Search Results (14,489)

Nitroguanidine-type energetic materials have broad application prospects in the propellant field, and their derivative structures are numerous, with intricate changes in macro-level properties. However, due to the unclear inherent evolution mechanisms of these macro-level properties, the structural optimization of compounds and the iteration of application systems face difficulties. This work systematically investigates the variations in density, thermal decomposition, and sensitivity among nitroguanidine (NQ), 1-amino-2-nitroguanidine (ANQ), and 1-amino-2-nitroguanidinium nitrate (ANGN). Hirshfeld surface and bond dissociation energy analyses reveal that strengthened electrostatic and inductive interactions enhance the hydrogen bonding network in ANGN, leading to its higher density compared to NQ. In contrast, weakened electrostatic interactions in ANQ result in a less robust hydrogen bonding network and a correspondingly lower density. The sensitivity trend is consistently explained from both molecular and crystalline perspectives: an increasingly inhomogeneous electrostatic potential distribution, coupled with a higher frequency of O···O contacts, provides a coherent explanation for the experimental observations. Furthermore, the electron-withdrawing -NH₃⁺ group in ANGN weakens the N–NO₂ bond, reducing its bond dissociation energy and leading to the most intense NO₂ mass spectral signal during thermal decomposition. ANQ exhibits the opposite behavior. A linear correlation (R² = 0.92) is observed between the N–NO₂ BDE and NO₂ mass spectral intensity across NQ, ANQ, and ANGN. This study provides unique insights into the intrinsic mechanisms governing variations in the properties of nitroguanidine derivatives. Full article

The MoonRay project is carrying out a concept study of a permanent lunar cosmic-ray (CR) and gamma-ray observatory, in view of the implementation of habitats on our satellite. The idea is to build a modular telescope that will be able to overcome the limitations, in available power and weight, of the present generation of CR instruments in Low Earth Orbit, while carrying out high-energy gamma-ray observations from a vantage point at the South Pole of the Moon. An array of fully independent modules (towers), with limited individual size and mass, can provide an acceptance more than one order of magnitude larger than instruments in flight at present. The modular telescope is designed to be deployed progressively, during a series of lunar missions, while collecting meaningful scientific data at the intermediate stages of its implementation. The operational power will be made available by the facilities maintaining the lunar habitats. With a geometric factor close to 15 m²sr and about 8 times larger sensitive area than FERMI-LAT, MoonRay will be able to carry out a very rich observational program over a time span of a few decades with an energy reach of 10 PeV allowing the exploration of the CR “knee” and the observation of the Southern Sky with gamma rays well into the TeV scale. Each tower (of approximate size 20 cm × 20 cm ×100 cm) is equipped with three instruments. A combined Charge and Time-of-Flight detector (CD-ToF) can identify individual cosmic elements, leveraging on an innovative two-layered array of pixelated Low-Gain Avalanche Diode (LGAD) sensors, with sub-ns time resolution. The latter can achieve an unprecedented rejection power against backscattered radiation from the calorimeter. It is followed by a tracker, providing also photon conversion, and by a thick crystal calorimeter (55 radiation lengths, 3 proton interaction lengths at normal incidence) with an energy resolution of 30–40% (1–2%) for protons (electrons) and a proton/electron rejection in excess of 10⁵. A time resolution close to 100 ps has been obtained, with prototypal arrays of 3 mm × 3 mm LGAD pixels, in a recent test campaign carried out at CERN with Pb beam fragments. Full article

Rubberized concrete is a novel green building material that enhances many features when rubber particles are incorporated into cement mortar, simultaneously yielding economic benefits through the recycling of waste tires. This study applies styrene–butadiene latex (SBL) for toughening treatment. The investigation delves into the mechanism by which SBL improves the interface between rubber and cement, encompassing macroscopic mechanical properties, microscopic structural characteristics, and nano-scale interfacial interactions. Macroscopic mechanical tests reveal a significant increase in flexural strength, shear strength, and compressive strength of the composite concrete upon the introduction of SBL and rubber. Specifically, the compressive strength improved by 8.8%, shear strength by 13.7%, and flexural strength by 18.9% at 28 days. Through electron microscopy observation of corresponding polymer cement concrete sections, observations reveal that SBL reinforces both interfaces and elucidates its bonding impact at the micro-level interface. Molecular dynamics (MD) modeling of SBL/rubber/CSH is employed at the nanoscale to compute and examine the local structure, dynamic behavior, and binding energy of the interface. The findings indicate that SBL mitigates interface impacts, enhances interface hydrogen bonds, van der Waals interactions, $\mathrm{C}\mathrm{a}-\mathrm{H}$ coordination bonds, and stability, consequently improving interfacial adhesion and fortifying the feeble interface bonding between organic polymers (rubber) and inorganic silicates (CSH). Full article

The numerical reservoir simulation is a valuable tool to enhance heavy oil recovery by assessing different production strategies (like SAGD and CSS) and operational scenarios. While numerous studies have developed complex models, a systematic review identifying the most critical parameters for achieving accurate production forecasts is lacking. In this work, diverse studies have been reviewed regarding the numerical models of steam injection technologies by examining various parameters (reservoir properties and operating conditions) employed and their impact on the results obtained. Additionally, the effect of using kinetic models in simulations, as well as the modeling of solvent and catalyst injection, is discussed. The outcomes highlight that oil recovery for steam injection methods requires effective steam chamber management and an understanding of geomechanical changes due to the significant role of thermal convection on energy transfer and oil displacement. Increasing steam injection pressures can enhance energy efficiency and reduce emissions, but controlling the gases generated during the reaction poses difficulties. The gas formation within the reservoir in simulations is crucial to prevent overestimating oil production and improving precision. This can be achieved using simple kinetic models, but it is essential to incorporate gas–water solubilities to mimic actual gas emissions and avoid gas buildup. Crucially, our synthesis of the literature demonstrates that incorporating gas–water solubilities and kinetic models for H₂S production can improve the prediction accuracy of gas trends by up to 20% compared to oversimplified models. Enhanced recovery methods (adding solvent and catalyst injection) provide advantages compared with conventional steam injection methods. However, suitable interaction models between oil components and solid particles are needed to improve steam displacement, decrease water production, and enhance recovery in certain circumstances. The use of complex reaction schemes in numerical modeling remarkably enhances the prediction of experimental reservoir data. Full article

The rapid global expansion of photovoltaic (PV) generation has increased the prevalence of PV-dominated weak-grid systems, where wideband oscillations (WBOs) pose a significant challenge to secure and reliable operation. Unlike conventional electromechanical oscillations, WBOs originate from inverter control loops and multi-inverter interactions, spanning sub-Hz to kHz ranges. This review provides a PV-focused and mitigation-oriented analysis that addresses this gap. First, it clarifies the mechanisms of WBOs by mapping oscillatory drivers such as phase-locked loop dynamics, constant power control, converter–grid impedance resonance, and high-frequency switching effects to their corresponding frequency bands, alongside their engineering implications. Second, analysis methods are systematically evaluated, including eigenvalue and impedance-based models, electromagnetic transient simulations, and measurement- and data-driven techniques, with a comparative assessment of their strengths, limitations, and practical applications. Third, mitigation strategies are classified across converter-, plant-, and system-levels, ranging from adaptive control and virtual impedance to coordinated PV-battery energy storage systems (BESS) operation and grid-forming inverters. The review concludes by identifying future directions in grid-forming operation, artificial intelligence (AI)-driven adaptive stability, hybrid PV-BESS-hydrogen integration, and the establishment of standardized compliance frameworks. By integrating mechanisms, methods, and mitigation strategies, this work provides a comprehensive roadmap for addressing oscillatory stability in PV-dominated weak grids. Full article

The advancing speed of the coal mining face has a significant impact on the mining-induced stress and energy accumulation of the surrounding rock. To explain the influence mechanism from a mesoscopic perspective, this study conducted a uniaxial compression test on the coal–rock combination body under different quasi-static loading rates, and analyzed their mechanical properties, failure characteristics, acoustic emission characteristics and energy evolution characteristics. The main findings are as follows: The uniaxial compressive strength and elastic modulus of the coal–rock combination body show a variation law of first increasing and then decreasing with the increase in loading rate, while the degree of impact failure significantly increases gradually as the loading rate rises. With the increase in loading rate, there is a tendency that the AE parameters concentrate from the first two stages to the latter two stages. The post-peak residual elastic energy density of the coal–rock combination body increases gradually with the increase in loading rate. The formation of the advancing speed effect of mining-induced stress concentration and elastic energy accumulation in coal–rock masses is caused by the “competitive” interaction between fracture propagation and coal matrix damage when the coal component in the coal–rock combination is deformed under stress. Full article

The flow of a two-dimensional incompressible hybrid nanofluid over a stretching cylinder containing microorganisms with parallel effect of inclined magnetohydrodynamic was examined in the current study in relation to chemical reactions, heat source effect, nonlinear heat radiation, and multiple convective boundaries. The main objective of this research is the optimization of heat transfer with inclined MHD and variation in different physical parameters. The governing partial differential equations are transformed into a set of ordinary differential equations by applying the appropriate similarity transformations. The Runge–Kutta method is recognized for using shooting as a technique. Surface plots, graphs, and tables have been used to illustrate how various parameters affect the local Nusselt number, mass transfer, and heat transmission. It is demonstrated that when the chemical reaction parameter rises, the concentration and motile concentration profiles drop. The least responsive is the rate of heat transfer to changes in the inclined magnetic field and most associated with changes in the Biot number and radiation parameter shown in contour plot. The streamline graph illustrates the way fluid flow is affected simultaneously by the magnetic parameter M and an angled magnetic field. Local Nusselt number and local skin friction are improved by the curvature parameter and mixed convection parameter. The contours highlight the intricate interactions between restricted magnetic field, significant radiation, and substantial convective condition factors by displaying the best heat transfer. The three-dimensional surface, scattered graph, pie chart, and residual plotting demonstrate the statistical analysis of the heat transfer. The results support their use in sophisticated energy, healthcare, and industrial systems and enhance our theoretical knowledge of hybrid nanofluid dynamics. Full article

Photovoltaic power generation prediction is crucial for the effective integration of renewable energy into the grid, real-time grid balancing, and the optimization of energy storage systems. However, PV power generation is highly dependent on environmental factors such as weather conditions. Photovoltaic power generation prediction is crucial for the effective integration of renewable energy into the grid, real-time grid balancing, and the optimization of energy storage systems. However, PV power generation is highly dependent on environmental factors such as weather conditions. Effectively integrating environmental information remains a major challenge for photovoltaic power forecasting. This study proposes a hybrid deep learning model that incorporates an adaptive neural network to capture the latent relationships between PV power generation and environmental variables, thereby enhancing forecasting accuracy. The adaptive graph neural network employs a data-driven directed graph structure, where TCN and variable interaction layers are alternately stacked to better model the spatiotemporal coupling among variables for long-term PV output forecasting. The proposed model was evaluated on three sites located in different regions, with a fixed input length of 96 and output horizons ranging from 96 to 768 steps. Compared with state-of-the-art baselines, the model achieved average improvements of 2.19% and 1.57% in MSE and MAE at a 384-step horizon, and 2.81% and 2.47% at a 768-step horizon, respectively, demonstrating superior performance in long-term PV output forecasting tasks. Full article

Digital Twin (DT) technology has emerged as a valuable tool for researchers and engineers, enabling them to optimize performance and enhance system efficiency. This paper presents a comprehensive Systematic Literature Review (SLR) following the PRISMA framework to explore current applications of DT technology in the power generation sector while highlighting key advancements. A new framework is developed to categorize DTs in terms of time-scale horizons and applications, focusing on power plant types (emissive vs. non-emissive), operational behaviors (including condition monitoring, predictive maintenance, fault detection, power generation prediction, and optimization), and specific components (e.g., power transformers). The time-scale is subdivided into a six-level structure to precisely indicate the speed and time range at which it is used. More importantly, each category in the application is further subcategorized into a three-level framework: component-level (i.e., fundamental physical properties and operational characteristics), system-level (i.e., interaction of subsystems and optimization), and service-level (i.e., value-adding service outputs). This classification can be utilized by various parties, such as stakeholders, engineers, scientists, and policymakers, to gain both a general and detailed understanding of potential research and operational gaps. Addressing these gaps could improve asset longevity and reduce energy consumption and emissions. Full article

Apixaban (APX) is a direct oral anticoagulant with low aqueous solubility and limited bioavailability. This study aimed to improve APX solubility by forming spray-dried inclusion complexes (ICs) with β-cyclodextrin (β-CD) derivatives. ICs were prepared using hydroxypropyl-β-CD (HP-β-CD), sulfobutylether-β-CD (SBE-β-CD), randomly methylated-β-CD (RM-β-CD), and heptakis(2,6-di-O-methyl)-β-CD (DM-β-CD). Complex formation (1:1 stoichiometry) was confirmed by phase solubility studies and Job’s plots. The ICs were characterized by SEM, PXRD, DSC, and FTIR, and their saturated solubility was evaluated. Molecular docking assessed host–guest interactions. Among the tested carriers, DM-β-CD exhibited the highest stability constant (K_C = 371.92 M⁻¹) and produced amorphous ICs. DM-ICs achieved the greatest solubility enhancement at all pH conditions, with a maximum solubility of 1968.7 μg/mL at pH 1.2 and ~78.7-fold increase in water compared with pure APX. Docking results supported stable inclusion with the lowest binding free energy (−8.01 kcal/mol). These findings indicate that DM-β-CD-based ICs effectively enhance APX dissolution and show potential as solubilizing carriers for oral dosage forms. Full article

The increased energy efficiency of electrified vehicles and their potential to reduce CO₂ emissions through the use of environmentally friendly materials are highlighted as reasons for the shift to electrified vehicles. Brief trends on the development of electric vehicles (EVs) have been discussed, presenting outstanding improvement towards the actualization of the green economy. The state of the art in lubrication has been thoroughly investigated as one of the factors influencing energy efficiency and the lifespan of machine components. As a result, many reports on the effectiveness of specific lubricants in electric vehicle applications have been developed. Good thermal and corrosion-resistant lubricants are necessary because of the emergence of several new tribological difficulties, especially in areas that interact with greater temperatures and currents. To avoid fluidity and frictional problems that may be experienced over its lifetime, a good viscosity level of lubricant was also mentioned as a crucial component in the formulation of EV lubricant. New lubricants are also necessary for the gearbox systems of electric vehicles. Furthermore, battery electric vehicles (BEVs) require a suitable cooling system for the batteries; thus, a compatible nano-fluid is recommended. Sustainable battery cooling options support global energy efficiency and carbon emission reduction while extending the life of EV batteries. The path for future advancements or the creation of the most useful and efficient EV lubricants is provided by this review study. Full article

Microencapsulation is a fundamental technology for protecting active compounds from environmental degradation by factors such as light, heat, and oxygen. This process significantly improves their stability, bioavailability, and shelf life by entrapping an active core within a protective matrix. Therefore, a thorough understanding of the physicochemical interactions between these components is essential for developing stable and efficient delivery systems. The composition of the microcapsule and the encapsulation method are key determinants of system stability and the retention of encapsulated materials. Recently, the application of computational tools to predict and optimize microencapsulation processes has emerged as a promising area of research. In this context, molecular dynamics (MD) simulation has become an indispensable computational technique. By solving Newton’s equations of motion, MD simulations enable a detailed study of the dynamic behavior of atoms and molecules in a simulated environment. For example, MD-based analyses have quantitatively demonstrated that optimizing polymer–core interaction energies can enhance encapsulation efficiency by over 20% and improve the thermal stability of active compounds. This approach provides invaluable insights into the molecular interactions between the core material and the matrix, ultimately facilitating the rational design of optimized microstructures for diverse applications, including pharmaceuticals, thereby opening new avenues for innovation in the field. Ultimately, the integration of computational modeling into microencapsulation research not only represents a methodological advancement but also pivotal opportunity to accelerate innovation, optimize processes, and develop more effective and sustainable therapeutic systems. Full article

Effective thermal management is essential in metal additive manufacturing to ensure process stability and desirable material properties. Directed energy deposition using a laser beam and wire (DED-LB/w) enables the production of large, high-performance components but remains sensitive to adverse thermal effects during multi-layer deposition due to heat accumulation. While prior studies have investigated interlayer temperature control and substrate preheating in DED modalities, including laser-powder and arc-based systems, the influence of substrate preheating in DED-LB/w has not been thoroughly examined. This study employs substrate preheating to simulate heat accumulation and assess its effects on melt pool geometry, wire–melt pool interaction, and the microstructural evolution of Alloy 718. Experimental results demonstrate that increased substrate temperatures lead to a gradual expansion of the melt pool, with a notable transition occurring beyond 400 °C. Microstructural analysis reveals that elevated preheat temperatures promote coarser secondary dendrite arm spacing and the development of wider columnar grains. Moreover, Nb-rich secondary phases, including the Laves phase, exhibit increased size but relatively unchanged area fractions. Observations from electrical conductance measurements and coaxial visual imaging show that preheat temperature significantly affects the process dynamics and microstructural evolution, providing a basis for advanced process control strategies. Full article

Shipping expansion, offshore energy generation, fish farming, and construction work radiate high levels of underwater noise, which may critically stress marine ecosystems. Tools for simulating, analyzing, and forecasting underwater noise can be of great help in understanding the impact of underwater radiated noise both on the environment and on man-made equipment, such as underwater communication and telemetry systems. To address this challenge, we developed a web-based Marine Noise Analysis Tool (MNAT) that models, simulates, and predicts underwater radiated noise levels. To reproduce realistic shipping conditions, MNAT combines real-time Automatic Identification System data with environmental data using broadly accepted underwater acoustic propagation models, including Bellhop and RAM. Moreover, MNAT can simulate other kinds of noise sources, such as seismic airguns. It features an intuitive interface enabling real-time tracking, noise impact assessment, and interactive visualizations. MNAT’s noise modeling capabilities allow the user to design resilient communication systems in different noise conditions, analyze maritime noise data, and forecast future noise levels, with potential contributions to the design of noise-resilient systems, to the optimization of environmental monitoring device deployments, and to noise mitigation policymaking. MNAT has been made available for the community at a public GIT repository. Full article

Background/Objectives: Photobiomodulation (PBM) is a non-invasive, low-level laser treatment shown to improve insulin resistance, glucose metabolism, and obesity-related inflammation. This study examined whether PBM could acutely enhance mitochondrial efficiency and energy metabolism in women with obesity. Methods: In a randomized, crossover within-subject design, 16 women with obesity (43 ± 5 years; BMI: 36 ± 4 kg/m²) and 16 sedentary normal-weight women (43 ± 5 years; BMI: 22.7 ± 2 kg/m²) underwent PBM (front and back exposure; red light, 633–660 nm; NIR, 850–940 nm) and a sham stimulation (SHAM), as a control, for 12 min. Resting energy expenditure (REE) was assessed via indirect calorimetry before and after exposure. Secondary measures included skin autofluorescence, heart rate, blood pressure, profile of mood states, rate of perceived exertion (RPE), and flexibility. Diet and physical activity were controlled. Results: A 2 × 2 × 2 ANOVA revealed a significant group × time interaction (F_3,60 = 3.054, p = 0.03) and a main effect of time (F_1,60 = 10.88, p = 0.001). Women with obesity showed a significant increase in REE post-PBM compared to pre-PBM (+9.3%, 1624 ± 314 vs. 1486 ± 327 kcal/day; p < 0.001), with no change in the respiratory exchange ratio. Additionally, RPE decreased and flexibility improved in both groups following PBM. Front and back skin temperatures increased significantly post-PBM, with greater changes observed in the back versus the front. Conclusions: These preliminary findings indicate that PBM acutely enhances energy utilization efficiency in women with obesity, increasing resting energy expenditure without modifying substrate oxidation. PBM may represent a promising non-invasive adjunctive strategy for improving the metabolic health of obese individuals. Full article