A DFT/PCM Study on the Affinity of Salinomycin to Bind Monovalent Metal Cations

The affinity of the polyether ionophore salinomycin to bind IA/IB metal ions was accessed using the Gibbs free energy of the competition reaction between SalNa (taken as a reference) and its rival ions: [M+-solution] + [SalNa] → [SalM] + [Na+-solution] (M = Li, K, Rb, Cs, Cu, Ag, Au). The DFT/PCM computations revealed that the ionic radius, charge density and accepting ability of the competing metal cations, as well as the dielectric properties of the solvent, have an influence upon the selectivity of salinomycin. The optimized structures of the monovalent metal complexes demonstrate the flexibility of the ionophore, allowing the coordination of one or two water ligands in SalM-W1 and SalM-W2, respectively. The metal cations are responsible for the inner coordination sphere geometry, with coordination numbers spread between 2 (Au+), 4 (Li+ and Cu+), 5/6 (Na+, K+, Ag+), 6/7 (Rb+) and 7/8 (Cs+). The metals’ affinity to salinomycin in low-polarity media follows the order of Li+ > Cu+ > Na+ > K+ > Au+ > Ag+ > Rb+ > Cs+, whereas some derangement takes place in high-dielectric environment: Li+ ≥ Na+ > K+ > Cu+ > Au+ > Ag+ > Rb+ > Cs+.

Adsorption Sites on Pd Nanoparticles Unraveled by Machine-Learning Potential with Adaptive Sampling

Catalytic properties of noble-metal nanoparticles (NPs) are largely determined by their surface morphology. The latter is probed by surface-sensitive spectroscopic techniques in different spectra regions. A fast and precise computational approach enabling the prediction of surface–adsorbate interaction would help the reliable description and interpretation of experimental data. In this work, we applied Machine Learning (ML) algorithms for the task of adsorption-energy approximation for CO on Pd nanoclusters. Due to a high dependency of binding energy from the nature of the adsorbing site and its local coordination, we tested several structural descriptors for the ML algorithm, including mean Pd–C distances, coordination numbers (CN) and generalized coordination numbers (GCN), radial distribution functions (RDF), and angular distribution functions (ADF). To avoid overtraining and to probe the most relevant positions above the metal surface, we utilized the adaptive sampling methodology for guiding the ab initio Density Functional Theory (DFT) calculations. The support vector machines (SVM) and Extra Trees algorithms provided the best approximation quality and mean absolute error in energy prediction up to 0.12 eV. Based on the developed potential, we constructed an energy-surface 3D map for the whole Pd55 nanocluster and extended it to new geometries, Pd79, and Pd85, not implemented in the training sample. The methodology can be easily extended to adsorption energies onto mono- and bimetallic NPs at an affordable computational cost and accuracy.

Х-ray Study and Computer Simulation of the Structure of Amorphous-Crystalline Titanite

The structure of amorphous-crystalline titanite obtained by mechanical activation was studied by X-ray diffraction and simulation methods. The short-range order characteristics were calculated using Finbak-Warren's method. It was found that the coordination numbers of metal atoms decreased as the result of titanite grinding. The atomic configurations of short-range order of ground titanite were constructed by translation of titanite unit cell. The theoretical X-ray patterns were calculated using Debye's method and were compared with the experimental curves. The structure of ground titanite in the mill with centrifugal factor 40 g was described satisfactorily by the model of mechanical mixture of clusters containing 2016 atoms, disordered during the molecular dynamics with clusters containing 12096 atoms. The increase of grinding intensity led to the sharp decrease of sizes of small cluster.

Visualization of magnesium and rubidium ion hydration in sulfocationite using quantum chemical calculation

Based on the data of ab initio calculation of the structure of (RSO3)2Mg (H2O)18 and (RSO3Rb)2(H2O)16 clusters, which simulate the structure of swollen sulfostyrene ion exchangers in the corresponding ionic forms and a water cluster of comparable size, the numbers of water molecules directly bound to cations and their coordination numbers, including the oxygen atoms of the sulfonic groups linked to the cation, were calculated. It is shown that the first molecular layer around the magnesium ion is formed from water molecules with the highest binding energy with the cluster, and around the rubidium ion – from the molecules of the nearest environment with the lowest binding energies. This is explained by the fact that the transfer of water molecules from its volume to magnesium hydrate is energetically favorable, but not to rubidium hydrate. Therefore, the magnesium ion builds its hydrate mainly from water molecules with the highest binding energy in order to obtain the greatest energy gain, and the rubidium ion – from molecules with the lowest energy, which provides the smallest energy loss.

Unravelling Morphological and Topological Energy Contributions of Metal Nanoparticles

Metal nanoparticles (NPs) are ubiquitous in many fields, from nanotechnology to heterogeneous catalysis, with properties differing from those of single-crystal surfaces and bulks. A key aspect is the size-dependent evolution of NP properties toward the bulk limit, including the adoption of different NP shapes, which may bias the NP stability based on the NP size. Herein, the stability of different Pdn NPs (n = 10–1504 atoms) considering a myriad of shapes is investigated by first-principles energy optimisation, leading to the determination that icosahedron shapes are the most stable up to a size of ca. 4 nm. In NPs larger than that size, truncated octahedron shapes become more stable, yet a presence of larger {001} facets than the Wulff construction is forecasted due to their increased stability, compared with (001) single-crystal surfaces, and the lower stability of {111} facets, compared with (111) single-crystal surfaces. The NP cohesive energy breakdown in terms of coordination numbers is found to be an excellent quantitative tool of the stability assessment, with mean absolute errors of solely 0.01 eV·atom−1, while a geometry breakdown allows only for a qualitative stability screening.

Evidence of a Unique Critical Fabric Surface for Granular Soils

The critical state of granular soils needs to make proper reference to the fabric that develops at critical state. This study substantializes the concept of critical fabric surface (CFS) which attracts the fabric state of granular soils upon continuous shearing. Numerical experiments using discrete element modelling (DEM) are conducted under drained and undrained conditions with varies Lode angles. Fabric tensors are defined based on the normals of all contacts and of the strong force contacts only. Both tensors have their spherical component preserved such that the information of coordination number can be carried. A separate series of low confining pressure undrained test are conducted to probe the fabric states of soils in the post-liquefaction regime. Finally, a single CFS spanning across a wide range of coordination numbers is established based on the DEM results. The CFS concept provides an important reference state for soils sheared to large strains in complementary to the traditionally defined critical state. It provides a new perspective to interpret and model the mechanics of granular soils in both pre- and post- liquefied regimes. The evolution of fabric shows that the normalized strong-contact fabric evolves linearly with the stress ratio even for liquefied or anisotropically consolidated soils.

Thermodynamic and Structural Modelling of Non-Stoichiometric Ln-Doped UO2 Solid Solutions,Ln = {La, Pr, Nd, Gd}

Available data on the dependence of the equilibrium chemical potential of oxygen on degrees of doping, z, and non-stoichiometry, x, y, in U1-zLnzO2+0.5(x-y) fluorite solid solutions and data on the dependence of the lattice parameter, a, on the same variables are combined within a unified structural-thermodynamic model. The thermodynamic model fits experimental isotherms of the oxygen potential under the assumptions of a non-ideal mixing of the endmembers, UO2, UO2.5, UO1.5, LnO1.5, and Ln0.5U0.5O2, and of a significant reduction in the configurational entropy arising from short-range ordering (SRO) within cation-anion distributions. The structural model further investigates the SRO in terms of constraints on admissible values of cation coordination numbers and, building on these constraints, fits the lattice parameter as a function of z, y, and x. Linking together the thermodynamic and structural models allows predicting the lattice parameter as a function of z, T and the oxygen partial pressure. The model elucidates contrasting structural and thermodynamic changes due to the doping with LaO1.5, on the one hand, and with NdO1.5 and GdO1.5, on the other hand. An increased oxidation resistance in the case of Gd and Nd is attributed to strain effects caused by the lattice contraction due to the doping and to an increased thermodynamic cost of a further contraction required by the oxidation.

The complex build algorithm to set up starting structures of lanthanoid complexes with stereochemical control for molecular modeling

When handling metallic centers of higher coordination numbers, one is commonly deluded with the presumption that any assembled metal complex geometry (including a crystallographic one) is good enough as a starting structure for computational chemistry calculations; all oblivious to the fact that such a structure is nothing short of just one out of several, sometimes dozens, or even thousands of other stereoisomers. Moreover, coordination chirality, so frequently present in complexes of higher coordination numbers, is another often overlooked property, rarely recognized as such. The Complex Build algorithm advanced in this article has been designed with the purpose of generating starting structures for molecular modeling calculations with full stereochemical control, including stereoisomer complete identification and coordination chirality recognition. Besides being in the chosen correct stereochemistry, the ligands are positioned by the Complex Build algorithm in a very unobstructed and unclogged manner, so that their degrees of freedom do not hinder or even choke one another, something that would otherwise tend to lead to negative force constants after further geometry optimizations by more advanced computational model chemistries. The Complex Build algorithm has been conceived for any metallic center, but at present is targeting primarily lanthanoids whose coordination numbers range mostly from 5 to 12 and often lead to a combinatorial explosion of stereoisomers.

Atomistic Nodal Approach: a General Molecular Dynamics Method For Computing Local Thermal Conductance at Atomistic Resolution

The Interfacial Thermal Conductance (ITC) is a fundamental property of materials and has particular relevance at the nanoscale. The ITC quantifies the thermal resistance between materials of different compositions or between fluids in contact with materials. Furthermore, the ITC determines the rate of cooling/heating of the materials and the temperature drop across the interface. Here we propose a method to compute local ITCs and temperature drops of nanoparticle-fluid interfaces. Our approach resolves the ITC at the atomic level using the atomic coordinates of the nanomaterial as nodes to compute local thermal transport properties. We obtain high-resolution descriptions of the interfacial thermal transport by combining the atomistic nodal approach, computational geometry techniques and "computational farming'' using Non-Equilibrium Molecular Dynamics simulations. We illustrate our method by analyzing various nanoparticles as a function of their size and geometry, targeting experimentally relevant structures like capped octagonal rods, cuboctahedrons, decahedrons, rhombic dodecahedrons, cubes, icosahedrons, truncated octahedrons, octahedrons and spheres. We show that the ITC of these very different geometries can be accurately described in terms of the local coordination number of the atoms in the nanoparticle surface. Nanoparticle geometries with lower surface coordination numbers feature higher ITCs, and the ITC generally increases with decreasing particle size.