Optimal Structure Research Articles

Enhanced current-carrying capability in YBCO coated conductor bilayers for high-field applications

We have investigated the impact of bilayer structures on the critical current density, J c, of YBa2Cu3O6+x (YBCO) coated conductor films, i.e. films grown on buffered metal substrates, under varying temperature and magnetic field conditions. The bilayers consisted of a YBCO layer free of artificial pinning centers and 8 wt% BaZrO3-added (BZO) layer on top, where the thickness percentage of the layers was varied from 0 to 100 %. The results reveal that the bilayer configuration enhances J c at temperatures below 60 K, with a significant improvement in high magnetic fields (5–8 T) and temperatures ≤20 K. The optimal BZO-added layer thickness was found to be approximately 70 %, reaching 80 % at 8 T. Structural examinations indicate improved growth of YBCO and BZO nanorods in the bilayer structure with BZO-added layer thickness ≤80 %. Theoretical model of the bilayer structure considering the layers as two parallel superconductors with different properties was developed. It was found that the model adequately explains all the experimentally observed tendencies, and thus the observed maximum in J c is due to better growth of the BZO-added layer. The study provides valuable insights for designing optimal bilayer structures for diverse applications operating in different temperature and magnetic field regimes.

Topology optimization of an elliptical parabolic shell structure: effect of penalty factor and load type

Topology optimization of an elliptical parabolic shell structure: effect of penalty factor and load type

Topology optimization of truss structure considering kinematic stability based on mixed-integer programming approach

Topology optimization of truss structure considering kinematic stability based on mixed-integer programming approach

Vascular remodelling in cardiovascular diseases: hypertension, oxidation, and inflammation.

Optimal vascular structure and function are essential for maintaining the physiological functions of the cardiovascular system. Vascular remodelling involves changes in vessel structure, including its size, shape, cellular and molecular composition. These changes result from multiple risk factors and may be compensatory adaptations to sustain blood vessel function. They occur in diverse cardiovascular pathologies, from hypertension to heart failure and atherosclerosis. Dynamic changes in the endothelium, fibroblasts, smooth muscle cells, pericytes or other vascular wall cells underlie remodelling. In addition, immune cells, including macrophages and lymphocytes, may infiltrate vessels and initiate inflammatory signalling. They contribute to a dynamic interplay between cell proliferation, apoptosis, migration, inflammation, and extracellular matrix reorganisation, all critical mechanisms of vascular remodelling. Molecular pathways underlying these processes include growth factors (e.g., vascular endothelial growth factor and platelet-derived growth factor), inflammatory cytokines (e.g., interleukin-1β and tumour necrosis factor-α), reactive oxygen species, and signalling pathways, such as Rho/ROCK, MAPK, and TGF-β/Smad, related to nitric oxide and superoxide biology. MicroRNAs and long noncoding RNAs are crucial epigenetic regulators of gene expression in vascular remodelling. We evaluate these pathways for potential therapeutic targeting from a clinical translational perspective. In summary, vascular remodelling, a coordinated modification of vascular structure and function, is crucial in cardiovascular disease pathology.

Discovery of Novel 2-Oxoacetamide Derivatives as B3GAT3 Inhibitors for the Treatment of Hepatocellular Carcinoma.

Beta-1,3-glucuronosyltransferase (B3GAT3), overexpressed in hepatocellular carcinoma (HCC) and negatively correlated to prognosis, is a promising target for cancer therapy. Currently, no studies have reported small molecule inhibitors of B3GAT3. In this study, we designed and synthesized a series of small-molecule inhibitors of B3GAT3 through virtual screening and structure optimization. The lead compound TMLB-C16 exhibited potent B3GAT3 inhibitory activity (KD = 3.962 μM) by effectively suppressing proliferation and migration, and inducing cell cycle arrest and apoptosis in MHCC-97H (IC50= 6.53 ± 0.18 μM) and HCCLM3 (IC50= 6.22 ± 0.23 μM) cells. Furthermore, compound TMLB-C16 demonstrated favorable pharmacokinetic properties with a relatively high bioavailability of 68.37%. It significantly inhibited tumor growth in both MHCC-97H and HCCLM3 xenograft tumor models without causing obvious toxicity. These results indicate that compound TMLB-C16 is an effective small molecule inhibitor of B3GAT3, providing a basis for the future development of B3GAT3-targeted drugs.

Optimization of H-type Anti-slide Pile Support Structure Based on RSM-NSGA-II

The parameters of an h-type anti-slide pile are optimized and analyzed with respect to its structural characteristics. Based on laboratory model tests, further analysis of its stress characteristics is carried out through numerical simulation methods. Then, the response surface methodology (RSM) combined with the NSGAII algorithm was employed to perform multiobjective optimization of the reinforcement performance and engineering cost for h-type anti-slide piles regarding landslide consideration. Finally, the entropy weight TOPSIS method was used for comprehensive evaluation to obtain the optimal structural parameters. The results show that the bending moment distribution of the front and rear rows of h-type piles first increases and then decreases with increasing depth, and the front soil pressure of the pile gradually increases. Using RSM-NSGA-II instead of finite element modeling can shorten the optimization time, and the accuracy of the surrogate model reaches 94.8%. NSGA-II is much stronger than that of the particle swarm optimization algorithm. The entropy weight method combined with the TOPSIS method can obtain the unique optimal solution from the Pareto solution set without prior knowledge. The h-type anti-slide pile with the best structural parameters reduces the cost by 75.19%, while the slope safety factor increases by 47.39%. This method provides a new idea for structural optimization.

A machine learning and CFD modeling hybrid approach for predicting real-time heat transfer during cokemaking processes

As the development of green energy and smart industries progresses, concerted efforts are being directed towards the transformation of coking industries. Effective management of heat transfer during the coking process is essential to fortify energy consumption management and augment the quality of coke produced. However, the process during cokemaking is a complex thermochemical conversion process where even minor deviations in operating conditions can lead to instability. Additionally, due to the environmental and chemical conditions inside the coke oven, temperature measurements during operation are difficult. As the fields of Industry 4.0 continue to evolve, numerous industries have begun to leverage intelligent strategies to enhance advanced process management. The digital twin (DG) technique stands as a pivotal technology in the realm of industrial transformation, paving the way for a smarter future. However, given the inherent difficulty in real-time data collection for process operations, the challenge of achieving synchronization becomes increasingly prevalent in the development of DG for the smart coking process. As a result, predicting changes in coal temperature during the cokemaking process is crucial. Currently, computer technology allows for the simulation of complex chemical processes. While some numerical models describe the cokemaking process, these models are limited by the time-consuming characteristic and computing resources. To address this issue, this study proposed a Machine Learning (ML) and Computational Fluid Dynamics (CFD) hybrid model for real-time prediction of heat transfer during the cokemaking process. A CFD model was built based on an industrial-scale coke oven, with cokemaking process data generated through CFD simulation serving as input for the ML models. This study used a total of nine ML models, including ensemble and deep learning models. The hyperparameters of each model were optimized by Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and JAYA algorithms to identify the optimal model structure. Both simulation and empirical experimentation using a coke oven, coupled with industrial data verification, have substantiated the superiority of the proposed model. It simulates the heat transfer during the coking process with high accuracy and efficiency, thereby demonstrating its capacity to significantly reduce energy consumption within the coking industry.

Local thermal resistance delamination method for precise analysis and optimization of heat transfer processes

Excessive carbon emissions are one of the global challenges. Promoting the optimization of high-efficiency heat transfer systems and improving the analysis and evaluation methods of heat exchangers is one of the effective ways to save energy and reduce carbon emissions. The traditional thermal resistance analysis method treats the fluid domain as a uniform whole, which makes it difficult to regulate the heat transfer characteristics in local regions, especially in the near-wall region. To address this problem, this paper obtained the thermal resistance mathematical equation for different regions in the flow field by magnitude analysis. The thermal resistance delamination method for dividing the thermal resistance field is proposed from the channel flow, and the conduction, mixing and advection zones in the flow field are identified. The delamination factor (α) is defined to determine the boundaries of thermal resistance zones. The α values which meet the delamination factor independence under forced convection and natural convection conditions are investigated. By studying the mechanism of the thermal resistance in conduction zone of the enhanced heat transfer structure, the critical value of fin height to achieve maximum enhancement efficiency is determined. The thermal resistance delamination facilitates a comprehensive analysis of heat transfer processes and structure optimization in the near-wall region.

A comprehensive overview of recent advances in generative models for antibodies

Therapeutic antibodies are an important class of biopharmaceuticals. With the rapid development of deep learning methods and the increasing amount of antibody data, antibody generative models have made great progress recently. They aim to solve the antibody space searching problems and are widely incorporated into the antibody development process. Therefore, a comprehensive introduction to the development methods in this field is imperative. Here, we collected 34 representative antibody generative models published recently and all generative models can be divided into three categories: sequence-generating models, structure-generating models, and hybrid models, based on their principles and algorithms. We further studied their performance and contributions to antibody sequence prediction, structure optimization, and affinity enhancement. Our manuscript will provide a comprehensive overview of the status of antibody generative models and also offer guidance for selecting different approaches.

Revisiting the Structure and Bonding in Li5H6- and the Exploration of Reactivity: Planar Pentacoordinate Hydrogen.

Recently, Guha and co-workers (Sarmah, K.; Kalita, A.; Purkayastha, S.; Guha, A. K. Pushing The Extreme of Multicentre Bonding: Planar Pentacoordinate Hydride. Angew. Chem. Int. Ed. 2024, e202318741) reported a highly intriguing bonding motif: planar pentacoordinate hydrogen (ppH) in Li5H6-, featuring C2v symmetry in the singlet state with two distinct H-Li (center-ring) bond distances. We herein revisited the potential energy surface of Li5H6- by using a target-oriented genetic algorithm. Our investigation revealed that the lowest-energy structure of Li5H6- exhibits a ppH configuration with very high D5h symmetry and a 1A1' electronic state. We did not find any electronic effect like Jahn-Teller distortion that could be responsible for lowering its symmetry. Moreover, our calculations demonstrated significant differences in the relative energies of other low-lying isomers. An energetically very competitive planar tetracoordinate hydrogen (ptH) isomer is also located, but it corresponds to a very shallow minimum on the potential energy surface depending on the used level of theory. Chemical bonding analyses, including AdNDP and EDA-NOCV, uncover that the optimal Lewis structure for Li5H6- involves H- ions stabilized by the Li5H5 crown. Surprisingly, despite the dominance of electrostatic interactions, the contribution from covalent bonding is also significant between ppH and the Li5H5 moiety, derived from H-(1s) → Li5H5 σ donation. Magnetically induced current density analysis revealed that due to minimal orbital overlap and the highly polar nature of the H-Li covalent interaction, the ppH exhibits local diatropic ring currents around the H centers, which fails to result in a global aromatic ring current. The coordination of Li5H6- with Lewis acids, BH3 and BMe3, instantly converts the ppH configuration to (quasi) ptH. These Lewis acid-bound ptH complexes show high electronic stability and high thermochemical stability against dissociation and, therefore, will be ideal candidates for the experimental realization.

Numerical Modeling and Topology Optimization for Designing the Anode Catalyst Layer in Proton Exchange Membrane Water Electrolyzers Considering Mass Transport Limitation

Abstract With the escalation of global warming primarily attributed to fossil fuel and other non-renewable energy consumption, the production of green hydrogen emerges as a mitigation strategy to reduce fossil fuel usage and effectively harness renewable energy sources for energy storage. The proton exchange membrane water electrolyzer (PEMWE) stands out as a promising technology, boasting high efficiency and a rapid response to variations in current density. Despite its stellar performance, the reliance on precious materials presents a cost challenge. To address this concern, we developed a numerical model considering mass transport limitations and temperature variation. The topology optimization (TO) method is employed to generate the optimal structure of the electrode by organizing the two primary constituent materials. Additionally, the impact of optimization points representing low (1.73 V) and high (2.03 V) operating voltage characteristics is analyzed. The optimal structure demonstrates a maximum performance improvement of up to 2.7 times at an operating voltage of 2.03 V compared to the homogeneous electrode structure. The gas coverage model influences the rearrangement of constituent materials, particularly the void fraction, creating channels to facilitate the reaction. Optimization at high voltage points yields a more significant improvement compared to the low voltage scenario.

Inspired by nacre: α-ZrP three-dimensional network structures for interlaminar toughening of laminates

This work study enhancing the mechanical properties of composite laminates by using a three-dimensional network structure that mimics the nacre-like structure. The optimal interlaminar structure was developed by utilizing α-ZrP with ultrasonic-assisted exfoliation for 6 minutes. The mode-I critical energy release rate (GIC) and mode-II critical energy release rate (GIIC) of the toughened composite are 104.44% and 74.20% higher than those of the unmodified sample. Observation of the region around the crack by SEM and OM reveals that the three-dimensional network structure across the fiber fabric undergoes energy dissipation behaviors, such as fracture, slip and pull-out when cracks penetrate thin walls. In addition, the effect of the thin-walled structure of the α-ZrP aerogel on interlaminar crack propagation is discussed, taking into account the inhomogeneous and multi-surface of the composite interfaces, and the toughening mechanism is investigated.

The Impact of Renewable Energy Development on Regional Carbon Emission Reduction: Based on the Spatio-Temporal Analysis of 30 Provinces in China.

The development of renewable energy has become an important means for the world to cope with climate change, ensure energy security, and protect the ecological environment. Using the panel data of 30 provinces in China from 2013 to 2021, this study used the mediating effect model and the spatial Durbin model (SDM) to explore the mechanism and spatial effects of renewable energy development on China's regional carbon emission reduction. The results show that: (1) Renewable energy development can help to reduce carbon emission intensity. (2) The results of mechanism analysis show that renewable energy development reduces carbon intensity by improving energy structure, promoting industrial structure optimization, and industrial structure upgrading. (3) The development of renewable energy can not only reduce the local carbon intensity but also have a positive spillover effect on the carbon intensity of neighboring regions. (4) Further analysis shows that the long-term effect of renewable energy development on carbon emissions is greater than the short-term effect. At the same time, the heterogeneity analysis shows that compared with the Yellow River basin, the development of renewable energy has a significant carbon emission reduction effect in the Yangtze River Economic Belt region. Energy-rich areas fall into the "resource curse", which makes the carbon emission reduction effect of renewable energy development not significant. This paper has certain reference significance for promoting reasonable decomposition between regions and formulating renewable energy development policies.

Developing Polymeric Carbon Nitrides for Photocatalytic H2O2 Production.

Hydrogen peroxide (H2O2) plays a crucial role in various applications, such as green oxidation processes and the production of high-quality fuels. Currently, H2O2 is primarily manufactured using the indirect anthraquinone method, known for its significant energy consumption and the generation of intensive by-products. Extensive research has been conducted on the photocatalytic production of H2O2via oxygen reduction reaction (ORR), with polymeric carbon nitride (PCN) emerging as a promising catalyst for this conversion. This review article is organized around two approaches. The first part main consists of the chemical optimization of the PCN structure, while the second focuses on the physical integration of PCN with other functional materials. The objective is to clarify the correlation between the physicochemical properties of PCN photocatalysts and their effectiveness in H2O2 production. Through a thorough review and analysis of the findings, this article seeks to stimulate new insights and achievements, not only in the domain of H2O2 production via ORR but also in other redox reactions.

Dynamic Mechanism Design for Repeated Markov Games with Hidden Actions: Computational Approach

This paper introduces a dynamic mechanism design tailored for uncertain environments where incentive schemes are challenged by the inability to observe players’ actions, known as moral hazard. In these scenarios, the system operates as a Markov game where outcomes depend on both the state of payouts and players’ actions. Moral hazard and adverse selection further complicate decision-making. The proposed mechanism aims to incentivize players to truthfully reveal their states while maximizing their expected payoffs. This is achieved through players’ best-reply strategies, ensuring truthful state revelation despite moral hazard. The revelation principle, a core concept in mechanism design, is applied to models with both moral hazard and adverse selection, facilitating optimal reward structure identification. The research holds significant practical implications, addressing the challenge of designing reward structures for multiplayer Markov games with hidden actions. By utilizing dynamic mechanism design, researchers and practitioners can optimize incentive schemes in complex, uncertain environments affected by moral hazard. To demonstrate the approach, the paper includes a numerical example of solving an oligopoly problem. Oligopolies, with a few dominant market players, exhibit complex dynamics where individual actions impact market outcomes significantly. Using the dynamic mechanism design framework, the paper shows how to construct optimal reward structures that align players’ incentives with desirable market outcomes, mitigating moral hazard and adverse selection effects. This framework is crucial for optimizing incentive schemes in multiplayer Markov games, providing a robust approach to handling the intricacies of moral hazard and adverse selection. By leveraging this design, the research contributes to the literature by offering a method to construct effective reward structures even in complex and uncertain environments. The numerical example of oligopolies illustrates the practical application and effectiveness of this dynamic mechanism design.

Impact of Land Use Change on Carbon Storage in Urban Agglomerations in the Guanzhong Plain

It is important to study the impact of land use change on terrestrial ecosystem carbon stocks in urban agglomerations for the optimization of land use structure and sustainable development in urban agglomerations. Based on the patch-generating land use simulation (PLUS) model and integrated valuation of ecosystem services and trade-offs (InVEST) model, a simulation was developed that predicted the land use change and carbon stock of the Guanzhong Plain urban agglomeration in 2040 under different scenarios and further analyzed the impact of land use change on carbon stock. The results showed that:① The land use types of the Guanzhong Plain urban agglomeration were mainly cultivated land, forest land, and grassland, which accounted for more than 90 % of the total study area. ② From 2000 to 2020, the carbon stock in the Guanzhong Plain showed a continuous downward trend, with cropland, woodland, and grassland being the main sources of carbon stock in the Guanzhong Plain, and the overall carbon stock declined by 15.12×106 t, with the spatial distribution presenting the distribution characteristics of "high in the north and south and low in the middle." ③ By 2040, the carbon stock would decrease the most under the urban development scenario, with a total reduction of 27.08×106 t, and the least under the ecological development scenario, with a total reduction of 4.14×106t. The research results can provide data support for the high-quality development and rational land use planning of the Guanzhong Plain urban agglomeration.

Can green finance improve eco-efficiency? New Insights from China.

Achieving economic development and ecological protection simultaneously is an inevitable selection for sustainable development in today's world, so it is crucial to improve eco-efficiency (EE). According to Chinese panel data at the provincial level between 2010 and 2020, this research explores the nexus between green finance (GF) and EE. The results denote that GF can significantly improve EE, and the higher the level of EE, the stronger the effect of improvement. The upgrading of industrial structure, optimization of energy structure, enterprises' concern for environmental protection and the public's attention to the environment are all favorable factors that can enhance the promotion effect of GF on EE. Additionally, this facilitation can only be played under a good external environment and mature internal conditions. Our findings can provide new insights for improving EE by developing GF.

An excellent triethylamine sensor based on composite nanotube WO3/SnO2

Triethylamine (TEA) is a volatile organic compound, widely used in production and life. But as a strong irritating and flammable gas, it is a serious threat to people's life safety, so the development of high-performance TEA gas sensor is of great significance to human life and production. In this work, pure WO3 and WO3/SnO2 composite nanofibers were prepared by electrospinning method. The morphology and microstructure of the obtained samples were characterized by SEM, XRD, XPS, PL, etc. Then the gas sensitivity of the samples was studied. The results show that both pure WO3 nanofibers and composite nanofibers have good selectivity for TEA. But compared with pure WO3 nanofibers, WO3/SnO2 nanofibers have higher response, faster response/recovery time (14 s/22 s) and better stability. The optimal temperature is 260 °C, 20 °C lower than that of pure WO3 nanofibers, and the response to 100 ppm TEA at this temperature is about 26, which is nearly 1 times higher than that of pure WO3 nanofibers. The improvement of gas-sensitive performance is mainly attributed to the successful construction of heterojunctions to improve electron transport efficiency and the optimization of morphology and structure through the composite.

Optimization of mechanical properties and pore structure of lightweight geopolymer concrete using GGBFS based on LF-NMR technology

Lightweight geopolymer can combine good physical and mechanical properties, good thermal and chemical stability, low CO2 emission and low energy. The development of high strength lightweight geopolymer concrete for load-bearing structures is important to expand the application range of geopolymer. This paper presents an improvement study on the mechanical properties and pore structure of lightweight geopolymer concrete (LGC) by adding ground granulated blast-furnace slag (GGBFS). The effect of GGBFS content on the mechanical properties of LGC was analyzed, including ultimate compressive stress and elastic modulus. The variation in the microscopic pore structure of LGC with different GGBFS content was further analyzed by low-field nuclear magnetic resonance (LF-NMR) technology. The lightweight geopolymer concrete with different strength grades was proposed including LC20, LC30 and LC40. The results show that as the GGBFS content increases, the ultimate compressive stress and specific strength of LGC increase while the strain corresponding to peak stress decreases, which means that the mechanical properties and deformation resistance of LGC are improved. The CO2 emissions of LGC are lower than that of cement-based lightweight concrete, which shows good sustainability. The results also show that the addition of GGBFS can produce more gel and reduce the volume proportion of capillary pores and air pores, resulting in the densification of the LGC. The recommended GGBFS contents corresponding to the strength grades of LC20, LC30 and LC40 are 0 ∼ 12.7 %, 12.7 % ∼ 24.6 % and 24.6 % ∼ 30%, respectively. The LGC has the characteristics of lightweight and high-strength, which has a potential application in civil engineering. Highlights [1] Mechanical properties and pore structure of LGC were improved by adding GGBFS. [2] The effect of GGBFS content on geopolymer concrete properties was discussed. [3] Effect of slag content on pore structure of geopolymer concrete were studied. [4] GGBGS content of geopolymer concrete with different strength grades was proposed.

Novel 3D textile structures and geometries for electrofluidics.

The integration of microfluidics with electric field control, commonly referred to as electrofluidics, has led to new opportunities for biomedical analysis. The requirement for closed microcapillary channels in microfluidics, typically formed via complex microlithographic fabrication approaches, limits the direct accessibility to the separation processes during conventional electrofluidic devices. Textile structures provide an alternative and low-cost approach to overcome these limitations via providing open and surface-accessible capillary channels. Herein, we investigate the potential of different 3D textile structures for electrofluidics. In this study, 12 polyester yarns were braided around nylon monofilament cores of different diameters to produce functional 3D core-shell textile structures. Capillary electrophoresis performances of these 3D core-shell textile structures both before and after removing the nylon core were evaluated in terms of mobility and bandwidth of a fluorescence marker compound. It was shown that the fibre arrangement and density govern the inherent capillary formation within these textile structures which also impacts upon the solute analyte mobility and separation bandwidth during electrophoretic studies. Core-shell textile structures with a 0.47mm nylon core exhibited the highest fluorescein mobility and presented a narrower separation bandwidth. This optimal textile structure was readily converted to different geometries via a simple heat-setting of the central nylon core. This approach can be used to fabricate an array of miniaturized devices that possess many of the basic functionalities required in electrofluidics while maintaining open surface access that is otherwise impractical in classical approaches.