Complex Multicomponent Systems Research Articles

Surface charge engineering for structural control of colloidal silica rod-decorated silica particles and their flexible integration into magnetic core–multishell structures

In this study, the effect of surface modification and the resulting surface charge of colloidal silica seeds on the growth of silica rods during an emulsion droplet-based process is investigated. Through electrostatic interactions between negatively charged poly(vinylpyrrolidone) (PVP) and seed surfaces, significant PVP adsorption occurs as the seed surfaces are modified with positively charged cationic substances and then aged with PVP. This surface charge-enhanced PVP adsorption promotes the anchoring of water emulsion droplets, serving as the starting point for the growth of silica rods on the seed surfaces and leading to a high yield of silica rod-decorated particles during emulsion droplet-based growth. The surface properties, including surface charge and hydrophobicity, can be further modified by applying different cationic modifiers, which influence the morphology, structural architecture, and yield of the final product. We further demonstrated the versatility of this approach by successfully generating silica rod-decorated magnetic core (Fe3O4)–multishell (silica/polystyrene/titania) particles. The findings of this study highlight the critical role of surface charge engineering in controlling the structure and morphology of silica rod-decorated silica particles as well as their integration into complex multicomponent systems.

Optimal predictive selective maintenance for fleets of mission-oriented systems

ABSTRACT In many settings, fleets of assets must perform series of missions with in-between finite breaks. For such fleets, a widely used maintenance strategy is the fleet selective maintenance (FSM). Under resource constraints, the FSM problem selects an optimal subset of feasible maintenance actions to be performed on a subset of components to minimise the maintenance cost while ensuring high system reliability during the upcoming mission. The majority of extant FSMP models are focussed on traditional physics-based reliability models. With recent advances in Machine Learning (ML) and Deep Learning (DL) algorithms, data-driven methods have shown accuracy in predicting remaining useful life (RUL). This paper proposes a predictive FSM strategy for fleets of complex and large multicomponent systems. It relies on a concurrent ML/DL and optimisation approach where a clustering algorithm is used to determine the health states of components and a probabilistic RUL prognostics model is used for component reliability assessment. An optimisation model is developed to solve the predictive FSM problem to ensure high reliability of all systems within the fleet. An efficient two-phase solution approach is developed to solve this complex optimisation problem. Numerical experiments show the validity of the approach and highlight the improved maintenance plans achieved.

Health Assessment for Multi-component Systems Based on Proportional Hazards Model and Dynamic Bayesian Network

As a key task of prognostics and health management, the health assessment of systems depends on both age and covariate processes. Cox’s proportional hazards model is an effective tool for system health assessment, capable of capturing both the failure rate and the effect of the covariate process. However, most existing literature assumes the system failure as a whole, which exhibits certain limitations when dealing with the failure interactions among components in complex systems. This paper develops a method that combines a dynamic Bayesian network with a proportional hazards model, where a dynamic Bayesian network is utilized to characterize the failure dependency among components failure, and the joint effect of age and covariate processes on component failure is quantified by the proportional hazards model. The integration of two models facilitates the health assessment of complex multi-component systems. Finally, the effectiveness of the proposed model is showcased through a numerical case study.

Mesoscopic Structure of Lipid Nanoparticle Formulations for mRNA Drug Delivery: Comirnaty and Drug-Free Dispersions.

Lipid nanoparticles (LNPs) produced by antisolvent precipitation (ASP) are used in formulations for mRNA drug delivery. The mesoscopic structure of such complex multicomponent and polydisperse nanoparticulate systems is most relevant for their drug delivery properties, medical efficiency, shelf life, and possible side effects. However, the knowledge on the structural details of such formulations is very limited. Essentially no such information is publicly available for pharmaceutical dispersions approved by numerous medicine agencies for the use in humans and loaded with mRNA encoding a mimic of the spike protein of the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) as, e.g., the Comirnaty formulation (BioNTech/Pfizer). Here, we present a simple preparation method to mimic the Comirnaty drug-free LNPs including a comparison of their structural properties with those of Comirnaty. Strong evidence for the liquid state of the LNPs in both systems is found in contrast to the designation of the LNPs as solid lipid nanoparticles by BioNTech. An exceptionally detailed and reliable structural model for the LNPs i.a. revealing their unexpected narrow size distribution will be presented based on a combined small-angle X-ray scattering and photon correlation spectroscopy (SAXS/PCS) evaluation method. The results from this experimental approach are supported by light microscopy, 1H NMR spectroscopy, Raman spectroscopy, cryogenic electron microscopy (cryoTEM), and simultaneous SAXS/SANS studies. The presented results do not provide direct insights on particle formation or dispersion stability but should contribute significantly to better understanding the LNP drug delivery process, enhancing their medical benefit, and reducing side effects.

Experimental investigation and thermodynamic assessment of phase equilibria in the Al–Cr–Ta ternary system

The Al–Cr–Ta ternary system was investigated through a combination of experiments and thermodynamic modeling. The determination of the liquidus projection involved the microstructural characterization of 29 as-cast alloys using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD). Sublattice models were proposed for the phases according to their crystal structures and homogeneity ranges. Based on the experimental results, the Al–Cr–Ta ternary system was thermodynamically assessed by the CALculation of PHase Diagrams (CALPHAD) approach. The liquidus projection, isothermal sections, and vertical sections of the ternary system were calculated. A set of self-consistent thermodynamic parameters for the Al–Cr–Ta ternary system was obtained with a satisfactory agreement between the experimental and calculated results. The present work could be significant for the practical application of novel cemented carbides and future thermodynamics assessment of complex multicomponent systems.

Al–Ni–Ti thermodynamic database from first-principles calculations

First-principles calculations have increasingly become an essential tool for providing additional thermodynamic data for assessing thermodynamic databases using the CALPHAD methodology. As computational power increases and becomes easily accessible, first-principles calculation results have become more presented along with the experimental procedures to determine the thermochemical properties of the phases within a target system. This can help accelerate the alloy development process, even in complex multi-component systems. By using only first-principles calculation data for the both thermodynamic description and interaction parameters of end-members, an Al–Ni–Ti ternary thermodynamic database is constructed. By relying on the existing materials database for ground state stability of the known compounds, the constructed Al–Ni–Ti thermodynamic database can mostly capture all the features related to the solid-state phases appear in the ternary phase diagram comparable to that of the published database from an experimental data assessment.

Revisiting Probabilistic Relation Analysis: Using Probabilistic Relation Graphs for Relational Similarity Analysis of Words in Short Texts

Relation graphs provide useful tools for structural and relational analyses of highly complex multi-component systems. Probabilistic relation graph models can represent relations between system components by their probabilistic links. These graph types have been widely used for the graphical representation of Markov models and bigram probabilities. This study presents an implication of relational similarities within probabilistic graph models of textual entries. The article discusses several utilization examples of two fundamental similarity measures in the probabilistic analysis of short texts. To this end, the construction of probabilistic graph models by using bigram probability matrices of textual entries is illustrated, and vector spaces of input word-vectors and output word-vectors are formed. In this vector space, the utilization of cosine similarity and mean squared error measures are demonstrated to evaluate the probabilistic relational similarity between lexeme pairs in short texts. By using probabilistic relation graphs of the short texts, relational interchangeability analyses of lexeme pairs are conducted, and confidence index parameters are defined to express the reliability of these analyses. Potential applications of these graphs in language processing and linguistics are discussed on the basis of the analysis results of example texts. The performance of the applied similarity measures is evaluated in comparison to the similarity index of the word2vec language model. Results of the comparative study in one of the illustrative examples reveal that synonyms with 0.18157 word2vec similarity value scored 1.0 cosine similarity value according to the proposed method.

Polysaccharide-Controlled Crystallization of Lactose in Sweetened Condensed Milk

Introduction: One of the main problems when storing sweetened condensed milk is the formation of organoleptically perceptible lactose crystals larger than 10 microns. To prevent this defect, the technology of introducing a fine-crystalline lactose seed has widely proven itself, ensuring the production of a high-quality product. However, this traditional technology is energy-intensive, requires large production areas and metal-intensive equipment in the form of vacuum crystallizers. In this regard, research into alternative approaches that prevent spontaneous crystallization of lactose during the production of sweetened condensed milk remains relevant.Purpose: The purpose of this study is to create a composition of polysaccharides to prevent the formation of organoleptically perceptible lactose crystals in sweetened condensed milkMaterials and Methods: The materials used were commercial samples of skimmed milk powder, sugar, polysaccharides and whey protein hydrolyzate powder. The work used the methods of rotational viscometry, electron microscopy and the method of sorption-capacitance determination of bound waterResults: The paper presents data on the influence of individual polysaccharides, as well as their complexes on the process of crystallization of lactose in concentrated milk systems with sugar on the formation of a stable structure of matrices, reflecting the ability to have both positive and negative effects of hydrocolloids on the process of crystallization of lactose and changes in dynamic viscosity. For multicomponent complex systems containing carboxymethylcellulose, sodium alginate, tara gum, locust bean gum and gum arabic, both a synergistic effect, consisting in the intermolecular interaction of polysaccharides and slowing down the spontaneous crystallization of lactose, and an antagonism effect, manifested in an increase in crystal size, have been establishedConclusion: The composition containing tara gum, carboxymethylcellulose and gum arabic showed the most pronounced properties for inhibiting the growth of lactose crystals, as well as high thixotropic properties. In practical terms, the use of this complex additive for the production of condensed milk products with sugar by the method of restoring dry components can replace the classical process of seeding fine-crystalline lactose, and, accordingly, reduce the energy and metal consumption of the process of crystallization of lactose in the product

HEAD OF THE COMPLEX LABORATORY OF THE SE «UKRNAUKAGEOCENTR»

Since 2011, the Integrated Analytical Laboratory of the Subsidiary Enterprise of PrJSC «NJSC «Nadra Ukrayny» «Ukrainian geological center of industry research» SE «Ukrnaukageocenter» has been headed by Sіra Nataliіa Vasylivna. Under the leadership and with the participation of Nataliіa Vasylivna, the Integrated Analytical Laboratory of SE «Ukrnaukageotsentr» conducts determination of physical and chemical properties of reservoir oils as complex multicomponent hydrocarbon systems, conducts experimental studies of samples. According to the analysis of the results of experimental studies of the water-gas system, in simulated reservoir conditions, it is proved that nitrogen, based on its physical and chemical characteristics, can be used as a geochemical indicator of the process of anticipatory watering of deposits of gas condensate deposits. The result of its growth was the defense of a candidate's thesis in 2015 on the topic «Development and application of complex geological and geochemical methods for forecasting the watering of gas condensate deposits».

Coupled Computational Fluid Dynamics and Computational Thermodynamics Simulations for Chemically Reacting Flows with Phase Transformations: A Fluoride Volatility Process Example

This study presents the development of a novel computational capability for chemically reacting flows with phase transformation that can be applied to complex multi-component systems. This is done by coupling Computational Fluid Dynamic (CFD) using OpenFOAM and Computational Thermodynamics (CT) using Thermochimica and the Joint Research Centre Molten Salt Database (JRCMSD). To illustrate this capability, the Fluorite Volatility Process (FVP) is modelled because it is the first step to clean up irradiated fuel salt in the Molten Salt Fast Reactor design and needs to be better understood. Local chemical equilibrium is assumed, which means that chemical kinetics is solely governed by mass transport. FVP recovers nuclear fuel UF4(liq) by injection of fluorine gas (F2), forming UF6(gas). The vaporization rate of UF4 is investigated on four different molten salt systems, using different dynamic viscosities and temperatures. The vaporization rate was shown to be clearly dependent on both thermochemical and thermophysical properties. Higher viscosities promoted larger bubbles, which delayed the chemical reaction. Results were compared to already available experimental data, showing good agreement. The applicability of coupling CFD with Thermochimica showed to be promising and expands interpretations on multiphysics systems and will be later applied to more complex scenarios.

Shocker─A Molecular Dynamics Protocol and Tool for Accelerating and Analyzing the Effects of Osmotic Shocks.

The process of osmosis, a fundamental phenomenon in life, drives water through a semipermeable membrane in response to a solute concentration gradient across this membrane. In vitro, osmotic shocks are often used to drive shape changes in lipid vesicles, for instance, to study fission events in the context of artificial cells. While experimental techniques provide a macroscopic picture of large-scale membrane remodeling processes, molecular dynamics (MD) simulations are a powerful tool to study membrane deformations at the molecular level. However, simulating an osmotic shock is a time-consuming process due to slow water diffusion across the membrane, making it practically impossible to examine its effects in classic MD simulations. In this article, we present Shocker, a Python-based MD tool for simulating the effects of an osmotic shock by selecting and relocating water particles across a membrane over the course of several pumping cycles. Although this method is primarily aimed at efficiently simulating volume changes in vesicles, it can also handle membrane tubes and double bilayer systems. Additionally, Shocker is force field-independent and compatible with both coarse-grained and all-atom systems. We demonstrate that our tool is applicable to simulate both hypertonic and hypotonic osmotic shocks for a range of vesicular and bilamellar setups, including complex multicomponent systems containing membrane proteins or crowded internal solutions.

Utilisation of livestock by-products for resource-saving biogas production in industrial pork production

Modern energy systems are complex multi-component systems that use mechanical, thermal, and electrical energy. As fossil fuel reserves decline, interest in renewable energy sources is growing everywhere, which is driving research into biogas production technologies. The purpose of this study was to evaluate technological approaches to reduce resource costs to produce renewable energy sources from organic waste (manure) from pig farms. This study employed the bibliographic method of research, laboratory method, biochemical methods (determination of the chemical composition of manure and wastewater), statistical, mathematical (calculation of economic efficiency), multicriteria analysis, and analytical method. Based on the conducted research, a resource-saving technology for the preparation of wastewater for use in the production of renewable energy in industrial pork production was developed, which ensures a reduction in capital and operating costs for biogas generation. It was found that the settling of the initial wastewater with a moisture content of 96.94±0.18% produces a sediment with a moisture content of 91.23±0.25% and a liquid fraction with a moisture content of 98.86±0.42%. At the same time, the organic matter in the sediment extracted from the original wastewater is about 87%, and in the liquid fraction – about 98% of the dry matter. The technological process includes centrifugal and gravity separation of the initial wastewater into fractions (sediment and liquid fraction), gravitational thickening of the sediment and its dosing for methane digestion to produce biogas. It was shown that the application of the proposed approaches can reduce capital investments by about 30%. Based on the multicriteria analysis, it was found that the proposed biogas production technology has a significant advantage in terms of the complex efficiency indicator of each of the considered options N(Ck) compared to the idealised one. For this technology, the objective function is the smallest according to the criteria considered and amounts to 0.1672, while the objective function of the baseline technology is 1.9 times worse. The findings of the study on the use of livestock by-products for resource-saving biogas production are planned to be used at pork production complexes and farms

Condition-based maintenance optimisation for multi-component systems using mean residual life

This paper aims to propose a Novel Condition-based maintenance (CBM) decision aid model for optimising the maintenance of complex multi-component systems. As the degradation level of each component is assumed to be independent and stochastic, it follows a specific probability distribution determined from historical data of experimental observations and inspection. The main objective is to optimise the total cost for providing maintenance actions and reducing the excess of spare parts usage. The decision support model consists of determining measurements on components with the aim of estimating the instant of time of removing predictively one or a group of components before they fail. The measurement model includes the mean residual lifetime (MRL) and some extensions developed for this purpose. For demonstrating the pertinency of the proposed model, we use a preventive maintenance strategy for one-component systems and a grouping/opportunistic maintenance for multi-component systems. Besides, a numerical comparative study performing these measurements is carried out using several examples and a case study from Electric energy distribution systems. The solution is illustrated as a decision-making optimal model for optimising the maintenance operations’ costs and the total number of spare parts. The numerical results and the comparison show the efficiency of the proposed approach.

Thermodynamic insights into selenium oxyanion removal from synthetic flue gas desulfurization wastewater with temperature-swing solvent extraction.

Temperature-swing solvent extraction (TSSE) is a cost-effective, simple, versatile, and industry-ready technology platform capable of desalinating hypersaline brines toward zero liquid discharge. In this work, we demonstrate the potential of TSSE in the effective removal of selenium oxyanions and traces of mercury with the coexistence of high contents of chloride and sulfate often encountered in flue gas desulfurization wastewater streams. We compare the rejection performance of the two common solvents broadly used for TSSE, decanoic acid (DA) and diisopropylamine (DPA), and correlate those with the solvent physicochemical properties (e.g., dielectric constant, polarity, molecular bulkiness, and hydrophobicity) and ionic properties (e.g., hydrated radii and H-bonding). The results show that TSSE can remove >99.5% of selenium oxyanions and 96%-99.6% of mercury traces coexisting with sulfate (at a sixfold Se concentration) and chloride (at a 400-fold Se concentration) in a synthetic wastewater stream. Compared to diisopropylamine, decanoic acid is more effective in rejecting ions for all cases, ranging from a simple binary system to more complex multicomponent systems with highly varied ionic concentrations. Furthermore, the H-bonding interaction with water and the hydrated radii of the oxyanions (i.e., selenate vs. selenite) along with the hindrance effects caused by the molecular bulkiness and hydrophobicity (or lipophilicity) of the solvents play important roles in the favorable rejection of TSSE. This study shows that TSSE might provide a technological solution with a high deionization potential for the industry in complying with the Environmental Protection Agency regulations for discharge streams from coal-fired power facilities.

Deep Reinforcement Learning for Dynamic Opportunistic Maintenance of Multi-Component Systems With Load Sharing

Opportunistic maintenance (OM), which shows its superiority on complex multi-component systems by integrating the maintenance activities of multiple components to reduce the maintenance cost, has been widely studied over the past decade. To our knowledge, most of the existing OM works are developed based on fixed maintenance thresholds without fully utilizing the health state of the multi-component system. This article presents an OM optimization problem of multi-component systems with load sharing, solved by a modified proximal policy optimization approach based on deep reinforcement learning algorithm. The load sharing effect is reflected in the hazard rate function, which further changes the failure probability of the components. Meanwhile, the health states can be recovered by executing imperfect maintenance and corrective maintenance. The optimization problem is formulated as an infinite-horizon MDP with mixed discrete and continuous state and action space to maximize the total discounted reward, taking into account the system reliability and the maintenance cost. The difficulty caused by the mixed action space is solved by designing a parameterized action space structure and multi-task reinforcement learning framework. The effectiveness of the proposed algorithm is tested on a four-component system and a real-world scenario configured with the high-pressure feedwater heater system in the nuclear power plant. The results show that the performance of the algorithm is stable when facing large-scale problems. The algorithm proposed in this study also contributes to the imperfect maintenance optimization with state-of-the-art optimization techniques.

VII congress of biophysicists of Russia

The problems and results of research in biophysics, which were devoted to the VII Congress of Biophysicists of Russia (Krasnodar, April 17-23, 2023, http://rusbiophysics.ru/db/conf.pl), are discussed. The results of fundamental and applied research in the field of molecular biophysics, cell biophysics, biophysics of complex multicomponent systems were presented at plenary, sectional and poster sessions. The structure and dynamics of biopolymers, the fundamental mechanisms underlying the impact of physicochemical factors on biological systems, membrane and transport processes were actively discussed. Much attention was paid to new experimental methods of biophysical research, methods of bioinformatics, computer and mathematical modeling as essential research tools at all levels of organization of living systems. Along with the fundamental problems of studying the biophysical mechanisms of regulation of processes at the molecular, subcellular and cellular levels, much attention was paid to applied research in the field of biotechnology and environmental monitoring. Works in the field of medical biophysics were especially widely represented. During the Congress, the National Council for Biophysics was formed.

ЦИФРОВОЙ ХИМИЧЕСКИЙ СПРАВОЧНИК ИНДИВИДУАЛЬНЫХ ВЕЩЕСТВ НА БАЗЕ МОДУЛЕЙ «THERMO» И «CHEMICALS»

The article discusses an approach to the development of mathematical models of technological processes in the Python programming language. The paper presents an overview of the Thermo and Chemical modules with a description of their main functions and capabilities. These modules are designed to calculate the physicochemical properties of various systems, as well as to model the phase states of complex multicomponent systems. With the help of these modules, a digital chemical reference book of the properties of individual substances was created in the form of a Bot program based on the Telegram messenger

Mechanism of structural colors in binary mixtures of nanoparticle-based supraballs.

Inspired by structural colors in avian species, various synthetic strategies have been developed to produce noniridescent, saturated colors using nanoparticle assemblies. Nanoparticle mixtures varying in particle chemistry and size have additional emergent properties that affect the color produced. For complex multicomponent systems, understanding the assembled structure and a robust optical modeling tool can empower scientists to identify structure-color relationships and fabricate designer materials with tailored color. Here, we demonstrate how we can reconstruct the assembled structure from small-angle scattering measurements using the computational reverse-engineering analysis for scattering experiments method and use the reconstructed structure in finite-difference time-domain calculations to predict color. We successfully, quantitatively predict experimentally observed color in mixtures containing strongly absorbing nanoparticles and demonstrate the influence of a single layer of segregated nanoparticles on color produced. The versatile computational approach that we present is useful for engineering synthetic materials with desired colors without laborious trial-and-error experiments.

The Role of Surface Functional Groups of Iron Oxide, Organic Matter, and Clay Mineral Complexes in Sediments on the Adsorption of Copper Ions

Heavy metal pollution is a global problem affecting the environment and human health. Sediment is the source sink of heavy metals in water. Under certain circumstances, the migration of heavy metals will cause water pollution. Therefore, it is of great significance to study sediment composition and composite complexes in the migration and transformation of heavy metals. To understand the adsorption mechanisms of composite complexes and improve the theoretical understanding of adsorption in multi-component complex systems, this study explored the characteristics and rules of Cu adsorption to organic–inorganic, inorganic minerals, and iron-oxide–clay complexes in the estuary sediments of the Dianchi Lake. The Langmuir and Freundlich isotherm models were used for Cu adsorption experiments on three complexes to study their adsorption kinetics. X-ray diffraction and Fourier transform infrared spectroscopy characterized the samples before and after adsorption. The relationship between adsorption capacity and sediment composition was analyzed through redundant analyses. The results showed that the Freundlich isothermal model was better than the Langmuir model in describing the adsorption behavior of the adsorbents. The contribution of iron and aluminum oxides to Cu adsorption was more than that of organic matter. The organic–inorganic complexes functional groups involved in copper adsorption are the most, which resulting in a higher adsorption capacity. The organic matter removal (organic degradation in sediment) will reduce the polar functional groups and reduce silicide activity, leading to heavy metal desorption and re-entry into the water body.

Provision of optimal dispatching scenarios for regional power systems in the face of uncontrollable power shortages

As of 2023, substantiation of the criteria for optimal load balancing of territorial energy islands in the face of uncontrolled power shortages due to unpredictable failure (destruction as a result of military bombardment) of the upper (trunk) level energy infrastructure is relevant. The research aims to substantiate approaches to ensuring the controlled operation of regional power systems under conditions of power shortages. The study applied the theory and methods of mathematical analysis of complex multicomponent systems in the form of mixed Boolean linear programming. A Mathematical Programming Language and modelling methods of the NEOS server based on the Gurobi solver were used to describe the modes of operation of power grids. A proportional power distribution method between load nodes of the post-emergency network configuration was used to determine the direction of electricity transit with the least losses. An algorithm and a program for solving the problem of addressing flows and power losses in multi-node regional power systems are proposed. The optimisation problem is formulated in the form of a mixed Boolean linear programming model with the criterion of minimising the power momentum, considering balance constraints and upper limits on the power between supply and load nodes. The developed transport matrix made it possible to find the optimal power distribution for emergency and post-emergency modes according to the criterion of minimum network losses. Algorithms and scenarios for the response of dispatching services were formed, considering the capacity of power grids and determining temporary power supply schemes, the configuration of which will ensure the “survivability” of energy islands. The levels of efficiency of the Boolean linear programming model associated with the connectivity of the cycles of the transport problem and the order of traversal of its vertices for test examples depending on the constraints imposed on the components of the network structure are established. The process of tracking electricity flows will allow for establishing routes connecting specific electricity supply nodes with load nodes and determining their shares in covering the energy island’s demand under conditions of power shortage