Search Results (406)

The Sichuan–Yunnan–Guizhou Pb-Zn metallogenic belt (SYG metallogenic belt), a crucial metallogenic unit on the southwestern margin of the Yangtze Block, is a key part of the South China low-temperature metallogenic domain. The incorporation mechanisms and distribution of trace elements (e.g., Ge, Ga, Cd) widely enriched in Pb-Zn sulfides throughout this region remain poorly understood. This study investigates main-ore-stage sulfides (sphalerite and pyrite) from the Maoping Pb-Zn deposit using in situ laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) analyses and mapping to systematically elucidate the partitioning and occurrence of these trace elements. The key findings are as follows: (1) Sulfides show distinct elemental partitioning: sphalerite preferentially concentrates Cd, Ag, Ge, Ga, and Se, whereas pyrite is significantly enriched in Mn, Ni, As, and Co. (2) Sphalerite is the primary host for many trace elements. Cadmium, Ge, Mn, Cu, and Ag mainly enter the sphalerite lattice by substituting for Zn²⁺. Coupled substitution mechanisms, such as Zn²⁺ ↔ Cd²⁺, 2Zn²⁺ ↔ Ge²⁺ + Cu²⁺, and 2Zn²⁺ ↔ Ga³⁺ + Cu⁺, facilitate the incorporation of Ge and Ga. (3) The sphalerite exhibits a trace element assemblage of high Cd-Ge and low Fe-Mn, which is geochemically similar to typical Mississippi Valley-type (MVT) deposits and differs significantly from sedimentary exhalative (SEDEX) and magmatic–hydrothermal deposits, indicating a medium- to low-temperature metallogenic environment. Based on these geochemical signatures and epigenetic textures, we confirm that the Maoping Pb-Zn deposit exhibits similarities with MVT deposits. Nevertheless, distinct differences in the tectonic setting and metal grades suggest it is a unique SYG-type Pb-Zn deposit. Full article

Steel pipes play a vital role in energy and industrial transportation systems, where undetected defects such as cracks and wall thinning may lead to severe safety hazards. Although ultrasonic guided waves enable long-range inspection, their defect imaging performance is often limited by dispersion, multimode interference, and strong noise. In this work, a multi-frequency fusion imaging method integrating time-reversal, topological derivative, and sparse Bayesian learning is proposed for guided wave-based defect detection in steel pipes. Multi-frequency guided waves are employed to enhance defect sensitivity and suppress frequency-dependent ambiguity. Time-reversal focusing is used to concentrate scattered energy at defect locations, while the topological derivative provides a global sensitivity map as physics-guided prior information. These results are further fused within a sparse Bayesian learning framework to achieve probabilistic defect imaging and uncertainty quantification. Dispersion compensation based on the semi-analytical finite element method is introduced to ensure accurate wavefield reconstruction at different frequencies. Domain randomization is also incorporated to improve robustness against uncertainties in material properties, temperature, and measurement noise. Numerical simulation results verify that the proposed method achieves high localization accuracy and significantly outperforms conventional TR-based imaging in terms of resolution, false alarm suppression, and stability. The proposed approach provides a reliable and robust solution for guided wave inspection of steel pipelines and offers strong potential for engineering applications in nondestructive evaluation and structural health monitoring. Full article

The Paleoproterozoic Aravalli Supergroup in northwest India hosts one of the oldest phosphorite deposits on Earth, located in the Jhamarkotra Formation, which was deposited after ca. 1762 Ma. Secondary enrichment is identified in the eastern region, resulting in upgradation of phosphate content, while primary stromatolitic columns are well-preserved in the western area of the Jhamarkotra mines. In this study, drill-core samples were collected from the unaltered western Block B and the upgraded eastern Block E to understand the alteration process. Petrographic studies reveal evidence of structural deformation and alteration. Elemental mapping of petrographic thin sections, employing SEM-EDS, indicates that dolomite has been leached out, resulting in phosphorite upgrading in the E-block. The major element oxide data support the leaching of dolomite. In the upgraded E-block, the weighted average P₂O₅ content nearly doubled (from 21% to 38%), while the MgO content decreased from 21% to 4% compared to the B-block. REE+Y contents in Block E are increased with minor Ce and Eu anomalies developed compared to the B Block. The U and Sr concentrations are also increased in Block E phosphorites. The petrographic and geochemical studies indicate that phosphorite enrichment was driven by structurally controlled, low-temperature hydrothermal alteration in the Jhamarkotra mines. Full article

Soil contamination by potentially toxic elements (PTEs) is a growing environmental concern, particularly in agricultural regions where soil quality directly affects crop safety and human health. This study evaluates PTE concentrations and ecological risks in agricultural soils of Hautat Sudair, central Saudi Arabia, using contamination indices, multivariate statistics, and GIS-based spatial modeling supported by RS-derived land use/land cover (LULC) mapping. The results show that the mean concentrations of Ni (35.97 mg/kg) and Mn (1230 mg/kg) exceed international thresholds in several locations, while Pb (8.34 mg/kg), Cr (33.00 mg/kg), Zn (60.09 mg/kg), and As (4.25 mg/kg) remain within permissible limits in most samples. Contamination indices, including the Enrichment Factor (EF), Contamination Factor (CF), and Geo-Accumulation Index (Igeo), highlight hotspot behavior, with isolated sites showing elevated concentrations approaching screening levels (e.g., Pb up to 32.0 mg/kg and Cr up to 52.0 mg/kg), whereas Ni and Mn exhibit the most pronounced local enrichment. The Pollution Load Index (PLI) varies from 0.24 to 0.80, indicating low to moderate contamination levels, while the Risk Index (RI) ranges from 10.43 to 41.38, signifying low ecological risk. Multivariate statistical analyses, including correlation matrices and principal component analysis (PCA), reveal that Ni, Cr, and Mn share a common source, possibly linked to anthropogenic inputs and natural geological background. Kaiser–Meyer–Olkin (KMO) and Bartlett’s test confirm the adequacy of the dataset for PCA (KMO = 0.797; χ² = 563.845, p < 0.001). Spatial distribution maps generated using GIS and RS highlight contamination hotspots, reinforcing the necessity for periodic monitoring. By integrating indices, multivariate patterns, and spatial context, this study provides a replicable, research-driven framework for interpreting PTE controls in arid agricultural soils. Full article

Gold nanoparticles (Au-NP) are frequently used as model nanomaterials to study nanoparticle behavior in plants due to their analytical detectability and negligible natural background in plant tissues. However, the feasibility of visualizing the spatial distribution of foliar-applied Au-NP at low exposure levels using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) remains insufficiently explored. In this study, commercially sourced Au-NP were applied to lentil leaves (Lens culinaris var. Beluga) at a low concentration of 0.5 mg·L⁻¹ using a controlled leaf submersion approach. Leaves were sampled at 1 h, 24 h, and 96 h post-exposure and analyzed by LA-ICP-MS imaging to assess time-dependent changes in gold-associated spatial signals, and to compare elemental distribution patterns with non-exposed controls. Untreated control leaves showed no detectable gold at any sampling time point, confirming negligible native Au background. In treated leaves, LA-ICP-MS imaging revealed an initially localized Au hotspot at 1 h, followed by progressive Au redistribution toward the leaf margins and petiole region by 24 h and 96 h. Gold signals persisted over the full 96 h period, indicating stable association of Au-NP with leaf tissue. Comparative elemental mapping of Ca, Mg, K, P, Fe, Zn, and Cu showed no persistent differences in spatial distribution patterns between treated and control leaves as detectable by LA-ICP-MS. This study demonstrates the feasibility of LA-ICP-MS imaging for visualizing the deposition and temporal spatial redistribution of low-dose foliar-applied nanoparticles in intact leaves. The results provide a methodological reference for future hypothesis-driven studies that apply nanoparticles under more controlled conditions, include increased replication, and combine multiple analytical techniques. Full article

The accumulation of heavy metals in fish tissues is widely recognized as an indicator of aquatic environmental pollution, and the analysis of their content provides a basis for assessing ecological risk and the safety of aquatic food. The European eel (Anguilla anguilla) is a species frequently used as a bioindicator in environmental studies due to its wide geographic distribution, long life cycle, and high capacity for bioaccumulation of heavy metals in various tissues. The aim of this study was to assess the variation in the accumulation of heavy metals, i.e., mercury (Hg), lead (Pb), arsenic (As), and cadmium (Cd), in the tissues (muscle, liver, gonads, and gills) of European eels caught in two locations in Polish inland waters. The obtained results showed significant differences both in the concentration levels of individual elements and in their co-occurrence in the examined tissues. The statistical methods used, including correlation analysis, heat maps, and principal component analysis (PCA), allowed for a comprehensive assessment of the relationships between metals and the identification of factors differentiating the studied populations. The obtained results clearly indicate that fish residing in similar environments for long periods exhibit significant differences in heavy metal content in various fish tissues. Fish obtained from environments with potentially higher levels of heavy metal inputs, such as the Oder River EMU compared with the Vistula River EMU, showed higher levels of heavy metal accumulation in tissues. This study also found that the concentration of heavy metals tested did not exceed the safe standards for human fish consumption. Full article

The ecological network serves as a vital spatial strategy for addressing climate change, biodiversity loss, and habitat fragmentation. Addressing limitations in existing ecological network studies—such as strong subjectivity and insufficient accuracy in structural element identification, cross-regional integration, and resistance surface weighting—this research uses the Zhengzhou–Kaifeng–Luoyang region (ZKLR) as a case study. It introduces the self-organizing map (SOM) model to identify ecological sources and employs the XGBoost-SHAP model to optimize resistance surface weights, thereby reducing subjective weighting biases. Subsequently, the Linkage Mapper tool is utilized to construct the regional ecological network. The superiority of the SOM model for identifying ecological sources was confirmed by comparison with a traditional network based on morphological spatial pattern analysis (MSPA). Further integrating complex network topology theory, nodes attack the simulations-assessed network resilience and proposed optimization strategies. The results indicate the following: (1) The area of ecological sources identified by the SOM model is three times that of the MSPA model; (2) SHAP feature importance analysis revealed that elevation (DEM) exerted the greatest influence on the composite resistance surface, contributing over 40%, followed by land use and slope, with each contributing approximately 15%. High-resistance areas were primarily distributed in western and central mountainous regions and built-up urban areas, while low-resistance areas were concentrated in the central and eastern plains; (3) topological analysis indicates that the integrated ecological network (IEN) exhibits superior robustness compared to the structural ecological network (SEN). The edge-adding strategy generated 22 additional ecological corridors, significantly enhancing the overall resilience of the integrated ecological network; and (4) based on ecological network construction and optimization results, a territorial spatial protection strategy of “one belt, two cores, two zones, and three corridors” is proposed. This study provides a novel methodological framework for ecological network construction, with findings offering reference for ecological conservation and spatial planning in the ZKLR and similar areas. Full article

Around eight million people—mainly in cities—die prematurely from pollution-related diseases; thus, studies of urban dust have become increasingly relevant over the last two decades. In this study, an assessment of heavy metal and metalloid contamination in urban dust was conducted in downtown Murcia, Spain. The objectives were to evaluate the level of contamination and the associated health risks, both with a spatially explicit focus. One hundred and twenty-eight urban dust samples were collected, each from a 1-square-meter area, using plastic tools to prevent contamination. The dust was dried and weighed, then acid-digested before analysis via inductively coupled plasma mass spectrometry. Corresponding maps were then generated using a geographic information system. The elements analyzed in the urban dust (with their median concentrations, given in mg/kg) were As (2.14), Bi (14.06), Cd (0.38), Co (1.88), Cr (71.17), Cu (142.60), Fe (13,752), Mn (316.64), Mo (3.90), Ni (21.94), Pb (106.27), Sb (6.54), Se (4.34), Sr (488.08), V (28.05), and Zn (357.33). The sequence of median concentrations for the analyzed elements was Fe > Sr > Zn > Mn > Cu > Pb > Cr > V > Ni > Bi > Sb > Se > Mo > As > Co > Cd. The pollution assessment reveals that the city is moderately polluted. Using local background levels, the elements with median values exceeding the threshold for considerable contamination were As, Cu, Pb, Sb, Se, and Zn. Using the global background level, the elements with median values exceeding the threshold for considerable contamination were Bi, Cu, Mo, Pb, Sb, Se, and Zn. The median value of the sum of the hazard index (1.82) indicates a risk to children’s health. The hazard index revealed that 43% of the sites pose a relative risk to children. In contrast to previous global studies, the present research provides a multi-scale assessment of urban pollution and health risks. Pollution is evaluated by metal, city, zone, and site, while health risks are assessed by metal, city, and site. We recommend a strategy for both local authorities and residents. Full article

Cracking occurred in the surface coating of a screw rotor during shale gas well operations. To determine whether the coating cracks could contribute to the failure of the 42CrMo substrate, the microstructure and morphology of surface cracks and local corrosion pits were examined and analyzed using a metallographic microscope, an SEM, and an EDS. To investigate the cross-sectional morphology and elemental distribution of corrosion pits, EDS mapping was performed. The composition of the corrosion products was characterized using Raman spectroscopy and XPS. In addition, four-point bend stress corrosion tests were conducted on screw rotor specimens under simulated service conditions. The results indicate that the P and S contents in the screw rotor substrate exceeded the specified limits, whereas its tensile and impact strengths satisfied the standard requirements. The microstructure consisted of tempered sorbite and ferrite, along with a small amount of sulfide inclusions. The corrosion products on the fracture surface were primarily identified as FeOOH, Fe₃O₄, and Cr(OH)₃. All specimens failed during the four-point bend tests. The chlorine (Cl) content in the corroded regions reached up to 8.05%. These findings demonstrate that the crack resistance of the 42CrMo screw rotor was markedly reduced under the simulated service conditions of 130 °C in a saturated, oxygenated 25% CaCl₂ solution. The study concludes that stress concentration induced by sulfide inclusions in the screw rotor, together with the combined effects of chloride ions, dissolved oxygen, and applied load, promotes the initiation and propagation of stress corrosion cracking. Therefore, it is recommended to strictly control the chemical composition and inclusion content of the screw rotor material and to reduce the oxygen content of the drilling fluid, thereby mitigating the risk of corrosion-induced cracking of the rotor. Full article

The FDA plans to gradually replace animal testing with organoid and organ-on-a-chip technologies for drug safety assessment, driving surging demand for gut-on-a-chip in food and drug safety evaluation and highlighting the need for efficient, precise chip designs. Oxygen gradients are central to these devices because they shape epithelial metabolism, microbial co-culture, and overall gut homeostasis. We coupled machine learning with finite element analysis to build a parametric COMSOL Multiphysics model linking channel geometry, transport coefficients, and cellular oxygen uptake to the resulting oxygen field. For numerical prediction, three models—Random Forest (RF), XGBoost, and MLP—were employed, with XGBoost achieving the highest accuracy (RMSE = 1.68%). SHAP analysis revealed that medium flow rate (39.7%), external flux (26.9%), and cellular oxygen consumption rate (24.8%) contributed most importantly to the prediction. For oxygen distribution mapping, an innovative Boundary-Guided Generative Network (BG-Net) model was employed, yielding an average concentration error of 0.012 mol/m³ (~4.8%), PSNR of 33.71 dB, and SSIM of 0.9220, demonstrating excellent image quality. Ablation experiment verified the necessity of each architectural component of BG-Net. This pipeline offers quantitative, data-driven guidance for tuning oxygen gradients in gut-on-a-chip. Future work will explore extensions including real experimental data integration, real-time prediction, and multi-task scenarios. Full article

Emerging research indicates that neuronal activity is maintained by an architectural system of protons in a multi-scale fashion. Proton architecture is formed when organelles (such as mitochondria, endoplasmic reticulum, lysosomes, synaptic vesicles, etc.) are coupled together to produce dynamic energy domains. Techniques have been developed to visualize protons in neurons; recent advances include near-atomic structural imaging of organelle interfaces using cryo-tomography and nanoscale resolution imaging of organelle interfaces and proton tracking using ultra-fast spectroscopy. Results of these studies indicate that protons in neurons do not diffuse randomly throughout the neuron but instead exist in organized geometric configurations. The cristae of mitochondrial cells create oscillating proton micro-domains that are influenced by the curvature of the cristae, hydrogen bonding between molecules, and localized changes in dielectric properties that result in time-patterned proton signals that can be used to determine the metabolic load of the cell and the redox state of its mitochondria. These proton patterns also communicate to the rest of the cell via hydrated aligned proton-conductive pathways at the mitochon-dria-endoplasmic reticulum junctions, through acidic lipid regions, and through nano-tethered contact sites between mitochondria and other organelles, which are typically spaced approximately 10–25 nm apart. Other proton architectures exist in lysosomes, endosomes, and synaptic vesicles. In each of these organelles, the V-ATPase generates steep concentration gradients across their membranes, controlling the rate of cargo removal from the lumen of the organelle, recycling receptors from the surface of the membrane, and loading neurotransmitters into the vesicles. Recent super-resolution pH mapping has indicated that populations of synaptic vesicles contain significant heterogeneity in the amount of protons they contain, thereby influencing the amount of neurotransmitter released per vesicle, the probability of vesicle release, and the degree of post-synaptic receptor protonation. Additionally, proton gradients in each organelle interact with the cytoskeleton: the protonation status of actin and microtubules influences filament stiffness, protein–protein interactions, and organelle movement, resulting in the formation of localized spatial structures that may possess some type of computational significance. At multiple scales, it appears that neurons integrate the proton micro-domains with mechanical tension fields, dielectric nanodomains, and phase-state transitions to form distributed computing elements whose behavior is determined by the integration of energy flow, organelle geometry, and the organization of soft materials. Alterations to the proton landscape in neurons (e.g., due to alterations in cristae structure, drift in luminal pH, disruption in the hydration-structure of the cell, or imbalance in the protonation of cytoskeletal components) could disrupt the intracellular signaling network well before the onset of measurable electrical or biochemical pathologies. This article will summarize evidence indicating that proton–organelle interaction provides a previously unknown source of energetic substrate for neural computation. Using an integrated approach combining nanoscale proton energy, organelle interface geometry, cytoskeletal mechanics, and AI-based multiscale models, this article outlines current principles and unresolved questions related to the subject area as well as possible new approaches to early detection and precise intervention of pathological conditions related to altered intracellular energy flow. Full article

Accurate prediction of the stress–strain fields in fiber-reinforced composites is crucial for performance analysis and structural design. However, due to their complex microstructures, traditional finite element analysis (FEA) entails a very high computational cost. Therefore, this study proposes a conditional generative adversarial network (cGAN) framework, named FiberGAN, to enable rapid prediction of the microscopic stress–strain fields in fiber-reinforced composites. The method employs an adaptive representative volume element (RVE) generation algorithm to construct random fiber arrangements with fiber volume fractions ranging from 30% to 50% and uses FEA to obtain the corresponding stress and strain fields as training data. A U-Net generator, combined with a PatchGAN discriminator, captures both global distribution patterns and fine local details. Under tensile and shear loading conditions, the R² values of FiberGAN predictions range from 0.96 to 0.99, while the structural similarity index (SSIM) values range from 0.95 to 0.99. The error maps show that prediction residuals are mainly concentrated in high-gradient regions with small magnitudes. These results demonstrate that the proposed deep learning model can successfully predict stress–strain field distributions for different fiber volume fractions under various loading conditions. Full article

This paper presents a validated, data-driven framework for the sustainable rehabilitation of corrosion-damaged marine infrastructure, demonstrated through a comprehensive study on a historic coastal structure. The implemented three-phase methodology—integrating advanced condition assessment, evidence-based intervention design, and rigorous performance validation—successfully addressed severe chloride-induced deterioration. Diagnostic quantification revealed that 30% of the primary substructure was severely compromised, with chloride concentrations reaching 1.94% by weight (970% above the corrosion threshold) and half-cell potential mapping confirming a >90% probability of active corrosion in critical elements. Guided by this data, a synergistic intervention combining galvanic cathodic protection, high-performance coatings, and structural strengthening was deployed. Post-repair validation confirmed exceptional outcomes: a complete electrochemical repassivation (potential shift from −385 mV to −185 mV), a 97.3% reduction in chloride diffusion rates, a 250% increase in surface resistivity, and the restoration of structural capacity to 115% of design specifications. The framework achieved a 65% reduction in projected lifecycle costs while establishing a new paradigm for preserving marine infrastructure through evidence-based, multi-mechanism strategies that ensure long-term durability and economic viability. Full article

Moss biomonitoring was conducted in 2002, 2005, 2010, 2015 and 2020 to evaluate atmospheric mercury (Hg) deposition across N. Macedonia as part of a comprehensive survey of potentially toxic elements (PTEs). More than 70 samples of the dominant moss species Hypnum cupressiforme and Homalothecium lutescens were collected during the summer field campaigns. Mercury concentrations were determined using cold vapour atomic absorption spectrometry and inductively coupled plasma mass spectrometry (ICP-MS). The results revealed marked temporal fluctuations: median Hg content increased from 56 µg/kg in 2002 to 68 µg/kg in 2005, peaked at 93 µg/kg in 2010, then decreased to 84 µg/kg in 2015, and further to 52 µg/kg in 2020. Over the study period, Hg concentrations ranged from 10 to 595 µg/kg, with the highest variability observed in 2010. Spatial distribution maps and regional comparisons indicate that elevated Hg contents correspond predominantly to anthropogenic sources, particularly in industrialised zones and regions affected by mining and metallurgical activities. The 2020 dataset shows a significantly lower median value (52 µg/kg) compared to previous surveys, indicating a slight improvement in air quality, although local hotspots persist. These results highlight the importance of long-term moss biomonitoring as a cost-effective approach for tracking atmospheric mercury trends and informing national environmental policy. Full article

Airborne magnetic particles (AMPs) are associated with potentially toxic elements, and their size, mineralogy, and concentration can significantly impact both the environment and human health. However, their spatial analysis is often limited by small datasets, non-normality, and pronounced local variability. In this work, two sites with distinct demographic and geographic characteristics, the city of Mar del Plata (Argentina) and the Aburrá Valley region (Colombia), were analyzed using the fuzzy Magnetic Pollution Index (IMC) as an indicator of the concentration of AMPs. Moreover, an original methodological framework that explicitly incorporates measurement uncertainty through fuzzy numbers, combined with an approach modeling fuzzy semivariances via α-cuts, performs spatial prediction via ordinary kriging. This study produces maps that simultaneously reflect the magnitude of IMC and its associated uncertainty. Unlike classical geostatistics, the fuzzy-based model captures the inherent imprecision of magnetic measurements and reveals spatial patterns where uncertainty becomes informative about the type and origin of pollution. In particular, this approach demonstrates that areas with higher IMC levels are associated with high anthropic activity (near industrial zones, main avenues, slow traffic). In contrast, lower values were found in residential areas. Overall, the fuzzy-driven approach provides an additional layer of information not accessible through traditional methods, improving spatial interpretation and supporting the identification of priority areas for environmental monitoring. Full article