Ionic liquids (IL) based on tetrahexyl-, tetraheptyl-, and tetraoctylammonium cations and dicyanamide anion were obtained as a result of the chemical interaction of ammonium bromides with dicyanamide salts. The purity of IL synthesized (>98%) was confirmed by atomic absorption spectrometry and ionic chromatography. The influence of such factors as solution acidity, extraction, temperature, and duration, the ratio of organic and aqueous phase volumes on the recovery of platinum ions was studied. It was shown, that ionic liquids with longer alkyl chains exhibit higher efficiency due to their improved hydrophobic properties. Spectroscopic analysis of the extracted systems revealed that the mechanism of platinum extraction is associated with the formation of stable ion pairs between metal halogen complexes and tetraalkylammonium cations. This interaction causes a hypsochromic shift in the absorption spectra (λ_max = 261 nm), confirming the nature of the formed compounds. Ionic liquids demonstrated stability and low solubility in the aqueous phase, which makes them reliable extractants. The application of the proposed eco-friendly approach can be useful for recycling secondary platinum sources, cleaning industrial wastewater and minimizing environmental pollution.

In the present work, textile halochromic materials were obtained, which were tested as indicators of acid-base titration. Textile materials were obtained by functionalization of cotton fabric with halochromic disazo derivatives of 4,4’-diaminostilbene-2,2’-disulfonic acid using direct dyeing technology. Rectangular fragments of halochromic textile material, 20 by 20 mm in size, were placed directly into the titrated solution. The range of color change and the rate of its change were studied in the process of potentiometric titration of sodium hydroxide, sodium bicarbonate and tap water. Testing of halochromic textile material as an indicator of acid-base titration was carried out using the example of determining the acidity of water. The correctness of the results was determined using the added-found method. The halochromic sensor material is characterized by a high rate of color change. The color change of the material is visually observed in all cases within the jump on the titration curve. The data obtained when determining the alkalinity of water with various indicators are in good agreement with each other. When assessing the correctness of the results using the recovery test, the degree of discovery close to 100 % was obtained. When using such an indicator, the halochromic dye is localized within the tissue fragment, which significantly facilitates the fixation of the end point of titration. The use of the developed material can be relevant in contactless control systems for the acidity of the medium through a transparent wall as an alternative to fluorescent sensors.

A set of solid reference materials of electrical steel with a wide list of standardized impurity and alloying components has been developed for determination of B, C, N, Al total, Al acid-soluble, Si, P, S, Ca, Ti, V, Cr, Mn, Ni, Cu, Nb, Mo, and Sn. The RM set allows monitoring the accuracy of determination of elements by spectral methods. The consistency and stability of the chemical composition of the set were confirmed, and the error values of the certified elements were evaluated. Using this RM set the linear calibration graphs for determination of all the elements by spark atomic emission spectrometry (SAES) (R² = 0.9817 – 0.9998) and X-ray fluorescence spectrometry (XRF) (R² = 0.8141 – 0.9999) with detection limits of 0.1 – 170 and 0.8 – 18 ppm correspondingly were plotted. Using the Wilcoxon statistical criterion, the possibility of joint use of the developed and Czech SPL SST-1A – SST-4A sets for the determination of components by SAES and XRF was demonstrated. The developed kit is used in the laboratories of a metallurgical enterprise.

The paper presents the results of the application of artificial intelligence (AI) and machine vision technologies for the detection of surface defects. Plates with a crystallographic surface of {100} monocrystalline GaAs grown by the Czochralski method with liquid encapsulation of the melt were analyzed. Based on the YOLOv8 open architecture, a neural network was trained to recognize pits formed as a result of selective etching of single-crystal GaAs plates, and a solution was proposed to automate the calculation of dislocation density based on the identified pits of selective etching. Monochrome images were used for processing by the neural network, the data array at the training stage amounted to about 40,000 objects. It was found that the average density of etching pits (detection objects) is (3 – 7) × 10⁴ cm^–2. When trained on a sufficient amount of data, AI and machine vision algorithms are able to recognize target objects with high confidence, including overlapping ones. Continuous counting (over the entire surface of the plate) and further software processing of the results made it possible to obtain a map of the density distribution of dislocation etching pits, as well as lines of density levels (isolines) with reference to its absolute value. It is shown that the use of the continuous counting method using AI and machine vision technologies in comparison with traditional averaging methods for analyzing the structural uniformity of single-crystal GaAs is justified and appropriate. The obtained results can be used for technological control of dislocation density and identification of patterns in the change of the dislocation structure of single-crystal GaAs depending on the growth modes and post-processing of ingots.

The development of gas analytical devices based on semiconductor metal oxide sensors is associated with the growth of their sensitivity, selectivity and energy efficiency. This paper presents the results of a study of a new metal oxide gas sensor. Improvement of the sensor characteristics was achieved through miniaturization of the dielectric substrate and temperature modulation. The electrical power of the sensor manufactured using the developed dielectric substrate based on aluminum oxide ceramics (dimensions 1.50 × 1.50 × 0.63 mm) was about 250 mW at an operating temperature of 723 K. To produce the microheater, we used a platinum resistive paste consisting of platinum-coated micron-sized particles of aluminum oxide and glass. The resistive film with a sheet resistance of about 4 Ω was produced by screen printing, and the gas-sensitive material of the sensor was produced by the sol-gel method. To process the training subsamples of the experimental data using the principal component method, we selected regions on the plane of the principal components corresponding to conventionally single-component gas systems. It was shown that qualitative and quantitative analysis of conventionally single-component gas systems is possible using a single metal oxide sensor based on tin dioxide with gold nanoparticles in the temperature modulation mode. The obtained results can be used in the development of new models of universal and compact gas analytical devices with increased energy efficiency and selectivity.

Surface plastic deformation is widely used in various branches of mechanical engineering to improve the operational properties of materials. The paper presents the results of the study of the influence of surface plastic deformation modes on the structure, microhardness and magnetic characteristics of austenitic steel 04Kh17N8T. The experiments included changing the parameter of surface plastic deformation — normal load on the indenter. Using microstructural analysis, including optical and electron microscopy, changes in the crystalline structure of the material after deformation were analyzed. The magnetic characteristics of the samples were determined using a Barkhausen noise analyzer. It was found that the hardness of the samples in the cross section is almost the same at indenter load of 250 N and more. In addition, there is a correlation between deformation modes and hardness. It is shown that changing the modes of surface plastic deformation has a significant effect on the structure and magnetic characteristics of 04Kh17N8T steel. Friction treatment leads to the formation of a gradient structure with a depth of up to 500 μm, and at a depth of up to 25 μm a highly deformed dispersed structure is formed. Due to the increase in the normal load on the indenter during friction machining, the content of defects in the material increases, which affects its magnetic properties. The obtained results can be used in the improvement of surface plastic deformation technologies and optimization of steel processing processes to achieve the required level of performance characteristics and increase the service life of parts and structural elements.

Based on the results of tensile, compression, bending and interlayer shear tests on carbon fiber plastic samples made on the basis of unidirectional carbon fabric and an epoxy binder (reinforcement direction 0), as well as on the results of tensile and compression tests on fiberglass samples based on glass fabric and an epoxy binder, the coefficients were determined by Spearmen’s factor of rank correlation, and the corresponding empirical regression equations of strength characteristics for various stress ranges were obtained. It is shown that to assess the close relationship (correlation) of the strength characteristics of composite materials, the use of Spearmen’s factor of rank correlation coefficient is appropriate and effective in the case where the distribution of samples of experimental values is different from normal or compliance with the normal distribution law is questionable due to high scatters. At the same time, in contrast to the Pearson linear correlation coefficient, the Spearmen’s factor of rank correlation coefficient makes it possible to evaluate not only the linear relationship of characteristics, but nonlinear monotonic functional relationships that are characteristic of the strength characteristics of most structural composite materials. For the considered composite materials, the Spearmen’s factor of rank correlation coefficient was determined and the corresponding empirical dependencies and regression equations of strength characteristics were obtained for various stress ranges, which can be used in evaluation calculations of strength, in mathematical modeling and planning mechanical tests. Establishing statistically substantiated functional (both linear and nonlinear) dependencies between the main, including between the calculated strength characteristics, assessing the type and parameters of distributions of experimental values of the strength and elasticity characteristics of composite materials is one of the main tasks aimed at ensuring effective and safe operation of products and structures made of polymer composite materials.

The influence of hydrogen embrittlement on Wierzbicki and Xue (W-X) model parameters is studied. This model has 4 parameters (D, C, δ*, and e 0/f) which have been obtained by tensile test on smooth specimens, tensile notch specimens, single notch tensile specimens (SENT) and pure shear specimens. Hydrogen embrittlement has been obtained by electrolytic method. For the steel API 5L X60, the relative values decrease for the parameters D and C of the W-X plasticity model is in the range after HE. The δ* parameter increases after HE but its influence is limited because the values of the Lode angle are low. For the studied pipe exhibiting a crack-like defect and submitted to internal pressure, soil reaction and lateral seismic displacement Δ, the local failure strain indicates that for high values of Δ, the effect of HE is hidden by the stress triaxiality effect. Improvement of the W-X plasticity model is necessary to consider the thickness and the geometry effects. This can be done by introducing a constrain parameter. Application of the (W-X) model has been done to get the local critical resistance used to predict fracture by the Volumetric Method (VM). An example is given for the case of an embedded pipe submitted to service pressure and lateral seismic displacement.

Changes in the strength and service life of mechanical objects are caused by the occurrence of physical, mechanical and chemical degradation processes in them. The existing formalization of these processes does not always provide sufficient accuracy in predicting and substantiating the methods and means for diagnosing damage, malfunction and catastrophic failures. Such events are caused by some uncertainty of the influencing factors and properties of the object. One of the methods for solving the problem of uncertainty is to use all the experimental information on changes in the corresponding properties and states of objects. Information must be identified and systematized to reflect the cause-and-effect relationships between the influencing factors, object properties and the degradation processes and phenomena caused by them. Additional concepts and classification features reflecting the main factors, properties and events accompanying the stages of changing the states of objects are substantiated. The set of influencing factors, material and design properties is defined as a degradation mechanism causing damage, malfunction, failure and the corresponding states of objects. The listed events are represented by features, and the states are represented by parameters. A tabular model reflecting their interrelationship has been constructed. A graphical model has been developed linking the relevant data with the degradation process as a sequence of events and states. The developed models represent an information and analytical basis for planning additional tests and specifying the properties of the objects being created, determining the methods and means for predicting and diagnosing catastrophic failures and preventing them. A conceptual algorithm for using models to predict the degradation processes of a specific object based on design and technological documentation is presented.

The issues of creating computationally efficient algorithms for implementing the least absolute deviation method for estimating regression dependencies are considered. The purpose of the work is to improve the performance of gradient descent along nodal lines by considering the geometry of the objective function near the minimum, as well as its comparative analysis with the gradient projection algorithm. A modified gradient descent algorithm for regression estimation using the least absolute deviation method is proposed. Efficiency was achieved by excluding the calculation of the objective function values in the minimums of the nodal lines and determining an improved initial approximation on part of the sample. As a result, it was possible to reduce the dependence of computation time on sample size and expand the application area of the least absolute deviation method. The gradient projection algorithm for constructing linear regression dependencies does not guarantee finding an exact solution and is significantly inferior in performance to algorithms for descending along nodal lines.