Search Results (786)

The crystal structure regulation of Ti₃O₅ by Al₂O₃ doping and its effect on the optical properties of TiO₂ films prepared by electron beam evaporation were systematically studied. Ti₃O₅ coating materials with different Al₂O₃ doping contents (0–50 at%) were prepared by vacuum melting, and the corresponding TiO₂ films were deposited on K9 glass substrates via electron beam vacuum evaporation. The phase structure, phase transition temperature, chemical composition and optical properties of the materials and films were characterized by XRD, DSC, EDS, XPS, UV-Vis and AFM. Results show that Al₂O₃ doping induces the phase transition of Ti₃O₅ from a room-temperature stable β-phase to a high-temperature stable λ-phase, with complete transition at 5 at% doping. Al³⁺ with a smaller ionic radius causes lattice contraction and local distortion of Ti₃O₅, enabling stabilization at room temperature of the λ-phase. For TiO₂ films, 12.5 at% doping is the optimal state with the stable composition transfer under this condition. With the increase in Al₂O₃ doping content, the refractive index and extinction coefficient of TiO₂ films decrease continuously, while the optical band gap and surface roughness show an increasing trend. The changes in optical properties are mainly ascribed to the low refractive index of Al₂O₃, lattice compressive strain effect and oxygen vacancy passivation induced by Al³⁺. This study clarifies the regulation effect of Al₂O₃ doping on Ti₃O₅ phase transition and TiO₂ film optical properties, and provides theoretical basis and experimental reference for the doping modification of TiO₂ films and their practical applications in consumer electronics and optical filter devices. Full article

This research aimed to evaluate the effects of formulation and process conditions on the physical and structural properties of starch–brewers’ spent grain films. Three factors were considered: BSG amounts (0, 1, 3, 5%), a possible ultrasonication pre-treatment, and different microwave gelatinization treatments (450 W for 80 and 90 s; 900 W for 45 and 50 s). An increase in BSG is responsible for increases in moisture (10.72 → 23.40%), water absorption (67.65 → 95.73%), density (0.90 → 1.27 g/cm³), browning index (5.86 → 85.88), UV blocking capacity (82.42% → 99.96% for UV_A; 61.28% → 99.86% for UV_B), and degradability in the first 7 days (58.72 → 66.57%), but dramatically decreases the Young’s modulus and tensile strength (fallen to 2.90 N/mm² and 0.21 N/mm², at 5% BSG). Sonication contributes to increased browning index (36.17 → 37.24), UV blocking capacity, solubility (49.35 → 51.49%) and Young’s modulus (4.40 → 4.77 N/mm²). The most severe microwave treatment (900 W, 50 s) minimizes moisture (15.83%) and water absorption (80.89%) and maximizes density (1.21 g/cm³), browning index (37.52), and Young’s modulus (5.37 N/mm²). SEM micrographs allow us to observe that the film surface appears rough, and the structure becomes increasingly porous as BSG % increases. The regression analysis indicates that the quadratic model effectively describes the relationships between the three factors and each of the most important properties of the films; it is suitable for predicting film behavior and optimizing their characteristics depending on the desired use. Full article

Fracture roughness is critical to fluid flow behavior in fractured rock masses. However, the mechanism by which such roughness heterogeneity influences fluid flow and amplifies cubic law deviation remains incompletely understood. Current theoretical analyses are focused on uniform roughness or smooth parallel plates, neglecting the roughness heterogeneity in natural fractures. The equivalent fracture geometry models with heterogeneous roughness are established based on the fracture walls of the smooth parallel plates and the sinusoidal profiles in this study. Based on the geometry models and derived from the Navier–Stokes equations, two simplified fracture models are proposed: the equivalent plate–sinusoidal walled and the sinusoidal–sinusoidal walled fracture model, validated via COMSOL Multiphysics. A roughness heterogeneity index Z_r is defined to quantify the roughness heterogeneity. The influence of roughness heterogeneity on hydraulic behavior (e.g., fluid flow rate, equivalent hydraulic aperture) is analyzed and compared with those of uniform roughness and smooth parallel plates. Additionally, the influence of roughness heterogeneity on the power–law exponent relationship between fracture mechanical aperture and flow rate is examined. The results indicate that the flow rate and hydraulic aperture decrease with increasing roughness heterogeneity, while the deviation of fluid flow from the cubic law increases. The power–law exponent can be as high as 15.5. This study provides theoretical models for understanding the effects of roughness heterogeneity and a reference for extending flow models to complex fracture morphologies. Full article

Globally, the combined pollution of fine particulate matter (PM_2.5) and ground-level ozone (O₃) poses severe challenges to public health and sustainable urban development. Recent data indicate that the annual average PM_2.5 concentration in the vast majority of cities worldwide fails to meet World Health Organization safety standards, with air pollution causing millions of premature deaths annually. As a nature-based solution, the purification efficacy of vegetation remains poorly quantified due to unclear coupling mechanisms with local meteorological conditions. This study systematically reviewed and synthesized 229 empirical studies published between 2000 and 2025 from Web of Science and China National Knowledge Infrastructure (CNKI), aiming to clarify the quantitative relationships and regulatory mechanisms of plant–meteorological synergistic purification of PM_2.5–O₃. Following double-blind independent screening (κ = 0.85) and data extraction, a quantitative minimal feasible synthesis approach was adopted due to high data heterogeneity. The results indicated the following. (1) The median canopy purification efficiency of urban vegetation for PM_2.5 was 18.2% (IQR: 12.5–30.1%, n = 17), with a median dry deposition velocity (Vd–PM) of 0.05 cm s⁻¹ (0.02–30 cm s⁻¹, n = 15). The median dry deposition velocity (Vd–O₃) for O₃ was 0.55 cm s⁻¹ (0.12–1.82 cm s⁻¹, n = 8), with non-stomatal deposition contributing approximately 35%. (2) Meteorological factors exhibit nonlinear regulation: relative humidity (RH) > 70% significantly enhances PM_2.5 adsorption, wind speeds of 1.5–3.0 m s⁻¹ are optimal for PM_2.5 deposition, and temperatures > 30 °C generally inhibit plant uptake of both pollutants (n = 7). (3) Functional traits strongly correlate with purification efficacy: species with high leaf roughness (R² = 0.8), high stomatal conductance, and low BVOC emissions (e.g., Ginkgo biloba, Platycladus orientalis) exhibit optimal synergistic purification potential. Species with high BVOC emissions (Populus przewalskii, Eucalyptus robusta) can increase daily net O₃ pollution equivalents by up to 86 g and must be strictly avoided. Based on quantitative evidence, a green space planning decision matrix indexed by climate zone and pollution type was developed, specifying vegetation configuration patterns, functional group selection, and key design parameters (canopy closure, green belt width, etc.) for different scenarios. This study provides an actionable scientific basis for precision planning and climate-adaptive management of urban green infrastructure. Full article

Alcoholic liver injury (ALI) represents a global public health crisis with limited therapeutic options. Polysaccharides from edible mushrooms have emerged as promising candidates for liver protection due to their multifaceted biological activities and low toxicity. A mouse model of ALI was established to investigate the protective effect of Agaricus subrufescens polysaccharide on liver injury. The polysaccharide exhibited a non-triple-helix structural, characterized by a rough surface morphology, crack-like features, and a wavy strip structure. The body growth, liver index, serum and liver biochemical parameters, hepatic histopathological characteristics, and hepatic mRNA levels were investigated. The results demonstrated that A. subrufescens polysaccharide significantly alleviated liver injury, decreased serum levels of ALT by 36.22% and AST by 31.65%, lowered hepatic MDA content by 33.19%, and increased the activities of antioxidant enzymes, including SOD, GSH-P_X, and Cat by 12.04%, 9.76% and 18.45%, respectively. Meanwhile, the polysaccharide also regulated the mRNA expression of key genes involved in fatty acid metabolism, oxidative stress, and inflammatory responses. These findings provide theoretical evidence for the efficacy of A. subrufescens polysaccharide against alcohol-induced liver injury. Full article

Nitridation of different materials using ion implantation is of considerable interest for many applications. As electronic components, oxynitride (SiO_xN_y) layers exhibit beneficial properties such as precise compositional variability, refractive index tunability, oxidation resistance, and low mechanical stress. In the present study we investigate nanoscale SiO_xN_y synthesized using ion implantation methods. To introduce N⁺ ions into a shallow Si subsurface region, both conventional ion beam implantation and plasma immersion ion implantation with subsequent high-temperature treatment in dry O₂ are used. The optical and morphological properties and chemical bonding of formed SiO_xN_y layers were studied by applying spectroscopic ellipsometry in the range of VIS-Near IR (SE) and IR (IR-SE), Raman spectroscopy and Atomic Force Microscopy (AFM). Monte Carlo modeling of implant profiles contributed to understanding physical and chemical processes and predicted different influences of the incorporated N⁺ ions on the oxidation mechanism, confirmed by the thickness dependence of SiO_xN_y/Si layers obtained from the SE data analysis. IR-SE spectral analysis established the formation of Si-O, Si-N, Si-N-O and Si-Si chemical bonds in the grown layers. The occurrence of amorphization of the Si crystal lattice due to incorporation of high-energy N⁺ ions into the Si lattice is confirmed by the Raman and ellipsometry results. The free Si atoms can congregate, forming nanocrystalline clusters. AFM imaging revealed that both implantation methods left the surface of the resulting SiO_xN_y layers considerably smooth with similar roughness parameter values. The results of the studies imply that the technological approaches used allow the production of high-quality nanoscale silicon oxynitride films with appropriate tunable composition and properties for possible application in advanced electronic devices for nanoelectronics, optoelectronics and sensor applications. Full article

With increasing traffic loads and increasingly complex climate conditions, accurate prediction of the International Roughness Index (IRI) of asphalt pavements is crucial for developing effective maintenance plans. However, traditional regression models have limitations in capturing the coupled effects of traffic, structure, and environmental factors. To overcome this limitation, this study constructed a dataset containing 10,836 samples based on the Long-Term Pavement Performance (LTPP) database, integrating traffic load, pavement structure parameters, and climate variables. The variance inflation factor (VIF) and correlation analysis were used to validate the effectiveness of feature selection. We trained nine machine learning models and optimized the hyperparameters using a Bayesian optimization method with five-fold cross-validation to ensure good generalization ability. Results show that the TabPFN model, based on prior information, achieved the best overall performance with a coefficient of determination $R^2$ = 0.9474 and a low prediction error (RMSE = 0.138) on the test set. Paired t-tests based on cross-validation further confirmed that TabPFN’s predictive performance is statistically superior to the baseline model. SHAP and generalized additive model (GAM) analyses indicate that traffic load is the main driver of IRI growth, while structural layer thickness, within a certain range, can mitigate pavement roughness. Climatic factors have indirect long-term effects through cumulative environmental exposure. Although the main drivers differ slightly among different pavement structures, traffic load consistently plays a dominant role. To enhance the model’s practical applicability, we also developed a user-friendly graphical interface (GUI) for fast and accurate IRI prediction. Full article

Urbanization alters the hydrological and structural functioning of tropical urban streams, influencing organic matter transport and retention processes. This study investigated leaf litter retention dynamics in the Bindá Stream in central Amazonia. A six-month leaf release experiment (100 leaves per 12 trial; 1200 leaves total) was conducted alongside hydrological monitoring and floristic surveys of riparian vegetation (adult and regeneration strata). Leaf retention remained consistently low (<33%) across sampling periods. Generalized linear models indicated that flow velocity and discharge were the primary predictors of retention probability, with higher hydrodynamic intensity significantly reducing in-stream storage. Riparian vegetation exhibited moderate structural complexity (Shannon H′ = 1.80; Structural Complexity Index = 3.80), yet limited channel roughness and physical obstructions constrained retention efficiency. Anthropogenic debris locally increased retention, but represents a structurally altered retention mechanism. Hydrodynamic forcing, rather than precipitation totals alone, governed organic matter transport dynamics. Reduced retention capacity suggests limited buffering of downstream material export under high-flow conditions. Although direct water-quality or epidemiological indicators were not measured, findings align with ecohydrological frameworks linking structural simplification and flow flashiness to diminished ecosystem regulation. These results inform riparian restoration and urban stormwater management strategies aimed at enhancing ecosystem regulation and water-quality buffering in tropical cities. Full article

To address rapid tool wear and unstable hole surface quality during roughing and semi-finishing operations using indexable shallow-hole drills, an optimization study on the spatial mounting posture of the insert is conducted, aiming to improve tool life and machined surface quality. Considering that tool life and surface quality are significantly influenced by cutting force and cutting temperature, radial cutting force and cutting temperature are selected as the multi-objective optimization criteria. A mapping model between the insert mounting posture parameters and cutting performance metrics is established. An improved LO-NSGA-II algorithm is employed to perform multi-objective optimization, yielding a Pareto-optimal solution set, and the entropy weighted-TOPSIS method is subsequently applied to determine the optimal insert mounting posture. Experimental results demonstrate that the optimized spatial mounting posture significantly enhances the overall cutting performance of the tool. Compared with the non-optimized tool, the optimized configuration exhibits a significant extension in tool life and a notable improvement in machined hole surface quality. This study provides an effective methodology for the structural optimization design of indexable shallow-hole drills. Full article

The suppression of residual reflectivity in optical elements has become a hot research topic as it addresses the degradation of optical system imaging quality caused by stray light. Antireflective coatings on the outer surface of window glasses require low reflectivity, high hardness, and resistance to mechanical wear. This study investigates the role of reactive gas stoichiometry in tailoring the structure and performance of ${\mathrm{SiO}}_x$${\mathrm{N}}_y$ antireflection (AR) coatings deposited on GG7i glass via capacitively coupled radio-frequency magnetron sputtering. First, the influence of three ${\mathrm{N}}_2$/${\mathrm{O}}_2$ flow ratios on the optical and mechanical properties of ${\mathrm{SiO}}_x$${\mathrm{N}}_y$ films discussed under identical process parameters. Results show that the refractive index, hardness, and surface roughness of the ${\mathrm{SiO}}_x$${\mathrm{N}}_y$ films increase with increasing ${\mathrm{N}}_2$/${\mathrm{O}}_2$ ratio and that the stress of the ${\mathrm{SiO}}_x$${\mathrm{N}}_y$ films increases according to the Stoney formula. The wear resistance of the ${\mathrm{SiO}}_x$${\mathrm{N}}_y$ films combined with an antifingerprint (AF) coating is tested using steel wool. Experimental results show that the water contact angle of the AF decreases with increasing surface roughness of the film. Finally, on the basis of a comprehensive evaluation of optical and mechanical properties, the antireflection coating on the outer surface of the window glass was prepared by optimizing the process parameters. At $0^{°}$ incidence, the average reflectivity from 420 to 680 nm is <1%, the maximum value is <1.2%, the surface hardness is 17.2 GPa, and the water contact angle is ${100}^{°}$ after the steel wool wear test, showing its suitability for durable antifingerprint applications. This work provides a strategic pathway for designing high-performance optical coatings with tailored mechanical robustness. Full article

The use of additive manufacturing for rapid prototyping of near-infrared and terahertz components provides seamless and error-free production. This article discusses the additive manufacturing and post-processing of axicons and their performance evaluation using attenuation and near-field-measurements based fundamental techniques. The axicons are manufactured using the materials cyclic olefin copolymer (TOPAS) and polymethyl methacrylate (PMMA), for their respective use in terahertz and near-infrared applications. The optical and terahertz components manufactured using traditional 3D-printing processes, e.g., fused filament fabrication or stereolithography apparatus exhibit high surface roughness in the range of 15 ± 2.5 µm, resulting in undesired propagation and scattering in the near infrared wavelengths. This research work proposes an economical post-processing technique for additively manufactured terahertz and near-infrared axicons for applications in multispectral characterization, e.g., bio-sensing. The authors used an enhanced method of dip-coating, which involves interval dipping and intermittent hardening to achieve better surface finish. An emphasis is placed on interval dipping and intermittent hardening, which lead to excellent transparency in case of additively-manufactured near-infrared axicons. The dip-coated samples exhibit surface roughness below 10 nm. With the use of heated resin material as the coating layer, due to reduced viscosity, the resin material distributes uniformly over the surface of the 3D-printed terahertz and near-infrared axicons. The authors also observed that the DOF length deviation between unprocessed and enhanced dip-coated axicons remains within the measurement error estimation from analytical calculations. In addition to the improved surface finish and transparency, the coatings are also closely matched in refractive index to the axicon material. Such post-processed axicons pave the way for producing a wide array of systems in the fields of communication, imaging, and bio-sensing. Full article

Numerical weather prediction (NWP) models are essential for precipitation forecasting but are constrained by coarse spatial resolutions (10–50 km), which fail to capture fine-scale variations required for regional disaster prevention, particularly in complex terrain. While statistical and machine learning downscaling methods have been developed to bridge this resolution gap, they predominantly operate as “black boxes” without explicit physical guidance, leading to predictions that violate meteorological principles and systematic underestimation of extreme precipitation events. To address these limitations, this study aims to develop a Physics-Informed Machine Learning framework that explicitly integrates multi-scale topographic modulation and physical consistency constraints into precipitation downscaling. Specifically, a Random Forest model enhanced with Multi-Scale Structural Similarity (MS-SSIM) loss and Physical Constraint Enhancement (MSSSIM-PCE-RF) was constructed. The model introduces elevation gradient weights at low-resolution layers and micro-topographic parameters (slope, surface roughness) at high-resolution layers, while enforcing physical consistency between precipitation intensity, radar reflectivity, and ground observations via the Z-R relationship. Based on hourly data from 2252 meteorological stations in Jiangxi Province (2021–2022), coupled with topographic factors (DEM, slope, aspect) and Normalized Difference Vegetation Index (NDVI), a technical framework of “data fusion–feature synergy–machine learning–spatial reconstruction” was established. Results demonstrate that the MSSSIM-PCE-RF model achieves a validation R² of 0.9465 and RMSE of 0.1865 mm, significantly outperforming the conventional RF model (R² = 0.9272). Notably, errors in high-altitude, steep-slope, and high-vegetation areas are reduced by 45.3%, 42.0%, and 43.1%, respectively, with peak precipitation period errors decreasing by 37.2%. Multi-scale topographic analysis reveals significant orographic lifting effects at 250–1000 m elevations, peak precipitation at 12–15° slopes, and abundant precipitation on south/southeast aspects. By explicitly embedding topographic modulation and physical consistency constraints, the model effectively alleviates systematic underestimation of extreme precipitation in complex terrain, providing high-resolution data support for transmission line disaster prevention and micro-meteorological risk assessment. Full article

Accurate, scalable pavement condition monitoring is essential for effective asset management, yet traditional methods of collecting metrics like the International Roughness Index (IRI), Pavement Condition Index (PCI), and Pavement Surface Evaluation and Rating (PASER) can be inefficient, expensive, and subjective. Recent efforts by Original Equipment Manufacturers have introduced crowdsourced approaches that estimate IRI at scale using connected vehicles (CVs). This study analyzes one month of CV-estimated IRI (IRI_CVe) data and compares it with manually collected PCI data from Marion County, Indiana, in 2024. The study includes four roadway classes: primary arterial, secondary arterial, primary collector, and local street, with 562, 147, 426, and 2402 centerline miles of data, respectively. IRI_CVe coverage was nearly complete for arterial and collector roads (93–100%) but was limited for local streets (37%). Threshold optimization revealed that the “needs maintenance” IRI category (IRI > 170 in/mi) correlates most strongly with PCI values below 50. The study found that 68%, 65%, 70%, and 59% of the roadway segments had PCI and IRI classifications in agreement. Spatial and categorical comparisons suggest some systematic biases between the metrics across roadway types, reflecting how they measure different dimensions of pavement condition. The results demonstrate near-term applications of IRI_CVe data for quality control in PCI-based asset management and support practical guidelines for integrating complementary pavement assessment metrics. Full article

Soil moisture is an essential climate variable exhibiting strong spatio-temporal dynamics, especially in the topsoil. Therefore, it is assessed multiple times by sensors within in situ networks, satellites, and by modeling of the Earth system. The resulting soil moisture fields from all methods are individual and non-congruent due to the imperfection of the methods and retrievals. But their spatial patterns have valuable similarities that call for investigation to foster intercomparison or even fusion of soil moisture products. In this research study, the similarity of spatial soil moisture patterns between passive microwave remote sensing products and Earth system modeling is investigated. We configure and apply spatial similarity metrics to enable a spatial comparison of the operational SMAP Dual Channel Algorithm (DCA) radiometer soil moisture product with the soil moisture output from IFS model runs of the ECMWF. The pattern assessment spans over the whole of Europe and aims to find the drivers behind the spatial soil moisture distributions at scales ranging from single grid cells (minimum) to continental (maximum) spatial scales, and between growing periods of wet (2021) and dry (2022) years. The two specifically configured metrics, total disagreement and mean category distance, showcase the opportunities and challenges when assessing spatial similarity in soil moisture fields across different scales. In addition, the potential drivers of the spatial moisture patterns were screened. Here, soil texture is the most influential single driver of spatial patterns in the IFS soil moisture runs, when analyzed in absolute terms [m³ m⁻³]. In relative terms of soil moisture [-] (soil wetness index), precipitation and soil temperature explain most of the variability of the IFS soil moisture for Europe. The SMAP retrievals are predominantly driven by the brightness temperatures, mostly influenced by surface temperature, vegetation water content, and soil roughness. These differences in drivers, as well as in methodology, culminate in an inherent discrepancy between the two soil moisture products. However, the assessment of their spatial patterns reveals the underlying similarity from the local to the continental scale. Full article

The Geological Strength Index (GSI) is a crucial tool for assessing jointed rock masses, but it is often hindered by subjectivity in visual assessments. In this study, we propose a novel quantitative GSI method wherein 3D laser-scanning point clouds are used to quantitatively derive empirical rock mass indices (SR and SCR) to estimate mechanical parameters. By integrating the GSI with the Rock Block Index (RBI) and joint spacing, a framework for quantifying the Structural Rating (SR) is established. Furthermore, the Analytic Hierarchy Process (AHP) is employed to assign weights to Surface Condition Rating (SCR) factors. The results indicate that infilling materials have the most significant impact on SCR (weight 0.6334), followed by weathering (0.2605) and roughness (0.1061). This method was applied to evaluate rock masses at depths of −915 to −960 m in the Sanshandao Gold Mine. The GSI values calculated for the foot wall, ore body, and hanging wall were 38.5, 33.8, and 37.8, respectively. Validation against conventional quantitative methods demonstrated high accuracy, with a maximum relative GSI difference of 1.5 and a deformation modulus difference of only 0.227 GPa. This data-driven approach effectively reduces subjectivity and provides a reliable tool for automated geotechnical parameter estimation. Full article