Hydration Structures on γ-Alumina Surfaces With and Without Electrolytes Probed by Atomistic Molecular Dynamics Simulations

A wide range of systems, both engineered and natural, feature aqueous electrolyte solutions at interfaces. In this study, the structure and dynamics of water at the two prevalent crystallographic terminations of gamma-alumina, [110] and [100], and the influence of salts─sodium chloride, ammonium acetate, barium acetate, and barium nitrate on such properties─were investigated using equilibrium molecular dynamics simulations. The resulting interfacial phenomena were quantified from simulation trajectories via atomic density profiles, angle probability distributions, residence times, 2-D density distributions within the hydration layers, and hydrogen bond density profiles. Analysis and interpretation of the results are supported by simulation snapshots. Taken together, our results show stronger interaction and closer association of water with the [110] surface, compared to [100], while ion-induced disruption of interfacial water structure was more prevalent at the [100] surface. For the latter, a stronger association of cations is observed, namely sodium and ammonium, and ion adsorption appears determined by their size. The differences in surface-water interactions between the two terminations are linked to their respective surface features and distributions of surface groups, with atomistic-scale roughness of the [110] surface promoting closer association of interfacial water. The results highlight the fundamental role of surface characteristics in determining surface-water interactions, and the resulting effects on ion-surface and ion-water interactions. Since the two terminations of gamma-alumina considered represent interfaces of significance to numerous industrial applications, the results provide insights relevant for catalyst preparation and adsorption-based water treatment, among other applications

Interactions between γ-alumina surfaces in water and aqueous salt solutions

Particle agglomeration is relevant to numerous industrial applications and consumer products. The present work explores interactions between and agglomeration of gamma (γ)-alumina nanoparticles in pure water and dilute aqueous salt solutions. To characterize surface- and salt-specific effects, potential of mean force (PMF) profiles between γ-alumina surfaces ([110] and [100] facets) are extracted using classical molecular dynamics (MD) simulations. Supporting experiments are conducted using dynamic light scattering (DLS) to investigate agglomeration at the macroscale. The ion pairs considered are sodium chloride, ammonium acetate, barium nitrate, and barium acetate; sampling a broad range of the Hofmeister series. As particle surfaces approach contact, free-energy fluctuations of the PMF profiles reflect structural adjustments of the intervening aqueous phase. We extract values for the cohesive energy from the MD results, and parse the resultant effective pair interactions into van der Waals and electrostatic contributions. Molecular scale findings from simulations correlate with hydrodynamic radii of γ-alumina nanoparticles, obtained from DLS experiments. The results highlight the applicability of molecular simulations to identify the origins of macroscale observables

Near-Fixed Point Results via Ƶ-Contractions in Metric Interval and Normed Interval Spaces

In this paper, using α—admissibility and the concept of simulation functions, some near-fixed point results in the setting of metric interval and normed interval spaces are established. The results have been proved using Z-contractions.This work was funded by the Basque Government with the grant number IT207-19

Ammonia mobility in chabazite: insight into the diffusion component of the NH ₃-SCR process

To assess the effect of counterion presence on NH3 mobility in commercial automotive emission control zeolite catalysts, NH3 mobility in NH3-SCR catalyst Cu-CHA was compared with H-CHA using quasielastic neutron scattering and molecular dynamics simulations.</p

Style Shift: A Comparative Cultural Analysis of Pride and Prejudice and Unmarriageable

The writing style is the core element of the authors as it varies from place to place under cultural representation through code-mixing and code-switching. The current study is qualitative in nature describing the style shift and the code mixing and code switching in the context of the non-native speakers. The study is being conducted comparatively on the analysis of two novels Pride and Prejudice by Jane Austen (1813) and Unmarriageable by Soniah Kamal (2019) to highlight the element of style shift from native to non-native context. The study concludes that the shift of the style and sense being presented in the native context of the English language is of a universal level while the style of non-natives is of local level with the inclusion of code-mixing and code-switching.

Near-Coincidence Point Results in Norm Interval Spaces via Simulation Functions

Recently, Wu in 2018 established interesting results in the framework of interval spaces. He initiated the idea of near-fixed points and proved some related basic results in metric interval, norm interval, and hyperspaces. In 2015, Khojasteh et al. gave the concept of simulation functions and studied some fixed-point results in metric spaces. Motivated by this work, we give some near-coincidence point results in norm interval spaces using the concept given by Khojasteh et al. Examples are also provided for the validation of the results

Perspective: Methods for large-scale density functional calculations on metallic systems

Current research challenges in areas such as energy and bioscience have created a strong need for Density Functional Theory (DFT) calculations on metallic nanostructures of hundreds to thousands of atoms to provide understanding at the atomic level in technologically important processes such as catalysis and magnetic materials. Linear-scaling DFT methods for calculations with thousands of atoms on insulators are now reaching a level of maturity. However such methods are not applicable to metals, where the continuum of states through the chemical potential and their partial occupancies provide significant hurdles which have yet to be fully overcome. Within this perspective we outline the theory of DFT calculations on metallic systems with a focus on methods for large-scalecalculations, as required for the study of metallic nanoparticles. We present early approaches for electronic energy minimization in metallic systems as well as approaches which can impose partial state occupancies from a thermal distribution without access to the electronic Hamiltonian eigenvalues, such as the classes of Fermi Operator Expansions and Integral Expansions. We then focus on the significant progress which has been made in the last decade with developments which promise to better tackle the length-scale problem in metals. We discuss the challenges presented by each method, the likely future directions that could be followed and whether an accurate linear-scalingDFT method for metals is in sight

Machine learning assisted high-throughput screening of zeolites for the selective adsorption of xylene isomers

The production of widely used polymers such as polyester currently relies upon the chemical separation of and transformation of xylene isomers. The least valuable but most prevalent isomer is meta-xylene which can be selectively transformed into the more useful and expensive para-xylene isomer using a zeolite catalyst but at a high energy cost. In this work, high-throughput screening of existing and hypothetical zeolite databases containing more than two million structures was performed, using a combination of classical simulation and deep neural network methods to identify promising materials for selective adsorption of meta-xylene. Novel anomaly detection techniques were applied to the heavily biased classification task of identifying structures with a selectivity greater than that of the best performing existing zeolite, ZSM-5 (MFI topology). Eight hypothetical zeolite topologies are found to be several orders of magnitude more selective towards meta-xylene than ZSM-5 which may provide an impetus for synthetic efforts to realise these promising materials. Moreover, the leading hypothetical frameworks identified from the screening procedure require a markedly lower operating temperature to achieve the diffusion seen in existing materials, suggesting significant energetic savings if the frameworks can be realised

Machine learning accelerated high-throughput screening of zeolites for the selective adsorption of xylene isomers

A combination of machine learning and high throughput simulation has identified several potential zeolite structures that appear to outperform the leading commercially used material and explained the key factors for high selectivity

Modification of O and CO binding on Pt nanoparticles due to electronic and structural effects of titania supports

Metal oxide supports often play an active part in heterogeneous catalysis by moderating both the structure and the electronic properties of the metallic catalyst particle. In order to provide some fundamental understanding on these effects, we present here a density functional theory (DFT) investigation of the binding of O and CO on Pt nanoparticles supported on titania (anatase) surfaces. These systems are complex, and in order to develop realistic models, here, we needed to perform DFT calculations with up to ∼1000 atoms. By performing full geometry relaxations at each stage, we avoid any effects of “frozen geometry” approximations. In terms of the interaction of the Pt nanoparticles with the support, we find that the surface deformation of the anatase support contributes greatly to the adsorption of each nanoparticle, especially for the anatase (001) facet. We attempt to separate geometric and electronic effects and find a larger contribution to ligand binding energy arising from the former. Overall, we show an average weakening (compared to the isolated nanoparticle) of ∼0.1 eV across atop, bridge and hollow binding sites on supported Pt55 for O and CO, and a preservation of site preference. Stronger effects are seen for O on Pt13, which is heavily deformed by anatase supports. In order to rationalize our results and examine methods for faster characterization of metal catalysts, we make use of electronic descriptors, including the d-band center and an electronic density based descriptor. We expect that the approach followed in this study could be applied to study other supported metal catalysts