Search Results (228)

Nuclear energy has emerged as a crucial technological solution for ensuring energy security and achieving carbon neutrality goals, given its ultra-high energy density and near-zero carbon emissions against the backdrop of rapid socioeconomic development, increasing energy demands, and accelerated global transition toward low-carbon energy structures. As the core component for energy conversion in nuclear reactors, fuel elements critically determine reactor efficiency and safety performance, with the fission product retention capability of silicon carbide layers in multilayer-coated fuel particles having been thoroughly validated through high-temperature gas-cooled reactor irradiation tests. The precise sphericity control of large-sized UO₂ fuel kernels represents a fundamental requirement for enhancing tristructural isotropic (TRISO) fuel particle performance and advancing Generation IV nuclear power plant development. This study presents a sphericity control strategy based on sol–gel processing that synergistically integrates physicochemical regulation of gelling media with multi-field washing flow field optimization. By implementing silicone oil-mediated interfacial tension gradient control, we effectively suppressed gel sphere destabilization while developing an innovative three-phase sequential washing technique involving kerosene washing, anhydrous ethanol interfacial transition, and ammonia solution replacement, which significantly enhanced mass transfer diffusion in stagnant liquid films and revolutionized fuel microsphere washing technology with improved efficiency and quality. Experimental results demonstrate that this integrated approach increases kernel sphericity qualification to 99.8%, reduces washing solution consumption by 79%, and achieves an average sphericity of 1.03. The research establishes a coupling mechanism between gelling media and multi-field washing processes, elucidating the synergistic effect between interfacial tension regulation and washing optimization, thereby providing both theoretical foundations and engineering application basis for the precision manufacturing of high-performance nuclear fuels. Full article

A spatially resolved investigation of bacterial inactivation using a radiofrequency (13.56 MHz) capacitively coupled plasma (RF CCP) discharge operating in ambient air at 4.0 mbar is presented. The plasma was generated in a parallel-plate reactor without external gas precursors and characterized using Langmuir probe diagnostics and optical emission spectroscopy (OES). Electron densities on the order of 109 cm³ were measured near the powered electrode, exhibiting pronounced axial and radial gradients across the discharge volume. OES revealed strong excitation of oxygen- and nitrogen-containing emitters, including O I (777 nm), N₂ s positive system (337–380 nm), and N₂⁺ first negative system features, with emission intensities increasing monotonically with applied RF power. The bactericidal performance was evaluated using Escherichia coli American Type Culture Collection (ATCC) 11775 exposed at different axial and radial positions within the reactor. At a fixed exposure time of 60 s, the log₁₀ reduction increased nonlinearly with RF power, rising from 0.29 at 20 W to 0.81 at 40 W, followed by a sharp transition to the assay reporting ceiling (≥2.95-log₁₀ under the adopted half-count correction) at 50 W and above. Time-resolved measurements at 50 W demonstrated rapid inactivation kinetics, with measurable reductions occurring within 5–10 s and reaching the reporting ceiling within 60 s. In contrast, samples positioned at the chamber periphery or approximately 20 cm from the discharge center exhibited negligible inactivation, confirming strong spatial localization of the biocidal effect. These results identify a threshold-like operating regime in which increased discharge intensity produces rapid inactivation in the plasma core while remaining strongly position dependent. The findings establish medium pressure, air-based RF CCP as an efficient, gas-free, and spatially controllable platform for localized surface decontamination under non-thermal conditions. Full article

Measurement of material thermodynamic parameters plays a crucial role in understanding the interactions between host materials and guest species. Therefore, developing a general-purpose system for thermodynamic parameter measurement is of great significance. In this work, a complete gas–solid interface thermodynamic parameter measurement platform was developed based on isothermal adsorption and a resonant microcantilever testing platform. Unlike conventional adsorption measurement systems that rely on manual, multi-cycle adsorption–desorption processes, the proposed platform integrates an automated hardware–software architecture together with a stepwise concentration-gradient protocol and on-chip thermal desorption, enabling continuous and efficient acquisition of adsorption isotherms. The study includes: (i) construction of an improved thermodynamic parameter extraction model based on the Sips model, (ii) development of an integrated resonant microcantilever control and acquisition module using a modified Fourier algorithm, and (iii) implementation of an automated testing and data analysis software framework developed in LabVIEW based on the Queued Message Handler (QMH) architecture. The system was validated from both hardware performance and material testing perspectives using CO₂ adsorption on H-SSZ-13 as a representative case. The results show that the system achieves a maximum sampling rate of 10,000 pts (points per second), with minimum root-mean-square (RMS) noise levels of 0.0083 Hz for frequency and 0.0109 °C for temperature. The PID temperature-control settling time (0.1%) is 24.9 ms, and the frequency-response settling time (0.01%) is 9.6 ms. Thermodynamic parameters including entropy change (ΔS), enthalpy change (ΔH), and Gibbs free energy change (ΔG) were successfully extracted during CO₂ adsorption at 294.15 K under different relative uptakes. Reproducibility was verified across three independent samples, yielding a standard deviation of 9.1 J·mol⁻¹ for ΔS at 2% relative uptake and relative standard deviations of 6.85% and 8.12% for ΔH and ΔG, respectively. These results demonstrate that the proposed thermodynamic measurement platform features a simple architecture, superior performance, and high reproducibility in gas–solid interface thermodynamic studies, showing strong potential for future commercialization. Full article

In this study, Ti-Co-Ce getter films were deposited via magnetron sputtering to investigate their activation mechanism—the thermal removal of surface passivation layers to restore gas sorption capability. The morphology before and after film activation was characterized using scanning electron microscopy (SEM) and atomic force microscopy (AFM). The oxygen content on the film surface before and after activation was measured using an energy-dispersive X-ray spectrometer (EDS), and gas desorption during activation was monitored with a quadrupole mass spectrometer (QMS). The combined results confirmed the absence of O₂ desorption during activation, suggesting oxygen migration into the film bulk. Crucially, in situ X-ray photoelectron spectroscopy (XPS) combined with controlled Ar⁺ ion sputtering depth profiling (0–30 nm) was employed to directly probe the chemical-state evolution within the thin film before and after thermal activation at 400 °C, thereby providing direct evidence of the activation dynamics. The data reveal that within the 0–10 nm near-surface region, a strong oxygen chemical potential gradient drives rapid oxide reduction and inward migration of lattice oxygen. At depths of 20–30 nm, moderate reduction coupled with oxygen enrichment induces phase separation, while around 30 nm, a dynamic equilibrium between oxygen inflow and outflow is established. These findings provide a theoretical basis for optimizing activation processes and guiding the development of low-temperature getter materials. This work is particularly relevant for MEMS, vacuum electronics, and other applications with stringent thermal budgets, expanding the design possibilities for heat-sensitive device integration. Full article

This study examines the hydro-sedimentological–radioecological controls governing the distribution of natural (K-40, Ra-226, Th-232) and anthropogenic (Cs-137) radionuclides in surface sediments of the western Black Sea shelf. Activity concentrations were determined by high-resolution gamma spectrometry, and radiological indices—including radium equivalent activity (Ra_eq), external hazard index (H_ex), and annual effective dose (AED)—were calculated to evaluate environmental safety. All indices remained well below internationally accepted thresholds, confirming the absence of radiological hazard in both coastal and offshore settings. Strong correlations between Ra-226 and Th-232 indicate dominant lithogenic control of natural radionuclides, whereas Cs-137 exhibits geochemical decoupling consistent with its behavior. A significant relationship between the fine-grained sediment fraction (<63 µm) and Cs-137 activity highlights the grain size effect, with offshore depositional zones acting as sediment-focusing areas where Cs-137 and excess Pb-210 co-accumulate under low-energy hydrodynamic conditions. Despite localized offshore enrichment, dose contribution analysis shows that natural radionuclides dominate the absorbed-dose budget, while Cs-137 contributes only marginally. Spatial predictive modeling using Artificial Neural Networks, validated under a Spatial Leave-One-Group-Out framework, yielded moderate generalization capacity (R² = 0.61 for Ra-226; R² = 0.41 for Cs-137), reflecting smoother spatial gradients of lithogenic radionuclides than heterogeneous radiocesium deposition. Furthermore, Machine Learning algorithms provided significant analytical value: a Random Forest (RF) model successfully classified environments (nearshore/shelf/depositional basin) based on distinct radionuclide signatures. At the same time, an optimized Artificial Neural Network (ANN-GA) enabled the nonlinear reconstruction of radiometric–granulometric patterns to identify local anomalies. The results show that radionuclide distributions are primarily structured by sediment provenance, grain size sorting, and hydrodynamic energy gradients rather than ongoing anthropogenic inputs. Full article

Coal mine gas disasters have always been a major threat to coal mine safety production. With the increasing depth and intensity of mining, the importance of studying gas geological laws is becoming increasingly prominent. In the actual mining process in coal mines, there is often a phenomenon of sudden increase in gas accumulation and gas emission in local areas. The study and prediction of the main influencing factors of gas enrichment are important research foundations for guiding the precise implementation of gas control engineering and avoiding coal and gas outburst accidents. Research shows that gas accumulates in local areas (such as abnormal structural and coal thickness areas), and gas pressure also increases locally; in areas where coal seam thickness changes dramatically, there is a sharp increase in gas content in mines. Prominent accidents all occurred in the coal seam area with a thickness exceeding 5 m. There is a significant spatial coupling between gas enrichment zoning and outburst accidents. The strip-shaped high-enrichment area based on gas content gradient division has a northeast southwest distribution consistent with the direction of structural extension. This study reveals the cross scale occurrence law of coalbed methane under multiple disturbances during the mining process, elucidates the non-equilibrium occurrence characteristics of methane, delineates local gas enrichment areas, uses theoretical models to predict gas emission and distribution laws, and provides parameter support for constructing gas geological attribute models. Full article

Model Predictive Control (MPC) has emerged as a promising approach for energy management in hybrid electric vehicles, enabling predictive optimization of powertrain operation. The energy management problem in parallel hybrid powertrains constitutes a Mixed-Integer Nonlinear Programming (MINLP) problem, combining continuous decision variables such as torque distribution with discrete decisions including engine on/off states and clutch engagement. This problem structure presents distinct challenges for different optimization approaches. Gradient-based methods such as Sequential Quadratic Programming (SQP) solve continuous, differentiable optimization problems and require auxiliary methods to handle integer variables, while metaheuristic approaches such as Genetic Algorithms (GA) can handle the mixed-integer structure directly at the cost of increased computational effort. This study presents a systematic comparison between GA and SQP as optimization solvers within an MPC framework for a P1P3 parallel hybrid powertrain. A multi-objective cost function is formulated to simultaneously optimize system efficiency, battery state of charge management, and noise emissions. Both approaches are evaluated across the WLTC as well as a real-world RDE scenario. On the WLTC, both MPC approaches reduce fuel consumption by 0.5–1.0% and improve system efficiency by 3.7–4.6% compared to a state-of-the-art deterministic reference strategy optimized for fuel consumption. At the same time, both approaches additionally achieve substantial reductions in noise emissions compared to the deterministic reference, which was not optimized for acoustic behavior. On both cycles, the GA-based MPC achieves favorable performance compared to SQP, with the performance gap widening from the WLTC to the RDE cycle. Both methods achieve real-time capability, yet SQP reduces computational time by a factor of four compared to GA. As long as computational resources in automotive ECUs remain constrained, this efficiency advantage positions gradient-based optimization for series production applications, whereas metaheuristic methods offer greater flexibility for concept development stages with relaxed real-time requirements. The findings contribute to the understanding of optimization algorithm selection for MINLP energy management problems in hybrid electric vehicles. Full article

The plugging of fractured gas reservoirs with severe lost circulation during oil and gas drilling and production has long been challenged by technical issues such as low plugging strength and short effective duration. This paper reports the preparation of a high-strength supramolecular gel plugging agent via micellar copolymerization based on the synergistic effects of hydrophobic association and hydrogen bonding. Systematic optimization determined the optimal synthesis formula: acrylamide (AM) 12%, 2-acrylamido-2-methylpropanesulfonic acid (AMPS) 2%, stearyl methacrylate (SMA) 0.4%, sodium dodecyl sulfate (SDS) 1.5%, and potassium persulfate 0.3%, with a reaction temperature of 60 °C. Performance evaluations revealed that the gel possesses a controllable gelation time (120 min) and excellent viscoelastic recovery properties. At a compressive strain of 87%, the compressive stress reached 1.43 MPa while maintaining structural integrity. Swelling behavior analysis indicated that the gel follows a non-Fickian diffusion mechanism, with its swelling process governed by the synergistic interplay of water molecule diffusion and polymer network relaxation. Core plugging experiments demonstrated that the gel achieved plugging efficiencies exceeding 95% for cores with permeabilities ranging from 0.18 to 0.90 μm², with a maximum breakthrough pressure gradient of up to 11.48 MPa/m. These results highlight the gel’s efficient and broad-spectrum plugging capability for fractured lost circulation zones. This preliminary study provides experimental foundations for the material design and performance optimization of supramolecular gel-based long-lasting plugging agents for severe lost circulation gas reservoirs, and further field-scale validation is required for engineering application. Full article

The soil–water interface (SWI) represents a critical biogeochemical hotspot where steep physicochemical gradients across millimetre-to micrometre-scales create diverse ecological niches controlling nutrient cycling, carbon stabilisation, and contaminant transformation. This review synthesises emerging understanding of micro-scale microbial dynamics, biofilm architecture, and functional processes shaping SWI ecosystems. We examine redox stratification driving microbial community assembly, biofilm-mediated nutrient trapping and soil aggregate stabilisation, and dynamic drivers including hydrological fluctuations, viral lysis, and differential transport at gas–water versus solid–water interfaces. Advanced methodologies, microsensor profiling, cryo-sectioning, spatially resolved metatranscriptomics, and non-destructive imaging, now enable unprecedented resolution of SWI microhabitat chemistry and microbial organisation. Horizontal gene transfer within interface biofilms accelerates adaptive responses to environmental stressors. Integration of micro-scale observations into ecosystem-level models remains challenging but essential for predicting soil carbon sequestration, contaminant fate, and microbial resilience under climate change. Strategic SWI management through biofilm engineering and controlled redox manipulation offers novel pathways for sustainable agriculture and bioremediation, though it requires careful balance of multiple ecosystem functions. Full article

Background: We sought to analyze the geographic distribution of HFE p.C282Y (homeostatic iron regulator c.845G>A; rs1800562) allele frequencies in Iberia. Methods: We analyzed published population/control cohorts of 50 or more subjects in mainland Spain and mainland Portugal and determined whether or not the p.C282Y genotypes in each cohort deviated from Hardy-Weinberg equilibrium (HWE) proportions. We defined combined p.C282Y allele frequencies from Spain and Portugal as representative of Iberia. We computed linear regressions (Pearson’s correlations) of allele frequencies vs. latitudes and longitudes of cohort recruitment sites, defined significant regressions as allele frequency gradients, and mapped regional allele frequencies. Results: There were 34 Iberian cohorts: 25 Spanish (12,297 subjects; 11 autonomous communities) and 9 Portuguese (1024 subjects; five administrative regions). p.C282Y genotypes in one of 34 cohorts (2.9%) deviated significantly from HWE proportions. Aggregate allele frequency in Iberia was 0.0292 (778/26,642) [95% confidence interval: 0.0272, 0.0313]. The correlation of allele frequencies with latitude in Iberia was significant (r₃₄ = 0.4184; p = 0.0138). The correlation of allele frequencies with longitude was not significant (r₃₄ = 0.0014; p = 0.9936). The range of 16 regional allele frequencies in Iberia was 0.0068 (Murcia) to 0.5000 (Galicia). Frequencies were highest in regions adjacent to the north and northwest coasts (Cantabria, Galicia, Norte) and lowest in the south (Algarve, Murcia). Conclusions: There is a significant decreasing linear north-to-south gradient of HFE p.C282Y allele frequencies in Iberia. p.C282Y allele frequencies are highest in regions adjacent to the north and northwest coasts. Full article

The tight gas reservoirs developed in the central Shenfu block are characterized by ultra-low porosity and permeability (typically < 10% porosity, <1 mD permeability), and high irreducible water saturation (40–60%). The frequent water blocking issue sharply reduces gas relative permeability during the production period, severely limiting well productivity. In this study, core flooding experiments using artificial cores were conducted to systematically evaluate the feasibility of CO₂ injection for enhanced gas recovery (EGR). The results show that the effectiveness of CO₂ EGR is sensitive to many factors, such as injection pressure, injection rate, total injection volume, and core permeability. The higher injection pressure and rate would improve the pressure gradient, CO₂ sweep efficiency, and EGR. An optimal total volume with the value (around 2.0 pore volumes, PV) was recommended as the amount of CO₂ injection are varied in the range of 0.5–2.5 PV. A higher permeable tight reservoir is prone to a higher nature gas recovery. The experimental findings, within the controlled conditions of this study, suggest that a flowback strategy of “slow startup and controlled depressurization” could be considered. Combining CO₂ injection with managed pressure drop of production and optimized fracturing process is proposed as a potential comprehensive strategy focused on “energy supplement, damage mitigation, and water control,” which may provide a useful reference for the efficient development of high-water-saturation tight gas reservoirs. Full article

Neutral atomic hydrogen (H I) regulates galaxy growth and quenching, but direct 21 cm measurements remain observationally expensive and affected by selection biases. We develop Bayesian neural networks (BNNs)—a type of neural model that returns both a prediction and an associated uncertainty—to infer the H I mass, ${log}_{10}\left(M_{\mathrm{HI}}\right)$, from widely available optical properties (e.g., stellar mass, apparent magnitudes, and diagnostic colors) and simple structural parameters. For continuity with the photometric gas fraction (PGF) literature, we also report the gas-to-stellar-mass ratio, ${log}_{10}(G/S)$, where explicitly noted. Our dataset is a reproducible cross-match of SDSS DR12, the MPA–JHU value-added catalogs, and the 100% ALFALFA release, resulting in 31,501 galaxies after quality controls. To ensure fair evaluation, we adopt fixed train/validation/test partitions and an additional sky-holdout region to probe domain shift, i.e., how well the model extrapolates to sky regions that were not used for training. We also audit features to avoid information leakage and benchmark the BNNs against deterministic models, including a feed-forward neural network baseline and gradient-boosted trees (GBTs, a standard tree-based ensemble method in machine learning). Performance is assessed using mean absolute error (MAE), root-mean-square error (RMSE), and probabilistic diagnostics such as the negative log-likelihood (NLL, a loss that rewards models that assign high probability to the observed H I masses), reliability diagrams (plots comparing predicted probabilities to observed frequencies), and empirical 68%/95% coverage. The Bayesian models achieve point accuracy comparable to the deterministic baselines while additionally providing calibrated prediction intervals that adapt to stellar mass, surface density, and color. This enables galaxy-by-galaxy uncertainty estimation and prioritization for 21 cm follow-up that explicitly accounts for predicted uncertainties (“risk-aware” target selection). Overall, the results demonstrate that uncertainty-aware machine-learning methods offer a scalable and reproducible route to inferring galactic H I content from widely available optical data. Full article

To address the issues of parameter setting, reliance on human experience, and the limitations of traditional model-driven control methods in handling complex nonlinear dynamics in the tin smelting industrial process, this paper proposes a data-driven control approach based on improved deep reinforcement learning (RL). Aiming to reduce the tin entrainment rate in smelting slag and CO emissions in exhaust gas, we construct a data-driven environment model with an 8-dimensional state space (including furnace temperature, pressure, gas composition, etc.) and an 8-dimensional action space (including lance parameters such as material flow, oxygen content, backpressure, etc.). We innovatively design a Dual-Action Discriminative Deep Deterministic Policy Gradient (DADDPG) algorithm. This method employs an online Actor network to simultaneously generate deterministic and exploratory random actions, with the Critic network selecting high-value actions for execution, consistently enhancing policy exploration efficiency. Combined with a composite reward function (integrating real-time Sn/CO content, their variations, and continuous penalty mechanisms for safety constraints), the approach achieves multi-objective dynamic optimization. Experiments based on real tin smelting production line data validate the environment model, with results demonstrating that the tin content in slag is reduced to between 3.5% and 4%, and CO content in exhaust gas is decreased to between 2000 and 2700 ppm. Full article

This study examines the influence of ignition position on explosion characteristics in linked vessels with a methane concentration gradient, aiming to support the safety of industrial combustible gas storage systems. A numerical simulation method was adopted, using a vessel-pipe-vessel linked device. Explosion parameters including pressure, pressure rise rate, temperature, and flame propagation speed were analyzed, with mechanism insights drawn from methane consumption rate and Reynolds number. Results indicate that maximum explosion pressure always occurs in the small vessel, decaying exponentially with increased dimensionless length of the ignition position, and ignition in the large vessel results in significantly higher pressure. The maximum pressure rise rate, maximum temperature rise rate, maximum flame speed, and maximum methane consumption rate each follow a quadratic trend, first decreasing and then increasing with the dimensionless length of the ignition position. Flame propagation is dominated by pipe acceleration, peaking at one end of the pipe near the small vessel at velocities up to 600 m/s. Turbulence intensity increases linearly with the dimensionless length of the ignition position and is highest when igniting in the small vessel. This research clarifies the influence mechanism of ignition position and provides theoretical support for the explosion prevention and control of linked vessel systems with concentration gradients. Full article

The response of selected physiological parameters to water deficit was investigated in three grapevine cultivars (Vitis vinifera L.): ‘Blue Portugal’, ‘Müller Thurgau’, and ‘Sauvignon Blanc’. The aim of the greenhouse experiment was to evaluate genotype-specific responses to reduced water availability and to assess the effects of water deficit on gas exchange, pigment content, chlorophyll fluorescence, and leaf water potential. Grapevine plants were grown in pots filled with perlite and subjected to five irrigation treatments ranging from 15 to 120 mL per container, applied to create a gradient of water availability. Measurements were performed over a one-month period at regular weekly intervals. Reduced irrigation generally resulted in decreased physiological performance compared to control plants. ‘Müller Thurgau’ showed the strongest reduction in chlorophyll content and gas exchange parameters under low irrigation, indicating high sensitivity to water deficit. In contrast, ‘Blue Portugal’ exhibited relatively stable pigment content under reduced water availability. Differences among cultivars in leaf water potential and gas exchange suggest contrasting water-use strategies. Overall, the results indicate higher drought tolerance in ‘Sauvignon Blanc’, while ‘Müller Thurgau’ appears to be the most sensitive cultivar. Full article