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.

Carbon Nanoparticle Oxidation by NO2and O2: Chemical Kinetics andReaction Pathways.

Carbon nanoparticle interactions with gases are central to many environmental and technical processes, but the underlying reaction kinetics and mechanisms are not well understood. Here, we investigate the oxidation and gasification of carbon nanoparticles by NO2and O2under combustion exhaust conditions. We build on a comprehensive experimental data set and use a kinetic multilayer model (KM-GAP-CARBON) to trace the uptake and release of gas molecules alongside the temporal evolution of particle size and surface composition. The experimental results are captured by a model mechanism that involves different types of carbon atoms (edge/plane-like) and the formation of a reactive oxygen intermediate (activated CO complex) as the rate-limiting step. A transition between distinct chemical regimes driven by NO2at lower temperatures and O2at higher temperatures is reflected by an increase in the observable activation energy from ~60 kJ/mol to ~130 kJ/mol. We derive energy profiles for three alternative reaction pathways that involve uni- or bimolecular decomposition of reactive oxygen intermediates.

Carbon Nanoparticle Oxidation by NO2 and O2: Chemical Kinetics and Reaction Pathways

Carbon nanoparticle interactions with gases are central to many environmental and technical processes, but the underlying reaction kinetics and mechanisms are not well understood. Here, we investigate the oxidation and gasification of carbon nanoparticles by NO2 and O2 under combustion exhaust conditions. We build on a comprehensive experimental data set and use a kinetic multilayer model (KM‐GAP‐CARBON) to trace the uptake and release of gas molecules alongside the temporal evolution of particle size and surface composition. The experimental results are captured by a model mechanism that involves different types of carbon atoms (edge/plane‐like) and the formation of a reactive oxygen intermediate (activated CO complex) as the rate‐limiting step. A transition between distinct chemical regimes driven by NO2 at lower temperatures and O2 at higher temperatures is reflected by an increase in the observable activation energy from ~60 kJ/mol to ~130 kJ/mol. We derive energy profiles for three alternative reaction pathways that involve uni‐ or bimolecular decomposition of reactive oxygen intermediates.

The structure of tetranuclear zirconium pivalate ZR<sub>4</sub>O<sub>2</sub> [(CH<sub>3</sub>)<sub>3</sub>CCO<sub>2</sub>]<sub>12</sub> according to X-ray diffraction analysis and quantum chemical calculations

The crystal and molecular structure of a polynuclear pivalate complex obtained by the interaction of ZrCl4 with pivalic acid was determined by X-ray diffraction analysis. The compound C71H124O28Zr4 (compound 1) crystallizes in the monoclinic crystal system. The crystal structure was refined in the nonstandard space group I2. The asymmetric part of the structure includes three Zr atoms, six pivalate ligands, a bridging µ3-O oxygen atom, as well as disordered crystallization molecules of pivalic acid with an occupancy of 50% and benzene with an occupancy of 50%. The zirconium complex molecule is a tetranuclear cluster that contains three types of Zr atoms that differ in ligand environment. Comparison of the results of quantum chemical calculations of the model reaction ZrCl4 with acetic acid with the literature data on reactions of ZrCl4 with aliphatic acids have shown the possibility of the formation of both mononuclear Zr(RCO2)4 and polynuclear clusters in this reaction, which is a new route for obtaining polynuclear zirconium clusters. The structure of the clusters formed depends on the steric properties of carboxylate ligands.

High-throughput screening of the potential alloying elements for high-strength Mg alloys based on solid-solution strengthening

Abstract 49 types of alloy atomic dopants and their effects on the doping stability and micro-mechanical behaviour of magnesium matrix were investigated using density functional theory and high throughput first-principles calculations. Geometry optimization was performed for each doping system, and the ability of atom doping into the magnesium matrix was assessed based on the doping energy and atomic radius. Results show that the transition metal elements have negative or near negative doping energy, especially for the elements with a radius that similar to Mg. The micro-mechanical properties of the doping system were evaluated by computing the fracture energy and theoretical tensile stress. Through a screening on the doping stability and strengthening effect of 49 types of alloy atoms, a set of elements (Re, Os, Ir, Tc and W, etc.) are screened out that could strengthen the magnesium matrix with a good doping stability. The high throughput screen results serve as a theoretical guide for the selection of appropriate alloy elements for designing the high-strength magnesium alloys in the regime of solid solution strengthening effects.

Impact of piezoelectric driving waveform on performance characteristics of vibrating mesh atomizer (VMA)

Vibrating mesh atomizer (VMA) is a specific type of ultrasonic atomizer known for its low power consumption and production of uniformly fine droplets. While previous research has provided a basic understanding of VMA operation, it has primarily focused on driving the piezoelectric actuator with continuous and symmetrical waveforms, such as sine and square waveforms. This study aims to experimentally investigate the impact of different driving waveforms on the ultrasonic atomization process and the associated performance characteristics. Specifically, the effects of pulse waveforms (Gauss and Lorentz pulse) were analyzed with high rates of energy deposition and asymmetrical hybrid waveforms (trapezia and absolute sine), featuring distinct negative cycles, by comparing them with conventional symmetrical waveforms (sine and square). Pulse waveforms suppress the growing stage but provide a high flux of input energy, facilitating the detachment of liquid into fine droplets, resulting in uniformly distributed droplets with VMDs of 5.84 μm and 4.71 μm for Gauss and Lorentz waveforms, respectively. Conversely, shorter negative cycles in asymmetrical hybrid waveforms reduce liquid suction into the micronozzle, leading to higher energy flux during subsequent positive cycles that promote the growing stage, producing larger droplets with VMDs of 10.82 μm and 11.86 μm for trapezia and absolute (abs) sine waveforms, respectively. Additionally, high-speed imaging reveals irregular pulsating behaviors in the atomization process when using pulse waveforms, suggesting a reciprocating-pump-like operation mechanism in VMA atomization. These new insights contribute to an improved understanding of the atomization mechanism in VMAs.

Determining the chemical ordering in nanoalloys by considering atomic coordination types.

The energetically most favorable chemical ordering of bimetallic nanoparticles can be characterized by combining global optimization algorithms and surrogate energy models. The latter approximate the energy of nanoalloys relying on structural descriptors, training models, and data. Here, we systematically evaluate the performance of highly data-efficient topological descriptors [Kozlov et al., Chem. Sci. 6, 3868 (2015)] for predicting the energies of metal nanoalloys with different chemical orderings. We also introduce a new descriptor based on atomic coordination types, which results in a less data-efficient and interpretable approach, but improves the general accuracy and the quantification of orderings in the inner parts of nanoparticles. The capacity of both the original and new approaches in combination with a basin hopping algorithm is illustrated by generating convex hulls of PdZn nanoalloys and predicting the resulting active surface site distribution as a function of particle composition. Finally, we show how these approaches can be combined with machine-learning adsorption models in electrocatalysis studies for a fast evaluation of the reactivity landscape of targeted nanoalloys.

Synthesis and structural study of the partially disordered complex hexagonal phase δ1-MnZn9.7.

A detailed structural analysis of the Zn-rich δ1-MnZn9.7 phase using single-crystal X-ray diffraction is presented. The δ1 phase has been synthesized by the high-temperature synthetic route. The structure crystallizes in space group P63/mmc (Pearson symbol hP556) with unit-cell parameters: a = b = 12.9051 (2) Å and c = 57.640 (1) Å. The 556 atoms are distributed over 52 Wyckoff positions in the hexagonal unit cell: seven ordered Mn sites, 37 ordered Zn sites and eight positionally disordered Zn sites. The structure predominantly consists of Frank-Kasper polyhedra (endohedral icosahedra Zn12 and icosioctahedron Zn16) and four distinct types of glue Zn atoms. The structure comprises a 127-atom supercluster (Mn13Zn114), a 38-atom extended Pearce cluster (Mn3Zn35), a 46-atom L-tetrahedron (Mn4Zn42), a Friauf polyhedron (Zn17), a disordered icosahedral cluster (MnZn12) and four glue Zn atoms. Positionally disordered Zn sites around an Mn site can be visualized as the superimposition of three differently oriented Zn12 icosahedra.

Empi: GPU-Accelerated Matching Pursuit with Continuous Dictionaries

This article introduces an effective approach to performing matching pursuit calculations with continuous (quasi-infinite) dictionaries. Simulating continuous parameter space is accomplished by combining optimal dictionary construction as introduced previously with local parameter optimization. At the same time, the calculations can be performed not only with Gabor atoms, but with any type of oscillating atoms, or even multiple types of atoms at once. The proposed method is confirmed by introducing a first stable version of a high-performance implementation empi , supporting calculations with continuous as well as discrete dictionaries, further accelerated by the use of multi-threading and GPU support.

Genetic algorithm optimization of langevin thermostat and thermal properties of graphene-aluminum nanocomposites: a molecular dynamics

Abstract The thermal properties of a laminated structure of graphene-coated aluminum composite nanomaterial were investigated through non-equilibrium molecular dynamics (NEMD) simulations to address the problem of temperature deviation in the thermostat volume applied. This paper presents a new insight into the best values of timestep and Langevin thermostat damping parameters for each atom in the nanomaterial with different size configurations using the genetic algorithm (GA) method by considering the timestep and thermostat damping parameters for each atom type, as well as the thickness of the nanomaterial, the thermostat, buffer, and heat flow lengths. The initial population results indicate that the thermostat temperature deviation increases with higher thermostat damping coefficients and timestep. However, the deviation decreases significantly with increased heat flow and thermostat lengths. Variations in buffer length and aluminum thickness do not have a significant effect on temperature. The application of a GA for optimization leads to a decrease in thermostat temperature deviation. The optimized parameters resulted in better thermostat temperature deviations when analyzing the temperature, aluminum thickness, and both buffer and thermostat lengths. Additionally, the thermal conductivity of aluminum-graphene nanomaterial decreases with increasing temperature, buffer length, and aluminum thickness, but increases by up to 9.85% with increasing thermostat length.

Predicting the properties of bitumen using machine learning models trained with force field atom types and molecular dynamics simulations

This study enhances the molecular analysis of bitumen by transitioning from traditional chemical descriptors, such as SARA (Saturates, Aromatics, Resins, and Asphaltenes) fractions and elemental compositions, to specific force field atom types in Molecular Dynamics (MD) models. This shift improves the precision in predicting material properties critical for bituminous material characterization. Machine Learning Models (MLMs) were developed to use these atom types as input features, inherently reflecting fundamental chemical characteristics. Trained on data from over 1,770 LAMMPS simulations of diverse bitumen types and conditions, these MLMs enable the prediction of properties like density, heat capacity, solubility parameters, and thermal expansion coefficients without the need for additional MD simulations. The models utilize 30 chemical descriptors corresponding to specific atom types in the PCFF force field, which collectively account for over 95% of the influence on these properties. By accurately predicting fundamental, thermodynamic, and kinetic properties, the use of MLMs and force field atom types allows researchers to efficiently tweak the chemical nature of organic molecules and mixtures to achieve desired properties. With near-instantaneous prediction times, these MLMs offer valuable insights for advancing bitumen research in the construction and petroleum industries, reducing the need for more intensive simulation techniques.

Isomerism in Molecular Metal Carbonyl Clusters

The present microreview focuses on different typologies of isomerism documented along the years for metal carbonyl clusters (MCCs), and outlines their analogies to other classes of ligand‐protected molecular clusters and nanoclusters. Isomerism in molecular MCCs is discussed within two main categories, that is, surface isomerism and core isomerism. The first Section presents some representative examples of surface isomerism involving inorganic (carbonyls and hydrides) and organic ligands, as well as isomerism due to ML fragments decorating the cluster surface. The second Section focuses on three major categories of core isomerism, that is: (1) isomers that mainly differ on M‐M distances; (2) isomers displaying different structures of the metal kernels; (3) isomers possessing almost identical metal kernels and ligand shells, but differing for the positions of different types of metal atoms within the metal kernel. The third Section briefly discusses two related and very rare cases of isomerism, that is, polymerisation and coordination isomerism. General conclusions are outlined in the final Section.

A Well-Defined Magnesium Complex of C70 6.

Controlling and understanding charge state and metal coordination in carbon nanomaterials is crucial to harnessing their unique properties. Here we describe the synthesis of the well-defined fulleride complex [{(Mesnacnac)Mg}6C70], 2, (Mesnacnac)=HC(MeCNMes)2, Mes=2,4,6-Me3C6H2, from the reaction of the β-diketiminate magnesium(I) complex [{(Mesnacnac)Mg}2] with C70 in aromatic solvents. The molecular structure of complex 2 was determined, providing the first high-quality structural study of a complex with the C70 6- ion. In combination with solution state NMR spectroscopic and DFT computational studies, the changes in geometry and charge distribution in the various atom and bond types of the fulleride unit were investigated. Additionally, the influence of the (Mesnacnac)Mg+ cations on the global and local fulleride coordination environment was examined.

Learning Joint 2-D and 3-D Graph Diffusion Models for Complete Molecule Generation.

Designing new molecules is essential for drug discovery and material science. Recently, deep generative models that aim to model molecule distribution have made promising progress in narrowing down the chemical research space and generating high-fidelity molecules. However, current generative models only focus on modeling 2-D bonding graphs or 3-D geometries, which are two complementary descriptors for molecules. The lack of ability to jointly model them limits the improvement of generation quality and further downstream applications. In this article, we propose a joint 2-D and 3-D graph diffusion model (JODO) that generates geometric graphs representing complete molecules with atom types, formal charges, bond information, and 3-D coordinates. To capture the correlation between 2-D molecular graphs and 3-D geometries in the diffusion process, we develop a diffusion graph transformer (DGT) to parameterize the data prediction model that recovers the original data from noisy data. The DGT uses a relational attention mechanism that enhances the interaction between node and edge representations. This mechanism operates concurrently with the propagation and update of scalar attributes and geometric vectors. Our model can also be extended for inverse molecular design targeting single or multiple quantum properties. In our comprehensive evaluation pipeline for unconditional joint generation, the experimental results show that JODO remarkably outperforms the baselines on the QM9 and GEOM-Drugs datasets. Furthermore, our model excels in few-step fast sampling, as well as in inverse molecule design and molecular graph generation. Our code is provided in https://github.com/GRAPH-0/JODO.

Enhancing Oxygen Reduction Reaction of Single-Atom Catalysts by Structure Tuning.

Deciphering the fine structure has always been a crucial approach to unlocking the distinct advantages of high activity, selectivity, and stability in single-atom catalysts (SACs). However, the complex system and unclear catalytic mechanism have obscured the significance of exploring the fine structure. Therefore, we endeavored to develop a three-component strategy to enhance oxygen reduction reaction (ORR), delving deep into the profound implications of the fine structure, focusing on central atoms, coordinating atoms, and environmental atoms. Firstly, the mechanism by which the chemical state and element type of central atoms influence catalytic performance is discussed. Secondly, the significance of coordinating atoms in SACs is analyzed, considering both the number and type. Lastly, the impact of environmental atoms in SACs is reviewed, encompassing existence state and atomic structure. Thorough analysis and summarization of how the fine structure of SACs influences the ORR have the potential to offer valuable insights for the accurate design and construction of SACs.

Exploring Molecular Heteroencoders with Latent Space Arithmetic: Atomic Descriptors and Molecular Operators.

A variational heteroencoder based on recurrent neural networks, trained with SMILES linear notations of molecular structures, was used to derive the following atomic descriptors: delta latent space vectors (DLSVs) obtained from the original SMILES of the whole molecule and the SMILES of the same molecule with the target atom replaced. Different replacements were explored, namely, changing the atomic element, replacement with a character of the model vocabulary not used in the training set, or the removal of the target atom from the SMILES. Unsupervised mapping of the DLSV descriptors with t-distributed stochastic neighbor embedding (t-SNE) revealed a remarkable clustering according to the atomic element, hybridization, atomic type, and aromaticity. Atomic DLSV descriptors were used to train machine learning (ML) models to predict 19F NMR chemical shifts. An R2 of up to 0.89 and mean absolute errors of up to 5.5 ppm were obtained for an independent test set of 1046 molecules with random forests or a gradient-boosting regressor. Intermediate representations from a Transformer model yielded comparable results. Furthermore, DLSVs were applied as molecular operators in the latent space: the DLSV of a halogenation (H→F substitution) was summed to the LSVs of 4135 new molecules with no fluorine atom and decoded into SMILES, yielding 99% of valid SMILES, with 75% of the SMILES incorporating fluorine and 56% of the structures incorporating fluorine with no other structural change.

Molecular Simulations Study of the Ionic Adsorption on Oscillating Carbon Electrodes

Molecular dynamics simulations are a method of choice to study, at microscopic scales, the adsorption of electrolyte ions onto the electrodes of capacitive storage systems such as supercapacitors1.Here, we use all-atom molecular dynamics simulations to model a pure ionic liquid electrolyte ([EMIM][TFSI], 1-ethyl-3-methylimidazolium trifluoromethanesulfonylimide) in contact with graphite electrodes (Figure: Supercapacitor composed of graphite electrodes (in light blue) in contact with a pure ionic liquid electrolyte ([EMIM][TFSI], atoms colored according to the atom type. The arrows illustrate the oscillations of electrodes used to probe the influence of such a motion on the interfacial liquid.)When applying a potential difference between the two carbon electrodes, a migration of counter ions is observed, ultimately leading to a compensation of the charges carried by the electrode atoms. The adsorption dynamics depend on several parameters (electrostatic interactions, coordination number, viscosity of the electrolyte, size of the ions etc.) and influence the power of these capacitive systems2. In order to improve the performance of supercapacitors, it is then important to describe and understand interfacial properties at a molecular scale.Experimentally, an Electrochemical Quartz Crystal Microbalance (EQCM) can be used to study ion flow on top of the electrode. The frequency variations recorded by the EQCM are linked to variations in the mass adsorbed on the quartz surface, thus making it possible to study the dynamics of ion adsorption during the charge of a supercapacitor.3 However, variations in quartz frequency can influence the stability of the electrode-electrolyte interface, thus modifying ionic interactions and adsorption dynamics at the electrode surface4.Here, thanks to molecular simulations, we have access to the adsorption rate of ions, to their re-orientation, to the variation of charge of the electrode atoms during adsorption etc. This allows us to study in detail the mechanisms governing the electrolyte-electrode interface during the charging of supercapacitors as part of EQCM. And study the extent to which perturbations caused by quartz oscillations in the ionic liquid affect electrode-electrolyte interfacial properties. References 1 K. Xu, H. Shao, Z. Lin, C. Merlet, G. Feng, et al.. ENERGY & ENVIRONMENTAL MATERIALS, 3, 235-246 (2020) 2C. Péan, B. Rotenberg, P. Simon, M. Salanne, ELECTROCHIM. ACTA, 206, 504-512 (2016) 3Y-C. Wu, J. Ye, G. Jiang, K. Ni, N. Shu, P-L. Taberna, Y. Zhu, P. Simon, ANGEW. CHEM. INT. ED., 60, 13317-13322 (2021) 4Nikolai V. Priezjev, MICROFLUIDICS AND NANOFLUIDICS, 14, 225-233 (2013) Figure 1

A new perspective on ductile high-Tc superconductors under ambient pressure: few-hydrogen metal-bonded hydrides

Superconducting materials have garnered widespread attention due to their zero-resistance characteristic and complete diamagnetism. After more than 100 years of exploration, various high-temperature superconducting materials including cuprates, nickelates, iron-based compounds, and ultra-high pressure multi-hydrides have been discovered. However, the practical application of these materials is severely hindered by their poor ductility and/or the need for high-pressure conditions to maintain structural stability. To address these challenges, we first provide a new thought to build high-temperature superconducting materials based on few-hydrogen metal-bonded hydrides under ambient pressure. We then review the related research efforts in this article. Moreover, based on the bonding type of atoms, we classify the existing important superconducting materials and propose the new concepts of pseudo-metal and quasi-metal superconductivity, which are expected to be helpful for the design of new high-temperature superconducting materials in the future.

Dark Matter Distribution in Milky Way analog Galaxies

Our current understanding of how dark matter (DM) is distributed within the Milky Way (MW) halo, particularly in the solar neighborhood, is based on either careful studies of the local stellar orbits, model assumptions on the global shape of the MW halo, or from direct acceleration measurements. In this work, we undertake a study of external galaxies, with the intent of providing insight into the DM distribution in MW-analog galaxies. For this, we carefully select a sample of galaxies similar to the MW, based on maximum atomic hydrogen (H i) rotational velocity ( vmax,HI=200–280 km s−1) and morphological type (Sab–Sbc) criteria. With a need for deep, highly resolved H i, our resulting sample is composed of five galaxies from the VLA Imaging of Virgo in Atomic Gas (VIVA) survey and The H i Nearby Galaxy Survey (THINGS). To perform our baryonic analysis, we use deep mid-IR images at 3.6 and 4.5 μm from the Spitzer Survey of Stellar Structures in Galaxies (S4G). Based on the dynamical three-dimensional modeling software 3D-Based Analysis of Rotating Objects via Line Observations (3D-BAROLO), we construct rotation curves (RCs) and derive the gas and stellar contributions from the galaxy's gaseous and stellar disks' mass surface density profiles. Through a careful decomposition of their RCs into their baryonic (stars, gas) and DM components, we isolate the DM contribution by using a Markov Chain Monte Carlo-based approach. Based on the Sun's location and the MW’s R 25, we define the corresponding location of the solar neighborhood in these systems. We put forward a window for the DM density (ρ dm = 0.21–0.55 GeV cm−3) at these galactocentric distances in our MW analog sample, consistent with the values found for the MW’s local DM density, based on more traditional approaches found in the literature.

Production of Cu/Zn Nanoparticles by Pulsed Laser Ablation in Liquids and Sintered Cu/Zn Alloy

As a preliminary step to establish technology for fabricating High-Entropy Alloys (HEAs) that can make a large-scale HEA using a pulse laser with high peak intensity and high-repetition in the future, we fabricated alloys in which two types of metal atom are mixed close together in the order of nanometers. For the method to produce the alloy, metal alloy nanoparticles were prepared by irradiating the material in liquid with focused high-repetition Q-switched laser pulses using an in-liquid laser ablation method. When brass powder was used as an original material, analysis results by TEM showed that numerous nanoparticles mixed with copper and zinc atoms could be produced. Furthermore, it was clarified by SEM EDS that copper and zinc atoms in the nanoalloy were maintained at a ratio of 3:1 in sintered alloy, and that the atoms were spatially uniformly distributed over a wide range in sintered metal.