Recently, the environmental situation in the world has been deteriorating everywhere and there is a need to find new effective means of detecting harmful substances in the air. Every year, the content of carbon dioxide in the air is growing, which in the end can lead to a deterioration in the health of people. Various types of sensor devices are currently used to timely fix the increase in the gas level. As the active material of such a sensor, modern unique materials can be used – nanotubes, which, due to their sorption properties, are able to detect the presence of harmful impurities in the air space of the premises. It is also possible to use such sensors as detectors of some human diseases by analyzing exhaled air, which makes their use in medicine possible. The results of a theoretical study of the sorption interaction of modified boronitride nanotubes with molecules of carbon dioxide and acetone, obtained using the quantum-chemical DFT method, are presented, which prove the possibility of using this type of nanotubes as a sensor material for sensor devices.

Using examples of an exothermic chemical reaction and self-heating of the region of a conducting filament of a memristor, heat-induced phase transitions, disadvantages of applying the classical Fourier approach on the nanoscale, and advantages of the molecular mechanics method at modeling the temperature factor are discussed. The correction for Arrhenius relationship, taking into account that the temperature becomes a random variable is proposed. Based on the introduced concepts (elementary act of heat release, distance and region of thermal impact) method for taking into account the thermal factor, is proposed.

The correction is based on splitting the entire pool of particles into several, each of which corresponds to a fixed temperature value taken from a certain range. Although continuous and discrete correction options are given both, but the discrete option is more preferable. This is due to the fact that the methodology focuses on the application of methods of molecular mechanics, and, intentionally, in the most primitive version. The role of amorphization is noted as an example of the structural restructuring of matter in nano-volumes. It is indicated that the phonon spectra themselves, which determine heat transfer, depend on temperature. The technique is consistent with the ideology of multiscale modeling. The integral temperature increase is calculated outside the region of thermal exposure, where nonequilibrium effects are significant, by solving the standard equation of thermal conductivity.

The electronic, magnetic, mechanical and other properties of crystalline substances are due to the feature of their structure — the periodicity and symmetry of the lattice, therefore, the determination of the metrical parameters is an important stage in the study of the characteristics of such materials. The paper considers a number of metals having a crystal lattice of the hcp structural type (hexagonal close packing) – beryllium, cerium, cobalt, dysprosium, erbium, gadolinium, hafnium, holmium, lanthanum, lutetium, magnesium, neodymium, osmium, praseodymium, rhenium, ruthenium, scandium, terbium, titanium, thallium, thulium, yttrium, zirconium. The paper shows the application of the annealing simulation algorithm to find the metric parameters of the materials under consideration using the dense packing model, which is widely used in crystallographic calculations. The own software implementation of the annealing simulation algorithm presented in the paper makes it possible to determine the coordinates of the atoms included in the unit cell of the crystal lattice, to calculate the lattice constants and the packing density of atoms in the cell of the crystal of the hcp structural type, using the given chemical formula and space symmetry group. These structural characteristics can be used as input parameters in modeling the electronic, magnetic, and other properties of the considered materials. The paper compares the values of the crystal lattice constants obtained as a result of numerical simulation with published data.

The work is devoted to the issues of simulation modeling of an analog impulse neural network based on memristive elements within the framework of the problem of pattern recognition. Simulation modeling allows you to configure the network at the level of a mathematical model, and subsequently use the obtained parameters directly in the process of operation. The network model is given as a dynamic system, which can consist of tens and hundreds of thousands of ordinary differential equations. Naturally, there is a need for an efficient and parallel implementation of an appropriate simulation model. OpenMP (Open Multi-Processing) is used as a technology for parallelizing calculations, since it allows you to easily create multi-threaded applications in various programming languages. The efficiency of parallelization is evaluated on the problem of modeling the process of learning the network to recognize a set of five images of size 128 by 128 pixels, which leads to the solution of about 80 thousand differential equations. On this problem, more than a sixfold acceleration of calculations was obtained.
According to experimental data, the character of memristor operation is stochastic, as evidenced by the spread in the current-voltage characteristics during switching between high-resistance and low-resistance states. To take this feature into account, a memristor model with interval parameters is used, which gives upper and lower limits on the quantities of interest, and encloses the experimental curves in corridors. When modeling the operation of the entire analog self-learning impulse neural network, each epoch of training, the parameters of the memristors are set randomly from the selected intervals. This approach makes it possible to do without the use of a stochastic mathematical apparatus, thereby further reducing computational costs.

The article considers the problem of developing synchronous and self-timed (ST) circuits that are tolerant to faults. Redundant ST coding and two-phase discipline ensures that ST circuits are more soft error tolerant than synchronous counterparts. Duplicating ST channels instead of tripling reduces the fault-tolerant ST circuits’ redundancy and retains their reliability level compared to synchronous counterparts.

Computational material science aims to simulate substances to understand their physical properties. Bioelectronics is an interdisciplinary field that studies biological material from the conductivity point of view. In case of proteins, the folding is an important feature that directly influences physical and chemical properties. The folding modelling is a hard task. The enormous number of degrees of freedom makes modelling impossible for classical computation due to resource limits. Quantum computations aim to process multidimensional data with logarithmic growth of quantum bits. Quantum operators (gates) form quantum programs, known as circuits that process the input data. In real quantum computers, the gates are noisy and expensive to execute. Thus, it is essential to reduce the number of quantum gates both for the quality of the result and the cost of computations. This work describes an approach to decrease the number of quantum gates based on their mathematical property. The matrix properties form the first optimization technique. In this case, the optimized quantum circuit predicts precisely the same protein folding as the not optimized circuit predicts. This happens because both of the circuits are mathematically equivalent. The removal of weakly-parametrized gates forms the second optimization technique. In such case the optimized quantum circuit calculates the approximate protein folding. The error depends on parameter’s amplitude of the gates. The first technique allows to decrease the circuit depth from 631 to 629 gates while modelling the part of Azurin peptide. The second technique allows to decrease the depth to 314 gates with the threshold parameter value 0.4 radians.

The aim of this work was to study the effect of vacuum sintering conditions and cerium concentration on the optical, luminescent and thermal properties of yttrium-aluminum garnet based ceramics doped with Се³⁺ cations. Series of ceramic powders were synthesized and samples of luminescent ceramics having the composition Y_3-хСе_хAl5O12 were synthesized where x was in the range 0.01 to 0.025 f.u. We show that the phase composition and grain size distribution of the ceramic powders do not depend on cerium concentration. Without sintering additives, an increase in vacuum sintering temperature from 1675 to 1800 °C leads to an increase in the optical transmittance of luminescent ceramic specimens from 5 to 55% at a 540 nm wavelength and an increase in the thermal conductivity of the samples from 8.4 to 9.5 W/(m ∙ K). It was found that an increase in cerium concentration leads to a shift of the luminescent band peak from 535 to 545 nm where as the width of the luminescent band decreases with an increase in vacuum sintering temperature from 1675 to 1725 °C.

The development pace of advanced electronics raises the demand for semiconductor single crystals and strengthens the requirements to their structural perfection. Dislocation density and distribution pattern are most important parameters of semiconductor single crystals which determine their performance as integrated circuit components. Therefore studies of the mechanisms of dislocation nucleation, slip and distribution are among the most important tasks which make researchers face the choice of suitable analytical methods. This work is an overview of advanced methods of studying and evaluating dislocation density in single crystals. Brief insight has been given on the main advantages and drawbacks of the methods overviewed and experimental data have been presented. The selective etching method (optical light microscopy) has become the most widely used one and in its conventional setup is quite efficient in the identification of scrap defects and in dislocation density evaluation by number of etch pits per vision area. Since the introduction of digital light microscopy and the related transfer from image analysis to pixel intensity matrices and measurement automation, it has become possible to implement quantitative characterization for the entire cross-section of single crystal wafers and analyze structural imperfection distribution pattern. X-ray diffraction is conventionally used for determination of crystallographic orientation but it also allows evaluating dislocation density by rocking curve broadening in double-crystal setup. Secondary electron scanning electron microscopy and atomic force microscopy allow differentiating etch patterns by origin and studying their geometry in detail. Transmission electron microscopy and induced current method allow obtaining micrographs of discrete dislocations but require labor-consuming preparation of experimental specimens. X-ray topography allows measuring bulky samples and also has high resolution but is hardly suitable for industry-wide application due to the high power consumption of measurements.
Digital image processing broadens the applicability range of basic dislocation structure analytical methods in materials science and increases the authenticity of experimental results.

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