Search Results (94)

The Bayan Obo Mining District, recognized as the largest rare-earth resource base worldwide, has experienced significant surface instability due to intensive mining and large-scale dumping activities. To address the challenges posed by complex geological conditions and mining-induced disturbances, this study employs dual-ascending Sentinel-1A C-band Synthetic Aperture Radar (SAR) datasets (Path 11 and Path 113) and applies the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to retrieve time-series deformation along the line-of-sight (LOS) direction for each track. Through temporal normalization and spatial matching, paired LOS observations from the two tracks were established. Based on the SAR observation geometry and under the assumption that the north–south component is negligible, a LOS projection model was constructed and a geometric decomposition was performed to derive the east–west and vertical two-dimensional deformation fields. The results indicate that the study area is generally stable, while significant subsidence occurs in the northern pit and adjacent waste-dump zones, with local maximum rates approaching 50 mm/year, predominantly controlled by the vertical component. The two-dimensional deformation analysis reveals that vertical displacement dominates surface motion, whereas east–west movement shows smaller amplitudes but clear directional concentration. In particular, the east–west slopes exhibit slightly higher velocities, suggesting a lateral adjustment tendency along this direction, likely related to the overall east–west geometric configuration of the open-pit and waste-dump areas. Time-series observations further reveal that precipitation-related surface deformation occurs with an approximate two-month delay, reflecting the hydrological–mechanical coupling processes of rainfall infiltration, pore-water pressure propagation, and dump-material consolidation. Overall, this study reveals the multi-dimensional deformation characteristics and precipitation-driven stage-wise response of the mining area, demonstrating the effectiveness of the dual-ascending SBAS-InSAR for two-dimensional deformation monitoring in highly disturbed environments, and providing a scientific basis for surface stability assessment and geohazard prevention. Full article

This study systematically investigates the effects of polishing slurry components on the material removal rate (MRR) and surface morphology of the C-face of 4H-SiC substrates during chemical mechanical polishing (CMP) based on the Fenton reaction. By regulating the particle size and concentration of colloidal silica abrasives, H₂O₂ concentration, and Fe₃O₄ catalyst content, the mechanisms of each component on MRR and surface roughness (Sa) were systematically analyzed. The results indicate that in an alkaline polishing slurry at pH = 9, Fe₃O₄ effectively catalyzes the decomposition of H₂O₂ to generate hydroxyl radicals (·OH), thereby significantly enhancing the material removal efficiency. When using colloidal silica with a particle size of 110 nm at a concentration of 8 wt%, H₂O₂ at 5 wt%, and Fe₃O₄ at 0.03 wt%, a maximum MRR of 701 nm/h was achieved along with a good surface quality of Sa = 0.79 nm. The study also found that the abrasive particle size and concentration, as well as the ratio of oxidant to catalyst, significantly influence the chemo-mechanical synergy. Excessively high H₂O₂ or Fe₃O₄ concentrations can trigger ·OH quenching reactions, thereby reducing polishing efficiency. This research provides a theoretical basis and process optimization direction for the application of heterogeneous Fenton reactions in SiC CMP under alkaline conditions. Full article

In bridge health monitoring, structural responses are strongly coupled with temperature effects and vehicle load effects, making it difficult for conventional fixed thresholds and single data-driven approaches to simultaneously achieve environmental adaptability and quantitative reliability assessment. To address this issue, this study proposes a deep-learning-based dynamic early-warning method for bridge structures, using health-monitoring data from an in-service long-span cable-stayed bridge as the research background. First, a two-month mid-span deflection time series is processed using variational mode decomposition optimized by the Porcupine Optimization Algorithm to separate temperature-induced effects. Subsequently, a hybrid prediction model integrating Informer and SEnet is constructed. Temperature and temperature-induced deflection components are used as input features, and a sliding-window strategy is adopted to achieve high-accuracy prediction of the temperature-induced deflection trend, which serves as the time-varying baseline of the dynamic threshold. On this basis, vehicle load effects are modeled by combining Pareto extreme value theory with finite element analysis and superimposed to establish a two-level dynamic early-warning threshold system that satisfies code requirements. Furthermore, a stochastic finite element Monte Carlo method is introduced to probabilistically model uncertainties associated with material parameters, load effects, and model prediction errors. The threshold failure probability at each time instant is taken as the evaluation metric, enabling quantitative characterization of threshold reliability. The results indicate that under combined multiple working conditions, the proposed method reduces the maximum failure probability of the first-level warning by 32.68% and that of the second-level warning by 93.48%, with more stable and consistent probabilistic responses. In engineering applications, simulation experiments based on stochastic traffic loading show that the warning accuracy is improved by up to 19.27%, while the error rate is reduced by up to 16.16%. The study demonstrates that the proposed method possesses a clear physical and statistical foundation as well as good engineering feasibility and provides a viable pathway for transforming bridge early-warning systems from experience-based schemes toward data-driven and risk-oriented frameworks. Full article

An ultrafast laser system combined with an optical delay line allowed ablation and in-scription at 1 kHz and 1 GHz pulse burst within transparent polyimide films. The two-photon-induced absorption results in clean surface ablation, while inscription results in polymer decomposition, creating carbonised regions within the polymer. Three pulse bursts at 1 GHz increased the observed coupling to the material significantly. Modified regions (with linewidths down to a few microns) were investigated using optical microscopy, white light interferometry, SEM and Raman spectroscopy, supporting the increasing carbon density relative to the pristine polymer. As depth of field was only a few microns at high NA, 3D micro-structuring was achieved. Polymer decomposition produces gaseous products, resulting in internal stress and thus affecting inscription fidelity. An inscribed subsurface electrode with dimensions of 5 mm × 0.3 mm × 3 μm connected to conducting vias had a resistance of R = 10.6 ± 0.2 kΩ, along with resistivity of ρ ~ 0.19 Ω cm; hence, it had DC conductivity, σ ~ 5.3 Scm⁻¹. This conductivity is similar to that of bulk graphite and could well form the basis of future flexible sensors, demonstrating single-step 3D subsurface inscription of carbon or laser-induced graphene structures. Full article

This study proposes a novel high-performance computational framework to address the computational challenges in probabilistic large-deformation landslide analysis. By integrating a GPU-accelerated material point method (MPM) solver with a parallelized covariance matrix decomposition (CMD) algorithm for decomposing symmetric matrices, the framework achieves exceptional efficiency, demonstrating speedups of up to 532× (MPM solver) and 120× (random field generation) compared to traditional serial methods. Leveraging this efficiency, extensive Monte Carlo simulations (MCSs) were conducted to quantify the effects of spatial variability in soil properties on landslide behaviors. Quantitative results indicate that runout and influence distances follow normal distributions, while sliding mass volume exhibits log-normal characteristics. Crucially, deterministic analysis was found to systematically underestimate the hazard; the probabilistic mean sliding volume significantly exceeded the deterministic value, with 73–80% of stochastic realizations producing larger failures. Furthermore, sensitivity analyses reveal that increasing the coefficient of variation (COV) and the cross-correlation coefficient (from −0.5 to 0.5) leads to a monotonic increase in both the mean and standard deviation of large-deformation metrics. These findings confirm that positive parameter correlation amplifies failure risk, providing a rigorous physics-based basis for conservative landslide hazard assessment. Full article

The degradation of bisphenol A (BPA), the main monomer of polycarbonate, was investigated under subcritical water conditions to better understand its decomposition as a function of process conditions and to provide useful data for designing a recycling process to convert polycarbonate into valuable products. Hydrothermal experiments were conducted in a batch reactor at temperatures ranging from 250 to 350 °C, with reaction times from 5 to 30 min and water-to-material ratios of 5, 10, and 15 (mL/g), following a Box–Behnken design with response surface methodology (RSM). The influence of process parameters on phase distribution, total carbon content, and product composition was evaluated. The results showed that temperature and reaction time were the most significant factors affecting BPA decomposition, while the water-to-material ratio had a minor effect. The recovery of the DEE (diethyl ether)-soluble phase decreased with increasing temperature and time, accompanied by a corresponding increase in the water-soluble phase yield and total carbon content. Analysis of the DEE-soluble fraction revealed the sequential transformation of BPA into 4-isopropenylphenol, 4-isopropylphenol, and phenol, with phenol becoming the dominant degradation product at higher temperatures. These findings provide new insights into the hydrothermal decomposition mechanism of BPA and form a basis for understanding polycarbonate degradation and developing sustainable subcritical water recycling processes for polymeric materials. Full article

A thorough understanding of the dispersion characteristics of graphene oxide (GO), its micro-pore enhancement mechanisms, and correlations with mechanical properties are crucial for advancing high-strength, durable green concrete. Introducing recycled concrete powder (RCP) can weaken the interfacial transition zone (ITZ) and inhibit hydration reactions, degrading the pore structure and affecting mechanical strength and durability. However, traditional methods struggle to accurately characterize and quantitatively analyze GO-modified pore structures due to their nanoscale size, microstructural diversity, and characterization technique limitations. To address these challenges, this study integrates deep learning-based backscattered electron image analysis with deep Taylor decomposition feature extraction. This innovative method systematically analyzes pore characteristic evolution and the correlation between porosity and mechanical strength. The results indicate that GO promotes Calcium Silicate Hydrate gel growth, refines pores, and reduces pore connectivity, decreasing the maximum pore size by 33.4–45.2%. Using a Convolutional Neural Network architecture, BSE images are efficiently processed and analyzed, achieving an average recognition accuracy of 94.3–96.9%. The optimized degree of GO coating on enhanced regions reaches 30.2%. Fitting porosity with mechanical strength and chloride ion permeability coefficients reveals that enhanced regions exhibit the highest correlation with mechanical strength and durability in regenerated cementitious materials, with R² values ranging from 0.79 to 0.99. The deep learning-assisted pore structure characterization method demonstrates high accuracy and efficiency, providing a critical theoretical basis and data support for performance optimization and engineering applications of recycled cementitious materials. This research expands the application of deep learning in building materials and offers new insights into the relationship between the microstructural and macroscopic properties of recycled cementitious materials. Full article

This study investigates the practical value of angular-velocity measurements in the dynamic identification of pedestrian footbridges, addressing the need for reliable yet cost-effective diagnostics for slender civil structures. A comprehensive experimental campaign on a steel footbridge in Gdynia combined ambient vibration tests, forced excitation (light and heavy shakers), and controlled pedestrian loading. Synchronous translational accelerations and rotational velocities from MEMS sensors enabled evaluation of both bending and torsional responses. Three identification techniques—Peak Picking (PP), Frequency Domain Decomposition (FDD), and Stochastic Subspace Identification (SSI)—were applied and compared with a validated beam–shell FEM developed in SOFiSTiK. The results show that rotational data improve mode-shape interpretation and classification, notably resolving a coupled torsional–vertical mode (VT₂) that was ambiguous in acceleration-only analyses. The fundamental frequency of 3.1 Hz places the bridge in a resonance-prone range; field tests confirmed predominantly vertical response, with horizontal accelerations < 0.05 m/s² and peak vertical accelerations exceeding comfort class CL3 during synchronised walking of six pedestrians (≈2.55 m/s²) and jumping (up to 3.61 m/s²). Overall, the outcomes highlight that low-cost gyroscopic sensing offers substantial benefits for structural system identification and mode-shape characterization, enriching acceleration-based diagnostics and strengthening the basis for subsequent analyses. By reducing the financial and material demands of vibration testing, the proposed approach contributes to more sustainable assessment and maintenance of pedestrian bridges, aligning with resource-efficient monitoring strategies in civil infrastructure. Full article

The development of rare-earth-free permanent magnets represents a strategic direction in advanced magnetic materials research. Among the most promising candidates, the metastable α″-Fe₁₆N₂ phase stands out due to its exceptionally high saturation magnetization. In this work, α″-Fe₁₆N₂ powders produced by gas atomization followed by nitriding were consolidated via Spark Plasma Sintering (SPS). The effects of sintering temperature (498–598 K) and pressure (40–80 MPa) on phase evolution, densification, microstructure, and magnetic properties have been systematically investigated. Optimal processing conditions were identified at 548 K and 60 MPa, providing a balance between densification (~80% of the theoretical density), phase stability, and magnetic performance. X-ray diffraction revealed that the α″-Fe₁₆N₂ phase remains stable up to ~523 K, while its decomposition into α-Fe and γ′-Fe₄N becomes significant at higher temperatures. The consolidated samples exhibited a saturation magnetization of ~230 Am²/kg, a maximum coercivity of ~86.5 kA/m, and a Mr/Ms ratio of 0.42. δM curve analysis indicated a transition from magnetostatic interactions (at low pressures) to exchange-dominated coupling (at intermediate and high pressures). These findings demonstrate the potential of SPS processing to preserve the α″-Fe₁₆N₂ phase and produce rare-earth-free magnetic compacts with competitive magnetic performance, providing a basis for further process optimization. Full article

Silicone rubber, primarily composed of polydimethylsiloxane (PDMS) chains, is widely used in sealing materials due to its excellent flexibility and durability. Its performance is significantly affected by environmental conditions, with humid-heat aging being a major factor of degradation. In this study, molecular dynamics simulations were conducted to systematically investigate the effects of water and temperature on PDMS at the molecular scale. The glass transition temperature (T_g) and free volume distribution were analyzed to evaluate the mobility of polymer chains under hydrated conditions. Mechanical simulations (including tensile and compressive deformation) indicate that the combined effect of elevated temperature and moisture significantly accelerates the degradation of rubber properties. Thermal decomposition simulations indicate that, under high-temperature and humid conditions, PDMS main chains gradually break into small molecules, with free radical reactions further promoting the aging process. The results elucidate the molecular mechanisms underlying silicone rubber performance deterioration under the coupled action of water and temperature, providing a theoretical basis for service-life prediction and durability design of sealing materials. Full article

Finding high-performance and low-cost materials is essential for high-quality photocatalytic water purification to expand the spectral response and improve light utilization. In this paper, we used relatively inexpensive materials such as Ag and TiO₂. The influence of particle spacing, core radius, shell thickness, environmental refractive index, and incident light direction angle on the light absorption and scattering properties, local electric field enhancement, and photothermal effect of the Ag@TiO₂ core–shell nanosphere dimer is investigated by using the finite element method and the finite difference time domain. The formation mechanism of multipole resonance mode of the dimer is revealed by means of the multipole decomposition theory and the internal current distribution of the particles. The results show that light absorption and scattering of the dimer can be tuned within the visible light range by changing the particle spacing, core radius, and shell thickness. With the azimuth angle of incident light increases, the longitudinal local surface plasmon resonance (L-LSPR) mode will transform into the transverse local surface plasmon resonance (T-LSPR) mode, and the L-LSPR mode makes the dimer have better local electric field enhancement. Strong light absorption can easily cause a sharp increase in the temperature around the dimer, accelerating the rate of catalytic oxidation reactions and the elimination of bacteria and viruses in water. Strong light scattering causes a significant enhancement of the electric field between the particles, making the generation of hydroxyl and other active oxides more efficient and convenient. This work establishes a theoretical basis for designing efficient water purification photocatalysts. Full article

This study investigates the thermal behavior of SnS₂ anodes for lithium-ion batteries at seven different states of charge (fully discharged (lithiated) at 0 mAh/g, partially charged at 100, 200, 300, 400, and 500 mAh/g, and fully charged (delithiated) at 550 mAh/g) using differential scanning calorimetry (DSC). To better understand the observed thermal behavior, complementary XRD and XPS analyses were performed. Generally, in all electrodes, the thermal decomposition of the electrode material is initiated by the exothermic decomposition of the SEI followed by a binder decomposition reaction around 265 °C. Interestingly, with increased states of delithiation from 400 mAh/g, endothermic peaks in the heat-flow signal of the DSC measurements are observed, which can be correlated with the structural and compositional changes in the electrode material as determined by XRD and XPS, respectively. These analyses confirmed the progressive formation of metallic tin on advanced delithiation. Additionally, the total heat generation from the electrodes decreased with increased delithiation. The results of this study serve as the basis for better understanding the thermal decomposition of SnS₂-based anodes, which are considered promising for advanced lithium-ion battery chemistries. Full article

Microwave-induced oxidation has emerged as an effective approach for water purification. In this study, bismuth ferrite-supporting carbon nanotubes with strong microwave absorption and magnetism were successfully fabricated for the degradation of brilliant green. The reactivity of bismuth ferrite in microwave fields and the role of carbon nanotubes was revealed by systematic characterization methods. Our results demonstrated that the addition of bismuth ferrite in microwave-induced system can enhance the ability of microwave-induced absorption and further induce the degradation and mineralization of brilliant green within 10 min, significantly surpassing conventional heating methods. The brilliant green decomposition by bismuth ferrite in microwave-induced process is a heterogeneous process. Its excellent performance achieved by active species-trap experiments can be attributed to microwave-induced holes. Overall, this study presented a promising material for microwave-induced elimination of brilliant green and other dyes in aqueous media, which can provide the basis for the environmental application of microwave radiation to water purification and wastewater treatment. Full article

Understanding the molecular structure and water transport behavior in anion-exchange membranes (AEMs) is essential for advancing efficient and cost-effective alkaline fuel cells. In this study, large-scale all-atom molecular dynamics simulations of QPAF-4, a promising AEM material, were performed at multiple water uptakes (λ = 2, 3, 6, and 13). The simulated systems comprised approximately 1.4 to 2.1 million atoms and spanned approximately 26 nm, thus enabling direct comparison with both wide-angle X-ray scattering (WAXS) and small-angle X-ray scattering (SAXS) experiments. The simulations successfully reproduced experimentally observed structure factors, accurately capturing microphase-separated morphologies at the mesoscale ($~$8 nm). Decomposition of the SAXS profile into atom pairs suggests that increasing water uptake may facilitate the aggregation of fluorinated alkyl chains. Furthermore, the calculated pair distribution functions showed excellent agreement with WAXS data, suggesting that the atomistic details were accurately reproduced. The water dynamics exhibited strong dependence on hydration level: At low water uptake, mean squared displacement showed persistent subdiffusive behavior even at long timescales ($~$200 ns), whereas almost normal diffusion was observed when water uptake was high. These results suggest that water mobility may be significantly influenced by nanoconfinement and strong interactions exerted by polymer chains and counterions under dry conditions. These findings provide a basis for the rational design and optimization of high-performance membrane materials. Full article

Most metal plates are orthorhombic aggregates of cubic crystallites. First, we discuss the representations of the stress tensor, the strain tensor, the elasticity tensor, and the rotation tensor under the Kelvin notation. Then, we give the decomposition of determining the material constants and the mesostructure tensors on the metal plate of cubic crystallites. Under the Voigt model and the Reuss model, we derive the volume average stiffness tensor and the volume average flexibility tensor’s inverse, respectively, of cubic crystallites based on the decomposition. The elasticity tensors of the Voigt model and the Reuss model are upper and lower bounds of the effective elasticity tensor, respectively. We make use of an FEM example to check the decomposition of the elasticity tensor on the material constants and the mesostructures. The results of our decomposition are consistent with the FEM simulation’s results. Full article