Different Types Of Atoms Research Articles

The influence of continuum lowering on the equilibrium composition of plasma: case study of laser-induced plasma on a WC–Cu target

Abstract Composition of plasma obtained by laser ablation of cermet WC-Cu is calculated under the assumption of local thermodynamical equilibrium. Special attention is paid to the effect of lowering the ionization potential, the influence of which in the Saha equations is reflected both through the exponential Boltzmann term and through the partition functions of atomic and ionic species present in the plasma. The effect on these two terms are separated and quantified. It has been shown that the correction of the partition functions due to the reduction of the ionization potential is of greater importance than the correction of Boltzmann term, at higher temperatures for atomic species. It is thus necessary for the precise determination of the plasma composition, especially at higher temperatures. The effect of lowering the ionization potential on different types of atoms as well as on different ionic states is analyzed. Тhe change of the partition function due to the lowering of ionization energy affects also the concentration of single charged ions in an amount comparable to the correction of the Boltzmann term.

Magnetic resonance frequency shifts in quantum magnetometers based on the phenomenon of the optical orientation of atoms

Subject of study. Magnetic resonance frequency shifts caused by spin exchange collisions involving optically oriented alkali atoms in the ground state are studied. Aim of study. Spin-exchange collisions involving optically oriented pairs of alkali atoms of different types are theoretically studied to determine the shifts in the magnetic resonance line frequencies as a function of temperature for various pairs of alkali atoms under various optical orientation conditions in order to determine optimum constraints for the construction of quantum magnetometers with optical pumping using mixtures of alkali atoms. Method. Collisions between optically oriented alkali atoms are analyzed within the framework of quantum scattering theory, and data on the interaction potentials of alkali-atom dimers are used to calculate the scattering phases for collisions involving these potentials and the imaginary parts of the complex spin-exchange cross-section. The resulting cross sections as a function of energy were used to determine the magnetic resonance frequency shifts as a function of temperature. Main results. The magnetic resonance line frequency shifts for the following pairs of alkali atoms were obtained as a function of temperature: 39K−133Cs, 39K−85Rb, and 133Cs−85Rb. The shift in the magnetic resonance line involving the F=1 hyperfine state for a 39K−85Rb alkali-atom pair was found to pass through zero near temperature 480 K. Practical significance. The results obtained in this paper can be used to develop zero-spin-exchange-shift quantum electronic devices based on the optical orientation of atoms. In particular, it is possible to develop co-magnetometers based on the optical orientation of alkali atoms.

Composition templating for heterogeneous nucleation of intermetallic compounds.

Refinement of intermetallic compounds (IMCs) through enhancing heterogeneous nucleation during casting process is an important approach to improve the properties of aluminium alloys, which greatly increases the economy value of recycled Al-alloys. However, heterogeneous nucleation of IMCs is inherently more difficult than that of a pure metal or a solid solution. It requires not only creation of a crystal structure but also the positioning of 2 or more different types of atoms in the lattice with specific composition close to that of the nucleated IMCs. Previous understanding on heterogeneous nucleation is based on structural templating, usually considering the small lattice misfit at the interface between the nucleating solid and substrate. In this work, we proposed a hypothesis and demonstrated that composition templating plays a critical role in heterogeneous nucleation of IMCs. The experimental results revealed that segregation of Fe atoms on the AlB2 surface, i.e., the Fe modified AlB2 particle, provides the required composition templating and hence enhances heterogeneous nucleation of α-Al15(Fe, Mn)3Si2, resulting in a significant refinement of the α-Al15(Fe, Mn)3Si2 particles in an Al-5Mg-2Si-1.0Mn-1.2Fe alloy.

Síntesis y análisis estructural de la ludwigita Co<sub>2</sub>InBO<sub>5</sub>

We report the study on the synthesis of indium-doped cobalt ludwigite (Co2InBO5). The Match program, applied to the XRD powder standard, determined that the compound consists of two phases: phase I, ludwigite Co2InBO5 and phase II, the indium oxide In2O3 impurity. The Rietveld refinement applied to the XRD powder standard determined that the percentage by weight of phase I was 98.18% and the percentage by weight of phase II was 1.82%. The atomic ratio between Co and in corresponded to Co/In=2/1, for a goodness of fit s = 1.64 and Chi2 ≅ 2.70. With these adjustment values, the chemical content of the experimental compound was adjusted to its theoretical chemical stoichiometric content. In the unit cell, the different types of atoms occupied the Wyckoff positions 2b, 2c, 4g, and 4h. Atoms of the same or different types can occupy the same Wyckoff position, but a group of them occupy equivalent positions forming a crystallographic orbit. The refined position of each of the different types of atoms, in the unit cell, showed that the B ions, Co and In, form bonds with oxygen generating structural units in the form of triangles (BO3) and in the form of octahedrons (CoO6, InO6). These structural units form low- dimensional substructures such as three-legged ladders 313, 424 and zigzag-shaped planes. Both, the three-legged ladders and the zigzag-shaped planes are formed by the (Co/In)O6 octahedrons, and extend along the crystallographic axis c. These substructures are responsible for their extraordinary physical properties.

Fast and Accurate Prediction of Refractive Index of Organic Liquids with Graph Machines.

The refractive index (RI) of liquids is a key physical property of molecular compounds and materials. In addition to its ubiquitous role in physics, it is also exploited to impart specific optical properties (transparency, opacity, and gloss) to materials and various end-use products. Since few methods exist to accurately estimate this property, we have designed a graph machine model (GMM) capable of predicting the RI of liquid organic compounds containing up to 16 different types of atoms and effective in discriminating between stereoisomers. Using 8267 carefully checked RI values from the literature and the corresponding 2D organic structures, the GMM provides a training root mean square relative error of less than 0.5%, i.e., an RMSE of 0.004 for the estimation of the refractive index of the 8267 compounds. The GMM predictive ability is also compared to that obtained by several fragment-based approaches. Finally, a Docker-based tool is proposed to predict the RI of organic compounds solely from their SMILES code. The GMM developed is easy to apply, as shown by the video tutorials provided on YouTube.

Data scheme and data format for transferable force fields for molecular simulation

A generalized data scheme for transferable classical force fields used in molecular simulations, i.e. molecular dynamics and Monte Carlo simulation, is presented. The data scheme is implemented in an SQL-based data format. The data scheme and data format is machine readable, re-usable, and interoperable. A transferable force field is a chemical construction plan specifying intermolecular and intramolecular interactions between different types of atoms or different chemical groups and can be used for building a model for a given component. The data scheme proposed in this work (named TUK-FFDat) formalizes digitally these chemical construction plans, i.e. transferable force fields. It can be applied to all-atom as well as united-atom transferable force fields. The general applicability of the data scheme is demonstrated for different types of force fields (TraPPE, OPLS-AA, and Potoff). Furthermore, conversion tools for translating the data scheme between .xls spread sheet format and the SQL-based data format are provided. The data format can readily be integrated in existing workflows, simulation engines, and force field databases as well as for linking such.

PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features.

Protein-DNA interactions play an important role in various biological processes such as gene expression, replication, and transcription. Understanding the important features that dictate the binding affinity of protein-DNA complexes and predicting their affinities is important for elucidating their recognition mechanisms. In this work, we have collected the experimental binding free energy (ΔG) for a set of 391 Protein-DNA complexes and derived several structure-based features such as interaction energy, contact potentials, volume and surface area of binding site residues, base step parameters of the DNA and contacts between different types of atoms. Our analysis on relationship between binding affinity and structural features revealed that the important factors mainly depend on the number of DNA strands as well as functional and structural classes of proteins. Specifically, binding site properties such as number of atom contacts between the DNA and protein, volume of protein binding sites and interaction-based features such as interaction energies and contact potentials are important to understand the binding affinity. Further, we developed multiple regression equations for predicting the binding affinity of protein-DNA complexes belonging to different structural and functional classes. Our method showed an average correlation and mean absolute error of 0.78 and 0.98kcal/mol, respectively, between the experimental and predicted binding affinities on a jack-knife test. We have developed a webserver, PDA-PreD (Protein-DNA Binding affinity predictor), for predicting the affinity of protein-DNA complexes and it is freely available at https://web.iitm.ac.in/bioinfo2/pdapred/.

Cluster classification by chemi-topology

The atomic-scale structure of disordered materials like liquids and glasses has often been studied using the Voronoi tessellation method. This method divides the space containing atoms into regions called Voronoi polyhedra. Each polyhedron contains one atom. When two polyhedra share a common face, the atoms in the polyhedra are regarded as being connected by a bond. The bonded atoms and their common neighbors form a bipyramidal cluster, whose topology is related to the shape of the common face: when a common face is an n-gon, the cluster is an n-gonal bipyramid. Therefore, the topological order of a material can be characterized by the frequency distribution of the shapes of common faces. However, this approach does not tell anything about the chemical order: how different types of atoms are arranged in the clusters. In this paper, we propose a method for classifying bipyramidal clusters by chemi-topology, namely according to not only the topology of the cluster but also the chemical arrangement in the cluster. Since a bipyramidal cluster is made up of edge-sharing tetrahedral clusters, we also propose a method for classifying tetrahedral clusters. The function to characterize the atomic structures of materials using the proposed methods has been added to the Vorotis software. We describe the usage of this function. As a demonstration, we apply the proposed methods to characterize the structural difference between the liquid and glass states of a model alloy.

Topologically protected vortex knots and links

In 1869, Lord Kelvin found that the way vortices are knotted and linked in an ideal fluid remains unchanged in evolution, and consequently hypothesized atoms to be knotted vortices in a ubiquitous ether, different knotting types corresponding to different types of atoms. Even though Kelvin’s atomic theory turned out incorrect, it inspired several important developments, such as the mathematical theory of knots and the investigation of knotted structures that naturally arise in physics. However, in previous studies, knotted and linked structures have been found to untie via local cut-and-paste events referred to as reconnections. Here, in contrast, we construct knots and links of non-Abelian vortices that are topologically protected in the sense that they cannot be dissolved employing local reconnections and strand crossings. Importantly, the topologically protected links are supported by a variety of physical systems such as dilute Bose-Einstein condensates and liquid crystals. We also propose a classification scheme for topological vortex links, in which two structures are considered equivalent if they differ from each other by a sequence of topologically allowed reconnections and strand crossings, in addition to the typical continuous transformations. Interestingly, this scheme produces a remarkably simple classification.

Novel magnetic covalent organic framework for the selective and effective removal of hazardous metal Pb(II) from solution: Synthesis and adsorption characteristics

The removal of lead(II) from wastewater is conducive to reduce the risk of harmful metals to the environment and people. A magnetic organic framework adsorbent (Ni0.6Fe2.4O4-HT-COF) with high selectivity, repeatability and facile solid–liquid separation was devised through the condensation process of hexachlorocyclo-triphosphazene, trithiocyanuric acid and Ni0.6Fe2.4O4. At 298 K and pH = 5.0, the adsorbent showed remarkable Pb(II) adsorption capability. The removal rate reached 95.64 % at 10 min and the maximum adsorption capacity was 411.80 mg/g. The partition coefficient (KQ) of Pb(II) among the seven coexisting ions was 34079 mL/g and the adsorption efficiency was 97.15 %, indicating that the adsorbent can accurately capture Pb(II) from wastewater containing multiple ions. The PSO kinetic and the Langmuir isotherm models agreed with Pb(II) adsorption on Ni0.6Fe2.4O4-HT-COF, demonstrating that Pb(II) was removed by monolayer chemisorption. The adsorption was a spontaneous exothermic process using thermodynamic analysis. In addition, the adsorbent was able to maintain 91.77 % adsorption efficiency for Pb(II) after four adsorption–desorption cycles. Zeta potential and XPS analysis revealed that Ni0.6Fe2.4O4-HT-COF removed Pb(II) by electrostatic attraction and chelation. Density functional theory (DFT) was utilized to simulate the adsorption process, and the effects of different types of atoms on adsorption were discussed. The results demonstrate that the chelation and electrostatic interaction between Ni0.6Fe2.4O4-HT-COF and lead(II) ions were involved in the selective adsorption. The novel magnetic COF has a lot of potential for removing heavy metal Pb(II).

Photocatalytic applicability of HfN and tuning it with Mg and Sc alloys: A DFT and molecular dynamic survey

In this work, we study the phononic, electronic, elastic, thermodynamic, and optical properties of two rock salt (B1) and zinc blend (B3) structures of Hafnium Nitride (HfN) and its alloys with Mg and Sc. Our calculations were performed using the density functional theory (DFT) method and molecular dynamic (MD) simulations. Raman and IR active modes for the B1 structure show high quality for hard materials and the powerful bond between different types of atoms. Electronic band structures exhibit semiconductor behavior for Mg alloys that could be appropriate for semiconductor devices. Moreover, the spatial dependence of elastic modulus for B3 structure represents lower anisotropy than rock salt. The absorption spectrum of the MD method for B1 at 100 K shows a higher amount of absorption than the lower temperatures but, after that, it tends to constant values for 300, 400, 500, and 600 K.

Investigation of droplet size distribution for vibrating mesh atomizers

Vibrating mesh atomizers (VMA) are increasing in demand for various aerosol applications due to their ability to generate uniform droplets. Currently there are two types of VMA (metallic membranes and silicon membranes). High uniformity and control of small droplet sizes are the basic requirements for many aerosol applications, for which ultrasonic or VMA are employed. However, there is limited research on understanding the droplet size distribution of different types of atomizers. In this study three ultrasonic aerosol generators were investigated initially: traditional high frequency ultrasonic, metallic VMA, and Si-based microelectromechanical systems VMA. The initial objective was to compare these devices with respect to droplet size distribution and mechanism of action. The primary goal was to systematically study and compare the performance of the two types of VMA based on droplet distribution and volumetric median diameter (VMD) using liquids with different physiochemical properties. Size distribution of the droplet produced from the metallic VMA had twice the span compared to silicon VMA for fluids with viscosity <2 cP. The metallic VMA also resulted in an increase in VMD as the viscosity increased, whereas the Si VMA did not illustrate any significant increase in VMD. The silicon-based VMA demonstrated a 4–15x increase in fine particle fraction control compared to metallic VMA. The results demonstrate that silicon based VMA has narrower droplet distribution with more uniform droplet size and lower span compared to metallic VMA.

The Role of Water Network Chemistry in Proteins: A Structural Bioinformatics Perspective in Drug Discovery and Development.

Although water is regarded as a simple molecule, its ability to create hydrogen bonds makes it a highly complex molecule that is crucial to molecular biology. Water molecules are extremely small and are made up of two different types of atoms, each of which plays a particular role in biological processes. Despite substantial research, understanding the hydration chemistry of protein-ligand complexes remains difficult. Researchers are working on harnessing water molecules to solve unsolved challenges due to the development of computer technologies. The goal of this review is to highlight the relevance of water molecules in protein environments, as well as to demonstrate how the lack of well-resolved crystal structures of proteins functions as a bottleneck in developing molecules that target critical therapeutic targets. In addition, the purpose of this article is to provide a common platform for researchers to consider numerous aspects connected to water molecules. Considering structure-based drug design, this review will make readers aware of the different aspects related to water molecules. It will provide an amalgamation of information related to the protein environment, linking the thermodynamic fingerprints of water with key therapeutic targets. It also demonstrates that a large number of computational tools are available to study the water network chemistry with the surrounding protein environment. It also emphasizes the need for computational methods in addressing gaps left by a poorly resolved crystallized protein structure.

Vorotis: Software for Voronoi tessellation analysis using the polyhedron code

The atoms in disordered structures such as liquids and glasses are arranged in a complex manner. Their atomic arrangements are often studied using the Voronoi tessellation method. This method divides the space containing atoms into regions called Voronoi polyhedra. Each polyhedron contains one atom. From the structure of the polyhedron, we can know the bond network, or the topology, of the cluster composed of the atoms in the polyhedron and its neighbors. Thus, the topological order can be characterized by identifying the most dominant Voronoi polyhedron. For this purpose, the Voronoi index has long been used. However, it does not specify how polygons are arranged in the polyhedron. Therefore, polyhedra with different graph structures can have the same index. This problem can lead to failure in characterizing the topological order. In addition, the Voronoi index does not tell anything about the chemical arrangement, namely how different types of atoms are arranged in the cluster. To characterize both of the topological and chemical order of disordered structures, a polyhedron-naming system called the polyhedron code has been developed and implemented in the Vorotis software. This paper presents the essential features of Vorotis. Program summaryProgram Title: VorotisCPC Library link to program files:https://doi.org/10.17632/4548pnvzpy.1Developer's repository link:https://doi.org/10.5281/zenodo.6069726Code Ocean capsule:https://doi.org/10.24433/CO.1700993.v1Licensing provisions: MITProgramming language: Fortran, Csh, PythonNature of problem: Characterizing the arrangement of atoms in substances.Solution method: The polyhedron code is combined with the Voronoi tessellation method.Additional comments including restrictions and unusual features: The current version of Vorotis assumes that every vertex of a Voronoi polyhedron meets three faces.

Theoretical investigation of HER/OER/ORR catalytic activity of single atom-decorated graphyne by DFT and comparative DOS analyses

Multifunctional catalysts for hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and oxygen reduction reaction (ORR) play a key role in the development of electrochemical energy systems. Moreover, single metal atoms embedded in a two-dimensional material substrate have emerged as outstanding catalysts. Owing to its large surface area and novel electronic properties, graphyne (GY) is a promising substrate for the fabrication of single-atom catalysts (SACs). By doping foreign atoms of different types on GY, their local electronic/chemical environment and catalytic performance would be improved. Based on recent experimental advances, we used density functional theory (DFT) simulations to identify a suitable series of co-decorated GY materials, with boron-, nitrogen-, phosphorus-, and sulfur-doped GY (3B-GY, 3N-GY, 3P-GY, 3S-GY) as the two-dimensional substrate, and transition metal (TM) atoms (Sc to Zn) as the single-atom centers. Our screening process showed that Co@GY and Ni@3B-GY would be highly promising multifunctional electrocatalysts for HER/OER/ORR. Then, we built quantitative structure–property relationship (QSPR) between electronic density of states (DOS) structure and catalytic performance using comparative DOS analysis (CoDOSA). The present results further support the development and application of d-band center theory. Our work identifies promising co-decorated GY for HER/OER/ORR and provides quantitative information on their DOS.

Describe Molecules by a Heterogeneous Graph Neural Network with Transformer-like Attention for Supervised Property Predictions.

Machine learning and deep learning have facilitated various successful studies of molecular property predictions. The rapid development of natural language processing and graph neural network (GNN) further pushed the state-of-the-art prediction performance of molecular property to a new level. A geometric graph could describe a molecular structure with atoms as the nodes and bonds as the edges. Therefore, a graph neural network may be trained to better represent a molecular structure. The existing GNNs assumed homogeneous types of atoms and bonds, which may miss important information between different types of atoms or bonds. This study represented a molecule using a heterogeneous graph neural network (MolHGT), in which there were different types of nodes and different types of edges. A transformer reading function of virtual nodes was proposed to aggregate all the nodes, and a molecule graph may be represented from the hidden states of the virtual nodes. This proof-of-principle study demonstrated that the proposed MolHGT network improved the existing studies of molecular property predictions. The source code and the training/validation/test splitting details are available at https://github.com/zhangruochi/Mol-HGT.

Self-diffusion in melts of Ni-Al and Ti-Al systems: molecular dynamics study

Self-diffusion in liquid alloys of Ni-Al and Ti-Al systems, depending on the concentration of the components, as well as in pure metals Al, Ni, and Ti, has been studied using the molecular dynamics method. Knowledge of the diffusion parameters in melts of binary systems, such as Ni-Al and Ti-Al, is necessary for a more detailed understanding and prediction of the processes occurring during combustion synthesis and in the manufacture of wares by melt casting. For the considered systems, self-diffusion characteristics (activation energy and preexponential factor in the corresponding Arrhenius equation) are found separately for atoms of different types. Good agreement of diffusion characteristics with experimental data was obtained for pure metal melts, which indicates the physical realism of the molecular dynamics model and EAM potentials used. In addition to pure metals, three component ratios were considered for each system: A75B25, A50B50 and A25B75. According to the data obtained, the activation energy of diffusion substantially depends on the concentration of the components, and the highest, among the considered compositions, for Ni75Al25 for Ni-Al system and for Ti50Al50 for Ti-Al system. At the same time, no significant predominance of diffusion mobility of atoms of different types was observed for all the mixture compositions considered.

Electronegativity under Confinement.

The electronegativity concept was first formulated by Pauling in the first half of the 20th century to explain quantitatively the properties of chemical bonds between different types of atoms. Today, it is widely known that, in high-pressure regimes, the reactivity properties of atoms can change, and, thus, the bond patterns in molecules and solids are affected. In this work, we studied the effects of high pressure modeled by a confining potential on different definitions of electronegativity and, additionally, tested the accuracy of first-order perturbation theory in the context of density functional theory for confined atoms of the second row at the Hartree–Fock level. As expected, the electronegativity of atoms at high confinement is very different than that of their free counterparts since it depends on the electronic configuration of the atom, and, thus, its periodicity is modified at higher pressures.

A dictionary learning method with atom splitting for seismic footprint suppression

The acquisition footprint causes serious interference with seismic attribute analysis, which severely hinders accurate reservoir characterization. Therefore, acquisition footprint suppression has become increasingly important in industry and academia. We have assumed that the time slice of 3D poststack migration seismic data mainly comprises two components: useful signals and the acquisition footprint. Useful signals describe the spatial distributions of geologic structures with local piecewise smooth morphological features. However, the acquisition footprint often behaves as periodic artifacts in the time-slice domain. In particular, the local morphological features of the acquisition footprint in marine seismic acquisition appear as stripes. Because useful signals and the acquisition footprint have different morphological features, we can train an adaptive dictionary and divide the atoms of the dictionary into two subdictionaries to reconstruct these two components. We have devised an adaptive dictionary learning method for acquisition footprint suppression in the time slice of 3D poststack migration seismic data. To obtain an adaptive dictionary, we use the K-singular value decomposition algorithm to sparsely represent the patches in the time slice of 3D poststack migration seismic data. Each atom of the trained dictionary represents certain local morphological features of the time slice. According to the difference in the variation level between the horizontal and vertical directions, the atoms of the trained dictionary are divided into two types. One type significantly represents the local morphological features of the acquisition footprint, whereas the other type represents the local morphological features of useful signals. Then, these two components are reconstructed using morphological component analysis based on different types of atoms, respectively. Synthetic and field data examples indicate that our method can effectively suppress the acquisition footprint with fidelity to the original data.

MOLECULAR DYNAMICS SIMULATIONS OF MECHANICAL PROPERTIES OF CuNi ALLOYS UNDER PRESSURE

Structural characterization of CuNi alloys during the cooling process at different pressures from 0 GPa to 45 GPa is performed by Molecular dynamics (MD) simulations. The interaction potential used in the calculations for interactions between different types of atoms in the sample is the Quantum Sutton-Chen embedded potential. The increase of the pressure increases crystal-forming temperature. Mechanical properties of these samples at 300 K and different pressures were determined by using the mechanical analysis. The Young modulus of these models increases with the increase of pressure. During the deformation process, the number of disorder atoms increases dramatically in the elastic region and unchanged in the plastic region. Sliding deformation is recognized at different pressure under tensile deformation.