Search Results (2,036)

Semi-continuous casting is an important method for the large-scale production of high-strength conductive copper-iron (Cu-Fe) alloys in the future. However, serious peeling defects were found on the surface of cold-rolled strips during industrial trials. Due to the multi-step complexity of the manufacturing process (from casting to final product), identifying the root cause of defect formation remains challenging. X-ray computed tomography (X-CT) was used to quantitatively characterize the pores and defects in the horizontal continuous casting Cu-Ni-Sn slab, the semi-continuous casting Cu-Fe alloy slab, and the hot-rolled slab of Cu-Fe, and the relationship between the defect characteristics and processes was analyzed. The results showed that the internal defect sphericity distribution of the Cu-Fe alloy slab after hot rolling was similar to that of the reference Cu-Ni-Sn slab. The main difference lies in the low sphericity range (<0.4). The volume of pore defects inside the Cu-Fe alloy after hot rolling was significantly larger than in the reference sample, with a 52-fold volume difference. This phenomenon may be the source of surface-peeling defects in the subsequent cold-rolling process. The occurrence of internal defects in the Cu-Fe alloy is related to both the composition characteristics and casting processes of the Cu-Fe alloy; on the other hand, it is also related to the hot-rolling process. Full article

With the depletion of high-grade iron ore resources, the efficient utilization of low-grade iron ore has become a critical demand in the steel industry. Due to its uniform particle size and chemical composition, pelletized iron ore significantly enhances both the utilization rate of iron ore and the efficiency of metallurgical processes. This paper presents a deep learning model based on ResUNet, which integrates three-dimensional CT images obtained through industrial computed tomography (ICT) to precisely segment hematite, liquid phase, and porosity. By incorporating residual connections and batch normalization, the model enhances both robustness and segmentation accuracy, achieving F1 scores of 98.37%, 95.10%, and 83.87% for the hematite, pores, and liquid phase, respectively, on the test set. Through 3D reconstruction and quantitative analysis, the volume fractions and fractal dimensions of each component were computed, revealing the impact of the spatial distribution of different components on the physical properties of the pellets. Systematic evaluation of model robustness demonstrated varying sensitivity to different CT artifacts, with the strongest resistance to beam hardening and highest sensitivity to Gaussian noise. Multi-scale resolution analysis revealed that segmentation quality and fractal dimension estimates exhibit phase-dependent responses to resolution changes, with the liquid phase being the most sensitive. Despite these dependencies, the relative complexity relationships among phases remained consistent across resolutions, supporting the reliability of our qualitative conclusions. The study demonstrates that the deep learning-based image segmentation method effectively captures microstructural details, reduces human error, and enhances automation, providing a scientific foundation for optimizing pellet quality and improving metallurgical performance. It holds considerable potential for industrial applications. Full article

To address the challenges of environmental adaptability and passivation in nanoscale zero-valent iron (nFe⁰) systems, we developed oxalate-modified nFe⁰ (nFe_oxa) through a coordination-driven synthesis strategy, aiming to achieve high-efficiency Cr(VI) removal with improved stability and reusability. Structural characterization (STEM and FT-IR) confirmed the formation of a FeC₂O₄/nFe⁰ heterostructure, where oxalate coordinated with Fe(II) to construct a semiconductor interface that effectively inhibits anoxic passivation while enabling continuous electron supply, achieving 100% Cr(VI) removal efficiency within 20 min at an optimal oxalate/Fe molar ratio of 1/29. Mechanistic studies revealed that the oxalate ligand accelerates electron transfer from the Fe⁰ core to the surface via the FeC₂O₄-mediated pathway, as evidenced by EIS and LSV test analyses. This process dynamically regenerates surface Fe(II) active sites rather than relying on static-free Fe(II) adsorption. XPS and STEM further demonstrated that Cr(VI) was reduced to Cr(III) and uniformly co-precipitated with Fe(II/III)-oxalate complexes, effectively immobilizing chromium. The synergy between the protective semiconductor layer and the ligand-enhanced electron transfer endows nFe_oxa with superior reactivity. This work provides a ligand-engineering strategy to design robust nFe⁰-based materials for sustainable remediation of metal oxyanion-contaminated water. Full article

Groundwater resources in the Kou sub-basin of southwestern Burkina Faso play a critical role in supporting domestic water supply, agriculture, and industry in and around Bobo-Dioulasso, the second-largest city in Burkina Faso. This study synthesizes over three decades of research on groundwater vulnerability, recharge mechanisms, hydrochemistry, and residence time across the region’s sedimentary aquifers. The Kou basin hosts a complex stratified system of confined and unconfined aquifers, where hydrochemical analyses reveal predominantly Ca–Mg–HCO₃ facies, alongside local nitrate (0–860 mg/L), iron (0–2 mg/L) and potassium (<6.5 mg/L–190 mg/L) contamination. Vulnerability assessments—using parametric (DRASTIC, GOD, APSU) and numerical (MODFLOW/MT3D) models—consistently indicate moderate to high vulnerability, especially in alluvial and urban/peri-urban areas. Isotopic results show a deep recharge for a residence time greater than 50 years with deep groundwater dating from 25,000 to 42,000 years. Isotopic data confirm a vertically stratified system, with deep aquifers holding fossil water and shallow units showing recent recharge. Recharge estimates vary significantly (0–354 mm/year) depending on methodology, reflecting uncertainties in climatic, geological, and anthropogenic parameters. This review highlights major methodological limitations, including inconsistent data quality, limited spatial coverage, and insufficient integration of socio-economic drivers. To ensure long-term sustainability, future work must prioritize high-resolution hydrogeological mapping, multi-method recharge modeling, dynamic vulnerability assessments, and strengthened groundwater governance. This synthesis provides a critical foundation for improving water resource management in one of Burkina Faso’s most strategic aquifer systems. Full article

This study developed iron-oxide-functionalized carbon xerogels for enhanced arsenic adsorption to mitigate global water contamination. The composites were synthesized by integrating magnetite nanoparticles (15–20 nm) into a resorcinol–formaldehyde matrix via sol–gel polycondensation, followed by controlled pyrolysis. Key parameters—magnetite/resorcinol ratios (0.03–0.07), carbonization conditions (temperature, heating rate, duration), and H₂O₂-induced surface modification—were optimized to maximize adsorption performance. Characterization (SEM/EDX, XRD, FTIR, BET, TEM) confirmed uniform magnetite dispersion (~5 wt%) and revealed that pyrolysis at 850 °C enhanced porosity (378.8 m²/g surface area) and refined surface chemistry. Adsorption kinetics followed Elovich (R² = 0.9396) and Power Function (R² = 0.9443) models, indicating chemisorption dominance. Response Surface Methodology optimized desorption parameters using a Central Composite Design with three factors and two center points with repetition. A kinetic study of As(V) desorption from carbon xerogels was conducted, yielding optimal conditions: 1.0 M KOH, 160 rpm agitation, and 90 min contact time. The adsorbent retained >88% regeneration efficiency over four cycles, demonstrating robust reusability. Synergistic effects of magnetite incorporation, tailored pyrolysis, and H₂O₂ modification significantly improved arsenic selectivity and capacity in complex matrices, while enabling magnetic recovery. Full article

In the era of VUCA (Volatility, Uncertainty, Complexity, and Ambiguity) and technological disruption, Indonesia’s construction sector faces significant challenges. This study aims to identify and evaluate the key components of human capital agility, leadership agility, and project success within this sector. Using a combination of systematic literature reviews, Focus Group Discussions (FGD), and the Fuzzy Delphi Method (FDM), this research develops and refines assessment frameworks for these variables. The findings highlight that project success extends beyond the traditional dimensions of time, cost, and quality (iron triangle) to include sustainability, environmental performance, and stakeholder satisfaction. From 20 proposed components, 17 were validated for human capital agility, emphasizing flexibility, responsiveness, and proactivity as key elements. Similarly, leadership agility is characterized by resilience, emotionally intelligent, adaptable, and responsive, with 17 validated components out of 22. Furthermore, this study identifies critical project success factors, including time, quality, profitability, health, cost and safety, with defuzzification scores exceeding 0.89. This research contributes both theoretically and practically by providing a strategic framework to enhance the competitiveness and sustainability of Indonesia’s construction sector. Stakeholders are encouraged to optimize human capital and leadership agility to effectively address dynamic project environments. Full article

From the lanthanide group, part of the rare earth elements (REEs), lanthanum is one of the most important elements given its application potential. Although it does not have severe toxicity to the environment, its increased usage in advanced technologies and medical fields and scarce natural reserves point to the necessity also of recovering lanthanum from diluted solutions. Among the multiple methods for separation and purification, adsorption has been recognized as one of the most promising because of its simplicity, high efficiency, and large-scale availability. In this study, a xerogel based on silicon and iron oxides doped with zinc oxide and polymer (SiO₂@Fe₂O₃@ZnO) (SFZ), obtained by the sol–gel method, was considered as an adsorbent material. Micrography indicates the existence of particles with irregular geometric shapes and sizes between 16 μm and 45 μm. Atomic force microscopy (AFM) reveals the presence of dimples on the top of the material. The specific surface area of the material, calculated by the Brunauer–Emmet–Teller (BET) method, indicates a value of 53 m²/g, with C constant at a value of 48. In addition, the Point of Zero Charge (pH_pZc) of the material was determined to be 6.7. To establish the specific parameters of the La(III) adsorption process, static studies were performed. Based on experimental data, kinetic, thermodynamic, and equilibrium studies, the mechanism of the adsorption process was established. The maximum adsorption capacity was 6.7 mg/g, at a solid/liquid ratio = 0.1 g:25 mL, 4 < pH < 6, 298 K, after a contact time of 90 min. From a thermodynamic point of view, the adsorption process is spontaneous, endothermic, and occurs at the adsorbent–adsorbate interface. The Sips model is the most suitable for describing the observed adsorption process, indicating a complex interaction between La(III) ions and the adsorbent material. The material can be reused as an adsorbent material, having a regeneration capacity of more than 90% after the first cycle of regeneration. The material was reused 3 times with considerable efficiency. Full article

A new complex, tetrakis(1,10-phenanthroline)-bis(succinate)-(µ₂-oxo)-bis(iron(III)) nonahydrate, [Fe₂(Phen)₄(Succinate)₂(μ-O)](H₂O)₉, was synthesized using the slow evaporation method. This study provides a comprehensive characterization of this coordination compound, focusing on its structural, spectroscopic, and thermal properties, which are relevant for applications in catalysis, material science, and chemical engineering processes. Single-crystal X-ray diffraction (XRD), Raman spectroscopy, Fourier-transform infrared (FT-IR), ultraviolet-visible (UV-Vis) spectroscopy, and thermoanalytical analyses were employed to investigate the material properties. Intermolecular interactions were further explored through Hirshfeld surface analysis. XRD results revealed a monoclinic crystal system with the C2/c space group, lattice parameters: a = 12.7772(10) Å, b = 23.0786(15) Å, c = 18.9982(13) Å, β = 93.047(2)°, V = 5594.27(7) ų, and four formulas per unit cell (Z = 4). The crystal packing is stabilized by C–H⋯O, C–O⋯H, C–H⋯π, and π⋯π intermolecular interactions, as confirmed by vibrational spectroscopy. The heteroleptic coordination environment, combining weak- and strong-field ligands, results in a low-spin state with an estimated crystal field stabilization energy of −4.73 eV. Electronic properties indicate direct allowed transitions (γ = 2) with a maximum optical band gap of 2.66 eV, suggesting potential applications in optoelectronics and photochemical processes. Thermal analysis demonstrated good stability within the 25–136 °C range, with three main stages of thermal decomposition, highlighting its potential for use in high-temperature processes. These findings contribute to the understanding of Fe(III)-based complexes and their prospects in advanced material design, catalytic systems, and process optimization. Full article

Carbonate-hosted clay-type lithium deposits have emerged as strategic resources critical to the global energy transition, yet their exploration faces the dual challenges of technical complexity and environmental sustainability. Traditional methods often entail extensive land disruption, particularly in ecologically sensitive ecosystems where vegetation coverage and weathered layers hinder mineral detection. This study presents a case study of the San Dan lithium deposit in central Yunnan, where we propose a hierarchical anomaly extraction and multidimensional weighted comprehensive analysis. This comprehensive method integrates multi-source data from GF-3 QPSI SAR, GF-5B hyperspectral, and Landsat-8 OLI datasets and is structured around two core parts, as follows: (1) Hierarchical Anomaly Extraction: Utilizing principal component analysis, this part extracts hydroxyl and iron-stained alteration anomalies. It further employs the spectral hourglass technique for the precise identification of lithium-rich minerals, such as montmorillonite and illite. Additionally, concealed structures are extracted using azimuth filtering and structural detection in radar remote sensing. (2) Multidimensional Weighted Comprehensive Analysis: This module applies reclassification, kernel density analysis, and normalization preprocessing to five informational layers—hydroxyl, iron staining, minerals, lithology, and structure. Dynamic weighting, informed by expert experience and experimental adjustments using the weighted weight-of-evidence method, delineates graded target areas. Three priority target areas were identified, with field validation conducted in the most promising area revealing Li₂O contents ranging from 0.10% to 0.22%. This technical system, through the collaborative interpretation of multi-source data and quantitative decision-making processes, provides robust support for exploring carbonate-clay-type lithium deposits in central Yunnan. By promoting efficient, data-driven exploration and minimizing environmental disruption, it ensures that lithium extraction meets the growing demand while preserving ecological integrity, setting a benchmark for the sustainable exploration of clay-type lithium deposits worldwide. Full article

In steel production, the blast furnace is a critical element. In this process, precisely controlling the temperature of the molten iron is indispensable for attaining efficient operations and high-grade products. This temperature is often indirectly reflected by the silicon content in the hot metal. However, due to the dynamic nature and inherent delays of the ironmaking process, real-time prediction of silicon content remains a significant challenge, and traditional methods often suffer from insufficient prediction accuracy. This study presents a novel Multi-Scale Fusion Convolutional Neural Network (MSF-CNN) to accurately predict the silicon content during the blast furnace smelting process, addressing the limitations of existing data-driven approaches. The proposed MSF-CNN model extracts temporal features at two distinct scales. The first scale utilizes a Convolutional Block Attention Module, which captures local temporal dependencies by focusing on the most relevant features across adjacent time steps. The second scale employs a Multi-Head Self-Attention mechanism to model long-term temporal dependencies, overcoming the inherent delay issues in the blast furnace process. By combining these two scales, the model effectively captures both short-term and long-term temporal dependencies, thereby enhancing prediction accuracy and real-time applicability. Validation using real blast furnace data demonstrates that MSF-CNN outperforms recurrent neural network models such as Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). Compared with LSTM and the GRU, MSF-CNN reduces the Root Mean Square Error (RMSE) by approximately 22% and 21%, respectively, and improves the Hit Rate (HR) by over 3.5% and 4%, highlighting its superiority in capturing complex temporal dependencies. These results indicate that the MSF-CNN adapts better to the blast furnace’s dynamic variations and inherent delays, achieving significant improvements in prediction precision and robustness compared to state-of-the-art recurrent models. Full article

In the context of the global food system transformation towards sustainable and healthy diets, risk–benefit assessment supported by quantitative dose–response relationships between nutrients and health outcomes is crucial for evaluating the public health impact of dietary modifications, particularly those involving novel foods. The primary aim of this review was to establish a foundational basis for RBA by compiling and synthesising quantitative dose–response relationships identified through a comprehensive literature review. This review encompassed the last 15 years up to March 2025, utilising databases such as PubMed, Scopus, and Web of Science. This review prioritised recent meta-analyses of observational studies and randomised controlled trials with a low risk of bias, as assessed using the ROBIS tool. This review identified significant dose–response relationships across 12 nutrients and their associations with various health outcomes. While dietary fibre has demonstrated broad protective effects, cereal fibre has been found to be the most beneficial for colorectal cancer prevention. Calcium has been shown to have inverse associations with several cancers, although high dairy intake may increase the risk of prostate cancer. Haem iron was linked to an increased risk of several chronic diseases, whereas non-haem iron showed less consistent associations. Zinc exhibited a potential U-shaped relationship with colorectal cancer risk. These findings underscore the complexity of nutrient–health relationships, highlighting the importance of considering dose–response gradients and nutrient sources. Although this overview primarily summarises quantitative associations without exploring in-depth mechanistic or population-specific details, it underscores the complexity of nutrient effects, including nonlinearity and source dependency. This analysis offers a valuable foundation for future risk–benefit assessments of various food scenarios, thereby informing dietary recommendations and public health strategies. Full article

This study presents a comprehensive kinetic analysis of sulfamethoxazole (SMX) degradation by the photo-Fenton process, highlighting its potential for removing emerging micropollutants in water treatment. The degradation of SMX followed pseudo-first-order kinetics, with increasing Fe(II) concentrations significantly accelerating the oxidation rate. A kinetic model was developed to describe SMX removal, aromaticity loss, and color changes during treatment. Although SMX was rapidly eliminated, intermediate aromatic and chromophoric compounds persisted, requiring extended reaction times for complete mineralization. The kinetic modeling of aromaticity and color revealed distinct degradation pathways and rate constants, showing a strong dependence on iron dosage. The formation of nitrate and sulfate was used to monitor nitrogen and sulfur mineralization, respectively. Optimal nitrate formation was achieved at 22 mol SMX: 1 mol Fe(II), beyond which excessive iron promoted radical scavenging and the formation of stable Fe–aminophenol complexes, inhibiting complete nitrogen oxidation and aromatic degradation. Moreover, excessive Fe(II) led to increased water coloration due to complexation with partially oxidized aromatic byproducts. These findings emphasize the need for optimized catalyst dosing to balance degradation efficiency and minimize secondary effects. The proposed kinetic models offer a predictive tool for improving photo-Fenton-based treatments and integrating them with biological processes to enhance micropollutant bioremediation. Full article

The characterisation of aluminium casting alloys with iron concentrations exceeding current standards is essential, as upcycling has recently become a significant concern in achieving a more circular economy. Secondary aluminium casting alloys often exhibit insufficient mechanical properties for load-bearing automotive applications due to contamination with iron, mainly due to alloy mixing or remnants from end-of-life products during downcycling. This trend is anticipated to soon lead to a surplus of scrap. This study aims to fully understand the microstructural changes, intermetallic phase morphologies, and defect formation in AlSiMg alloy highly contaminated with Fe that exists in Al scraps and is detrimental for upcycling purposes. The investigation examined the AlSi7Mg0.3 alloy with Fe concentrations ranging from 0.1 to 3.8 wt.% Fe, employing thermodynamic simulations, hardness testing, quantitative image analysis, and corrosion tests. Among these alloys, the AlSi7Mg0.3-3.8Fe, containing the highest level of contamination, exhibited the most complex microstructure. This microstructure is characterised by the presence of two distinct Fe-rich intermetallic phases with diverse shapes and sizes: petal-like α′-Al₈Fe₂Si, long and thick β-Al_4.5FeSi plaques, and very thin β-Al_4.5FeSi needles. The significant growth in these phases with higher Fe concentration resulted in increases in hardness (15 HBW), porosity (1.39%), and corrosion rate (approximately 12 times). Full article

Psoriasis, a chronic immune-mediated inflammatory skin disorder characterized by keratinocyte hyperproliferation and inflammatory cell infiltration, involves multiple distinct programmed cell death pathways in its pathogenesis. Following the Nomenclature Committee on Cell Death recommendations, we analyzed the current literature examining diverse modes of cellular death in psoriatic lesions, with particular focus on keratinocyte cell death patterns and their molecular signatures. Analysis revealed several distinct cell death mechanisms: autophagy dysfunction through IL-17A pathways, decreased apoptotic activity in lesional skin, medication targeting anoikis in psoriasis, upregulated necroptosis mediated by RIPK1/MLKL signaling, gasdermin-mediated pyroptosis with enhanced IL-1β secretion, coordinated PANoptotic activation through specialized complexes, PARP1-mediated parthanatos promoting cutaneous inflammation, iron-dependent ferroptosis correlating with Th22/Th17 responses, copper-dependent cuproptosis with elevated MTF1/ATP7B/SLC31A1 expression, and NETosis amplifying immune responses through interaction with the Th17 axis. The intricate interplay between these cell death mechanisms has led to the development of targeted therapeutic strategies, including mTOR inhibitors for autophagy modulation, RIPK1 inhibitors for necroptosis, and various approaches targeting ferroptosis and NETosis, providing new directions for more effective psoriasis treatments. Full article

This study explores the design and performance of an underwater magnetic sensor network (UMSN) tailored for intrusion detection in complex environments such as riverbeds and areas with dense vegetation. The system utilizes wireless sensor network (WSN) principles and integrates AMR-based magnetic sensors (e.g., LSM303AGR) with MEMS-based accelerometers to provide accurate and high-resolution magnetic field measurements. Extensive calibration techniques were employed to correct hard-iron and soft-iron distortions, ensuring reliable performance in fluctuating environmental conditions. Field tests included both controlled setups and real-world scenarios, such as detecting intrusions across river sections, shorelines, and coordinated land-water activities. The results showed detection rates consistently above 90%, with response times averaging 2.5 s and a maximum detection range of 5 m. The system also performed well under adverse weather conditions, including fog and rain, demonstrating its adaptability. The findings underline the potential of UMSN as a scalable and cost-efficient solution for monitoring sensitive areas. By addressing the limitations of traditional surveillance systems, this research offers a practical framework for enhancing security in critical regions, laying the groundwork for future developments in magnetic sensor technology. Full article