Constituent Blocks Research Articles

Structural Model of the Decision Support System for Managing the Supply of Material Resources of a Machine-Building Enterprise

In the context of digital transformation, the problem of supply management remains one of the urgent problems of any machine-building enterprise, requiring both theoretical and practical solutions. The potential of artificial intelligence to improve the efficiency of supply management in companies is obvious, but the level of implementation of solutions based on it remains low today. Enterprises are not ready to invest and allocate significant resources to implement elements of artificial intelligence in their activities, which is facilitated by the lack of transparency regarding the requirements for the implementation of artificial intelligence in the supply system of enterprises and experience with such technologies. The article proposes a structural model of a decision support system for supply management of machine-building enterprises, which assumes the possibility of making decisions automatically. Thanks to the use of artificial intelligence elements, the speed of the system’s response to changes in the initial data, both in the external and internal environment of the enterprise, will be significantly increased. This model has a number of advantages, the most significant of which are the integrated management of the supply, taking into account the features of its two constituent blocks: procurement management and supplier management, improving the quality of decisions made in the supply management due to the use of accumulated experience in the system, which will reduce the time and material costs of enterprises, as well as transaction costs arising when solving the problem of finding a supplier and supplies, negotiating, and concluding contracts with suppliers. The system will also allow solving new, complex problems in the field of management of the supply, using or adapting existing solutions in the precedent base of the decision support system, and explaining the decisions obtained. The proposed intelligent system makes decisions automatically, freeing up managers’ time to make strategic decisions in supply in other areas of the enterprise. The results presented in the article can be useful to researchers and managers in the field of supply activities, which can be used for more effective enterprise management in the process of digital transformation.

BB-SAR: An Application for Data-driven Analysis and Rational Design of Medicinal Chemistry Series.

In drug discovery, medicinal chemists face the challenge of generating and analyzing large data sets, often exceeding a thousand molecules and numerous physicochemical and biological properties. To address this, we introduced BB-SAR, an interpolative methodology that tackles both data complexity and interpretability, by breaking down molecules into their constituent building blocks (BBs). Establishing a direct correlation between molecules and their constituent BBs enables the association of these BBs with their respective biological and physicochemical properties. This facilitates more intuitive data analysis and enables the identification of critical trends between molecular features and their associated properties. While individual BBs rarely dictate property behavior, their combinations do. BB-SAR identifies impactful combinations for designing new, improved compounds. Additionally, it simplifies traditional medicinal chemistry analysis strategies and enhances the efficiency of drug discovery by providing a more inherent understanding of complex data sets within a concise framework.

Wood and Cellulose: the Most Sustainable Advanced Materials for Past, Present, and Future Civilizations.

Wood, with its constituent building block cellulose, is by far the most common biomaterial on the planet and has been the most important material used by humans to establish civilization. If there is one single biomaterial that should be studied and used by materials scientists across disciplines to achieve a sustainable future, it is cellulose. This perspective provides insights for the general materials science community about the unique properties of wood and cellulose and how they may be used in advanced sustainable materials to make a substantial societal impact. The focus is on sawn wood or cellulose fibers produced at scale by industry and the more recent cellulosic nanomaterials, highlighting the areas where these cellulose-based materials can be valorized into higher-order functions. Numerous articles have comprehensively reviewed different areas where cellulose is currently used in advanced materials science. The objective here is to provide general insight for all material scientists and to provide the opinions about the areas in which cellulose and wood have the largest potential to make a significant societal impact, especially to realize next-generation sustainable materials for construction, food, water, energy, and information. Discussing key areas where future research is needed to open avenues toward a more sustainable future is ended.

Design of switched capacitor converter-based wireless power transfer using mutual inductance for micropower applications

Abstract Short-range wireless DC-to-DC power transmission using a switched capacitor converter (SCC) for various voltage ranges is presented in this letter. In wireless power transfer (WPT) applications, the main factors to consider are the target’s distance from the source coil and the voltage received at the load. In this work, the receiving voltage can be varied according to the application by selecting the voltage ratios (VRs) using the multiphase controller. The different VRs are selected using the Fibonacci series having the arbitrary value of (0, 1). A proposed circuit is designed and simulated using SIMULINK to validate the proposed WPT converter and to verify its transfer efficiency. The constituent blocks of the transmitter and receiver modules are designed, simulated, and examined for WPT. In many studies, step-up voltages are presented and discussed, but not the step-down WPT. This letter discusses the wireless power supply system with different step-down/step-up VRs, which can be used to supply auxiliary systems such as battery charging, audio systems, mobile phone calls, and general voltage regulators. The proposed converter with WPT provides high efficiency, voltage regulation, and target distances. The power of the proposed converter varies from 80 μW to 100 μW for the input voltages 12 V–24 V.

Hydrogel-Assisted Robust Supraparticles Evolved from Droplet Evaporation.

Supraparticles, formed through the self-assembly of nanoparticles, are promising contenders in catalysis, sensing, and drug delivery due to their exceptional specific surface area and porosity. However, their mechanical resilience, especially in dimensions spanning micrometers and beyond, is challenged by the inherently weak interactions among their constituent building blocks, significantly constraining their broad applicability. Here, we have exploited a robust supraparticle fabrication strategy by integrating hydrogel components into the assembly system and evaporating on the superamphiphobic surface. The resultant SiO2/SA (sodium alginate) supraparticles, achieved by evaporating a 15% volume fraction dispersion of SiO2 nanoparticles containing 18.46 mg/mL of sodium alginate and subsequently cross-linking with Ca2+, demonstrate mechanical robustness with a fracture force of 6.04 N, representing a mechanical strength enhancement of 60 times higher than that prior to the incorporation of the hydrogel component. The supraparticles maintain their original morphology after 30 min of ultrasonic treatment (200 W), demonstrating mechanical stability. This method exhibits generalizability, enabling the customization of supraparticles with various building blocks and hydrogel backbone materials. Based on such a methodology, we have synthesized enzyme-carrying supraparticles, further expanding the potential applications in intricate cascade reactions. The encapsulated glucose oxidase and horseradish peroxidase maintained their inherent reactivity, and such hydrogel-assisted robust supraparticles exhibited exceptional performance in accurate glucose assays, indicating great practical application in biocatalysis.

Regiospecific Incorporation of Fluorine Atoms in Polythiophene Derivatives for Efficient Organic Solar Cells.

Derivatives of polythiophene (PT) have garnered considerable attention in organic solar cells (OSCs) because of their relatively uncomplicated molecular structures and cost-effective synthesis. Herein, we have developed two regioisomeric fluorinated PT donors, PEI3T-FITVT and PEI3T-FOTVT, to realize efficient OSCs. PEI3T-FITVT and PEI3T-FOTVT are strategically designed with different fluorine atom arrangements on thiophene-vinyl-thiophene (TVT) units. Notably, PEI3T-FOTVT possesses enhanced backbone planarity induced by F···S noncovalent interactions between two constituent building blocks. Consequently, PEI3T-FOTVT with the higher aggregation and crystalline properties leads to a 2.5-fold increase in hole mobility over PEI3T-FITVT (from 1.4 × 10-4 to 3.6 × 10-4 cm2 V-1 s-1). Furthermore, PEI3T-FOTVT exhibits higher domain purity than PEI3T-FITVT, leading to faster charge transport and reduced charge recombination in OSC devices. These characteristics lead to a higher power conversion efficiency of 14.4% for PEI3T-FOTVT-based OSCs, compared to 12.9% for PEI3T-FITVT-based OSCs.

Metal–Organic Frameworks and Derivative Materials in Perovskite Solar Cells: Recent Advances, Emerging Trends, and Perspectives

The power conversion efficiency (PCE) of perovskite solar cells (PSCs) has reached an impressive value of 26.1%. While several initiatives such as structural modification and fabrication techniques helped steadily increase the PCE and stability of PSCs in recent years, the incorporation of metal–organic frameworks (MOFs) in PSCs stands out among other innovations and has emerged as a promising path forward to make this technology the front‐runner for realizing next‐generation low‐cost photovoltaic technologies. Owing to their unique physiochemical properties and extraordinary advantages such as large specific surface area and tunable pore structures, incorporating them as/in different functional layers of PSCs endows the devices with extraordinary optoelectronic properties. This article reviews the latest research practices adapted in integrating MOFs and derivative materials into the constituent blocks of PSCs such as photoactive perovskite absorber, electron‐transport layer, hole‐transport layer, and interfacial layer. Notably, a special emphasis is placed on the aspect of stability improvement in PSCs by incorporating MOFs and derivative materials. Also, the potential of MOFs as lead absorbents in PSCs is highlighted. Finally, an outlook on the critical challenges faced and future perspectives for employing MOFs in PSCs in light of the commercialization of PSCs is provided.

Benzodithiophene‐Based Copolymers as Dopant‐Free Hole‐Transporting Materials for Perovskite Solar Cells

Over a decade, single‐junction perovskite solar cells (PSCs) have achieved a remarkable power conversion efficiency of 26.4%. However, the pressing challenge remains to address their long‐term stability for large‐scale industrial production. A significant factor contributing to this instability is the use of doped hole‐transporting materials (HTMs), which often introduce moisture into contact with the perovskite film, leading to its degradation. To address this issue, research has focused on developing novel and stable “dopant‐free” HTMs. These materials possess inherent conductivity and can function without the need for additional dopants. These HTMs, constructed with donors and acceptors, have properties like film‐forming abilities, tunable molecular weights, unique stacking, and high hole mobility. Among the classic donor units, benzodithiophene (BDT) has emerged as a widely utilized component for the synthesis of molecules and polymers for photovoltaic applications. To stimulate further research and optimization of these materials, this overview presents a comprehensive examination of various BDT‐based dopant‐free polymeric HTMs with different acceptor units within the field of PSCs. It provides a concise historical overview, followed by an in‐depth classification and examination of polymeric structures. The exploration delves into their constituent building blocks, aiming to uncover the relationships between structure and activity wherever feasible.

Depths in random recursive metric spaces

AbstractAs a generalization of random recursive trees and preferential attachment trees, we consider random recursive metric spaces. These spaces are constructed from random blocks, each a metric space equipped with a probability measure, containing a labelled point called a hook, and assigned a weight. Random recursive metric spaces are equipped with a probability measure made up of a weighted sum of the probability measures assigned to its constituent blocks. At each step in the growth of a random recursive metric space, a point called a latch is chosen at random according to the equipped probability measure, and a new block is chosen at random and attached to the space by joining together the latch and the hook of the block. We use martingale theory to prove a law of large numbers and a central limit theorem for the insertion depth, the distance from the master hook to the latch chosen. We also apply our results to further generalizations of random trees, hooking networks, and continuous spaces constructed from line segments.

Streamlining the Generation of Advanced Polymer Materials Through the Marriage of Automation and Multiblock Copolymer Synthesis in Emulsion.

Synthetic polymers are of paramount importance in modern life - an incredibly wide range of polymeric materials possessing an impressive variety of properties have been developed to date. The recent emergence of artificial intelligence and automation presents a great opportunity to significantly speed up discovery and development of the next generation of advanced polymeric materials. We have focused on the high-throughput automated synthesis of multiblock copolymers that comprise three or more distinct polymer segments of different monomer composition bonded in linear sequence. The present work has exploited automation to prepare high molar mass multiblock copolymers (typically>100,000 g mol-1) using reversible addition-fragmentation chain transfer (RAFT) polymerization in aqueous emulsion. A variety of original multiblock copolymers have been synthesised via a Chemspeed robot, exemplified by a multiblock copolymer comprising thirteen constituent blocks. Moreover, libraries of copolymers of randomized monomer compositions (acrylates, acrylamides, methacrylates, and styrenes), block orders, and block lengths were also generated, thereby demonstrating the robustness of our synthetic approach. One multiblock copolymer contained all four monomer families listed in the pool, which is unprecedented in the literature. The present work demonstrates that automation has the power to render complex and laborious syntheses of such unprecedented materials not just possible, but facile and straightforward, thus representing the way forward to the next generation of complex macromolecular architectures.

Streamlining the Generation of Advanced Polymer Materials Through the Marriage of Automation and Multiblock Copolymer Synthesis in Emulsion

AbstractSynthetic polymers are of paramount importance in modern life ‐ an incredibly wide range of polymeric materials possessing an impressive variety of properties have been developed to date. The recent emergence of artificial intelligence and automation presents a great opportunity to significantly speed up discovery and development of the next generation of advanced polymeric materials. We have focused on the high‐throughput automated synthesis of multiblock copolymers that comprise three or more distinct polymer segments of different monomer composition bonded in linear sequence. The present work has exploited automation to prepare high molar mass multiblock copolymers (typically>100,000 g mol−1) using reversible addition‐fragmentation chain transfer (RAFT) polymerization in aqueous emulsion. A variety of original multiblock copolymers have been synthesised via a Chemspeed robot, exemplified by a multiblock copolymer comprising thirteen constituent blocks. Moreover, libraries of copolymers of randomized monomer compositions (acrylates, acrylamides, methacrylates, and styrenes), block orders, and block lengths were also generated, thereby demonstrating the robustness of our synthetic approach. One multiblock copolymer contained all four monomer families listed in the pool, which is unprecedented in the literature. The present work demonstrates that automation has the power to render complex and laborious syntheses of such unprecedented materials not just possible, but facile and straightforward, thus representing the way forward to the next generation of complex macromolecular architectures.

Modular Circuit Synthesis Oriented Modelling Approach for Non-Isolated DC-DC Converters in DCM

The continued and envisioned large-scale integration of renewable energy sources as a reaction to rising global temperatures and climate change will need a readily available DC grid to increase commissioning and operating efficiency. The effective operation of these grids is predicated on the correct control of its main control points. A plethora of DC-DC converters that find use in DC microgrids act as the main control points. DC-DC converters are non-linear and can operate in different modes with completely unique characteristics. To utilise classical control techniques, laborious equivalent linear models are derived for DC-DC converters using averaging modelling schemes. The application and limitations of these modelling techniques are well captured in the available literature. The most common limitation of the available modelling schemes is that more focus is dedicated to converter attributes like order, functionality and operating mode, even when optimal power flow and voltage regulation within the DC network are of more interest. Structure-based modelling techniques like the use of basic building blocks nullify converter attributes in the modelling process which translates to modelling efficiency. In light of the merits seen with the use of basic building blocks when modelling converters in CCM, the current study extends these merits to converters operation in DCM. Similar to modelling converters in CCM, modelling techniques that are available in the literature continue to consider converter attributes in the modelling process for DCM operation. Moreover, the two modes of operation are treated as unique entities and often modelled in a non-unified manner, which compromise modelling efficiency since the same converter can operate in a different state solely based on loading. The aim is to increase modelling efficiency but also nullify operating mode in the modelling process. The same basic building blocks are now modelled as two-port networks for DCM operation and adopted based on the exact configuration of a specified converter to compute its steady-state and dynamic models. All the advantages seen when modelling converters in CCM using basic building blocks are retained and augmented when considering DCM operation. Thus, any converter with well-defined basic building blocks can be easily modelled solely based on the connection of constituent basic building blocks.

Gas phase electronic structure of the DTDCTB small-molecule donor for vacuum-processed organic photovoltaics compared to its constituent building blocks

Experimental and calculated gas phase photoemission and photoabsorption spectra of DTDCTB small-molecule donor compared to DPTA and BTD building blocks.

Mathematical model of thermophysical loading of a small-caliber artillery barrel with variant discretization of half-integer layers of the computational domain

In the conditions of continuous financing of the programs of the Ministry of defense of the Russian Federation, the question of finding the most effective ways to modernize weapons and military (special) equipment, the developments in which are maximum and the processes of their improvement can take no more than a few years, is particularly acute. Such products, in particular, include aviation artillery weapons (AAO), the prospects for the use of which remain for the entire period of the army's existence with conventional weapons. The main factor influencing the quality of the AAO functioning is considered to be the thermophysical loading of a small-caliber artillery barrel (hereinafter referred to as the barrel) during firing. The problem of increasing the accuracy of determining the temperature field of the barrel is again updated by tightening the conditions for striking targets. Issues closely related to the intensification of AAO application regimes have come to the fore. These are issues of heating, cooling, thermal strength, wear, barrel survivability, issues of safety and firing efficiency. Despite the methodological evidence of analytical and numerical approaches to formalizing heat transfer in the wellbore, their practical implementation is rather complicated. The physical and mathematical meaning of this reason is as follows: possible instability of solutions; manifestation of oscillations in areas of large gradients; simultaneous presence in the solution regions of supersonic, sonic and subsonic zones; the existence of laminar, turbulent flows and other non-linear formations; non-triviality of setting boundary conditions; the presence of thermal resistance of surfaces, etc. However, the practical needs of ensuring safety and increasing the efficiency of fire operation of AAO dictate the need to obtain a close approximation of the problem under consideration to its possibly existing exact analytical solution. The aim of the work is to improve the mathematical apparatus that simulates the temperature field of the shaft based on a combination of heat transfer methods and mathematical physics. By verifying the reliability of the developed mathematical model (hereinafter referred to as the model, if from the context of the presentation of the material it is clear that we are talking about the proposed tools), the facts of the absence of methodological errors in the formation of the constituent blocks of the model and the increase in the accuracy of determining the thermal loading of the wellbore by 9.4% were established. Based on the accents of the stated problem, the directions for improving the model are argued.

SEISMICITY of YAKUTIA in 2018–2019

According to the information received from 20 stationary digital seismic stations of the Yakutia Branch of Geophysical Survey of the Russian Academy of Sciences, two-year (2018–2019) seismic monitoring of the territory of the Republic of Sakha (Yakutia) was carried out. During this time interval, more than 730 earthquakes with an energy class of KR≥7.2 (magnitude M≥1.8) were registered. During the previous two years, their number was 801. Manifestations of seismicity fixed the spatial position of the contact zones and their constituent blocks of known lithospheric plates: the Eurasian and North American plates in the northeastern and arctic regions, as well as the Eurasian and Amur plates – in the southern areas of Yakutia. The epicentral fields traced individual relief lineaments and identified active boundary structures combined into two large-scale seismic belts – BaikalStanovoy and Arctic-Asian. Their outlines are seen as bands of earthquake epicenters, elongated from the Olekma River to the Sea of Okhotsk in the south and as ensembles of separate groups and chains of earthquakes between the Laptev and Okhotsk Seas in the northeast and in the Arctic. Significant seismic activity has been detected in the northeast of the region – in the Olekminsk region and the Aldan Highlands (South Yakutia), as well as in the Yano-Oymyakon Highlands, the Chersky Ridge and the Laptev Sea. A map of epicenters was constructed for the entire territory of the region, where 12 districts were identified and an analysis of the seismotectonic situation in each of them was given. The map of Arctic earthquakes in the Lena River delta is considered separately. It is compiled according to the processing of digital seismograms obtained at 25 autonomous field stations installed in accordance with the Russian-German project "Seismicity and Neotectonics of the Laptev Sea Region" (SIOLA). In this area during 2016–2019 more than 2 thousand shocks of small energy classes KR≥7.2–10.7 (M≥1.8–3.7) were recorded within the depth interval of 1–36 km of the Earth's crust. The characteristics of the most seismically active local tectonic structures developed in this region are given.

Structural Control of Plasmon Resonance in Molecularly Linked Metal Oxide Nanocrystal Gel Assemblies.

Nanocrystal gels exhibit collective optical phenomena based on interactions among their constituent building blocks. However, their inherently disordered structures have made it challenging to understand, predict, or design properties such as optical absorption spectra that are sensitive to the coupling between the plasmon resonances of the individual nanocrystals. Here, we bring indium tin oxide nanocrystal gels under chemical control and show that their infrared absorption can be predicted and systematically tuned by selecting the nanocrystal sizes and compositions and molecular structures of the link-mediating surface ligands. Thermoreversible assemblies with metal-terpyridine links form reproducible gel architectures, enabling us to derive a plasmon ruler that governs the spectral shifts upon gelation, predicated on the nanocrystal and ligand compositions. This empirical guide is validated using large-scale, many-bodied simulations to compute the optical spectra of gels with varied structural parameters. Based on the derived plasmon ruler, we design and demonstrate a nanocrystal mixture whose spectrum exhibits distinctive line narrowing upon assembly.

Blockchain-Secure Mobility Data in Smart Campus

Acquiring knowledge of the patterns of human mobility within a university setting is a critical endeavor that can facilitate the development of effective strategies for future work programs. It is essential to ascertain the positions of campus inhabitants as they engage in daily activities that align with the institution’s work plan framework. Nonetheless, the paramount challenge associated with the presence of personal data on human movement is ensuring the utmost security of this sensitive information. Blockchain technology offers a solution by enabling the safeguarding of personal data through the decentralization of information, wherein individuals act as controllers in a distributed cloud network. In the present study, a straightforward system comprising GPS sensors and a Raspberry Pi is employed to detect personnel’s location data. The SHA256 algorithm is utilized to generate a hash that connects the constituent blocks, thereby significantly enhancing data security. The intricate hash computation is validated through the implementation of proof-of-work, which generates pertinent binary data at an expeditious block mining time of 16.64 milliseconds. This approach effectively thwarts cyberattacks and ensures the maximum protection of data.

Formation and characterization of polymetallic {CrxMy} rings in vacuo

Understanding the (dis)assembly mechanisms of large metallosupramolecules is critical in their design, stability and application. The inherent complexity of these structures leads to many potential pathways for combining (or separating) the constituent building blocks, which makes this task difficult. Here we use collision-induced dissociation mass spectrometry to study the disassembly of heterometallic complexes. Collisional activation leads to the formation of a series of previously unknown smaller ring products and we characterize their geometry using ion mobility. The disassembly of both {CrxCu2} hourglass structures (x = 10, 12) and of a {Cr12Gd4} cluster shows the formation of rare closed, heptametallic species {Cr6Cu}, {Cr5Cu2} and {Cr5Gd2} as dominant products, as well as other closed ions such as {Cr5Cu}, {Cr10Cu}, {Cr12Cu}, {Cr10}, {Cr12} and {Cr6Gd2}. The collision cross-section of cyclic products and precursors has a linear correlation with ion mass—a relationship that does not hold for acyclic systems. As these rings are non-trivial to synthesize individually in solution, we propose the presented workflow to identify and characterize feasible molecules for bulk phase synthesis.

A multiresolution framework for the analysis of community structure in international trade networks

International trade networks are complex systems that consist of overlapping multiple trade blocs of varying sizes. However, the resulting structures of community detection in trade networks often fail to accurately represent the complexity of international trade. To address this issue, we propose a multiresolution framework that integrates information from a range of resolutions to consider trade communities of different sizes and reveal the hierarchical structure of trade networks and their constituent blocks. In addition, we introduce a measure called multiresolution membership inconsistency for each country, which demonstrates the positive correlation between a country’s structural inconsistency in terms of network topology and its vulnerability to external intervention in terms of economic and security functioning. Our findings show that network science-based approaches can effectively capture the complex interdependencies between countries and provide new metrics for evaluating the characteristics and behaviors of countries in both economic and political contexts.

State-of-the-Art in Smart Contact Lenses for Human–Machine Interaction

Contact lenses have traditionally been used for vision correction applications. Recent advances in microelectronics and nanofabrication on flexible substrates have now enabled sensors, circuits, and other essential components to be integrated on a small contact lens platform. This has opened up the possibility of using contact lenses for a range of human–machine interaction (HMI) applications, including vision assistance, eye tracking, displays, and healthcare. In this article, we systematically review the range of smart contact lens materials, device architectures, and components that facilitate this interaction for different applications. In fact, evidence from our systematic review demonstrates that these lenses can be used to display information, detect eye movements, restore vision, and detect certain biomarkers in tear fluid. Consequently, whereas previous state-of the-art reviews in contact lenses focused exclusively on biosensing, our systematic review covers a wider range of smart contact lens applications in HMI. Moreover, we present a new method of classifying the literature on smart contact lenses according to their six constituent building blocks, which are the sensing, energy management, driver electronics, communications, substrate, and the input/output interfacing modules. Based on recent developments in each of these categories, we speculate the challenges and opportunities of smart contact lenses for HMI. Moreover, based on our analysis of the state-of-the-art, we develop guidelines for the future design of a self-powered smart contact lens concept with integrated energy harvesters, sensors, and communications modules. Therefore, our review is a critical evaluation of current data and is presented with the aim of guiding researchers to new research directions in smart contact lenses.