Search Results (5,218)

To address the demand for lightweight, high-performance Al-Si-Mg alloys in aerospace and automotive industries, this work proposes a novel synergistic strengthening strategy by combining rare-earth Y microalloying and in situ synthesized ZrB₂ nanoparticles to construct a hybrid reinforcement architecture. The effects of Y-ZrB₂ additions on the microstructure, crystallographic orientation evolution, and mechanical properties of Al-Si-Mg alloys were systematically investigated via XRD, SEM, EBSD, and tensile/hardness tests. Results show that compared with the base alloy and single-modified alloys, the co-addition of Y and ZrB₂ simultaneously enhances mechanical properties and optimizes grain structure. The optimal comprehensive performance is achieved at 0.3 wt.% Y + 2 wt.% ZrB₂ after T6 heat treatment, with ultimate tensile strength of 332.87 MPa, yield strength of 271.35 MPa, elongation of 16.24%, and Vickers hardness of 153.9 HV. Phase analysis and SEM-EDS confirm a synergistic coupling relationship between Y-rich phases and ZrB₂ nanoparticles. EBSD characterization reveals that Y-ZrB₂ modification has negligible effect on the morphology and crystallographic orientation stability of primary α-Al grains, but effectively regulates the lattice rotation, texture redistribution, and growth behavior of eutectic Si. At the optimal composition, the fraction of high-angle grain boundaries (HAGBs) reaches a maximum of 34.3%. Furthermore, the synergistic effect significantly increases the geometrically necessary dislocation (GND) density and reduces the Schmid factor of the dominant {111}⟨110⟩ slip system, thus enhancing dislocation strengthening and plastic deformation resistance. This work clarifies the intrinsic strength-ductility synergy mechanism of Y-ZrB₂ co-modified Al-Si-Mg alloys, paving a new pathway for the development of advanced lightweight aluminum alloys. Full article

High-resolution, long-term gridded meteorological datasets from in situ observations are crucial for ecosystem monitoring, soil diagnostics, hydrological modelling, and Earth system model evaluation. This study presents two enhanced real-time adaptations of Thornton’s Daymet V4 interpolation method. Daymet4-r1 uses a traditional calibration strategy with exhaustive parameter search, while Daymet4-r2 applies a global optimization algorithm (find_min_global from the dlib library) to adjust parameters automatically at each time step. Both methods were tested over Tuscany using high-resolution terrain and a dense observation network. Validation with leave-one-out method was carried out for the period 1995–2011 for both versions, while Daymet4-r2 underwent extended evaluation from 1991 to 2024 to assess seasonal dynamics and long-term variability. Results show that Daymet4-r2 outperforms Daymet4-r1 and the original Daymet V4 for all variables (mean absolute error of 1.24 mm, 1.06 °C, 1.29 °C, 6.26%, 0.78 m/s, and 2.04 hPa for precipitation, maximum and minimum temperature, relative humidity, wind speed, and sea level pressure, respectively). The largest improvement was observed in minimum temperature due to an enhanced approach for detecting and modelling thermal inversions. The high performance, flexibility, and ability of Daymet4-r2 to operate without prior calibration highlight its potential for model verification, real-time environmental monitoring, and integration into climate services. Full article

In the process of deep hard rock mining, dynamic disasters, such as rockbursts and large-scale collapses, pose a serious threat to the production safety and sustainable development of mines. Microseismic monitoring has been widely used in mines as an efficient disaster monitoring tool. However, microseismic monitoring signals exhibit obvious nonlinear and disordered attributes due to the complex rock behavior, mine structure, and excavation disturbance. This poses great challenges for precise monitoring and forewarning of disasters in deep hard rock mines. This study introduced fractal theory and methods to characterize the spatiotemporal energy information of microseismic monitoring signals. Theoretical analysis, numerical simulation, in situ testing, and field monitoring were integrated to establish a comprehensive spatiotemporal energetic fractal characterization model of microseismic monitoring sources. A scale conversion method for the spatial and energy parameters of microseismic events was developed, and the fractal evolution of microseismic monitoring events induced by deep mining activities was systematically investigated. On this basis, a fractal-based grading forewarning system for deep mines was established, providing theoretical and methodological support for accurate disaster prediction in deep hard rock mines. Full article

With deep coal mining in China, high in situ stress frequently causes severe floor deformation, bolt-cable support failure, and excessive floor heave, which critically threaten mine safety. In this study, we use physical experiments, numerical simulation, and theoretical analysis to explore how hydraulic fractures with different azimuth angles affect stress transfer in roadways under floor dynamic pressure. Prefabricated fractures simulate weak planes induced by hydraulic fracturing. Uniaxial compression tests and PFC2D fluid–solid coupling simulations analyze mechanical properties, failure modes, acoustic emission behavior, and stress distribution. Results show that fracture azimuth significantly controls rock damage and failure modes. As the angle increases from 0° to 90°, failure changes from gradual degradation to sudden instability. Peak strength first decreases then increases, reaching the minimum at 22.5°, while roadway damage is minimal at 45°. Small-angle fractures lead to shear failure with clear precursors, and large-angle fractures cause sudden tensile failure. Hydraulic fractures form directional stress-relief zones and enable effective stress transfer and pressure relief. The results support parameter optimization of hydraulic fracturing and stability control for deep roadways under floor dynamic pressure. Full article

Methane combustion over palladium-based catalysts is a critical process for reducing greenhouse gas emissions from lean-burn engines and natural gas installations, yet the role of oxide support in controlling both the population and the intrinsic reactivity of Pd active centres remains incompletely understood. In this work, Pd catalysts at two series of higher and lower loading were prepared on five oxide supports—Al₂O₃, CeO₂, SiO₂, TiO₂, and ZrO₂—and characterised by a complementary suite of techniques including SEM-EDX, XRD, BET, AAS, in situ CO-FTIR, DRIFTS with methanol as a probe molecule, and Raman spectroscopy. Catalytic activity testing revealed the order Pd/CeO₂ > Pd/ZrO₂ > Pd/Al₂O₃ > Pd/TiO₂ > Pd/SiO₂. In situ CO-FTIR site quantification showed that active site density spans nearly an order of magnitude across the series, with Pd/CeO₂ reaching 105.44 µmol g⁻¹ and Pd/Al₂O₃ only 11.63 µmol g⁻¹. Turnover frequency analysis revealed a striking inversion: Pd/Al₂O₃ exhibited the highest TOF (0.1327 s⁻¹), approximately six times greater than Pd/CeO₂ (0.0226 s⁻¹). DRIFTS/methanol profiling demonstrated that CeO₂ and ZrO₂ expose cooperative redox and basic centres that promote methane activation, while SiO₂ supports only weakly bound methoxy species, consistent with its lowest activity. These results establish that the oxide support simultaneously governs Pd dispersion—and hence site density—and the electronic environment of each Pd centre, thereby modulating intrinsic reactivity. High specific surface area alone does not guarantee catalytic performance, and rational support selection is therefore the decisive lever for optimising methane combustion catalysts at ultra-low Pd loadings. In all, our findings provide a quantitative, molecular-level framework that disentangles support-controlled site density from intrinsic site reactivity under identical reaction conditions. By combining in situ CO-FTIR, DRIFTS, and Raman spectroscopy with kinetic analysis on well-defined, high-purity oxide supports, this work transforms previously qualitative “support effects” in Pd-catalysed methane combustion into predictive structure–activity relationships. Full article

In arid and semi-arid regions, groundwater plays an important ecohydrological role in sustaining ecosystem stability under climate-warming-induced surface-water uncertainty. Disentangling precipitation and groundwater recharge effects on vegetation growth remains challenging, limiting robust identification of groundwater-dependent vegetation ecosystems (GDVEs) and quantitative ecological groundwater level estimation. Taking the Daihai Basin, a typical inland closed-lake basin, as a case study, we integrated multi-source remote-sensing data (2005–2025) with in situ groundwater monitoring to develop a comprehensive framework for ecohydrological response analysis and management quantification. Using an improved Mann–Kendall test together with spatiotemporal correlation analyses, we analyzed the spatial relationships between vegetation dynamics and groundwater depth. Results show: (1) basin-wide vegetation exhibits a greening trend (Sen’s slope = 0.00014) with spatial heterogeneity; (2) vegetation dependence on groundwater displays a clear threshold behavior, with low-cover areas (fractional vegetation cover, FVC < 0.3) showing relatively strong groundwater dependency (r = 0.698) whereas high-cover areas exhibit a weaker relationship; and (3) approximate ecological groundwater reference thresholds are estimated as 1.0 m (90% assurance) for forest land and 0.6 m for grass land (80% assurance). The proposed GDVE identification scheme provides a scientific reference for adaptive groundwater management and ecological assessment. Full article

To improve the operational efficiency of in situ soil remediation, this study investigated the operating parameters of the crushing–mixing working element of a novel tracked multi-axis in situ soil remediation device according to the soil contamination characteristics and process requirements. A DEM-based numerical simulation model was established, and response surface methodology (RSM) and machine learning algorithms were further integrated to model the response relationships, predict the evaluation indicators, and optimise the operating parameters. Single-factor experiments were conducted using the dispersion coefficient and soil fragmentation rate as the main evaluation indicators to determine the parameter range for the steepest ascent test. The steepest ascent test was used to rapidly approach the optimal parameter region, and RSM was then applied to establish the nonlinear mapping relationships between the operating parameters and response indicators. On this basis, machine learning models were introduced to further analyse and predict the experimental data, thereby improving the multi-objective optimisation process. A comparative analysis showed that, under the same dataset and evaluation metrics, the machine learning models achieved higher prediction accuracy than the RSM model. Among them, the decision tree model exhibited the best overall performance and provided a more stable optimisation result than the random forest and support vector regression models. The optimal parameter combination, identified by the decision tree model, was a rotational speed of 81 rpm, an average mixing pitch of 195 mm, a descent speed of 0.061 m/s, and an average mixing time of 1.1 s. Under this parameter combination, the dispersion coefficient was 0.171, and the residual bond count was 1511. The comparison of the RSM and machine learning models showed that the machine learning models achieved higher prediction accuracy. The relative errors between the optimal and actual simulation values were 2.56% and 4.92%, respectively. These results demonstrate that machine learning algorithms are applicable to the parameter optimisation of soil remediation working elements. The proposed DEM–RSM–machine learning framework can improve the efficiency and accuracy of equipment development and process optimisation, providing a scientific and technical basis for the development of intelligent agricultural equipment and sustainable agricultural engineering. Full article

The NMR resonator is the sensor responsible for transmitting RF pulses and receiving detection signals, and its tuning and matching are crucial to acquiring high-sensitivity NMR signals. Automated tuning and matching (ATM) is therefore essential for rapid, accurate, and continuously efficient testing. Existing NMR ATM methods still primarily rely on iterative search strategies, whose dominant cost arises from repeated hardware measurements and waiting periods, often requiring multiple measurement cycles before convergence. The emergence of in situ NMR detection of high-concentration ionic samples has further increased the demand for real-time, rapid ATM with a large dynamic range, posing a major challenge to conventional approaches. This paper proposes a physics-informed few-shot learning method for automatic tuning and matching over wideband and multi-resonance-frequency NMR scenarios. The tuning-and-matching problem is formulated as a structure and frequency-conditioned function regression task, and a conditional neural process (CNP) is introduced to learn cross-task priors and directly predict the states of tunable components from only a small number of real-machine context measurements. A physics regularizer based on the local sensitivity of the input impedance is further designed to impose stronger penalties on errors under high-Q narrowband operating conditions without relying on proprietary analytical circuit models. Simulation studies and real NMR experiments are conducted on multiple circuit topologies and multiple target frequencies using only a small number of NMR samples. The results demonstrate consistent improvements in key metrics, including accuracy of tuning and matching and the number of collected real-machine samples required per task. In particular, with only 100 sampled tuning/matching capacitor points and 20 on-hardware collected samples, the proposed method already delivers satisfactory tuning-and-matching performance. The method achieves an attractive accuracy–cost tradeoff across both cross-topology and cross-frequency scenarios, and shows strong potential for few-shot, rapid, real-time detection. Full article

Accurate estimation of the weighted mean temperature (Tm) is essential for retrieving precipitable water vapor (PWV) from ground-based Global Navigation Satellite System (GNSS) observations. Machine learning (ML) techniques excel in modeling nonlinear relationships among Tm time series, station geographic coordinates, and surface meteorological parameters, and recent studies have demonstrated that ML and neural network models outperform conventional linear Tm models. However, the full potential of surface meteorological measurements at GNSS stations for high-precision Tm retrieval remains to be fully explored. This study develops regional Tm empirical models using two ML methods—random forest (RF) and Temporal Mixture of Experts with Sequential Attention (TMESA)—to generate reliable real-time Tm estimates and enhance the accuracy of operational GNSS-PWV retrievals over China. A traditional linear model is adopted as the baseline to evaluate the performance improvements of the proposed models. The models are trained and tested using 10-year (2014–2023) hourly ERA5-derived Tm products and in-situ surface pressure, temperature, and relative humidity from 2377 meteorological stations, with Tm diurnal variations, station coordinates, and day of year integrated as auxiliary predictive features. Validation is conducted using 2024 ERA5 reanalysis data and radiosonde profiles from 120 stations across China. Results show that the RF model yields a bias (RMSE) of −0.11 K (2.67 K) against ERA5 and −0.21 K (2.67 K) against radiosonde data, while the TMESA model achieves superior performance with bias (RMSE) of −0.02 K (2.34 K) and 0.09 K (2.46 K), respectively, whose performance levels comparable to state-of-the-art studies. Compared with the traditional linear model, the RF model reduces Tm RMSE by 32% against ERA5 and 25% against radiosonde data, while the TMESA model achieves reductions of 40% and 33%, respectively. These findings confirm that the proposed ML models can provide high-accuracy Tm estimates for reliable GNSS-PWV retrieval. Future work will focus on the operational application of these models for near-real-time GNSS-PWV estimation. Full article

In industrial applications such as in situ leaching and uranium mining, permanent magnet synchronous motors (PMSMs) for submersible pumps are frequently connected to frequency converters via long cables. During this long-distance transmission, traveling wave reflections induced by high-frequency pulse width modulation (PWM) generate severe transient overvoltages that threaten motor insulation. Because installation space at deep-well motor terminals is severely restricted, overvoltage suppression must be implemented at the inverter output. Here, the parameter design and optimization of a passive LC filter specifically developed for 250 Hz high-frequency PMSMs are presented. The optimal inductance and capacitance parameters were determined by balancing multiple operational constraints, including fundamental voltage drop, high-frequency harmonic attenuation, and the avoidance of low-order harmonic resonance. Furthermore, the anti-saturation performance of the magnetic core material, evaluated thermal characteristics through electromagnetic-thermal co-simulation, and analyzed the risk of self-excited oscillation between the filter capacitors and the motor was analyzed. Finally, hardware experiments conducted on a 20 m cable test bench validate that the designed LC filter effectively mitigates terminal overvoltage. The peak terminal voltage was reduced from 900 V to 505 V, and total harmonic distortion (THD) was limited to below 5%. This design provides a highly reliable, space-efficient solution for overvoltage suppression in high-speed, long-cable motor drive systems. Full article

The present study addresses the fabrication of porous gyroid architectures by additive manufacturing from preceramic polymer feedstocks. Photocurable emulsions were engineered by combining a silicone powder with acrylate monomers and dispersing an emulsified secondary phase of calcium nitrate. The formulations showed light-curing behaviour compatible with digital light processing vat photopolymerization (DLP-VPP), enabling high-fidelity replication of triply periodic minimal surface (TPMS) gyroids (designed porosity: 85 vol.%). After pyrolysis in nitrogen at 700 °C, the lattices converted into CaO–SiO₂-derived amorphous matrices embedding an in situ turbostratic/pyrolytic carbon fraction, as suggested by the photothermal response and preliminary impedance behaviour, although the latter was measured in liquid electrolyte and therefore does not isolate electronic transport. To improve robustness during polymer-to-ceramic conversion, pharmaceutical borosilicate waste glass (BASG) was added as a passive filler (30–70 wt.%). The waste-glass phase acts as a passive filler that improves processing robustness and can mitigate shrinkage-induced damage during pyrolysis, while remaining electrically insulating (dielectric) and therefore not directly contributing to electronic conduction. The resulting structures combine high surface-to-volume ratio, controlled open porosity, and structural integrity with electrochemical responsiveness under the adopted test conditions, making them promising architected platforms for electrochemical components where interconnected porosity is advantageous. Full article

The emergence of selective laser melting (SLM) has enabled the fabrication of complex structures with exceptional mechanical performance. However, process-induced defects, including porosity and geometric deviations, pose significant challenges to structural reliability, and their dynamic evolution under loading remains poorly understood. In this study, helical layered square honeycomb structures were fabricated via SLM. The effects of process conditions on defect characteristics, as well as the influence of porosity and wall thickness defects on mechanical properties, were investigated using X-ray computed tomography (CT), in situ loading tests, and finite element simulation. The results indicate that the investigated high-quality process conditions minimize porosity, optimize pore morphology, and improve wall thickness uniformity, thereby substantially reducing the adverse effects of pores on tensile properties. Under compressive loading, defect evolution, including pore expansion and wall thickness thinning, is primarily concentrated at structural corners, with more pronounced variations observed under coarse process conditions. Increased porosity, wall thickness reduction, and uneven thickness distribution all degrade the quasi-static compressive performance and medium to high-velocity impact resistance of the structure. Furthermore, thickness distribution exerts an independent influence on mechanical properties beyond the effect of overall average thickness. Full article

Background/Objectives: The rapid emergence of multidrug-resistant (MDR) bacteria has increased the demand for non-antibiotic antibacterial strategies. Although photothermal therapy (PTT) and photodynamic therapy (PDT) are promising alternatives, each modality alone may show limited antibacterial efficacy. This study aimed to construct a flexible dual-light-responsive nanofibrous membrane integrating PTT and PDT for improved in vitro antibacterial activity against MDR bacteria. Methods: A silica nanofibrous membrane (SNF) was prepared by electrospinning followed by calcination. An Fe-doped sulfonated TpPa covalent organic framework (SCOF-Fe) was then grown in situ on the SNF surface via an interfacial diffusion strategy to obtain SNF@SCOF-Fe. The membrane was characterized in terms of morphology, structure, optical absorption, photothermal performance, Fe loading, Fe leaching, and reactive oxygen species (ROS) generation. In vitro antibacterial activity against supplier-reported MDR Escherichia coli (E. coli) and methicillin-resistant Staphylococcus aureus (MRSA) was evaluated under 420 nm, 808 nm, and dual-light (420 + 808 nm) irradiation. Results: Fe doping broadened the optical absorption of the COF-functionalized membrane into the near-infrared region and improved its photothermal response. Under dual-light irradiation, SNF@SCOF-Fe generated singlet oxygen, superoxide radicals, and hydroxyl radicals, together with a greater temperature increase than the undoped membrane. Within 15 min, SNF@SCOF-Fe achieved antibacterial rates of 99.29% against E. coli and 99.62% against MRSA. In addition, controlled dual-light cytocompatibility testing yielded 78.76% viability in L929 fibroblasts and 82.86% viability in MC38 murine colon carcinoma cells after SNF@SCOF-Fe treatment. Conclusions: SNF@SCOF-Fe combines dual-light-triggered photothermal heating and ROS generation within a flexible nanofibrous membrane and demonstrated effective in vitro antibacterial activity against two representative resistant bacteria. These findings support further investigation of SNF@SCOF-Fe as a light-responsive antibacterial membrane in relevant in vitro and in vivo models. Full article

Permeability is the key factor controlling coalbed methane (CBM) production, and hydraulic fracturing is an essential technique for permeability enhancement in low-permeability coal reservoirs. In situ stress plays a dominant role in both coal reservoir permeability evolution and hydraulic fracture propagation. By taking the Yangquan Mining Area in the northeastern Qinshui Basin, China, as the study area, this study integrated injection/falloff well testing and microseismic monitoring data to investigate the controlling effect of in situ stress on coal reservoir permeability and hydraulic fracture geometry. The results show that the in situ stress and reservoir pressure generally increase with burial depth. The measured maximum and minimum horizontal principal stresses ranged from 4.04 to 23.41 MPa and 3.72 to 13.24 MPa, with average values of 14.54 MPa and 8.64 MPa, respectively, while the vertical principal stress ranged from 6.50 to 19.87 MPa, averaging 13.31 MPa. The reservoir pressure ranged from 0.55 to 6.09 MPa, with an average value of 2.32 MPa. The lateral stress coefficient (λ) ranged from 0.45 to 1.68 (average of 0.89), exhibiting a pattern of dispersion in shallow strata and convergence in deeper strata. The regional stress regime is dominated by strike-slip faulting, with approximately 60.5% of the deeper measurement points characterized by σ_H > σ_v > σ_h. The coal seam permeability ranged from 0.03 to 15.52 mD, with an average value of 1.62 mD, and decreased exponentially with increasing effective stress. The controlling effect of the coal seam thickness on the permeability exhibited distinct segmented characteristics. The microseismic monitoring results from 23 hydraulic fracturing treatments indicate that approximately 74% of the fractures trended NE5–NE55°, consistent with the regional maximum horizontal principal stress direction. The fracture lengths ranged from 124.9 to 447.7 m, with an average of 344.7 m, while the fracture heights ranged from 12.9 to 79.09 m. All the induced fractures were predominantly vertical. A greater horizontal principal stress difference was associated with a lower permeability and promoted the formation of longer and more planar hydraulic fractures. These results demonstrate that the regional in situ stress field exerts significant control on both the coal reservoir permeability and hydraulic fracture morphology, providing important guidance for stress-based optimization of CBM development and hydraulic fracturing design. Full article

Accurate and reliable groundwater-level monitoring in deep observation wells remains difficult for conventional non-contact ultrasonic systems because narrow tubular geometries intensify multipath reflections, signal attenuation, and echo ambiguity. This study proposes a dual-signal direct time-of-flight (ToF) method that combines radiofrequency (RF) synchronization with one-way airborne ultrasonic propagation to a floating receiver located at the groundwater surface. In the proposed architecture, the RF signal provides a near-instantaneous time reference, whereas the ultrasonic signal defines the propagation delay, thereby eliminating dependence on echo-based ranging. The system integrates a wellhead surface unit for synchronized transmission and control, a floating unit for ToF acquisition and embedded processing, and an optional reference channel for in situ estimation of the effective sound speed. A duty-cycled power architecture is used to support low-power long-term deployment, while a multi-shot acquisition strategy with a median-like estimator improves robustness against startup transients, timing jitters, and false detections. Field validation was conducted over a 12-month period under actual groundwater-monitoring conditions, during which the groundwater depth varied between 14 m and 30 m below the wellhead datum. Within this field-validation interval, the proposed system achieved a mean absolute error of 0.048 m, a maximum absolute error of 0.050 m, and an overall valid detection rate of 99.4% over 358 valid cycles out of 360 scheduled cycles. In addition, a separate range-dependent confined-tubular propagation test was conducted to evaluate the extended detection capability of the RF-synchronized one-way ultrasonic ToF architecture. This test demonstrated stable acoustic-link ToF detection up to 300 m inside the tested 170 mm confined plastic pipeline. Therefore, the 300 m result should be interpreted as a range-dependent valid-detection result rather than as a 12-month groundwater-depth validation over the full 300 m interval. These results demonstrate that the proposed direct-ToF method provides an RF-synchronized one-way ultrasonic ToF framework with a floating receiver for groundwater-level monitoring in deep observation wells, while remaining compatible with low-power and IoT-based environmental monitoring systems. Full article