Search Results (24,927)

In Chile, Eucalyptus globulus stands out as a significant forest species, yielding around 2 million tonnes of bark; this by-product is a valuable source of phenolic compounds. This research evaluated the valorization of E. globulus bark using alkali-assisted extraction (AAE) and obtained extracts intended to protect the wood against fungal degradation and ultraviolet (UV) radiation. The chemical and thermal properties of the extracts were characterized using total phenolic content (TPC), antioxidant capacity, FTIR spectroscopy, LC-LTQ-Orbitrap-MS, and thermal analyses (TGA and DSC). Pine wood samples were impregnated using the Bethel process, and their absorption, retention, leaching, UV resistance, gloss, and antifungal efficacy were evaluated. The AAE showed an extraction yield of 8.79%, almost double that of aqueous extraction, with a phenolic content of 970 mg GAE/100 g dry bark and good antioxidant capacity. The MS/MS analysis tentatively identified low-molecular-weight organic acids, phenolic acids, a hydrolyzable tannin derivative, ellagic acid, methylated flavonol glycosides, and an iridoid non-phenolic metabolite. Thermal analysis indicated greater stability of the alkaline extracts, with a mass loss of less than 10% up to 200 °C, and significant degradation between 220 and 300 °C. Leaching tests showed a lower release of polyphenols from alkali-treated wood, indicating reduced mobility and/or greater retention of the extractives within the wood structure. Biological assays demonstrated effective inhibition of stain fungi and strong resistance to brown rot. Furthermore, UV aging tests showed less color change (Delta E*) and greater resistance to surface degradation. These results demonstrate the potential of alkaline extracts from E. globulus bark as sustainable additives for wood protection. Full article

The composition of the surface, optical response, and size of the carbon dots synthesized from Valencia orange peels (Citrus sinensis L. Osbeck) were studied. The peels used in the hydrothermal synthesis were at three ripeness stages, and the synthesis was carried out at 220 °C and 3 MPa. Infrared spectroscopy results showed that carbon dots synthesized from the peels of unripe oranges are functionalized with oxygenated groups, and the carbonization process was effective. Instead, carbon dots obtained from peels of ripe oranges exhibit a nitrogen-functionalized surface. These results were confirmed by the bond-breakdown analysis in photoelectron spectroscopy. Additionally, the self-doped surface modified the optical response of the carbon dots, exhibiting an enhancement of the absorption band located at 283 nm corresponding to the contribution from n-π* transitions in nitrogen. Also, the excitation and emission wavelengths present a red shift for the ripe peels. Based on the above and the transmission electron microscopy results, it is concluded that the emission mechanism is associated with surface states and not particle size. Statistical analysis yielded an average size of less than 10 nm, regardless of the orange peels’ ripeness stage. It was observed that the CDs-N3 sample has more crystalline nuclei, which is justified because ripe peels follow a shorter carbonization pathway. Full article

This paper presents the results of apatite fission-track (AFT) and zircon fission-track (ZFT) analysis (dating) on samples collected from the surface exposures of six Precambrian intrusions in the southeastern Fennoscandian Shield: the Avdeevo and Shala dykes, the Valaam sill, the Salmi and Wiborg batholiths, and the Kuznechenskii massif. The short mean track lengths in apatite (10.7–13.5 μm) indicate that the studied rocks resided for a prolonged period within the apatite partial annealing zone (APAZ, 60–120 °C). We suggest that the AFT ages obtained from two of the granitic intrusions—the Salmi batholith and the Kuznechenskii massif—are apparent due to α-radiation-enhanced annealing (REA), as evidenced by an inverse correlation between single-grain AFT age and effective uranium (eU) concentration, and high dispersion and a negative chi-square test. An attempt to minimize the contribution of the REA effect to the AFT data for the Salmi batholith allowed its AFT age to be estimated as 1251 ± 125 (2σ) Ma, but the same approach was unsuccessful for the Kuznechenskii massif. In contrast, the mafic intrusions show no such correlation and yield reliable AFT ages: the Avdeevo dyke, 1040 ± 104 Ma; the Shala dyke, 1145 ± 89 Ma; and the Valaam sill, 1184 ± 78 Ma. The AFT data from the Wiborg batholith can be regarded as preliminary only. The most reliable AFT ages and thermal evolution models for the studied intrusions are similar and indicate prolonged exhumation of the intrusions to the surface over more than 1 billion years, with a marked increase in cooling rates around 300 Ma, which possibly has far-field causes, such as mantle dynamics related to the initial fragmentation of Pangea. Our data, as a first approximation, suggest a similar tectono–thermal evolution for intrusions located both within the northeastern margin of the Svecofennian orogen and on the Archean Karelian craton. Full article

High-entropy alloy (HEA) coatings exhibit enhanced chemical stability when doped with carbon, primarily due to the strong bonding between carbon and transition metals. Typical transition metals used in these coatings include Cr, Fe, Co, Ni, Cu, Ti, V, W, Nb, Ta, and Zr. Owing to their excellent chemical stability, HEA coatings are widely employed to protect component surfaces operating in highly corrosive environments. Against this backdrop, the present study investigates the effect of carbon doping introduced via methane gas flow during the physical vapor deposition of TiAlTaZrNb HEA coatings on corrosion resistance. The morphology and structure of the coatings were analyzed by field emission scanning electron microscopy, X-ray diffraction, and Raman spectroscopy. Corrosion protection and coating resistance were assessed through potentiodynamic polarization and electrochemical impedance spectroscopy. While increasing the methane flow resulted in an approximately 34% reduction in coating thickness, the overall coating resistance increased by one order of magnitude, reaching a maximum at a methane flow rate of 9 sccm, corresponding to the carbon solubility limit. This improvement was evidenced by a decrease in the corrosion rate from 8.02 × 10⁻² mm y⁻¹ for the uncoated H13 steel to 8.00 × 10⁻⁴ mm y⁻¹ for the HEA-coated samples. However, at higher methane flow rates, carbon precipitation and the formation of parallel microcracks contributed to an increase in corrosion rate. Full article

The generation of high-quality maritime samples is gradually becoming a key and challenging issue, due to the data thirst for training maritime intelligent models. However, existing methods mainly focus on static sample generation, which cannot meet the requirements of algorithms for dynamic decision. In this paper, an innovative method for generating high-quality marine dynamic data is proposed based on diffusion models. Considering the sensitivity of the diffusion model to prompts, a text enhancement module is first designed to perform semantic enhancement on the input text from the perspective of an expert in maritime climatology. Meanwhile, a directional image editing module is proposed to extract masks of interest from the input image, resulting in separate sea surface and sky regions. Then the image, mask and the enhanced text are sent together into the diffusion model to generate a high-quality directionally edited image. Finally, a video generation diffusion model is designed to convert the edited image into a dynamic data sequence. The entire framework has a clear sense of hierarchy and stable generation effect. We performed quantitative and qualitative experiments to prove that our method has significant advantages in data quality and controllability against existing SOTA methods. Full article

The manuscript deals with the bending behavior of beams with relatively less investigated cellular topologies based on triply periodic minimal surfaces (TPMSs). Three types of sandwich-type specimens (namely Schoen IWP, Fischer–Koch S, and Schoen F-RD) with five different volume fractions of 10, 15, 20, 25, and 35% (±1%) made of aluminum alloy AlSi10Mg by selective laser melting (SLM) technology were investigated. Three-point bending tests were performed at room temperature on a Zwick/Roell 1456 universal testing machine. The force–deflection dependences were plotted, while in addition to nominal stresses, the effective flexural stiffness and energy absorption to failure were evaluated to compare the properties of the investigated cellular beams. In the preparatory phase, critical aspects of possible premature failure of the samples with the smallest and highest selected volume fractions were addressed, while the manufacturability and fracture surfaces of the samples were assessed in order to improve the input conditions of the setup. By comparing the results obtained in the experimental testing in the second phase, it was found that the highest nominal bending stresses were achieved by the Schoen F-RD structure (although not significantly higher than Fischer–Koch S), but in terms of stiffness and amount of absorbed energy, the Fischer–Koch S structure showed the highest values. The improvement of input parameters led to an increase in the achieved nominal bending stresses by at least 100 MPa for all types of investigated structures compared to the first phase. The combined use of preliminary SLM process optimization, bending tests, and fracture surface/EDX analysis made it possible to relate the flexural response of the investigated TPMS topologies to manufacturing-related defects and premature-failure mechanisms in thin-walled AlSi10Mg cellular structures. The presented specimen configuration is intended as a comparative experimental benchmark for flexural performance of sandwich-type TPMS beams under quasi-static loading. Full article

Point-cloud analysis of sedimentary outcrops using Unmanned Aerial Vehicle (UAV) oblique photogrammetry is a crucial approach to sedimentary system characterization, stratigraphic correlation, and petroleum exploration analog studies. In large-scale field settings, however, outcrops are often scattered and fragmented, vegetation and soil cover is extensive, and class imbalance is pronounced. Manual interpretation is labor-intensive, while existing clustering algorithms, conventional machine learning methods, and general-purpose point-cloud segmentation networks struggle to simultaneously ensure geometric fidelity, rare-class recognition, and multi-scale feature integration. To address these challenges, we propose a method for extracting sedimentary outcrop point clouds from field surface point clouds using a UAV oblique photogrammetry acquisition strategy. The core segmentation module of the method, sedimentary cross-scale self-attention network (SedCSA-Net), is an enhanced version of PointNet++ that integrates collaborative improvements across four dimensions: data augmentation, sampling strategy, feature encoding, and loss optimization. Taking the Cretaceous Qingshuihe Formation in the Louzhuangzi area of the southern Junggar Basin as a case study, our experimental results indicate that SedCSA-Net overcomes the natural variability of UAV oblique photogrammetry point clouds—such as shadows, voids, and uneven density—achieving a mean Intersection over Union(mIoU) of 89.51% and an Overall Accuracy(OA) of 96.08%, with an outcrop-class Intersection over Union(IoU) of 86.90%. Attitude measurements derived from segmentation results deviate by less than 3° from manually annotated references, demonstrating that the proposed framework provides an end-to-end, generalizable approach for intelligent segmentation, geometric reconstruction, and attitude extraction of large-scale sedimentary outcrop point clouds. Full article

Sustainable agriculture increasingly relies on organic amendments that integrate circular economy principles. Municipal Solid Waste (MSW)-derived compost (MSW-compost) represents a promising candidate as soil amendment in viticulture, yet its impact on soil microbiota remains poorly investigated. This study assessed the effects of MSW-compost application on the bacterial microbiota of a Mediterranean vineyard soil over a twelve-month period, comparing two application methods (surface mulching and tillage incorporation). Soil DNA was analyzed by 16S rRNA gene metabarcoding, complemented by functional prediction (Picrust2) and the Tea Bag Index to assess soil decomposition activity. MSW-compost significantly increased alpha-diversity and affected beta-diversity (p = 0.001) of the microbiota, regardless of the application method, with significant effects persisting throughout the entire observation period despite a clearly diminishing trend. Devosia emerged as the hub taxon of the co-occurrence network and was increased by compost addition. MSW-compost application mode remarkably affected the microbial network, with mulched treatment leading to a more complex, denser, and more interconnected network. While a similar number of taxa were increased or decreased, functional prediction revealed a notable enrichment of metabolic pathways, both synthetic and degradative, in the MSW-compost amended samples; this finding was supported by the enhanced red tea decomposition data (p = 0.007). Our results indicate that MSW-compost acts as a beneficial soil amendment, simultaneously enhancing microbial diversity and soil decomposition activity. This study provides novel evidence supporting the use of MSW-compost as a sustainable tool for improving soil microbiological quality in productive vineyards. Full article

Surface-enhanced Raman scattering (SERS) offers a powerful route for biomolecular detection because it combines molecular specificity with high sensitivity, rapid optical readout, and multiplexing capability. In real biological samples, however, analytical performance is rarely determined by signal enhancement alone. Biofluids such as serum, plasma, saliva, urine, and interstitial fluid contain complex biomolecular mixtures that interfere with target capture, spectral response, and data interpretation. A practical SERS biosensor must therefore localize targets, stabilize spectral responses, tolerate matrix-induced variation, and convert complex spectra into reliable analytical information. This review discusses recent progress in SERS biosensing from an integrated system perspective, with particular focus on artificial intelligence/machine learning (AI/ML)-assisted interpretation. Direct label-free SERS provides chemically transparent readouts but is limited by stochastic adsorption, hotspot heterogeneity, and spectral variation in complex samples. Bio-recognition interfaces improve target localization, while signal-transduction strategies based on nanotags, immunoassays, clustered regularly interspaced short palindromic repeats (CRISPR) systems, nanozymes, and lateral-flow formats decouple molecular recognition from spectral generation. Digital SERS further improves measurement robustness by converting fluctuating intensities into countable, event-based outputs. AI/ML-assisted analysis can support full-spectrum classification, calibration transfer, explainability, and patient-level decision-making. We frame AI/ML-assisted SERS biosensing as an integrated architecture connecting substrate design, interface engineering, signal transduction, digital measurement, and clinical validation. Future progress will depend as much on validation-ready workflows as on plasmonic enhancement itself, especially for systems intended to operate across different samples, instruments, and clinical settings. Full article

The Chishui River Basin, a core production area for Chinese sauce-aroma Baijiu (exemplified by Moutai), supports sorghum cultivation critical to the liquor’s distinctive quality. The soil environment quality within this region, therefore, directly impacts the safety and quality of both raw material and the final distilled spirit. To underpin the safe production and sustainable development of this iconic beverage, it is essential to assess soil heavy metal contamination in the soils and quantify the contributions from various sources. In this study, 172 surface soil samples were collected from typical sorghum planting bases in the Renhuai area. Concentrations of eight heavy metals (loids) (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) were determined. The contamination status was evaluated using the geostatistical inverse distance weighting interpolation, the Nemerow pollution index (P_N), and the potential ecological risk index (RI). Source identification and quantification were performed using the positive matrix factorization receptor model (PMF). Results revealed significant enrichment of Cd and Hg in the soil, with mean concentrations 2.07 times and 2.54 times the soil background values for Guizhou Province, respectively. Pollution index results (P_i, P_N) indicated that soil Cd contamination is relatively severe, whereas contamination from other elements is minimal. Overall, approximately 86.5% of the study area was classified as clean or only slightly polluted. Cd poses a moderate ecological risk and was the primary contributor to the total ecological hazard. Other elements exhibited lower risk, resulting in a slight overall potential ecological risk. The soil environmental quality in certified organic sorghum bases was generally favorable. PMF analysis identified three principal sources: historic industrial emissions and traffic-related sources (contributing 46%), weathering of carbonate rocks combined with agricultural activities (37%), and natural background coupled with organic fertilizer application (17%). In conclusion, while the overall soil heavy metal pollution level in the sorghum planting areas is low, the notable enrichment and higher ecological risk of Cd necessitate enhanced dynamic monitoring and targeted risk control measures to ensure long-term soil health and product safety. Full article

Agricultural intensification in Eastern Africa has raised concerns about the transport of pesticides and nutrients from farmland into surface waters, posing risks to ecosystems and human health. This study systematically reviews the peer-reviewed literature published between 2010 and 2024 to assess the extent, patterns, and drivers of agrochemical contamination in rivers, lakes, and reservoirs across the region. Reported pesticide concentrations ranged from <0.01 to 0.55 μg L⁻¹, with several studies indicating exceedances of drinking-water or environmental guideline values, particularly for organophosphate and carbamate compounds. Nutrient enrichment was widespread, with nitrate concentrations of 0.99–5.6 mg L⁻¹ and phosphate levels of 0.16–2.0 mg L⁻¹, frequently linked to eutrophication. Many studies showed strong seasonal variability, with higher concentrations during rainy periods due to increased runoff and erosion. Variability among findings reflected differences in land use, catchment characteristics, sampling design, and analytical approaches. Where evaluated, mitigation measures such as vegetated buffer strips, cover cropping, and improved nutrient management were associated with reductions in agrochemical runoff of approximately 50–80%. Overall, agrochemical contamination is widespread across Eastern African basins and influenced by agricultural practices and hydrological dynamics, highlighting the need for strengthened monitoring, improved stewardship, and broader adoption of mitigation strategies. Full article

Ultrasonic thickness resonance can be effectively used to detect and quantify the level of corrosion in steel nuclear storage containers as well as other corrosion-prone thin-walled structures, such as pipes and storage tanks. Electro-Magnetic Acoustic Transducers (EMATs) have several advantages over more traditional piezoelectric-based transducers; namely, they can be used in a non-contact fashion on robotic platforms, allowing for measurements regardless of surface conditions or temperature. The major challenge of EMAT application is the power required to counteract the low actuation efficiency, which is achieved with a high-power ultrasonic pulse generator and a transformer circuit. Resonance techniques, in which most of the energy is concentrated near structural resonance frequencies, are preferable to improve efficiency of electro-magnetic acoustic measurements. This methodology was applied to 316L stainless steel thin plates subjected to uniform corrosion as well as pitting corrosion imitating different damage scenarios in a nuclear waste container. The resonant peak frequency shift was found to be proportional to the severity of corrosion for minimally corroded samples. However, the complete disappearance of the resonance peak was observed in the samples with severe corrosion damage. The EMAT liftoff distance was studied to quantify its effect on the amplitude, spread, and frequency of resonant peaks. Recommendations for use of EMATs for assessing corrosion damage are presented. The study demonstrates the success of frequency-based detection of corrosion damage in 316L stainless steel used in fabrication of nuclear waste storage containers. Full article

High-resolution surface soil moisture (SM) is needed for local hydrological and agricultural applications, but reliable retrieval at 100 m remains challenging. Within this broader methodological context, radiometer-constrained SAR change detection remains a practical and interpretable option for high-resolution soil moisture retrieval. It uses SAR-derived temporal changes to describe fine-scale wetting and drying processes, while passive microwave observations provide volumetric moisture references. This study proposes an improved SMAP-anchored Sentinel-1 change-detection framework (ISSF) for 100 m SM mapping. ISSF addresses these limitations by fitting NDVI-binned upper-envelope samples with a nonlinear quadratic function to normalize the vegetation-dependent backscatter-change range and by using multi-year SMAP dry/wet quantiles to scale the normalized relative wetness into volumetric SM. ISSF was evaluated using in situ measurements, a near-concurrent airborne reference, SMAP-based products, and direct transfer to OzNet. In the Shandian River Basin, ISSF achieved R = 0.549 and ubRMSE = 0.062 m³ m⁻³ at the point scale. Relative to three benchmark change-detection methods, ISSF increased R by 11–53% and reduced ubRMSE by 7–15%. For the airborne-referenced event, ISSF showed R = 0.635 and ubRMSE = 0.027 m³ m⁻³. Under direct transfer to OzNet, ISSF achieved mean R = 0.55 and mean ubRMSE = 0.05 m³ m⁻³. These results indicate that ISSF provides a practical and interpretable approach for 100 m soil moisture mapping in semi-arid regions with sparse to moderate vegetation. Full article

This study presents a material-level characterization of recycled aluminum briquettes produced by cold pressing Al–Si–Mg machining chips and investigates their behavior during subsequent remelting. The study evaluates density, porosity, chemical composition, and metallurgical yield before and after remelting, with the aim of assessing material-related prerequisites for potential metallurgical reuse applications. The cold-pressed briquette (Sample A) exhibited a bulk density of 2.29 g·cm⁻³ and an estimated porosity of 14.6%, attributed mainly to intergranular voids and residual surface contaminants. After melting and resolidification (Sample B), the density increased to 2.388 g·cm⁻³, while the estimated porosity decreased to 10.9%. Handheld ED-XRF analysis indicated no substantial compositional variation within the instrumental uncertainty range after remelting. SEM–EDS observations revealed Al-rich surface regions containing minor oxygen contributions associated with naturally formed surface oxides, while no pronounced intermetallic features were observed at the analyzed surface locations. The remelting process achieved a metallurgical yield of 94.2% with low dross generation. The results indicate that appropriately preprocessed and compacted aluminum machining chips can form mechanically stable briquettes with favorable remelting characteristics and potential applicability in secondary metallurgical processing. However, the present study does not evaluate deoxidation efficiency under molten steel conditions, which remains a subject for future investigation. Full article

High-fidelity computational fluid dynamics (CFD) remains computationally expensive for steady aerodynamic prediction under multi-condition and variable-geometry configurations, which limits rapid design iteration. To address this issue, this study proposes a data-driven surrogate framework for aircraft surface flow-field prediction on irregular meshes. The framework combines a geometry-unification strategy for variable rudder-deflection configurations with KM-GAT, a hybrid neural architecture that integrates graph attention and KAN-based nonlinear feature transformation. Geometry unification maps the surface flow fields associated with different rudder-deflection states onto a common zero-deflection reference template, thereby establishing consistent mesh correspondence and fixed prediction locations across samples while retaining the rudder angle as an operating-condition variable. The KM-GAT model further combines topology-aware message passing with localized nonlinear refinement, while the Huber loss is adopted to improve training robustness for CFD-derived data. Experiments on the F-22 research model show that the proposed framework achieves lower prediction errors and more concentrated error distributions than baseline MLP and GNN-based models. Qualitative comparisons further indicate that KM-GAT better preserves localized high-gradient structures, including pressure transitions and vortex-dominated regions. These results suggest that the proposed framework provides an effective surrogate modeling strategy for variable-geometry aerodynamic flow field prediction. Full article