Doklady Physical Chemistry最新文献

Laser electrodispersion has been used as an alternative to the chemical synthesis of palladium-containing catalysts. The thus produced catalysts supported on alumina and HZSM-5 zeolite have high catalytic activity and stability at ultralow palladium content (0.03 wt %) in a model reaction of CO oxidation under conditions of prompt thermal aging. According to X-ray photoelectron spectroscopy and transmission electron microscopy data, palladium in the catalyst samples predominantly occurs in the Pd⁰ state as fine particles about 2.0 nm in size, which almost completely cover the support surface. The textural characteristics of both supports are retained after the deposition of palladium. The modification of zeolite with palladium increases the adsorption capacity for hydrocarbons, which gives rise to a sorption effect in the temperature dependences of the CO conversion. The palladium-containing alumina-based catalyst demonstrated the best stability during heat treatment up to 1000°C.

The possibility of partial replacement of rhodium by iridium in palladium–rhodium bimetallic nanoparticles acting as active sites of three-way catalysts has been studied. Pd–Ir–Rh trimetallic alloy particles were obtained by the thermolysis of a solid solution of double complex salts containing palladium and the second metal (iridium or rhodium) under reducing conditions. It has been determined that all the three metals are uniformly distributed in the bulk of the catalyst in places of location of alloy nanoparticles. The trimetallic catalyst samples are not inferior in thermal stability to the bimetallic catalyst Pd–Rh/γ-Al₂O₃, provided that iridium is incorporated into the active cluster as Ir³⁺ ions. The partial replacement of rhodium by iridium reduces the cost of the new material.

The aldol condensation of acetone under supercritical conditions at 300–400°C and 11.0 MPa was carried out on BaSnO₃-450 and BaSnO₃-750 catalysts prepared by calcination of BaSn(OH)₆ at 450 and 750°C, respectively. Conducting the reaction under these conditions makes it possible to overcome the problem of catalyst deactivation by coking and to obtain valuable chemicals with high selectivity. At 300°C, both catalysts provide selectivity of 85–87% to isomeric mesityl oxides (C₆ products). At 400°C with the BaSnO₃-450 catalyst, the selectivity significantly changes from C₆ to C₉ (phorones). Powder X-ray diffraction and TEM data attest to the multiphase nature of the samples, which contain barium stannates, barium carbonate, and tin oxide. It was found that the catalyst structure rearranges in situ, which greatly affects the catalytic properties and is most pronounced for BaSnO₃-450.

Nickel-containing supported catalysts based on oxides La₂O₃ and La₂O₃–Mn₂O₃ (n_La/n_Mn = 1/1) synthesized by various methods have been studied in dry reforming of methane (DRM). The effect of the synthesis method on the phase composition and structure of the supports and the catalysts has been studied by low-temperature nitrogen adsorption, X-ray powder diffraction, transmission electron microscopy, and Raman spectroscopy. Deficient lanthanum manganite synthesized by the citrate method interacts with nickel ions to form LaNi_xMn_1–xO₃ in the surface layer. In the course of reduction of the oxidized precursor with hydrogen, particles of the active component are formed from NiO and LaNi_xMn_1 – xO₃, and the proportion between them depends on the ordering of the LaMnO_3 + δ support. The H₂ : CO ratio changes from 0.7 to 0.8 for the Ni/LaMnO₃ and Ni/La₂O₃ catalysts, respectively; however, for the perovskite sample, no carbon deposition is observed for 8.5 h.

The Pt–SnO_x, Pd–SnO_x, and Au–SnO_x composite catalysts were synthesized by pulsed laser ablation. The catalyst testing in the CO + O₂ reaction showed that the action of the reaction medium can induce both partial deactivation (Pt–SnO_x) and activation (Au–SnO_x) of the catalysts. The Pd–SnO_x catalyst has a high activity even in the initial state, and the effect of the reaction medium is slight. It was shown that gold and platinum mainly exist in the metallic state, while palladium exists as PdO nanoparticles. Electron transfer between the active component and support particles was detected for the Pt–SnO_x and Au–SnO_x catalysts. Electron donation effect from the support, enhanced by the action of the reaction medium, was found for Au–SnO_x. This effect was assumed to determine the low-temperature activity of the catalyst towards the CO oxidation.

Nanocomposite catalysts based on highly dispersed platinum and ceria particles supported on carbon nanotubes were studied. The composites were prepared using the complex (Мe₄N)₂[Pt₂(μ-OH)₂(NO₃)₈] as the platinum precursor. This approach ensured stabilization of platinum nanoparticles, clusters, and single atoms/ions on the surface of both ceria and the carbon nanomaterial. Study of the catalytic activity of the samples showed that highly dispersed metallic platinum species stabilized directly on the surface of carbon nanotubes can efficiently oxidize CO present in low concentrations in a reaction mixture at room temperature, in particular, in the presence of water vapor. However, low-temperature CO oxidation at higher CO concentrations requires formation of new active sites through interaction of platinum ions with ceria particles.

Redox properties of bis(1-naphtylimino)acenaphthene have been studied using various voltammetry techniques. Radical anion of diimine ligand has been obtained in situ through electrochemical reduction, and its optical properties have been studied. A new sodium complex having the anion-radical form of bis(1-naphthylimino)-acenaphthene has been obtained and structurally characterized. We have found that the solvent nature affects the long-wavelength absorption band of the sodium complex, which is determined by the contribution of the coordinated solvent molecules to the lowest unoccupied molecular orbital (LUMO) energy.

By the example of 14 compounds representing the main classes of explosives containing H, C, N, and O, it has been shown that the impact sensitivity correlates with the critical temperature T_cr of spontaneous ignition, which, in turn, is determined by the kinetic parameters and heat of the decomposition reaction. At the same time, the reaction rate is the dominant factor, and against its background the effect produced on the sensitivity by the thermophysical properties of the compounds and by the specific coefficient of friction becomes barely noticeable.

A polymer nanocomposite based on a symmetrical AB diblock copolymer filled with planar nanoparticles (NPs) has been studied by the dissipative particle dynamics method. The developed model predicts that NPs can reduce the threshold of thermodynamic incompatibility of blocks A and B, above which microphase separation of the polymer matrix occurs to give a lamellar phase. Depending on the features of the polymer/NP interaction, two types of stable orientations of the NP plane are formed: along and across the lamellar domains. This effect can be used to control the distribution of NPs in multiphase polymeric materials.

One of the most exciting tools that have entered the arsenal of modern science and technology in recent years is machine learning, which can efficiently solve problems of approximation of multidimensional functions. There is a rapid growth in the development and application of machine learning in physics and chemistry. This review is devoted to the possibilities of predicting interatomic interactions in multielement substances and high-entropy alloys using artificial intelligence based on neural networks and their active machine learning, which provides a comprehensive overview and analysis of recent research on this topic. The relevance of this direction is due to that the prediction of the structure and properties of materials by means of atomistic quantum mechanical modeling based on density functional theory (DFT) is difficult in many cases because of the rapid increase in computational costs with increasing size in accordance with the size of the object. Machine learning methods make it possible to reproduce real interparticle interaction potentials of the system using the available DFT calculations, and then, on their basis, to model the required properties by the molecular dynamics method on a multiply increased spatiotemporal scale. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. The design of the potential energy surface and interatomic interaction potentials in solid solutions, high-entropy alloys, high-entropy metal compounds with carbon, nitrogen, and oxygen, as well as in bulk amorphous materials, is described.