Advanced Theory and Simulations最新文献

We report a first-principles investigation of the structural, mechanical, electronic, and optical properties of the double perovskite hydrides Cs₂RbAlH₆ and Rb₂CaNiH₆. Density functional theory calculations were performed within the GGA-PBE framework, with an on-site Hubbard correction applied to Ni-3d states, using the FP-LAPW method as implemented in the Wien2k code and plane-wave calculations in CASTEP. Both compounds are found to crystallize in the cubic Fm-3m (No. 225) structure and satisfy the mechanical stability criteria, exhibiting brittle behavior according to Pugh's and Poisson's ratios. The calculated hydrogen storage capacities amount to 1.55 and 2.14 wt.% for Cs₂RbAlH₆ and Rb₂CaNiH₆, respectively, with corresponding volumetric densities of 56.16 and 73.36 g H₂/L, comparable to those reported for related hydride perovskites. Electronic structure calculations indicate that Cs₂RbAlH₆ is an indirect-gap semiconductor with a bandgap of 2.35 eV, while Rb₂CaNiH₆ exhibits an indirect bandgap of 1.10 eV after inclusion of the Hubbard U. The optical response is characterized by pronounced absorption in the ultraviolet region, finite optical conductivity, and distinct plasmonic features. These results establish Cs₂RbAlH₆ and Rb₂CaNiH₆ as representative model systems for exploring structure-property relationships in double perovskite hydrides relevant to hydrogen-based energy applications.

Although an excellent corrosion inhibitor, alumina deteriorates in the presence of chlorine. This systematic study probes how the most common chlorine-containing corroders, namely $�{\mathrm{Cl}}_2$, HCl, NaCl, KCl, $�{\mathrm{MgCl}}_2$, and $�{\mathrm{CaCl}}_2$ interact with an $�\alpha$-$��{\mathrm{Al}}_2�O_3�$ (0001) surface. Using a host of density functional theory-computed parameters, including adsorption energies, integrated crystal orbital Hamilton population (ICOHPs), combined with ab initio molecular dynamics studies, the adsorption of the corrosive species and their stability on the inhibitor surface is probed in great depth. Adsorption energies indicate that a higher ionic character of the corroder leads to a stronger adsorption on the alumina surface. However, ICOHP analyses of various bond pairs indicate that HCl, with a lower adsorption energy is more detrimental to the inhibitor slab in line with experimental observations. Overall, the study shows that ICOHPs are a more reliable predictor of corrosive effects than adsorption energies.

Understanding how gas mixtures diffuse and distribute within porous frameworks is central to designing advanced separation and storage materials. Here, the transport and spatial distribution of binary gas mixtures in a porous metal-organic framework, viz., ZIF-90, using molecular simulations is investigated. We performed grand canonical Monte Carlo (GCMC) simulations to examine the competitive adsorption of carbon dioxide (CO₂) and nitrogen (N₂) from a binary gas mixture in ZIF-90, while molecular dynamics (MD) simulations are conducted to investigate the transport behavior of the adsorbed molecules within the framework. These integrated simulations reveal that the framework topology and pore chemistry jointly dictate diffusion pathways and preferential occupancy of gas species, underscoring their intrinsic interdependence. Competitive adsorption leads to distinct spatial partitioning within the pores, which in turn modulates mixture diffusivity inside the porous medium compared to their bulk properties. These results provide molecular-level insight into how ZIF-90 accommodates and separates gas mixtures, offering design principles for optimizing metal-organic frameworks in energy and environmental applications.

Remarkable properties of 2D silicene have made it a potential future nanoscale interconnect, required for next-generation electronic devices. In this present work, a comprehensive density-functional theory (DFT) and non-equilibrium Green's function (NEGF) approaches have been implemented to analyse the the impact of P-type (Boron, Aluminium) and N-type (Nitrogen, Phosphorus) doping in zigzag silicene nanoribbons (ZSiNRs), focusing on their structural stability, electronic characteristics, and transport properties along with dynamical parameters and device-level metrics, including signal delay, power–delay product, and maximum frequency of operation (MFOO). Out of six possible doping sites, edge-site doping is found to be energetically favorable, with N-doped ZSiNRs exhibiting the highest thermodynamic stability. Electronic structure analyses show enhanced metallicity upon doping, while transport calculations indicate distinct doping-dependent behavior. B-doped ZSiNRs exhibit the lowest delay and MFOO, suggesting reduced signal delay, whereas N-doped ZSiNRs demonstrate improved thermodynamic stability with linear current–voltage (I–V) characteristic, low delay, and high MFOO, making them potential candidates for the interconnect applications.

The Monte Carlo (MC) simulation is an effective technique for capturing the dynamic evolution of Particulate-Filled Systems (PFS), as it allows detailed tracking of individual droplet histories. This flexible method is widely applied across many fields and, owing to its simple implementation and robust performance, often has no suitable alternative. However, the long computational time, especially when a large number of droplets is simulated, remains a significant drawback. The primary aim of this study is to address this limitation by introducing a novel strategy for incorporating large droplet populations into MC simulations, thereby reducing the time required to model extensive systems. A new mathematical procedure is combined with a time-increment MC model to develop a hybrid framework, referred to as the Combinatorial Approach (CAx), which enables the use of sufficiently large initial sample populations to predict the dynamic evolution of particle size distributions in suspension polymerization. The key feature of CAx is the combination of multiple distinct simulation runs into a single vector, providing higher precision than conventional MC simulations. This framework significantly reduces computational time while maintaining high accuracy in the predicted results.