Kintech Lab (Russia)

Laser powder bed fusion (LPBF) is an advanced additive manufacturing method, but its productivity is relatively low, which limits its application. Performance can be increased without hardware modifications by enlarging the powder-layer thickness. However, this approach requires deeper investigation because the probability of defects (keyhole porosity, lack of fusion) rises substantially, and experiments become costly since each thickness value requires a separate LPBF run. High-fidelity simulation under such conditions can reduce the experimental workload. Reliable predictions, however, require numerous thermophysical parameters; reported values are often inconsistent or unavailable, and few studies have quantified their influence on simulation outcomes. A Lattice Boltzmann-based model is adopted to simulate the keyhole melting mode of AlSi10Mg. The effects of laser spot diameter, laser absorptivity, and the temperature dependence of thermal diffusivity and surface tension on the results are investigated. Predicted melt-pool morphologies are compared with cross-sections of experimental single tracks.

The results of the comparison of two big chemical databases PubChem-Compound (general chemical information) and Aurora Fine Chemical (commercially available compounds) are presented. Each database contains about 115M records. The performed comparison shows that about 50% of the structures in these databases are identical while the rest are unique to each database, which is unexpected for such large samples. The PubChem compounds database contains many structures that are unstable at room temperature. For a more detailed comparison, the chemical structures are decomposed into circular fragments with a radius of up to 3 chemical bonds. It was found that the PubChem compounds database contains 1.5 times more fragments, than AFC. It is explained that the average size of a chemical structure in PubChem Compounds is larger than in Aurora Fine Chemicals. We also find that among the 30 most distributed fragments, 28 are common to both databases. Analysis of the unique fragments allowed us to find structures that are poorly represented in each of the databases. These are triple-bonded compounds in the PubChem Compounds database and organosilicon compounds in the Aurora Fine Chemicals database. Our study provides important information for the development of in silico engineering approaches for new polymers based on virtual synthesis from simple chemical fragments.

In recent years numerous satellite data on the yellow glow of the sodium layer (located at an altitude of 85–95 km from the Earth’s surface) have become available. Studies of optical activity at sodium D‑line frequencies are necessary for a better understanding of the plasma-chemical processes occurring in the mesosphere. It should be taken into account that these processes occur in a neutral environment, where molecular nitrogen is the main component. In this study the analytical numerical expressions for the elements of a 3 × 3 matrix of the interaction between Na(2Pj) and N2( $$X{}^{1}\Sigma _{g}^{ + }$$ ) and the interaction potential between Na(2S1/2) and N2( $$X{}^{1}\Sigma _{g}^{ + }$$ ) are obtained at medium and large interparticle distances that determine the collisional broadening of the radiation lines. The exchange, quadrupole–quadrupole, dispersion, and spin–orbit interactions are taken into account. The exchange interaction between the valence Na electron and N2( $$X{}^{1}\Sigma _{g}^{ + }$$ ) molecule is described by the local Hellman pseudopotential. The effect of the overlap between Na(2S1/2,2Pj) and N2( $$X{}^{1}\Sigma _{g}^{ + }$$ ) electron densities is taken into account in the evaluation of long-range quadrupole–quadrupole and dispersion interactions.

Finding new efficient thermoelectric materials is a significant challenge for materials science. It is crucial to have a comprehensive understanding of material-property relationships to develop new materials successfully, given that minor structural or compositional changes can result in significant property changes. This paper extensively utilizes advanced theoretical approaches and investigates the impact of n- and p-type impurities on the mechanical characteristics of PbTe thermoelectric materials. n- and p-type doping of PbTe were studied using various techniques, including elastic tensor calculations, crystal orbital Hamilton population method, and local vibrational theory. Our findings reveal the specific ways in which doping type affects the material's mechanical properties. This information can aid researchers in optimizing PbTe doping strategies.

We present a theoretical approach for the in silico generation of new polymer structures for the systematic search for new materials with advanced properties. It is based on Bicerano’s Regression Model (RM), which uses the structure of the smallest repeating unit (SRU) for fast and adequate prediction of polymer properties. We have developed the programs (a) GenStruc, for generating the new polymer SRUs using the enumeration and Monte Carlo algorithms, and (b) PolyPred, for predicting properties for a given input polymer as well as for multiple structures stored in the database files. The structure database from the original Bicerano publication is used to create databases of backbones and pendant groups. A database of 5,142,153 unique SRUs is generated using the scaffold-based combinatorial method. We show that using only known backbones of the polymer SRU and varying the pendant groups can significantly improve the predicted extreme values of polymer properties. Analysis of the obtained results for the dielectric constant and refractive index shows that the values of the dielectric constant are higher for polyhydrazides than for polyhydroxylamines. The high value predicted for the refractive index of polythiophene and its derivatives is in agreement with the experimental data.

The electrical conductivity of the polyimide R-BAPB polymer filled with\nsingle-wall carbon nanotubes (CNT) with chirality (5,5) is modeled using a\nmulti-scale approach. The modeling starts with molecular dynamics simulations\nof time-dependent fluctuating atomic configurations of polymer filled CNTs\njunctions. Then the atomic positions obtained in the first step are used to\nperform fully first-principles microscopic calculations of the CNTs junctions\ncontact resistances using the Green's function based quantum transport\ntechnique. And finally, those contact resistances are supplied as an input to a\nstatistical calculation of a CNTs ensemble conductivity using a Monte Carlo\npercolation model. The results of the first-principles calculations show a very\nstrong dependence of the polymer filled CNTs junctions contact resistance on\nthe geometry of CNTs junctions, including an angle $\\varphi$ between nanotubes\naxes and the positions of polymer atoms around CNTs. Incorporating into the\npercolation model this strong dependence as well as CNTs agglomeration, pushed\nthe calculated values of electrical conductivity just above the percolation\nthreshold below 0.01 S/m, which is within the experimental range for composites\nwith various base polymers. Possible mechanisms for further reduction of\ncomposites conductivity are discussed.\n

Structural organization of hydrogen and oxygen functionalized nanodiamond (ND) particles in hydrosols was investigated using a cryo-TEM method. The formation of chain-like structures was observed for hydrogen functionalized NDs while oxygen functionalized NDs tend to form more compact structures. In order to understand possible interaction mechanisms between NDs in hydrosols and to explain these experimental results, first-principles calculations were performed. Charged H-terminated ND particles and particles with partially dissociated hydroxyl and carboxyl groups on the surface were investigated as models of a real ND particle in solution. For positively charged H-terminated particles, it was established that charge distribution is determined by the values of valence band maximum for the particle facets. For negatively charged oxygen functionalized particles, the charge is localized near functional groups. In both cases, interaction is determined by the interplay between Coulomb interaction and van der Waals attraction. For detailed analysis of the ND interaction, the continual model considering this interplay was developed. The results obtained with this model indicate that the formation of chain structures from linked ND particles is caused by charge separation inside the ND particle. For the H-terminated ND particles in water solution, strongly anisotropic distribution of electrostatic potential around the particles promotes formation of non-compact chain structures of particles via interaction between facets with opposite charges. This effect of charge separation is lower in the oxygen functionalized particles as the charge is localized at the uniformly distributed O-containing functional groups, thus, more compact structures can be formed. These general qualitative statements address the problem of interactions between the large number of ND particles and explain the presented cryo-TEM experimental results.

IntroductionCoronavirus disease 2019 (COVID‐19) is characterized by a high contagiousness requiring isolation measures. At this time, diagnosis is based on the positivity of specific RT‐PCR and/or chest computed tomography scan, which are time‐consuming and may delay diagnosis. Complete blood count (CBC) can potentially contribute to the diagnosis of COVID‐19. We studied whether the analysis of cellular population data (CPD), provided as part of CBC‐Diff analysis by the DxH 800 analyzers (Beckman Coulter), can help to identify SARS‐CoV‐2 infection.MethodsCellular population data of the different leukocyte subpopulations were analyzed in 137 controls, 322 patients with proven COVID‐19 (COVID+), and 285 patients for whom investigations were negative for SARS‐CoV‐2 infection (COVID−). When CPD of COVID+ were different from controls and COVID− patients, we used receiver operating characteristic analysis to test the discriminating capacity of the individual parameters. Using a random forest classifier, we developed the algorithm based on the combination of 4 monocyte CPD to discriminate COVID+ from COVID− patients. This algorithm was tested prospectively in a series of 222 patients referred to the emergency unit.ResultsAmong the 222 patients, 86 were diagnosed as COVID‐19 and 60.5% were correctly identified using the discriminating protocol. Among the 136 COVID− patients, 10.3% were misclassified (specificity 89.7%, sensitivity 60.5%). False negatives were observed mainly in patients with a low inflammatory state whereas false positives were mainly seen in patients with sepsis.ConclusionConsideration of CPD could constitute a first step and potentially aid in the early diagnosis of COVID‐19.