Search Results (7,375)

Background/Objectives: Understanding the molecular interactions between small bioactive compounds and serum albumins is essential for drug development and pharmacokinetics. Noraucuparin, a biphenyl-type phytoalexin with promising pharmacological properties, has shown a strong binding affinity to bovine serum albumin (BSA), a model protein for drug transport. This study aims to elucidate the structural and energetic characteristics of the noraucuparin–BSA complex under physiological and slightly elevated temperatures. Methods: Microsecond-scale molecular dynamics (MD) simulations and Molecular Mechanics Generalized Born Surface Area (MMGBSA)-binding-free energy calculations were performed to investigate the interaction between noraucuparin and BSA at 298 K and 310 K. Conformational flexibility and per-residue energy decomposition analyses were conducted, along with interaction network mapping to assess ligand-induced rearrangements. Results: Noraucuparin preferentially binds to site II of BSA, near the ibuprofen-binding pocket, with stabilization driven by hydrogen bonding and hydrophobic interactions. Binding at 298 K notably increased the structural mobility of BSA, affecting its global conformational dynamics. Key residues, such as Trp213, Arg217, and Leu237, contributed significantly to complex stability, and the ligand induced localized rearrangements in the protein’s intramolecular interaction network. Conclusions: These findings offer insights into the dynamic behavior of the noraucuparin–BSA complex and enhance the understanding of serum albumin–ligand interactions, with potential implications for drug delivery systems. Full article

GTD 111 has been employed in first-stage blades in different gas turbines. The study of microstructural evolution is essential for the lifetime assessment and development of turbine blades. The microstructural stability of a 130 MW gas turbine first-stage blade at 800 °C was studied. The microstructure’s evolution was analyzed using scanning electron microscopy (SEM), transmission electron microscopy (TEM), and thermodynamic calculation. As thermal exposure time increases, the shape of γ′ precipitates changes from square to spherical. During thermal exposure, MC particles formed and coarsened along the grain boundaries, and primary MC carbide decomposed into the η phase and M₂₃C₆. The stability of MC carbide at the grain boundaries was lower than that within the grains. MC carbide precipitated at the grain boundaries tends to grow along the boundaries and eventually forms elongated carbide. High-resolution transmission electron microscopy (HRTEM) images indicate that the orientation of the γ′ precipitate changes during the coarsening process. The GTD 111 alloy can be deformed through dislocation shearing at 800 °C. The hardness value initially increases, then decreases with further exposure, which is related to the reduced precipitation strengthening by γ′ precipitates and the reduction in the hardness of the γ matrix. Full article

Power hardware-in-the-loop (PHIL) creates a safe test environment to connect simulations with real hardware under test (HuT). Therefore, an interface algorithm (IA) must be chosen. The ideal transformer method (ITM) and the partial circuit duplication (PCD) are popular IAs, where a distinction is made between voltage- (V-) and current-type (C-) IAs. Depending on the sample time of the simulator and further delays, simulation accuracy is reduced and instability can occur due to negative feedback in the V-ITM and C-ITM control loops, which makes PHIL operation impossible. In the case of positive feedback, such as with the V-PCD and C-PCD, the delay causes destructive interference, which results in a phase shift and attenuation of the output signal. In this article, a novel damped Smith predictor (SP) for positive feedback PHIL IAs is presented, which significantly reduces destructive interference while allowing stable operation at low linking impedances at V-PCD and high linking impedances at C-PCD, thus reducing losses in the system. Experimental results show a reduction in phase shift by 21.17° and attenuation improvement of 24.3% for V-PCD at a sample time of 100 µs. The SP transfer functions are also derived and integrated into the listed negative feedback IAs, resulting in an increase in the gain margin (GM) from approximately one to three, which significantly enhances system stability. The proposed methods can improve stability and accuracy, which can be further improved by calculating the HuT impedance in real-time and dynamically adapting the SP model. Stable PHIL operation with SP is also possible with SP model errors or sudden HuT impedance changes, as long as deviations stay within the presented limits. Full article

A novel single-walled carbon nanotube (SWNT), silver (Ag) nanoparticle, and zinc benzene carboxylate (ZnBDC) metal–organic framework (MOF) composite was synthesised and systematically characterised to develop an efficient platform for mercury ion (Hg²⁺) detection. X-ray diffraction confirmed the successful incorporation of Ag nanoparticles and SWNTs without disrupting the crystalline structure of ZnBDC. Meanwhile, field-emission scanning electron microscopy and energy-dispersive spectroscopy mapping revealed a uniform elemental distribution. Thermogravimetric analysis indicated enhanced thermal stability. Electrochemical measurements (cyclic voltammetry and electrochemical impedance spectroscopy) demonstrated improved charge transfer properties. Electrochemical sensing investigations using differential pulse voltammetry revealed that the SWNTs/Ag@ZnBDC-modified glassy carbon electrode exhibited high selectivity toward Hg²⁺ ions over other metal ions (Cd²⁺, Co²⁺, Cr³⁺, Fe³⁺, and Zn²⁺), with optimal performance at pH 4. The sensor displayed a linear response in the concentration range of 0.1–1.0 nM (R² = 0.9908), with a calculated limit of detection of 0.102 nM, slightly close to the lowest tested point, confirming its high sensitivity for ultra-trace Hg²⁺ detection. The outstanding sensitivity, selectivity, and reproducibility underscore the potential of SWNTs/Ag@ZnBDC as a promising electrochemical platform for detecting trace levels of Hg2+ in environmental monitoring. Full article

Slope stability analysis is conventionally performed using the strength reduction method with the proportional reduction in shear strength parameters. However, during actual slope failure processes, the attenuation characteristics of rock mass cohesion ($c$) and internal friction angle ($\phi$) are often inconsistent, and their reduction paths exhibit clear nonlinearity. Relying solely on proportional reduction paths to calculate safety factors may therefore lack scientific rigor and fail to reflect true slope behavior. To address this limitation, this study proposes a novel approach that considers the non-proportional reduction of $c$ and $\phi$, without dependence on predefined reduction paths. The method begins with an analysis of slope stability states based on energy dissipation theory. A Bayesian Gaussian Mixture Model (BGMM) is employed for intelligent interpretation of the dissipated energy data, and, combined with energy mutation theory, is used to identify instability states under various reduction parameter combinations. To compute the safety factor, the concept of a “reference slope” is introduced. This reference slope represents the state at which the slope reaches limit equilibrium under strength reduction. The safety factor is then defined as the ratio of the shear strength of the target analyzed slope to that of the reference slope, providing a physically meaningful and interpretable safety index. Compared with traditional proportional reduction methods, the proposed approach offers more accurate estimation of safety factors, demonstrates superior sensitivity in identifying critical slopes, and significantly improves the reliability and precision of slope stability assessments. These advantages contribute to enhanced safety management and risk control in slope engineering practice. Full article

This study theoretically investigates the potential of a polyacrylamide copolymerized with amylose, a primary component of starch, to evaluate its efficiency in removing heavy metals from industrial wastewater. This material concept seeks to combine the high adsorption capacity of polyacrylamide with the low cost and biodegradability of starch, ultimately aiming to offer an economical, efficient, and sustainable alternative for wastewater treatment. To this end, a computational model based on density functional theory (DFT) was developed, utilizing the B3LYP functional with the 6-311++G(d,p) basis set, a widely recognized combination that strikes a balance between accuracy and computational cost. The interactions between an acrylamide-amylose (AM/Amy) polymer matrix, as well as the individual polymers (AM and Amy), and the metal ions Pb, Hg, and Cd in their hexahydrated form (M·6H₂O) were analyzed. This modeling approach, where M represents any of these metals, simulates a realistic aqueous environment around the metal ion. Molecular geometries were optimized, and key parameters such as total energy, dipole moment, frontier molecular orbital (HOMO-LUMO) energy levels, and Density of States (DOS) graphs were calculated to characterize the stability and electronic reactivity of the molecules. The results indicate that this proposed copolymer, through its favorable electronic properties, exhibits a high adsorption capacity for metal ions such as Pb and Cd, positioning it as a promising material for environmental applications. Full article

To enhance the specific energy and rate performance of lithium primary batteries, the development of advanced cathode materials with superior electrochemical properties is essential. Fluorides, composed of light fluorine elements and multivalent cations, exhibit multi-electron reaction characteristics, possess a high theoretical voltage, and demonstrate high discharge-specific energy. However, owing to fluorine’s high electronegativity, which leads to the formation of strong ionic bonds with other elements, most fluorides exhibit poor electronic conductivity, thereby constraining their electrochemical performance when used as cathode materials. Copper fluoride (CuF₂) exhibits a high theoretical specific capacity and discharge voltage but is constrained by its large bandgap, poor electronic conductivity, and difficulties in synthesizing anhydrous CuF₂ materials, which significantly limit its electrochemical activity. In this study, zinc (Zn) was chosen as a dopant for copper fluoride. By combining theoretical calculations with experimental validation, the impacts of Zn doping on the structural stability and electrochemical performance of copper fluoride were comprehensively analyzed. The resultant highly active Zn-doped copper fluoride achieved a discharge specific capacity of 528.6 mAh/g at 0.1 C and 489.1 mAh/g at 1 C, showcasing superior discharge-specific energy and good rate performance. This material holds great potential as a promising cathode candidate for lithium batteries, providing both high specific energy and power density. Full article

The erosion resistance of materials against solid particles is a very important property, especially in the transportation of powders or in aeronautics (dust inside jet engines). There is a strong need to introduce new materials that have higher solid particle erosion resistance than state-of-the-art materials. Thus, in the present work, the solid erosion particles of high entropy alloys (HEAs) based on the Al0.7CoCrFeNi matrix were studied compared to the state-of-the-art stainless steel AISI 304. Furthermore, the effect of the addition of Ti to HEAs on hardness and erosion resistance was investigated. Current research included the development of the chemical composition of a new kind of HEA designed on the basis of thermodynamical calculations performed in CALPHAD, its manufacturing, full characterization involving microstructural and phase analyses, hardness measurements, solid particle erosion tests, and finally, the elucidation of erosion mechanisms. It was found that HEAs showed higher hardness as well as erosion resistance than AISI 304. Moreover, it was found that the increase in Ti content in an HEA resulted in an increase in the hardness and resistance to the erosion of the studied HEA. As the main reason for this phenomenon, the stabilization of the β-BCC phase, suppression of the α-FCC phase, and the appearance of the Ni₃Ti phase in the studied HEA were claimed. Full article

The one-dimensional (1D) Sb(III)-based organic–inorganic hybrid perovskite (AImd)₂¹_∞[SbI₅] (AImd = 1-allylimidazolium) crystallizes in the orthorhombic, centrosymmetric space group Pnma. The structure consists of corner-sharing [SbI₆] octahedra forming 1D chains separated by allylimidazolium cations. Void analysis through Mercury CSD software confirmed a densely packed lattice with a calculated void volume of 1.1%. Integrated quantum theory of atoms in molecules (QTAIM) and non-covalent interactions index (NCI) analyses showed that C–H···I interactions between the cations and the ¹_∞[SbI₅]²⁻ network predominantly stabilize the supramolecular assembly followed by N–H···I hydrogen bonds. The calculated growth morphology (GM) model fits very well to the experimental morphology. UV–Vis diffuse reflectance spectroscopy allowed us to determine the optical band gap to 3.15 eV. Density functional theory (DFT) calculations employing the B3LYP, CAM-B3LYP, and PBE0 functionals were benchmarked against experimental data. CAM-B3LYP best reproduced Sb–I bond lengths, while PBE0 more accurately captured the HOMO–LUMO gap and the associated electronic descriptors. These results support the assignment of an inorganic-to-organic [Sb–I] → π* charge-transfer excitation, and clarify how structural dimensionality and cation identity shape the material’s optoelectronic properties. Full article

Accurate pre-drilling mud weight window (MWW) prediction is crucial for drilling fluid design and wellbore stability in complex geological formations. Traditional physics-based approaches suffer from subjective parameter selection and inadequate handling of multi-mechanism over-pressured formations, while machine learning methods lack physical constraints and interpretability. This study develops a novel physics-guided deep learning framework integrating rock mechanics theory with deep neural networks for enhanced MWW prediction. The framework incorporates three key components: first, a physics-driven layer synthesizing intermediate variables from rock physics calculations to embed domain knowledge while preserving interpretability; second, a geological sequence-matching algorithm enabling precise stratigraphic correlation between offset and target wells, compensating for lateral geological heterogeneity; third, a long short-term memory network capturing sequential drilling characteristics and geological structure continuity. Case study results from 12 wells in northwestern China demonstrate significant improvements over traditional methods: collapse pressure prediction error reduced by 40.96%, pore pressure error decreased by 30.43%, and fracture pressure error diminished by 39.02%. The proposed method successfully captures meter-scale pressure variations undetectable by conventional approaches, providing critical technical support for wellbore design optimization, drilling fluid formulation, and operational safety enhancement in challenging geological environments. Full article

Compound steering technology has been extensively adopted in military logistics and related applications, owing to its superior maneuverability and enhanced stability compared to conventional systems. To enhance the steering efficiency and dynamic response of distributed-drive unmanned platforms under low driving torque conditions, this study investigates their unique compound steering system. Specifically, a compound steering dynamics model is established, and a hierarchical stability control strategy, along with a model predictive control-based trajectory tracking algorithm, are innovatively proposed. First, a compound steering platform dynamics model is established by combining the Ackermann steering and skid yaw moment methods. Then, a trajectory tracking controller is designed using model predictive control algorithm. Finally, the additional yaw moment is calculated based on the lateral velocity error and yaw rate error, with stability control allocation performed using a fuzzy control algorithm. Comparative hardware-in-the-loop experiments are conducted for compound steering, Ackermann steering, and skid steering. The experimental results show that the compound steering technology enables unmanned platforms to achieve trajectory tracking tasks with a lower torque, faster speed, and higher efficiency. Full article

Cancer remains one of the leading causes of death globally, highlighting the urgent need for novel anticancer therapies with higher efficacy and reduced toxicity. Similarly, the rise in multidrug-resistant pathogens and emerging infectious diseases underscores the critical demand for new antimicrobial agents that target resistant infections through unique mechanisms. This study used computational approaches to investigate twenty quinolone–triazole and conazole–triazole hybrid derivatives as antimicrobial and anticancer agents (1–20) with nine reference drugs. By studying their interactions with 6 bacterial DNA gyrase and 10 cancer-inducing target proteins (E. faecalis, M. tuberculosis, S. aureus, E. coli, M. smegmatis, P. aeruginosa and EGFR, MPO, VEGFR, CDK6, MMP1, Bcl-2, LSD1, HDAC6, Aromatase, ALOX15) and comparing them with established drugs such as ampicillin, cefatrizine, fluconazole, gemcitabine, itraconazole, ribavirin, rufinamide, streptomycin, and tazobactam, compounds 15 and 16 emerged as noteworthy antimicrobial and anticancer agents, respectively. In molecular dynamics simulations, compounds 15 and 16 had the strongest binding at −10.6 kcal mol⁻¹ and −12.0 kcal mol⁻¹ with the crucial 5CDQ and 2Z3Y proteins, respectively, exceeded drug-likeness criteria, and displayed extraordinary stability within the enzyme’s pocket over varied temperatures (300–320 K). In addition, we used density functional theory (DFT) to calculate dipole moments and molecular orbital characteristics and analyze the thermodynamic stability of putative antimicrobial and anticancer derivatives. This finding reveals a well-defined, possibly therapeutic relationship, supported by theoretical and future in vitro and in vivo studies. Compounds 15 and 16, thus, emerged as intriguing contenders in the fight against infectious diseases and cancer. Full article

This study proposed a sea cliff stability model that accounted for the coupled effects of sea erosion and rainfall, offering an improved quantitative assessment of the toppling risk. The approach integrated the notch morphology (height and depth) and rainfall infiltration to quantify stability, validated by field data from six toppling sites near Da’ao Bay, where the maximum erosion distance error between model predictions and measurements ranged from 0.81% to 48.8% (with <20% error for Sites S2, S3, and S4). The results indicated that the notch morphology and rainfall exerted significant impacts on the sea cliff stability. Site S4 (the highest site) corresponded to a 17.5% decrease in K per 0.1 m notch depth increment. The rainfall infiltration reduced the maximum stable notch depth, decreasing by 8.86–21.92% during prolonged rainfall. This model can predict sea cliff stability and calculate the critical notch depth (e.g., 0.56–1.22 m for the study sites), providing a quantitative framework for coastal engineering applications and disaster mitigation strategies under climate change scenarios. Full article

As a key insulating material in power equipment, epoxy resins (EP) are often limited in practical applications due to space charge accumulation and mechanical degradation. This study systematically investigates the effects of SiO₂ nanoparticle doping on the electrical and mechanical properties of SiO₂/EP composites through molecular dynamics simulations and first-principles calculations. The results demonstrate that SiO₂ doping enhances the mechanical properties of EP, with notable improvements in Young’s modulus, bulk modulus, and shear modulus, while maintaining excellent thermal stability across different temperatures. Further investigations reveal that SiO₂ doping effectively modulates the interfacial charge behavior between EP and metals (Cu/Fe) by introducing shallow defect states and reconstructing interfacial dipoles. Density of states analysis indicates the formation of localized defect states at the interface in doped systems, which dominate the defect-assisted hopping mechanism for charge transport and suppress space charge accumulation. Potential distribution calculations show that doping reduces the average potential of EP (1 eV for Cu layer and 1.09 eV for Fe layer) while simultaneously influencing the potential distribution near the polymer–metal interface, thereby optimizing the interfacial charge injection barrier. Specifically, the hole barrier at the maximum valence band (VBM) after doping significantly increased, rising from the initial values of 0.448 eV (Cu interface) and 0.349 eV (Fe interface) to 104.02% and 209.46%, respectively. These findings provide a theoretical foundation for designing high-performance epoxy-based composites with both enhanced mechanical properties and controllable interfacial charge behavior. Full article

Flying wing Unmanned Aerial Vehicles (UAVs) are an interesting flight configuration, considering its benefits over aerodynamic, structural and added stealth aspects. The existing configurations are thoroughly studied from the literature survey and useful observations with respect to design and analysis are obtained. The proposed design method includes distinct calculations of the UAV and modelling using 3D experience. The created innovative models are simulated with the help of computational fluid dynamics techniques in ANSYS Fluent to obtain the aerodynamic parameters such as forces, pressure and velocity. The optimization process continues to add more desired modifications to the model, to finalize the best design of flying wing frame for the chosen application and mission profile. In total, nine models are developed starting with the base model, then leading to the conventional, advanced and nature inspired configurations such as the falcon and dragonfly models, as it has an added advantage of producing high maneuverability and lift. Following this, fluid structure interaction analysis has been performed for the best performing configurations, resulting in the determination of variations in the structural behavior with the imposition of advanced composite materials, namely, boron, Kevlar, glass and carbon fiber-reinforced polymers. In addition to this, a hybrid material is designed by combining two composites that resulted in superior material performance when imposed. Control dynamic study is performed for the maneuvers planned as per mission profile, to ensure stability during flight. All the resulting parameters obtained are compared with one another to choose the best frame of the flying wing body, along with the optimum material to be utilized for future analysis and development. Full article