaminant sequestration and mobility, but the process is poorly understood at the molecular level. Experimental studies of absorption of Pb onto bulk minerals have indicated significant differences in reactivity, but the molecular basis for these differences has remained elusive due to the challenges of observing and modeling the complex chemistry that exists at the water-oxide interface. In this work, we present a detailed ab initio theoretical investigation aimed at understanding the fundamental physical and chemical characteristics of Pb adsorption onto the (0001) surface of two common minerals, R-Al 2 O 3 and R-Fe 2 O 3 . The results of our periodic density functional theory (DFT) calculations show that the adsorption energy of Pb(II) on hematite is more than four times the value on isostructural alumina with the same fully hydroxylated surface stoichiometry due to bonding interactions enabled by the partially occupied Fe d-band. Site preference for Pb(II) adsorption on alumina is shown to depend strongly on the cost to disrupt highly stable hydrogen bonding networks on the hydrated surface, but is less of a factor for the stronger Pb-hematite interaction. † Part of the special section "Physical Chemistry of Environmental Interfaces".
We report the bulk properties and ab initio thermodynamics surface free energies for α-Fe2O3(0001) using density functional theory (DFT) with calculated Hubbard U values for chemically distinct surface Fe atoms. There are strong electron correlation effects in hematite that are not well-described by standard DFT. A better description can be achieved by using a DFT + U approach in which U represents a Hubbard on-site Coulomb repulsion term. While DFT + U calculations result in improved predictions of the bulk hematite band gap, surface free energies using DFT + U total energies result in surface structure predictions that are at odds with most experimental results. Specifically, DFT + U predictions stabilize a ferryl termination relative to an oxygen termination that is widely reported under a range of experimental conditions. We explore whether treating chemically distinct surface Fe atoms with different U values can lead to improved bulk and surface predictions. We use a linear response technique to derive specific U d values for all Fe atoms in several slab geometries. We go on to add a Coulomb correction, U p, to better describe the hybridization between the Fe d and oxygen p orbitals, accurately predicting the structural and electronic properties of bulk hematite. Our results show that the site-specific U d is a key factor in obtaining theoretical results for surface stability that are congruent with the experimental literature results of α-Fe2O3(0001) surface structure. Finally, we use a model surface reaction to trace how the various DFT + U methods affect the surface electronic structure and heterogeneous reactivity.
The rapid increase in use of Li-ion batteries in portable electronics has created a pressing need to understand the environmental impact and long-term fate of electonic waste (e-waste) products such as heavy and/or reactive metals. The type of e-waste that we focus on here are the complex metal oxide nanomaterials that compose Li-ion battery cathodes. While in operation the complex metal oxides are in a hermetically sealed container. However, at the end of life, improper disposal can cause structural transformations such as dissolution and metal leaching, resulting in a significant exposure risk to the surrounding environment. The transformations that occur between operational to environmental settings gives rise to a stark knowledge gap between macroscopic design and molecular-level behavior. In this study we use theory and modeling to describe and explain previously published experimental data for cation release from Li(NiMnCo)O (NMC) nanoparticles in an aqueous environment ( Chem. Mater. 2016 (28) 1092-1100). To better understand the transformations that may occur when this material is exposed to the environment, we compute the free energy of surface dissolution, Δ G, from the complex metal oxide NMC for a range of surface terminations and pH.
This work investigates the biological impact of LixNiyMnzCo1−y−zO2, a class of cathode materials used in lithium ion batteries.
We report an ab initio thermodynamic analysis of the ␣-Al 2 O 3 ͑1102͒ surface aimed at understanding the experimentally observed terminations over a range of surface preparation conditions as well as a stoichiometric model for the ͑2 ϫ 1͒ surface reconstruction observed after high-temperature annealing. As temperature is increased under both ultrahigh vacuum and ambient hydrated conditions, the predicted minimum-energy structural model goes through the same series of changes: from the hydroxylated "missing-Al" surface model ͑or half-layer model in which the topmost Al site of the stoichiometric surface has zero occupancy͒, to the hydroxylated stoichiometric model, to another hydroxylated missing-Al surface model with tetrahedral coordinated surface Al, and finally to the clean ͑1 ϫ 1͒ stoichiometric model. These results are in agreement with observations of both missing-Al and bulklike stoichiometries under wet conditions and in agreement with similar trends reported for isostructural hematite. However, we observe that the models with excess oxygen have a relatively higher surface-free energy and distinct surface relaxations in the case of alumina as compared to hematite. At very high temperatures where oxygen defects are generated, we find that a stoichiometric, charge-neutral ͑2 ϫ 1͒ structure becomes the most thermodynamically stable. This is consistent with the observation of a ͑2 ϫ 1͒ electron diffraction pattern when the surface is annealed at 2000 K while a ͑1 ϫ 1͒ pattern persists at lower annealing temperatures. A general rule that emerges from our modeling results is that while the full phase space of hydrated and defective surfaces is expansive, model stoichiometries that can be made charge neutral through either hydration or defects offer the greatest thermodynamic stability. However, the unique trends in structure and relative energies of alumina surface stoichiometries as compared to hematite can be understood based on the difference in electronic structure of the substrate.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2023 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.