Search Results (7,036)

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

Resveratrol (RSV) is a poorly water-soluble polyphenolic compound with various potential health benefits, but its pharmaceutical application is limited by low aqueous solubility and poor oral bioavailability. Additive manufacturing (AM), particularly fused deposition modeling (FDM) 3D printing, offers a flexible approach for fabricating oral dosage forms with customized geometry and internal architecture. In this study, hot-melt extrusion (HME) combined with fused deposition modeling (FDM) 3D printing was used to prepare RSV-loaded tablets with different infill patterns. Hydroxypropyl methylcellulose acetate succinate and hydroxypropyl cellulose were selected as polymeric carriers to prepare RSV-loaded filaments suitable for FDM printing. The effects of infill pattern on the solid-state characteristics, dimensional accuracy, mechanical properties, floating behavior, and in vitro drug release of the printed tablets were systematically investigated. Differential scanning calorimetry, powder X-ray diffraction, and polarized light microscopy indicated that RSV was mainly converted into an amorphous or molecularly dispersed state after HME and FDM processing. All designed tablets were successfully printed and showed acceptable shape fidelity, while different infill patterns resulted in variations in tablet weight, mechanical strength, floating duration, and release behavior. In vitro dissolution studies showed that the RSV release profiles were dependent on the internal infill architecture. Tablets with more complex infill patterns generally exhibited slower drug release, which may be related to differences in internal pore structure, medium penetration pathways, matrix hydration, and diffusion distance. Release kinetic analysis further suggested that RSV release from the printed tablets involved a combination of diffusion and polymer relaxation processes. These results demonstrate that infill pattern is an important structural parameter for modulating the mechanical performance and drug release behavior of FDM 3D-printed RSV tablets. This study provides useful guidance for the design of 3D-printed oral dosage forms with tunable release characteristics. Full article

Glycolysis of poly(ethylene terephthalate) (PET) offers a promising route for chemical recycling, yet conventional homogeneous catalysts often suffer from low selectivity, severe side reactions (especially diethylene glycol, DEG formation), and detrimental metal residues that compromise the quality of recycled products. To address these challenges, we herein develop dipotassium phenylphosphonate (PPOA-K) as an efficient homogeneous catalyst for PET glycolysis. Under optimized conditions (1 wt% catalyst, 197 °C, EG/PET mass ratio 3:1, 90 min, atmospheric pressure), PPOA-K achieves 100% PET depolymerization and a high BHET yield of 86.0%, and the reaction follows apparent first-order kinetics with an activation energy of 70.3 kJ·mol⁻¹. Beyond its high catalytic activity, PPOA-K effectively suppresses the acid-catalyzed etherification of ethylene glycol to DEG, a common side reaction that reduces monomer purity and degrades recycled polyester properties. Remarkably, the trace amount of PPOA-K remaining in the recovered BHET (17.3 ppm) is not detrimental; instead, it continues to inhibit DEG formation during repolymerization and acts as a thermal stabilizer, improving the melting point and thermal stability of recycled PET. The advantages of PPOA-K are further demonstrated in a partial (in situ) glycolysis–repolymerization process, where it reduces the DEG content in the final rPET to 1.78% (vs. 2.25% for conventional Zn(OAc)₂), yielding rPET with a higher melting point, higher crystallinity, and better color. This work demonstrates that dipotassium phenylphosphonate uniquely combines high catalytic activity, side reaction suppression, and beneficial residue effects, offering a new catalyst design strategy for high-quality PET recycling. Full article

The Gebel Shalman-Wadi Biarn (GSh-WB) area in Egypt’s South Eastern Desert hosts NYF-type rare-metal pegmatites with significant U, Th, Nb-Ta, and REEs mineralization. This study integrates field observations, petrography, mineralogy, whole-rock geochemistry, and gamma-ray spectrometry to characterize these pegmatites and evaluate their economic potential. The pegmatites occur as veins, dykes, and zoned pockets hosted entirely within syenogranites. Petrography, pegmatites, and syenogranites are primarily composed of K-feldspar, albite, and quartz with trace amounts of biotite and muscovite. The environmental scanning electron microscope (ESEM) revealed the presence of the following minerals: autunite, kasolite, thorite, monazite-(Ce), parisite, xenotime-(Y), ferrocolumbite, hydroxyplumbobrtafite, aeschynite-(Y), and zircon, which are the major U-Th, Nb-Ta, and REE-bearing minerals. Additionally, gold, cassiterite, wolframite, pyrrhotite, chalcopyrite, and brass alloy were identified as sources of precious and base metals. Both groups’ chondrite-normalized REE patterns, which display slightly elevated LREE patterns and negative Eu anomalies, point to fractional crystallization involving plagioclase fractionation. Consequently, pegmatite and syenogranites are believed to have mostly formed from the partial melting of a reconstituted juvenile crust and its weathered sediments associated with Neoproterozoic magmatism. The marginally positive Ce anomaly in the (GSh-WB) pegmatites (1.02–0.98) may be associated with monazite crystallization resulting from enhanced fractionation. The Th and U levels range from 101 to 28.6 ppm and from 51 to 5.8 ppm, respectively. The magnitude of the tetrad effect in the rare earth elements of the analyzed rocks exceeds one (T1 = 1.12–1.02, T3 = 0.92–1.08, and T1,3 = 1.01–1.05), suggesting an M-type tetrad effect. The presence of this tetrad effect is indicative of granite that has been significantly altered by hydrothermal processes and is extensively fractionated. Chondrite-normalized REE patterns of the pegmatites (average ΣREE = 439 ppm) and their host syenogranites (average ΣREE = 192 ppm) show similar trends characterized by enrichment of light rare earth elements (LREEs) relative to heavy rare earth elements (HREEs) and pronounced negative Eu anomalies (Eu/Eu* = 0.09–0.22). These features, together with negative Sr and Ba anomalies, likely reflect extensive fractional crystallization of feldspars and feature anorogenic rocks. Spectrometric analysis reveals eU values of 2.0–288 ppm and eTh values of 7.0–455 ppm in pegmatite samples, with eU/eTh ratios (0.49–0.39) exceeding the typical continental crust value of 0.25, indicating uranium enrichment. Both magmatic and hydrothermal processes contributed to the observed radioactivity. The spatial distribution of uranium shows lithological and structural controls. The GSh-WB pegmatites represent a potential target for uranium exploration. Full article

This work reports the combination of pentagonal grain morphology, high phase purity, and non-monotonic thermal conductivity behavior over a wide temperature range (25–1200 °C). The SrZrO₃ coatings with different processing parameters are deposited using atmospheric plasma spraying (APS). Unlike conventional atmospheric plasma-sprayed oxide coatings, distinct pentagonal-shaped grains with multi-directional orientation suggest a unique solidification pathway and anisotropic growth mechanism. The pentagonal morphology may come from the impingement of five radially columnar grain sectors during rapid solidification of a highly undercooled melt splat, constrained by local thermal gradients. This atypical morphology, not commonly reported for SrZrO₃ coatings, is further supported by electron backscatter diffraction (EBSD) results, which confirm a remarkably high phase fraction (~94.5%) of SrZrO₃ despite rapid quenching inherent to APS processing. The combination of high phase purity and unusual grain geometry represents a significant advancement in tailoring the microstructures of environmental barrier materials. Moreover, the non-linear thermal conductivity response with temperature shows a pronounced decrease up to ~800 °C (0.737 W·m⁻¹·K⁻¹) stabilization between 800 and 900 °C, and a subsequent increase at higher temperatures. This behavior indicates a complex interplay between phonon scattering, defect structures, and possible radiative heat transfer contributions at elevated temperatures. Full article

Selective laser melting (SLM) equipment operates as a complex cyber–physical system, wherein strong implicit coupling among internal subsystems presents significant challenges for condition monitoring and fault diagnosis. Existing deep learning methods often suffer from feature submersion when processing multi-source heterogeneous data and lack the capability for system-level topological causal inference. To address these issues, we propose a multisystem coupling modeling and anomaly traceability method based on a decoupled graph attention network (ST-DBGAE). Independent local spatiotemporal feature alignment modules are constructed to map heterogeneous sensory data into a unified latent space. This eliminates dimensional discrepancies while strictly maintaining the feature independence of underlying hardware subsystems, such as optical and gas circuits. A dynamic graph attention mechanism with sparse priors is subsequently introduced to adaptively capture time-varying coupling weights triggered by implicit interactions (e.g., thermal fluids), bypassing the need for predefined rigid physical connections. Furthermore, a dual-branch two-stage decoupled optimization architecture is designed. By blocking the cross-interference of global backpropagation, this architecture outputs a continuous equipment health index (HI) based on reconstruction errors and employs a topological difference matrix inference mechanism to reversely anchor the root-cause nodes responsible for cross-system cascading degradation. Experimental results based on over 310,000 real operational monitoring records from industrial SLM equipment demonstrate that the proposed model achieves a comprehensive diagnostic Macro-F1 score of 96.5% across eight operating states. The single-class detection rates (ACCs) of specific underlying anomalies are significantly improved. This method not only enables high-precision equipment health warnings but also provides a physically interpretable microscopic fault propagation mapping for predictive maintenance. Full article

Titanium-bearing blast furnace slag is rich in high-melting-point titanium-containing minerals including perovskite, melilite and spinel, which result in the loss of titanium resources and hinder the comprehensive utilization of such slag. On this basis, combined with process mineralogy theories, this study adopted multiple characterization methods, including a polarized light microscope with transmitted and reflected light, XRD and EPMA. These simulations reveal that the bulk SiO₂ content dictates titanium distribution among the mineral phases, thereby laying a solid foundation for the subsequent experiments. Meanwhile, quantitative analyses were performed on the microstructure, mineral composition and perovskite grain size of the slag. The occurrence state and migration law of titanium in the slag were systematically investigated. The results show that the microstructure of titanium-bearing blast furnace slag presents a porphyritic structure at different SiO₂ levels. Its main mineral phases include perovskite, pyroxene, spinel and glass. Titanium is predominantly hosted in perovskite, with small amounts distributed in the pyroxene, spinel and glass phases. Reducing the SiO₂ content facilitates the formation and grain coarsening of perovskite and promotes the migration of titanium from pyroxene and glass into perovskite. When the SiO₂ content is 20%, the perovskite content reaches 44.3%. Among them, the proportion of grains larger than 40 μm is 59.94%, and the distribution ratio of titanium in perovskite is 86.78%. Under the experimental conditions of this study, 20% SiO₂ is the optimal level. These findings can provide a theoretical reference for the efficient separation and recovery of titanium from titanium-bearing blast furnace slag. Full article

Remote-sensing land surface deformation (LSD) is a powerful and effective approach for investigating regional alpine permafrost variations. However, alpine permafrost is often distributed in areas characterized by earthquakes, and the LSD of alpine permafrost is potentially contaminated or diminished by earthquake-related LSD. Therefore, this study aimed to derive the effective LSD in the alpine permafrost of the Source Area Yellow River (SAYR) by removing LSD originating from the M_w 7.4 Maduo earthquake in 2021-05-22 and analyzing the spatiotemporal variations in LSD during 2017–2024. Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) was used to obtain the initial LSD time series from Sentinel-1 images acquired during 2017–2024. The LSD of the M_w 7.4 Maduo earthquake, its aftershocks and the post-seismic relaxation in SAYR was simulated separately by considering its temporal process and removed from the LSD time series in SAYR. The final LSD was validated against in situ Global Navigation Satellite System (GNSS) measurements, and the spatiotemporal variations in LSD in SAYAR were subsequently analyzed. The study found the following: (1) the removal of the earthquake-related LSD was successful both spatially and temporally and the final LSD has mean absolute error (MAE) of 3.22 mm and root mean squared error (RMSE) of 3.92 mm; (2) during 2017–2024, the vertical LSD in SAYR was mostly −8–8 mm/y; (3) soil moisture determined the spatial distribution of the LSD direction in SAYR as a result of local drainage conditions, air temperature, precipitation and snow melt. This study demonstrated the necessity of removing the earthquake-related LSD when investigating the alpine permafrost LSD in tectonically active areas. The strategy adopted in this study serves as a technical reference for future investigations of this kind. The findings in this study provide insight for a thorough understanding of permafrost evolution on the Tibetan Plateau in the context of climate change. Full article

Expanded snack extrusion is governed by tightly coupled interactions among raw material composition, moisture, barrel temperature, screw speed, feed rate, screw configuration, die geometry, and energy input. These variables affect not only final responses such as expansion ratio, bulk density, hardness, crispness, and water absorption or solubility indices, but also intermediate state variables including specific mechanical energy (SME), melt temperature, die pressure, melt viscosity, and bubble growth dynamics. As a result, modelling has become essential for product design, process optimisation, and scale-up. This review critically evaluates the major classes of models used to describe process–structure–quality relationships in the extrusion of expanded snacks. The literature shows that empirical regression and response surface methodology (RSM) remain the most widely applied tools because they are experimentally efficient and easy to interpret. However, mixture-process designs are more appropriate when formulation and operating variables are changed simultaneously, while phenomenological and mechanistic approaches provide better physical insight into expansion and structure development. More recently, machine-learning and interpretable artificial intelligence approaches have demonstrated strong predictive capability when large, well-curated datasets are available. Across model families, a consistent theme is that operating variables act on final product quality through intermediate process state variables rather than independently. On that basis, this review proposes a practical hybrid framework for expanded snack extrusion: a mixture-process quadratic model augmented with SME, die pressure, melt temperature and shear-related state variables, and structured in three levels linking (i) controllable inputs to state variables, (ii) state variables to measurable quality attributes, and (iii) quality attributes to a gold-standard product target or sensory-control criterion. Such a model offers a realistic balance between predictive performance, physical interpretability, experimental burden, and industrial usefulness, while also providing a clear pathway toward future digital twin and machine-learning-enabled optimisation. Full article

Banana (Musa acuminata), the second most cultivated fruit worldwide, generates approximately 220 tons of agricultural waste per hectare annually, with nearly 80% of the plant biomass remaining underutilised after harvest. Banana inflorescence, an underutilised by-product of banana cultivation, is commonly discarded despite its rich nutritional and bioactive composition, contributing to agricultural waste and environmental concerns. This study aimed to develop and evaluate banana inflorescence lozenges as a nutraceutical supplement while promoting sustainable agricultural waste valorisation. Freeze-dried banana inflorescence powder was incorporated into a hard lozenge formulation using the melt-and-mould method, and the formulation was optimised through physical evaluation. The optimised lozenges demonstrated acceptable mechanical properties, including friability of 0.13%, hardness of 55.16 kg/cm², and disintegration time of 35 min. Fourier-transform infrared spectroscopy with attenuated total reflectance (FTIR–ATR) confirmed the compatibility between the active ingredient and excipients. The formulated lozenges exhibited a total phenolic content of 22.74 ± 0.74 mg GAE/g DW and moderate antioxidant activity, with ABTS and DPPH IC₅₀ values of 30.65 mg/mL and 72.53 mg/mL, respectively. In vitro antidiabetic assays demonstrated α-glucosidase inhibition of 45.80% and α-amylase inhibition of 98.11%. Mineral analysis further revealed appreciable levels of potassium, magnesium, calcium, and iron. Although some reduction in bioactivity was observed following processing and formulation, banana inflorescence still demonstrated potential as a sustainable functional ingredient for nutraceutical applications and agricultural waste valorisation. Further studies involving stability assessment and in vivo validation are recommended. Full article

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

Laser Powder Bed Fusion (LPBF) is a complex manufacturing process that depends on precise control of printing parameters and melt pool geometry, which directly influence defect formation and final part quality. This study evaluated the reliability of a simplified thermal conduction-based melt pool model by combining post-process metallographic analysis with in situ dual-wavelength infrared thermal imaging. Experimental data were obtained through single-track printing on 316L, IN625, and CoCr alloys across a wide range of parameters. The simulated melt pool length showed strong agreement with thermal camera measurements (R²_adj > 0.78), while the width showed moderate but consistent correlation (R²_adj > 0.52). For melt pool depth, the model systematically deviated due to its inability to capture keyhole melting, although a strong linear correlation was still observed (R²_adj > 0.86). Cross-validation between metallographic measurements and thermal imaging revealed only a 6–9% discrepancy, confirming the reliability of both methods and the potential of dual-wavelength cameras for industrial process monitoring. Overall, the model proves to be a reliable tool for predicting melt pool surface geometry specifically within the conduction melting regime, while its predictive capability degrades significantly in the keyhole regime, where simulated peak temperatures reach up to 7000 °C and melt pool depth errors escalate due to the disregard of recoil pressure, liquid and vapor dynamics. Full article

Lack of fusion (LOF) is a dominant defect in Laser Powder-Bed Fusion (PBF-LB/M) caused by insufficient overlapping between adjacent melt pools. This study introduces a rapid, first-principles model based on Rosenthal’s analytical solution for a moving point heat source to predict melt pool geometry. Using geometric criteria, the model evaluates whether the melt pool width exceeds the hatching distance and whether the melt pool depth exceeds the layer thickness. Based on these conditions, LOF-based process windows are constructed by plotting laser power against scanning speed and classifying each parameter combination as either LOF or no LOF. The process developed here for constructing LOF process windows can be applied to metallic PBF-LB/M systems. As PBF-LB/M of copper is commonly associated with LOF defects, the approach is examined for pure copper by evaluating a range of laser powers and scanning speeds for both near-infrared (NIR) (1064 nm) and green (515 nm) lasers using copper-specific absorptivity values. The resulting process windows are validated against literature-reported relative density data for pure copper, using high relative density values as indicators of full fusion and lower relative density values reported with LOF characteristics as indicators of lack of fusion. For a 30 µm layer thickness, the predicted LOF boundary agreed with 43 of 46 literature-reported copper PBF-LB/M data points when the data were classified using relative density and reported defect morphology. Sensitivity analysis showed that the agreement changed modestly when the relative-density threshold was reduced from 99% to 98.5% and 98% and that near-boundary classifications were sensitive to the selected absorptivity within the reported NIR range. The agreement supports the use of the framework as a preliminary screening tool for identifying LOF-prone parameter regions. By providing a fast, physics-based screening tool for LOF-limited process windows, this framework offers a computationally efficient alternative to high-fidelity numerical simulations commonly used in PBF-LB/M process development. Full article

Auxetic metamaterials exhibit unique mechanical behavior due to their negative Poisson’s ratio, but reliable determination of their effective elastic properties remains challenging. In this study, an experimental–numerical approach is proposed to characterize additively manufactured polylactic acid (PLA)-based auxetic sandwich structures. Material properties were first assessed using tensile testing, melt flow rate/volume rate (MFR/MVR) measurements, Fourier-transform infrared (FTIR) spectroscopy, differential scanning calorimetry (DSC), dilatometry, and nanoindentation, revealing stable mechanical behavior, good processability, and slight increases in crystallinity induced by the printing process. Impulse excitation technique (IET) measurements provided highly reproducible resonant frequencies, demonstrating a strong dependence on core geometry and orientation. However, classical ASTM-based evaluation yielded non-physical elastic properties, highlighting its limitations for architected metamaterials. Finite element modal analyses, combined with inverse parameter identification, enabled the determination of effective elastic properties using a transversely isotropic homogenized model. This approach significantly improved the agreement between experimental and numerical results. The findings revealed pronounced anisotropy and orientation-dependent auxetic behavior, including a negative Poisson’s ratio for specific configurations. The proposed methodology provides a suitable framework for the reliable characterization and design of complex metamaterials. Full article

Refractory high-entropy alloys (RHEAs) have attracted increasing attention for structural applications under extreme conditions; however, the uniformity and reliability of their mechanical properties remain critical challenges, particularly when processed by additive manufacturing. In this work, the microstructural heterogeneity and mechanical uniformity of a selective laser melting (SLM)-fabricated MoNbZrTaW RHEA were systematically investigated. Microstructural characterization revealed a dual-phase BCC structure with dendritic and interdendritic regions distributed along the build direction. Statistical analyses were employed to quantify variations in microstructure and mechanical properties, including hardness, fracture strength, yield strength, and fracture strain. The effects of strain rate and specimen aspect ratio on mechanical behavior were further examined through compression testing. Weibull statistical analysis demonstrated that strength-related properties exhibit high uniformity despite pronounced microstructural heterogeneity, whereas fracture strain shows comparatively greater scatter. The results indicate that solid-solution strengthening governs the mechanical response and helps mitigate the influence of microstructural non-uniformity. These findings provide important insights into the mechanical reliability of SLM-fabricated RHEAs under room-temperature quasi-static loading, and support their potential for further investigation in advanced structural applications. Full article