Search Results (2,522)

Characterising and quantifying complex multiphase steels is a challenging and time-consuming process, which is often open to subjectivity when based on image analysis of optical metallographic or SEM images. The properties of multiphase steels are highly sensitive to their individual phase properties and fractions, necessitating the development of robust characterisation tools. This paper presents a method for classifying nanoindentation maps into proportional fractions of up to five distinct microstructural regions in dual-phase and complex-phase steels. The phases/regions considered are ferrite, ferrite containing mobile dislocations, bainite, tempered martensite, and untempered martensite. A range of microstructures with varying fractions of phases were evaluated using both SEM/EBSD and nanoindentation. A machine learning (ML) approach applied to EBSD data showed good consistency in characterising a two-phase system. However, as the microstructural system complexity increased, variations were observed between different analysts and the sensitivity to the ML training data increased when four phases were present (reaching up to ~11% difference in the ferrite phase fraction determined). The proposed nanoindentation mapping technique does not show operator sensitivity and enables the quantification of additional microstructural features, such as identifying and quantifying ferrite regions with a high density of mobile dislocations and the degree of martensite tempering. Full article

Urban planners are progressively recognizing the significant effects of the built environment and land use on PM_2.5 levels. However, in analyzing the drivers of PM_2.5 levels, researchers’ reliance on annual mean and seasonal means may overlook the monthly variations in PM_2.5 levels, potentially impeding accurate predictions during periods of high pollution. This study focuses on the area within the Sixth Ring Road of Beijing, China. It utilizes gridded monthly and annual mean PM_2.5 data from 2019 as the dependent variable. The research selects 33 independent variables from the perspectives of the built environment and land use. The Extreme Gradient Boosting (XGBoost) method is employed to reveal the driving impacts of the built environment and land use on PM_2.5 levels. To enhance the model accuracy and address the randomness in the division of training and testing sets, we conducted twenty comparisons for each month. We employed Shapley Additive Explanations (SHAP) and Partial Dependence Plots (PDP) to interpret the models’ results and analyze the interactions between the explanatory variables. The results indicate that models incorporating both the built environment and land use outperformed those that considered only a single aspect. Notably, in the test set for April, the R² value reached up to 0.78. Specifically, the fitting accuracy for high pollution months in February, April, and November is higher than the annual mean, while July shows the opposite trend. The coefficient of variation for the importance rankings of the seven key explanatory variables exceeds 30% for both monthly and annual means. Among these variables, building density exhibited the highest coefficient of variation, at 123%. Building density and parking lots density demonstrate strong explanatory power for most months and exhibit significant interactions with other variables. Land use factors such as wetlands fraction, croplands fraction, park and greenspace fraction, and forests fraction have significant driving effects during the summer and autumn seasons months. The research on time scales aims to more effectively reduce PM_2.5 levels, which is essential for developing refined urban planning strategies that foster healthier urban environments. Full article

This study investigates the potential of thermally treated olive husk (OH)—a heterogeneous agro-industrial by-product comprising olive stones, pulp, and fibrous residues—as a multifunctional component in lightweight bio-concrete. Uniquely, this work harnesses the intrinsic dual nature of OH as both a fibrous reinforcement and a porous aggregate, without further fractionation, to evaluate its influence on the hygrothermal and mechanical behavior of cementitious composites. While prior studies have often focused selectively on thermal conductivity, this work provides a comprehensive assessment of all major thermal parameters; including diffusivity, effusivity, and specific heat capacity; offering deeper insights into the full thermal behavior of bio-based concretes. OH was incorporated at 0%, 10%, and 20% by weight, and the resulting concretes were subjected to a comprehensive characterization of their thermal, hygric, mechanical, and microstructural properties. Thermal performance metrics included conductivity, specific heat capacity, diffusivity, effusivity, time lag, and predicted energy savings. Hygric behavior was assessed through the moisture buffering value (MBV), while density, porosity, and mechanical strengths were also evaluated. At 20% OH content, thermal conductivity decreased to 0.405 W/m·K (a 72% reduction), thermal diffusivity dropped by 87%, and thermal effusivity reached 554 W·s^0.5/m²·K, collectively enhancing thermal inertia and increasing the time lag by 77% (to 2.32 h). MBVs improved to 2.18 g/m²·%RH, rated as “Excellent” for indoor moisture regulation. Despite the higher porosity, the bio-concrete maintained adequate mechanical integrity, with compressive and flexural strengths of 11.68 MPa and 3.58 MPa, respectively, attributed to the crack-bridging action of the fibrous inclusions. Microstructural analysis (SEM/XRD) revealed improved paste continuity and denser C–S–H formation, attributed to enhanced matrix compatibility following oil removal via thermal pre-treatment. These findings demonstrate the viability of OH as a new bio-based, multifunctional additive for fabricating thermally efficient, hygroscopically active, and structurally sound concretes suitable for sustainable construction. Full article

Despite efforts to recycle, boro-alumino-silicate pharmaceutical glass (BASG) results in a significant portion of glass cullet currently landfilled. Highly contaminated fractions of BASG cullet are largely unemployed because of the presence of metals in their composition that prevents recycling. This waste glass can be eligible to produce sustainable alkali-activated materials (AAMs) reducing at the same time consumption of raw materials and CO₂ emissions. The ‘weak’ alkaline attack (NaOH < 3 M) determines the gelation of glass suspensions. Condensation reactions occur in hydrated surface layers, leading to strong bonds (Si-O-Si, Al-O-Si, etc.) between individual glass particles. Alkali are mostly expelled from the gel due to the formation of water-soluble hydrated carbonates. Microwave treatment has been implemented on samples after precuring at 40 °C, saving time and energy and achieving better mechanical properties. To improve the stability and reduce the release of glass components into solution, the consolidated monoliths were subjected to boiling/drying cycles. The chemical stability, cytotoxicity and antibacterial behavior of the final products have been investigated with the purpose of obtaining new competitive and sustainable materials. For further stabilization and for finding new applications, the activated and boiled samples can be fired at low temperature (700 °C) to obtain, respectively, a homogeneous foam or a compact material with glass-like density and microstructure. Full article

Despite significant advancements in designing tandem catalysts for CO₂ hydrogenation to aromatics, the role of zeolite acid property in regulating the selectivity of light aromatics (benzene, toluene, and xylene, abbreviated as BTX) remains unclear. Herein, we report H-ZSM-5 zeolite (denoted as HZ-X, where X represents the Si/Al ratio) integrated with a Na-promoted FeCo-based catalyst (NaFeCo) for CO₂ hydrogenation into aromatics via a modified Fischer–Tropsch synthesis pathway. This study systematically modulates the Si/Al ratio of acidic zeolite and examines its critical role in influencing the light aromatics selectivity. The optimized NaFeCo/HZ-50 catalyst achieves a CO₂ conversion of 43% with an aromatics selectivity of 41%, including a BTX fraction of 57% in total aromatics. Multiple characterization techniques (NH₃-TPD, Py/DTBPy-IR, ²⁷Al NMR, etc.) clarify that acidic zeolite HZ-50 exhibits appropriate acid density and lower external surface acid sites, which synergistically boost the efficient aromatics and BTX synthesis while suppressing the undesirable alkylation and isomerization reactions on the external acid sites. This work develops a highly efficient multifunctional catalyst for CO₂ hydrogenation to light aromatics, especially offering guidance for the rational design of acidic zeolite with unique shape-selective functions. Full article

To investigate the microscopic mechanism of aging-induced “dewetting” at the matrix/filler interface in Nitrate Ester Plasticized Polyether (NEPE) propellant, this study decoupled the aging process into two factors: crosslinking density evolution and nitrate ester decomposition. Molecular dynamics (MD) simulations were employed to construct all-component matrix models and matrix/filler interface models with varying aging extents. Key parameters including crosslinking density, mechanical properties, free volume fraction, diffusion coefficients of the matrix, as well as interfacial binding energy and radial distribution function (RDF) were calculated to analyze the effects of both aging factors on “debonding”. The results indicate the following: 1. Increased crosslinking density enhances matrix rigidity, suppresses molecular mobility, and causes interfacial binding energy to initially rise then decline, peaking at 40% crosslinking degree. 2. Progressive nitrate ester decomposition expands free volume within the matrix, improves binder system mobility, and weakens nitrate ester-induced interfacial damage, thereby strengthening hydrogen bonding and van der Waals interactions at the interface. 3. The addition of a small amount of bonding agent improved the interfacial bonding energy but did not change the trend of the bonding energy with aging. Full article

Starch is one of the leading nutritional carbohydrates in the human diet; its characteristics, such as digestion rate, depend on molecular structure, and in particular, the molecular composition, type and length of amylopectin chains, which are known to present a parabolic behavior with respect to digestion rate. Amylopectin with a higher density of small branches (Chains A) and those abundant in long chains (B2/B3) often present a marked resistance to digestion and could be a challenge in bread production since both fermentation and digestion could be further modulated in the presence of hydrocolloids or gluten. The objective of this work was to analyze different mixtures of starches (rice, potato, and corn) with hydrocolloids (guar and xanthan gum) and vital gluten to understand the relationship between chain length and molecular characteristics with respect to speed of digestion and glycemic index, and their incorporation into a bread loaf at 50 and 100% wheat flour substitution. A Plackett–Burman design was used to design the mixtures. Mixtures were characterized in terms of amylose/amylopectin content, fast, slow, and resistant (SDS, RS) starch digestion fractions, in vitro glycemic index, molecular weight (Mw), radius of gyration (Rz) of amylopectin, chain length distribution, and textural analysis. In the bread, a tendency to increase the SDS was observed when the mixtures included rice or potato, which can be related to the relationship between Mw and size and the prevalence of B2 and B3 chains. The Rz and RS content were related to average chain size and amylose content. The use of vital gluten was a determinant in achieving volume and textural characteristics in the final products and significantly affected the proportions of SDS and RS. By combining the molecular characteristics of starch with hydrocolloids, we can obtain food ingredients for specific applications, such as gluten-free products. Full article

This study investigates the effect of duo-ceramic zirconia and silicon nitride (ZrO₂-Si₃N₄) particles and their reinforcement proficiencies on a Ti6Al4V alloy, consolidated using the spark plasma sintering (SPS) technique. The selected sintering parameters are, viz., 900 °C temperature, 50 MPa pressure, 10 min of holding time, and 100 °C/min of sintering rate. SEM/EDS and XRD equipment were used to disclose the microstructural evolution and phase identification of created composites. The mechanical characteristics of the resulting composites were determined using the nanoindentation technique. All consolidated sintered composites showed excellent densification, with sample relative densities reaching 96.65%. Significant improvements were also made in their nanomechanical characteristics; among the composite samples with different volume fractions, the ceramics with the lowest volume percentage had the best mechanical characteristics, whereas the sintered samples with the highest ceramic volume percentage showed a decrease in mechanical proficiencies and relative density. Composite S1, with the lowest volume fraction of the duo-ceramic particles, was seen to have a significant mechanical property improvement better than other composites, S2 and S3, in terms of measured Vickers microhardness, elastic modulus, and nano hardness values at a sintering temperature of 900 °C. Consequentially, composite specimens S2 and S3’s mechanical characteristics and relative densities dropped as the volume fractions of the duo-ceramic particles increased. Full article

In this paper, we introduce a general fractional master equation involving regularized general fractional derivatives with Sonin kernels, and we discuss its physical characteristics and mathematical properties. First, we show that this master equation can be embedded into the framework of continuous time random walks, and we derive an explicit formula for the waiting time probability density function of the continuous time random walk model in form of a convolution series generated by the Sonin kernel associated with the kernel of the regularized general fractional derivative. Next, we derive a fractional diffusion equation involving regularized general fractional derivatives with Sonin kernels from the continuous time random walk model in the asymptotical sense of long times and large distances. Another important result presented in this paper is a concise formula for the mean squared displacement of the particles governed by this fractional diffusion equation. Finally, we discuss several mathematical aspects of the fractional diffusion equation involving regularized general fractional derivatives with Sonin kernels, including the non-negativity of its fundamental solution and the validity of an appropriately formulated maximum principle for its solutions on the bounded domains. Full article

The study of the excited-state properties of diamond is crucial for understanding its electronic structure and surface physicochemical properties, providing theoretical support for its applications in optoelectronic devices, quantum technologies, and catalysis. This research employs Density Functional Theory (DFT) with the fixed electron occupation method to simulate the electron excitation. Using the Generalized Gradient Approximation (GGA) within DFT, we systematically investigated the excited-state characteristics of diamond by simulating the transfer of a fraction of electrons from the Highest Occupied Crystal Orbital (HOCO) to the Lowest Unoccupied Crystal Orbital (LUCO). Theoretical calculations indicate that with increasing electron excitation levels, the diamond crystal structure transitions from cubic to tetragonal, accompanied by a gradual decrease in the bandgap. Mechanical property analysis reveals that both Young’s modulus and shear modulus decrease with increasing excitation rate, while the bulk modulus remains nearly constant. These findings indicate a significant impact of electronic excitation on the mechanical stability of diamond. Phonon dispersion curves exhibit reduced degeneracy in high-frequency optical branches and a marked decrease in crystal symmetry upon excitation. This study not only advances the understanding of diamond’s excited-state properties but also offers valuable theoretical insights into its structural evolution and performance tuning under such extreme conditions. Full article

The advancement of topology optimization (TO) and additive manufacturing (AM) has significantly enhanced structural design flexibility and the potential for lightweight structures. However, challenges such as intermediate density, mesh dependency, checkerboard patterns, and local extrema in TO can lead to suboptimal performance. Moreover, existing AM technologies confront geometric constraints that limit their application. This study investigates minimum compliance as the objective function and volume as the constraint, employing the solid isotropic material with penalization method, density filtering, and the method of moving asymptotes. It examines how factors like mesh type, mesh size, volume fraction, material properties, initial density, filter radius, and penalty factor influence the TO results for a metallic gooseneck chain. The findings suggest that material properties primarily affect numerical variations along the TO path, with minimal impact on structural configuration. For both hexahedral and tetrahedral mesh types, a recommended mesh size is identified where the results show less than a 1% difference across varying mesh sizes. An initial density of 0.5 is advised, with a filter radius of approximately 2.3 to 2.5 times the average unit edge length for hexahedral meshes and 1.3 to 1.5 times for tetrahedral meshes. The suggested penalty factor ranges of 3–4 for hexahedral meshes and 2.5–3.5 for tetrahedral meshes. The optimal geometric reconstruction model achieves weight reductions of 23.46% and 22.22% compared to the original model while satisfying static loading requirements. This work contributes significantly to the integration of TO and AM in engineering, laying a robust foundation for future design endeavors. Full article

Male fertility is strongly influenced by environmental exposures, lifestyle, and advancing age. While advanced paternal age (APA) has been linked with a progressive decline in male fertility, poor reproductive outcomes, and decreased offspring health, the molecular mechanisms underlying these alterations remain unclear. In this work, we investigated the impact of men’s age on human sperm protein expression and phosphorylation to identify molecular alterations possibly responsible for the age-associated decline in male fertility. Semen samples from volunteers attending fertility consultations at the Hospital of Aveiro were collected, analyzed according to WHO’s guidelines, and processed by the density gradient technique. The proteome and phosphoproteome of 19 normozoospermic human sperm samples divided into four age groups were evaluated by mass spectrometry: ≤30 years old; 31–35 years old; 36–40 years old; and >40 years old. Proteomic analysis revealed 46 differentially expressed proteins (DEPs) between groups, some of them associated with infertility-related phenotypes. Gene ontology (GO) analysis, performed using the DAVID database, revealed that DEPs in older men were enriched in pathways related to stress response, metabolism, and embryo implantation. Additionally, 94 differentially phosphorylated sites corresponding to 76 differentially expressed phosphorylated proteins between the groups were identified, related to key reproductive processes such as sperm motility, spermatogenesis, and sperm binding to zona pellucida, and involved in metabolic and stress response pathways, like HSF1 activation. The set of proteins and phosphorylated residues altered in the sperm fraction usually used in assisted reproductive technology (ART) highlights the need to consider the age of the male partner during fertility assessment and treatment planning. These markers can also be used to explain cases of idiopathic infertility, failure in ART, or repeated abortion associated with APA, overcoming the subjectivity of the conventional semen analysis. Full article

Background/Objectives: Camellia nitidissima Chi (C. nitidissima), a traditional Chinese “food and medicine homology” plant, has demonstrated potential anti-tumor properties. However, its mechanisms of anti-lung cancer activity via ferroptosis remain unclear. This study aimed to construct an integrated research system of “natural product extraction-purification, bioactivity evaluation, and computational drug screening” to explore the bioactive compounds in C. nitidissima leaves targeting HMOX-1-mediated ferroptosis and their anti-lung cancer mechanisms. Methods: Active fractions were prepared using ethanol extraction combined with polyamide column chromatography. The anti-lung cancer activity was evaluated using the NCI-H1975 cell model. Ferroptosis was verified via transmission electron microscopy (TEM), biochemical indicators, a PCR Array, and immunofluorescence. The bioactive compounds were identified using UPLC-Q Exactive MS, and their binding affinity to HMOX-1 was evaluated via molecular docking and dynamics simulations, followed by cellular validation. Results: The 95% F1 fraction from the extracts of C. nitidissima leaves exhibited the strongest anti-lung cancer activity, which could be significantly reversed by Ferrostatin-1. Furthermore, it induced typical ferroptosis-related structural damage in mitochondria, including shrinkage and a reduction in size, increased membrane density, and a reduction or even the disappearance of cristae structures. At the molecular level, this fraction significantly increased the levels of oxidative stress markers (ROS↑, MDA↑, Fe²⁺↑, and GSH↓) and upregulated the expression of key ferroptosis-related genes, including HMOX-1, CHAC1, and NOX1. Using UPLC-Q Exactive MS combined with computational simulation methods, four bioactive compounds with high affinity for HMOX1 were successfully identified, including isochlorogenic acid A (−8.4 kcal/mol), isochlorogenic acid C (−8.4 kcal/mol), apigenin (−7.8 kcal/mol), and chrysin (−7.3 kcal/mol). Cellular experiments validated that these compounds exhibited dose-dependent anti-proliferative effects. Conclusions: The leaves of C. nitidissima induce anti-lung cancer effects via HMOX-1-mediated ferroptosis. Isochlorogenic acid A/C, apigenin, and chrysin were identified as key bioactive components. These findings lay the foundation for the development of natural ferroptosis-targeted drugs. Full article

This study aims to investigate the effectiveness of two machine learning methods for the prediction of the mechanical and fracture properties of Cenosphere-reinforced lightweight thermoset polyurethane composites. To evaluate the effectiveness of the models, datasets from our experimental study of composites made of five different volume fractions (0% to 40%) of Cenospheres (hollow Aluminum Silicate particles) in increments of 10% are fabricated. Experiments are conducted to determine the effect of the volume fraction of Cenospheres on Young’s modulus (both in tension and compression), percentage elongation at break, tensile strength, specific tensile strength, and fracture toughness of the composites. Two machine learning models, shallow artificial neural network (ANN) and the non-linear deep neural network (DNN), are employed to predict the above properties. A parametric study was performed for each model and optimized parameters were identified and later used to predict the properties beyond 40% volume fraction of Cenospheres. The predictions of non-linear DNN demonstrated less slope than shallow ANN and, for mass density, the non-linear DNN had unexpected predictions of increasing mass density with the addition of lighter Cenospheres. Hence, a double-hidden-layer DNN is used to predict the mass density beyond 40%, which provides the expected behavior. Full article

This study proposes a novel unified constitutive model that systematically integrates the microstructure evolution and macroscopic stress–strain response during the hot deformation of a Ni-based superalloy. The proposed model incorporates a suite of microstructural variables, including damage fraction, recrystallization fraction, δ phase content, average grain size, and dislocation density. Furthermore, the model explicitly considers critical macroscopic stress state parameters, specifically the magnitude and orientation of maximum principal stress, hydrostatic stress component, and Mises equivalent stress. A comparative analysis of rheological curves derived from uniaxial tension and compression experiments reveals that the prediction errors of the proposed model are less than 3%. The model is subsequently implemented to investigate the evolution characteristics of the damage accumulation fraction and δ phase content under varying stress directions and initial δ phase contents. An advanced computational framework integrating the finite element method with the proposed constitutive model is established through customized subroutines. The framework exhibits exceptional predictive accuracy across critical regions of disk forging, as evidenced by a close agreement between computational and experimental results. Specifically, the relative errors for predicting recrystallization fraction and average grain size remain consistently below 8% under varying stress–strain conditions. Testing results from four representative regions demonstrate a good alignment of high-temperature tensile properties with the macroscopic stress–strain distributions and microstructure characteristics, thereby confirming the model’s reliability in simulating and optimizing the forging process. Full article