Multiscale Computational Screening of Metal-Organic Frameworks for Kr/Xe Adsorption Separation: A Structure-Property Relationship-Based Screening Strategy

The separation of radioactive noble gases, such as Xe and Kr, has attracted special attention in the context of used nuclear fuel (UNF). In this study, 180 metal-organic frameworks (MOFs) formally used for selective adsorptions of ethane and ethylene, with a similar kinetic diameter to Kr and Xe, were initially screened for the Kr/Xe separation using the grand canonical Monte Carlo (GCMC) method. Then, the structure-adsorption property relationships were generalized, that is, the MOFs of higher Kr/Xe selectivity are with the porosity at 0.2-0.4 and the ratio of the largest cavity diameter/pore limiting diameter at 1.0-2.4. Based on the relationships, six reported MOFs with large Kr uptakes and Kr/Xe selectivities were experimentally screened out and validated by GCMC simulations within the CoRE-MOF database, which are higher than most reported MOFs under conditions pertinent to nuclear fuel reprocessing of an 80/20 v/v mixture of Kr/Xe at normal temperature and pressure. Further simulations reveal that higher Kr uptakes and Kr/Xe selectivities of six MOFs result from the confinement effect of the pores. Molecular dynamic simulations showed that the six MOFs are ideal membrane separation materials of Kr from Xe, which are driven by adsorption and diffusion. Analyses of electronic structure-based density functional theory calculations showed that the main interaction between Kr and the six MOFs is van der Waals force dominated by dispersion and induction interactions. Therefore, the generalized structure-adsorption property relationships may assist the screening of MOFs for the separation and production of Kr/Xe from UNF industrially.

Development of Theoretical Descriptors for Cytotoxicity Evaluation of Metallic Nanoparticles

Motivated by the recent development of quantitative structure-activity relationship (QSAR) methods in the area of nanotoxicology, we proposed an approach to develop additional descriptors based on results of first-principles calculations. For the evaluation of the biochemical activity of metallic nanoparticles, we consider two processes: ion extraction from the surface of a specimen to aqueous media and water dissociation on the surface. We performed calculations for a set of metals (Al, Fe, Cu, Ag, Au, and Pt). Taking into account the diversity of atomic structures of real metallic nanoparticles, we performed calculations for different models such as (001) and (111) surfaces, nanorods, and two different cubic nanoparticles of 0.6 and 0.3 nm size. Significant energy dependence of the processes from the selected model of nanoparticle suggests that for the correct description we should combine the calculations for several representative models. In addition to the descriptors of chemical activity of the metallic nanoparticles for the two studied processes, we propose descriptors for taking into account the dependence of chemical activity from the size and shape of nanoparticles. Routes to minimization of computational costs for these calculations are also discussed.

Computational studies on the interactions of glycine amino acid with graphene, h-BN and h-SiC monolayers

In the present work, the adsorption of glycine amino acid and its zwitterionic form onto three different hexagonal sheets, namely graphene, boron-nitride (h-BN) and silicon carbide (h-SiC), has been investigated within the framework of density functional theory (DFT) calculations.

Room-temperature metal-free ferromagnetism, stability, and spin transport properties in topologically fluorinated silicon carbide nanotubes

A new topologically fluorinated armchair single-walled silicon carbide nanotube has been predicted via first principles density functional theory (DFT) and nonequilibrium Green's function method, as well as ab initio molecular dynamic (MD) simulations.

Trends of amino acid adsorption onto graphene and graphene oxide surfaces: a dispersion corrected DFT study

First-principle calculations based on DFT were performed to investigate the adsorption properties of amino acids onto graphene surfaces.