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Today, one of the problems of modern implants is their high rigidity, which can lead to bone resorption at the interface between the implant and the bone and to the gradual detachment of the implant. In addition, implant detachment can occur due to the positive Poisson's ratio of the implant along its entire length. This phenomenon is described in detail in [1]. As a possible solution to these problems, it is proposed to use lattice structures with negative Poisson's ratio from TiNi alloy. This alloy has a fairly low modulus of elasticity - about 48 GPa. The use of a porous (lattice) structure of an implant made of TiNi alloy will reduce the modulus of elasticity and bring it closer to the modulus of human cortical bone – 12-17 GPa [2], and possibly the modulus of elasticity of cancellous bone – 0.1-5 GPа [3]. In this work, a computer numerical simulation of strut based lattice structures with several variants of unit cell topology with a negative Poisson's ratio is carried out. For the obtained structures, the following characteristics were calculated - elastic modulus (Young's modulus), modulus of elasticity in shear, Poisson's ratio. The modeling process is implemented using the ANSYS 2019 R2 SpaceClaim finite element analysis package. The data obtained confirmed the promising possibility of modeling and fabricating lattice structures with a low elastic modulus and negative Poisson's ratio from a TiNi alloy. Also, on the basis of this data, conclusions about the influence of the topology and porosity of unit cells on the resulting characteristics of the lattice structure were made.

The research of deposition of a nitinol sample in an equiatomic ratio from a powder mixture of nickel and titanium 55Ni-45Ti (in wt.%) is aimed at studying the heterogenicity of the chemical composition in the cross-section of a thin-walled and multilayer (bulk) cylindrical sample. The main task of the study was to determine the presence or absence of the chemical composition deviation from layer to layer, and mechanical properties. Analysis on an optical microscope, EDS analysis, and microhardness measurement, a thin-walled sample was studied. A chemical gradient was detected in the sample from the base along with its entire height. An increase in the content of the Ti element and a decrease of the Ni element was detected with an increase in the number of layers and the height of the sample, and a change in the microstructure and hardness were found. The increase in hardness from the base to the top point of the sample reaches 50%. X-ray phase analysis (XRD) showed the presence of NiTi phases in the martensitic and austenitic state, the side phases of NiTi2 in a thin-walled sample, and the presence of the Ni4Ti3 phase and the TiO2 oxide phase in a cylindrical bulk sample. The chemical composition of the cylindrical bulk sample agrees with the chemical composition of the mixture loaded into the powder feeder 55:45 Ni and Ti in wt. %. To indirectly determine the shape memory effect of the final alloy, mechanical tests were carried out for compression of cylindrical samples with subsequent heating, which confirmed the presence of the shape memory effect with a degree of reversible deformation of about 40%.

Increasing antimicrobial resistance (AMR) represents a global healthcare threat. To decrease the spread of AMR and associated mortality, methods for rapid selection of optimal antibiotic treatment are urgently needed. Machine learning (ML) models based on genomic data to predict resistant phenotypes can serve as a fast screening tool prior to phenotypic testing. Nonetheless, many existing ML methods lack interpretability. Therefore, we present a methodology for visualization of sequence space and AMR prediction based on the non‐linear dimensionality reduction method – generative topographic mapping (GTM). This approach, applied to AMR data of >5000 S. aureusisolates retrieved from the PATRIC database, yielded GTM models with reasonable accuracy for all drugs (balanced accuracy values ≥0.75). The Generative Topographic Maps (GTMs) represent data in the form of illustrative maps of the genomic space and allow for antibiotic‐wise comparison of resistant phenotypes. The maps were also found to be useful for the analysis of genetic determinants responsible for drug resistance. Overall, the GTM‐based methodology is a useful tool for both the illustrative exploration of the genomic sequence space and AMR prediction.

Treatment of hydrogen sulfide (H2S) is important in many industrial processes including oil refineries, natural and biogas processing, and coal gasification. The most mature technology for the selective capture of H2S is based on its absorption by chemical or physical solvents. However, only several compounds are currently used as physical (co)solvents in industry, and the search for new ones is an important task. The experimental screening of physical (co)solvents requires much time and many resources, while solubility modeling might enable one to reduce the number of solvents for the experimental evaluation. In this study, a workflow for the in silicodiscovery of new physical solvents for H2S absorption was suggested and experimentally validated. A data set composed of 99 H2S physical solvents was collected and predictive quantitative structure–property relationships for H2S solubility were built using a random forest algorithm and two types of molecular descriptors: ISIDA fragments and quantum-chemical descriptors. Virtual screening of industrially produced chemicals and their structural analogues enabled identification of the ones with predicted high solubility values. They can be suggested as starting points for further exploration of the H2S physical solvents chemical space. The predicted solubility value for one of the compounds found in virtual screening, 1,3-dimethyl-2-imidazolidinone, was confirmed experimentally.

Metal structures, pipelines and dies are used for a long time wear out and deteriorate. At the end of the service life it is necessary to repair or replace the metal component. The article discusses the method of applying nickel super-alloy Inconel 625 by direct laser deposition on high-alloy austenitic Mn-Cr steel. The article discusses the method of applying nickel super-alloy Inconel 625 by direct laser deposition on high-alloy austenitic Mn-Cr steel. Inconel 625 alloy is used as a buffer layer or repair material. But there is a high probability of cracks in the cladding material and the substrate material when the DLD method is used. One of the frequent causes of these cracks is high tensile stresses and oxides. To eliminate defects, the substrate was heated with resistive heaters to 400 °C. The result was a defect-free adhesion of the cladding material and the substrate. The hardness of the substrate before and after heating has not changed (312 HV 0,5/10). After heating the hardness of the substrate in the heat-affected zone decreased by 14% and was equal to 267 HV 0,5/10. The hardness of the cladding decreased by 12% and was equal to 230 HV 0,5/10.

Hepatitis B surface antigen (HBsAg) is the most clinically relevant serological marker of hepatitis B virus (HBV) infection. Its detection in blood is extremely important for identification of asymptomatic individuals or chronic HBV carriers, screening blood donors, and early seroconversion. Rapid point-of-care HBsAg tests are predominantly qualitative, and their analytical sensitivity does not meet the requirements of regulatory agencies. We present a highly sensitive lateral flow assay based on superparamagnetic nanoparticles for rapid quantification (within 30 min) of polyvalent HBsAg in serum. The demonstrated limit of detection (LOD) of 80 pg mL−1in human serum is better than both the FDA recommendations for HBsAg assays (which is 0.5 ng mL−1) and the sensitivity of traditional laboratory-based methods such as enzyme linked immunosorbent assays. Along with the attractive LOD at lower concentrations and the wide linear dynamic range of more than 2.5 orders, the assay features rapidity, user-friendliness, on-site operation and effective performance in the complex biological medium. These are due to the combination of the immunochromatographic approach with a highly sensitive electronic registration of superparamagnetic nanolabels over the entire volume of a 3D test structure by their non-linear magnetization and selection of optimal antibodies by original optical label-free methods. The developed cost-efficient bioanalytical technology can be used in many socially important fields such as out-of-lab screening and diagnosis of HBV infection at a point-of-demand, especially in hard-to-reach or sparsely populated areas, as well as highly endemic regions.

Accurate and reliable identification of chemical compounds is the ultimate goal of mass spectrometry analyses. Currently, identification of compounds is usually based on the measurement of the accurate mass and fragmentation spectrum, chromatographic elution time, and collisional cross section. Unfortunately, despite the growth of databases of experimentally measured MS/MS spectra (such as MzCloud and Metlin) and developing software for predicting MS/MS fragments in silico from SMILES patterns (such as MetFrag, CFM-ID, and Ms-Finder), the problem of identification is still unsolved. The major issue is that the elution time and fragmentation spectra depend considerably on the equipment used and are not the same for different LC-MS systems. It means that any additional descriptors depending only on the structure of the chemical compound will be of big help for LC-MS/MS-based omics. Our approach is based on the characterization of compounds by the number of labile hydrogen and oxygen atoms in the molecule, which can be measured using hydrogen/deuterium and 16O/18O-exchange approaches. The number of labile atoms (those from −OH, −NH, ═O, and −COOH groups) can be predicted from SMILES patterns and serves as an additional structural descriptor when performing a database search. In addition, distribution of isotope labels among MS/MS fragments can be roughly predicted by software such as MetFrag or CFM-ID. Here, we present an approach utilizing the selection of structural candidates from a database on the basis of the number of functional groups and analysis of isotope labels distribution among fragments. It was found that our approach allows reduction of the search space by a factor of 10 and considerably increases the reliability of the compound identification.

We present a novel approach for the increasing reliability of compound identification for LC-MS and MALDI imaging lipidomics. Our approach is based on the characterization of compounds not only by the elution time, accurate mass, and fragmentation spectra but also by the number of labile hydrogens that can be measured using the hydrogen/deuterium (H/D) exchange approach. The number of labile hydrogens (those from −OH and −NH groups) serves as an additional structural descriptor used when performing a database search. For LC-MS experiment, the H/D exchange was performed in the heating capillary of the modified electrospray ionization (ESI) source, while for MALDI imaging, the exchange was performed in the ion funnel at 10 Torr pressure. It was observed that such an approach allowed one to achieve a considerable degree of deuteration, enough to unambiguously distinguish between different classes of lipids. The proposed analytical approach may be successfully used for the identification not only of lipids but also of peptides and metabolites. A special software for the automatic filtration of molecules based on the number of functional groups was also developed.

Geometric features of mesh structures allow them to be used as a material for medical purposes. In this paper, the study of the properties of lattice structures, performed by selective laser melting was carried out. The range of high accuracy of manufactured samples and microstructure was determinated. The results of changes in porosity and mechanical properties after hot isostatic pressing in comparison with cortical bone and heat treated material and microstructure of samples are represented.

The relevance of the work is due to the need in the modern world to obtain products with gradient properties for aerospace and medical applications. The method of laser-assisted cold spraying allows obtaining composite materials by additive technology. As a result of this work, a layer of material was constructed from a mechanical mixture of aluminum Al powder with corundum powder Al2O3, then the structure and properties of the material obtained were studied, and zones of gradient transition were identified.

The Nb-Si in-situ composite samples were fabricated by laser metal deposition additive manufacturing technology from mechanically alloyed powders in vario-planetary ball mill. A laser metal of samples was carried out at various laser powers: 500 W, 1000 W, 1400 W. The microstructure of a sample grown at a laser power of 500 W consists of a solid solution of Nb, Nb3Si, and dendritic chains of Ti. When the laser power is increased to 1000 W, the volume fraction of silicides increases, the structural heterogeneity decreases. With further increase in laser power to 1400 W, Ti dissolves in Nb, dendritic chains disappear, Nb5Si3 silicide stabilizes.

Due to the additive manufacturing process concept - layered synthesis of products, it becomes necessary to apply new approaches to the design of parts. One of the main tools that need to operate is numerical simulation, capable, with a skilful approach, to give an engineer an integrated procedure to the development of new products. Numerical modeling, in addition to carrying out strength calculations, includes topology optimization and the creation of lattice structures, through which it is possible to create lightweight products. New design meets requirements of strength characteristics. The use of this tool leads to a reduction in the amount of initial material and as a result - cost saving. In this paper, using the bracket as an example, was used the topology optimization method with subsequent redesign. The paper presents the results of calculations of the stress-strain state of the initial and final structures, allowing estimating the possible reduction in the mass of the product and the amount of consumable material in the manufacture of additive technologies.

Within this work the results of the composition and structural study and mechanical tests of the specimens made from the heat-resistant iron based alloy powder derived from secondary raw material (chips) are presented. The crystalline transformations for the initial condition as well as after several heat treatment conditions were analyzed. Phase and chemical composition were investigated. Mechanical characteristics of the standard flat test bars were defined.

Many immunoassay platforms require time- and labor-consuming tuning of parameters for operation in complex mediums (food, whole blood, etc.), but no universal method has been proposed to accelerate that “trial-and-error” stage. We present a lateral flow platform, applicable to the multitude of assays comprising immunomagnetic separation, as a tool to establish quantitative relationship between analytical characteristics, sample volume, and magnetic enrichment time. The tool permits a user, prior to the analysis, to knowingly select from a “menu” of parameters’ values a particular combination that better suits a purpose. Besides, the platform showed quantitative detection in various food of staphylococcal enterotoxin B (SEB) as a model up to 6 pg/mL at the dynamic range of 3.5 orders with minimal sample pretreatment. Such performance is achieved due to using the same magnetic nanoparticles through all stages of analysis in contrast to the traditional approaches that engage these agents either for separation or as labels. The unique combination of broad benefits of magnetic particles, e.g., rapid enrichment and purification of analyte, reduction of matrix effect, extremely high signal-to-noise ratio, etc., are joined in one platform due to the method of their registration by nonlinear magnetization. The platform also retains the advantages of lateral flow principle such as extraordinary simplicity, on-site operation, affordable consumables, and permits samples of virtually any volume. Although tested here for SEB detection, the platform can be extended to other analytes for point-of-care in vitro diagnostics, food analysis, biosafety, environmental applications, etc.

The results of the analysis of the plasma atomized powder material are shown. An analysis of the microstructure in lightweight constructions made of VT6 alloy by selective laser melting was carried out. The microstructure and mechanical characteristics of the obtained structures were studied before and after heat treatment. Measurement of grain size in the microstructural analysis gave a more complete picture of the state of the structure in different parts of the sample formed as a result of growing by selective laser melting, in a mesh and compact. In the course of the work, the peculiarities of the growth of grains in bars of the mesh construction are revealed depending on their location.

Introduction:The concept of ‘chemical space’ reveals itself in two forms: the discrete set of all possible molecules, and multi-dimensional descriptor space encompassing all the possible molecules. Approaches based on this concept are widely used for the analysis and enumeration of compound databases, library design, and structure–activity relationships (SAR) and landscape studies. Visual representations of chemical space differ in their applicability domains and features and require expert knowledge for choosing the right tool for a particular problem.Areas covered:In this review, the authors present recent advances in visualization of the chemical space in the framework of current general understanding of this topic. Attention is given to such methods as van Krevelen diagrams, descriptor plots, principal components analysis (PCA), self-organizing maps (SOM), generative topographic mapping (GTM), graph and network-based approaches. Notable application examples are provided.Expert opinion:With the growth of computational power, representations of large datasets are becoming more and more common instruments in the toolboxes of chemoinformaticians. Every scientist in the field can find the method of choice for a particular task. However, there is no universal reference representation of the chemical space currently available and expert knowledge is required.

We study the guided modes in a wire medium (WM) slab, taking into account the nonlocality and losses in the structure. We show that due to the fact that the WM is an extremely spatially dispersive metamaterial, the effect of nonlocality plays a critical role, since it results in coupling between the otherwise orthogonal guided modes. We observe the effects of strong and weak coupling, depending on the level of losses in the system.

We introduce multiperiodicity in periodic metal-dielectric multilayers by stacking more than two types of metal and/or dielectric layers into the unit cell. A simple way to characterize arbitrary multiperiodic multilayers using permutation vectors is suggested and employed. Eifects of multiperiodicity up to its fourth order are investigated. We demonstrate that various topologies of multiple-sheet isofrequency and dispersion surfaces exist for such plasmonic multilayers, including a photonic realization of nontrivial isolated Dirac cones. [ABSTRACT FROM AUTHOR]

We study theoretically the propagation of large-wave-vector waves (volume plasmon polaritons) in multilayer hyperbolic metamaterials with two levels of structuring. We show that when the parameters of a subwavelength metal-dielectric multilayer (substructure) are modulated (superstructured) on a larger, wavelength scale, the propagation of volume plasmon polaritons in the resulting multiscale hyperbolic metamaterials is subject to photonic-band-gap phenomena. A great degree of control over such plasmons can be exerted by varying the superstructure geometry. When this geometry is periodic, stop bands due to Bragg reflection form within the volume plasmonic band. When a cavity layer is introduced in an otherwise periodic superstructure, resonance peaks of the Fabry-Pérot nature are present within the stop bands. More complicated superstructure geometries are also considered. For example, fractal Cantor-like multiscale metamaterials are found to exhibit characteristic self-similar spectral signatures in the volume plasmonic band. Multiscale hyperbolic metamaterials are shown to be a promising platform for large-wave-vector bulk plasmonic waves, whether they are considered for use as a kind of information carrier or for far-field subwavelength imaging. [ABSTRACT FROM AUTHOR]

The removal of CO2from gases is an important industrial process in the transition to a low-carbon economy. The use of selective physical (co-)solvents is especially perspective in cases when the amount of CO2is large as it enables one to lower the energy requirements for solvent regeneration. However, only a few physical solvents have found industrial application and the design of new ones can pave the way to more efficient gas treatment techniques. Experimental screening of gas solubility is a labor-intensive process, and solubility modeling is a viable strategy to reduce the number of solvents subject to experimental measurements. In this paper, a chemoinformatics-based modeling workflow was applied to build a predictive model for the solubility of CO2and four other industrially important gases (CO, CH4, H2, and N2). A dataset containing solubilities of gases in 280 solvents was collected from literature sources and supplemented with the new data for six solvents measured in the present study. A modeling workflow based on the usage of several state-of-the-art machine learning algorithms was applied to establish quantitative structure–solubility relationships. The best models were used to perform virtual screening of the industrially produced chemicals. It enabled the identification of compounds with high predicted CO2solubility and selectivity toward other gases. The prediction for one of the compounds, 4-methylmorpholine, was confirmed experimentally.

Discovery of drugs against newly emerged pathogenic agents like the SARS‐CoV‐2 coronavirus (CoV) must be based on previous research against related species. Scientists need to get acquainted with and develop a global oversight over so‐far tested molecules. Chemography (herein used Generative Topographic Mapping, in particular) places structures on a human‐readable 2D map (obtained by dimensionality reduction of the chemical space of molecular descriptors) and is thus well suited for such an audit. The goal is to map medicinal chemistry efforts so far targeted against CoVs. This includes comparing libraries tested against various virus species/genera, predicting their polypharmacological profiles and highlighting often encountered chemotypes. Maps are challenged to provide predictive activity landscapes against viral proteins. Definition of “anti‐CoV” map zones led to selection of therein residing 380 potential anti‐CoV agents, out of a vast pool of 800 M organic compounds.

Recent outbreaks of dangerous viral infections, such as Ebola virus disease, Zika fever, etc., are forcing the search for new antiviral compounds. Preferably, such compounds should possess broad‐spectrum antiviral activity, as the development of drugs for the treatment of dozens of viral infections lacking specific treatment would require significant resources. Antiviral activity data present in public resources are very sparse and further investigation of structure‐activity relationships is necessary. One of the strategies could be the investigation of chemical space around known active compounds and assessment of activity against closely related viruses in order to fill in the antiviral activity matrix. Here we present an investigation of antiviral activity using universal maps built with generative topographic mapping (GTM) algorithm. The GTM‐based maps were used to find commercially available compounds in close proximity to already known compounds with anti‐flaviviral and anti‐enteroviral activities. Selected compounds were then assessed in cell‐based assays against tick‐borne encephalitis virus (TBEV) and a panel of enteroviruses. This approach allowed us to identify 23 new compounds showing anti‐TBEV activity with EC50values in micromolar and submicromolar range.