Search Results (994)

Superhydrophobic and superoleophilic nanofibrous or microfibrous membranes are regarded as ideal oil/water separation materials owing to their controllable porosity, superior separation efficiency, and ease of operation. However, developing efficient, scalable, and environmentally friendly strategies for fabricating such membranes remains a significant challenge. In this study, isotactic polypropylene (iPP) fibrous membranes with morphologies ranging from ellipsoidal stacking to microfiber stacking were successfully fabricated via a multistage stretching extrusion and leaching process using in situ microfibrillar composites (MFCs). The results establish a significant relationship between microfiber morphology and membrane oil adsorption performance. Compared with membranes formed from high-aspect-ratio microfibers, those comprising short microfibers feature larger pores and a more open structure, which enhances their oil adsorption capacity. Among the fabricated membranes, the iPP membrane with an ellipsoidal stacking morphology exhibits optimal performance, achieving a porosity of 65% and demonstrating both hydrophobicity and superoleophilicity, with a silicone oil adsorption capacity of up to 312.5%. Furthermore, this membrane shows excellent reusability and stability over ten adsorption–desorption cycles using chloroform. This study presents a novel approach leveraging in situ microfibrillar composites to prepare high-performance oil/water separation membranes in this study, underscoring their considerable promise for practical use. Full article

Fly-ash-based bubble lightweight soil is widely used due to its environmental friendliness, load reduction, ease of construction, and low costs. In this study, 41 sets of 28 d compressive strength data on lightweight soils with different water–cement ratios, blowing agent dosages, and fly ash dosages were collected through a literature search and indoor tests. Using the compressive strength index and SEM tests, the correlation between the mix ratio design and the microscopic particle components was investigated. The findings were as follows: carbonation reactions occurred in lightweight soil during the maintenance process, and the particles were spherical; increasing the dosage of blowing agent increased the soil’s porosity and pore diameter, leading to the formation of through-holes and reducing the compressive strength and mobility; increasing the fly ash dosage and water–cement ratio increased the soil’s mobility but reduced its compressive strength; and the strength decreased significantly when the fly ash dosage was more than 16% (e.g., the strength at a 20% dosage was 17.8% lower than that at a 15% dosage). Feature importance analysis showed that the water–cement ratio (57.7%), fly ash dosage (30.9%), and blowing agent dosage (11.1%) had a significant effect on strength. ExtraTrees, LightGBM, and Bayesian-optimized Random Forest models were used for 28d strength prediction with coefficients of determination (R²) of 0.695, 0.731, and 0.794, respectively. The Bayesian-optimized Random Forest model performed optimally in terms of the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE), and the prediction performance was best. The accuracy of the model is expected to be further improved with expansions in the database. A 28 d compressive strength prediction platform for fly-ash-based bubble lightweight soil was ultimately developed, providing a convenient tool for researchers and engineers to predict material properties and mix ratios. Full article

The global atmospheric concentration of carbon dioxide (CO₂) has exceeded 420 ppm. Adsorption-based carbon capture technologies, offer energy-efficient, sustainable solutions. Relying on classical adsorption models like Langmuir to predict CO₂ uptake presents limitations due to the need for case-specific parameter fitting. To address this, the present study introduces a universal machine learning (ML) framework using multiple algorithms—Generalized Linear Model (GLM), Feed-forward Multilayer Perceptron (DL), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosted Trees (GBT)—to reliably predict CO₂ adsorption capacities across diverse zeolite structures and conditions. By compiling over 5700 experimentally measured adsorption data points from 71 independent studies, this approach systematically incorporates critical factors including pore size, Si/Al ratio, cation type, temperature, and pressure. Rigorous Cross-Validation confirmed superior performance of the GBT model (R² = 0.936, RMSE = 0.806 mmol/g), outperforming other ML models and providing comparable performance with classical Langmuir model predictions without separate parameter calibration. Feature importance analysis identified pressure, Si/Al ratio, and cation type as dominant influences on adsorption performance. Overall, this ML-driven methodology demonstrates substantial promise for accelerating material discovery, optimization, and practical deployment of zeolite-based CO₂ capture technologies. Full article

Rapid socio-economic development and the impact of human activities have exerted tremendous pressure on the groundwater system of the Dawen River Basin (DRB), the largest tributary in the middle and lower reaches of the Yellow River. Hydrochemical studies on the DRB have largely centered on the upstream Muwen River catchment and downstream Dongping Lake, with some focusing solely on karst groundwater. Basin-wide evaluations suggest good overall groundwater quality, but moderate to severe contamination is confined to the lower Dongping Lake area. The hydrogeologically complex mid-reach, where the Muwen and Chaiwen rivers merge, warrants specific focus. This region, adjacent to populous areas and industrial/agricultural zones, features diverse aquifer systems, necessitating a thorough analysis of its hydrochemistry and origins. This study presents an integrated hydrochemical, isotopic investigation and EWQI evaluation of groundwater quality and formation mechanisms within the multiple groundwater types of the central DRB. Central DRB groundwater has a pH of 7.5–8.2 (avg. 7.8) and TDSs at 450–2420 mg/L (avg. 1075.4 mg/L) and is mainly brackish, with Ca²⁺ as the primary cation (68.3% of total cations) and SO₄²⁻ (33.6%) and NO₃⁻ (28.4%) as key anions. The Piper diagram reveals complex hydrochemical types, primarily HCO₃·SO₄-Ca and SO₄·Cl-Ca. Isotopic analysis (δ²H, δ¹⁸O) confirms atmospheric precipitation as the principal recharge source, with pore water showing evaporative enrichment due to shallow depths. The Gibbs diagram and ion ratios demonstrate that hydrochemistry is primarily controlled by silicate and carbonate weathering (especially calcite dissolution), active cation exchange, and anthropogenic influences. EWQI assessment (avg. 156.2) indicates generally “good” overall quality but significant spatial variability. Pore water exhibits the highest exceedance rates (50% > Class III), driven by nitrate pollution from intensive vegetable cultivation in eastern areas (Xiyangzhuang–Liangzhuang) and sulfate contamination from gypsum mining (Guojialou–Nanxiyao). Karst water (26.7% > Class III) shows localized pollution belts (Huafeng–Dongzhuang) linked to coal mining and industrial discharges. Compared to basin-wide studies suggesting good quality in mid-upper reaches, this intensive mid-reach sampling identifies critical localized pollution zones within an overall low-EWQI background. The findings highlight the necessity for aquifer-specific and land-use-targeted groundwater protection strategies in this hydrogeologically complex region. Full article

Luminescent metal–organic frameworks (MOFs) are used for the detection of organophosphorus pesticides (OPs) due to their large surface area and pore volume as well as their special optical properties. However, most self-luminescent MOFs are not only complex to synthesize and unstable in water but also feature a “turn-off” sensing system, which has highly restricted their practical applications in OP detection. Herein, a “turn-on” fluorescence sensor based on the guest-induced luminescence MOF Ru(bpy)₃²⁺@UiO-66 was constructed, which realized the sensitive detection of OPs through a dual-enzyme system for the first time. Compared with self-luminescent MOFs, Ru(bpy)₃²⁺@UiO-66 was not only more easily synthesized but also had higher chemical and photostability in water. In this strategy, by means of the hydrolysis of AChE and ChOx, H₂O₂ will be produced, which can oxidize Fe²⁺ to Fe³⁺, thereby quenching the fluorescence of Ru(bpy)₃²⁺@UiO-66. In the presence of OPs, the activity of AChE can be inhibited, resulting in the inability to generate H₂O₂ and Fe³⁺, which will turn on the fluorescence signal of Ru(bpy)₃²⁺@UiO-66. As a result, the Ru(bpy)₃²⁺@UiO-66 sensing system not only had high sensitivity for OPs detection but also possessed a satisfactory detection recovery rate for parathion-methyl in real samples, which provides a new approach for OP detection in food safety as well as environmental monitoring. Full article

As a key component in the hydraulic brake system of automobiles, the brake joint directly affects the braking performance and driving safety of the vehicle. Therefore, improving the quality of brake joints is crucial. During the processing, due to the complexity of the welding material and welding process, the weld seam is prone to various defects such as cracks, pores, undercutting, and incomplete fusion, which can weaken the joint and even lead to product failure. Traditional weld seam detection methods include destructive testing and non-destructive testing; however, destructive testing has high costs and long cycles, and non-destructive testing, such as radiographic testing and ultrasonic testing, also have problems such as high consumable costs, slow detection speed, or high requirements for operator experience. In response to these challenges, this article proposes a defect detection and classification method for laser welding seams of automotive brake joints based on machine vision inspection technology. Laser-welded automotive brake joints are subjected to weld defect detection and classification, and image processing algorithms are optimized to improve the accuracy of detection and failure analysis by utilizing the high efficiency, low cost, flexibility, and automation advantages of machine vision technology. This article first analyzes the common types of weld defects in laser welding of automotive brake joints, including craters, holes, and nibbling, and explores the causes and characteristics of these defects. Then, an image processing algorithm suitable for laser welding of automotive brake joints was studied, including pre-processing steps such as image smoothing, image enhancement, threshold segmentation, and morphological processing, to extract feature parameters of weld defects. On this basis, a welding seam defect detection and classification system based on the cascade classifier and AdaBoost algorithm was designed, and efficient recognition and classification of welding seam defects were achieved by training the cascade classifier. The results show that the system can accurately identify and distinguish pits, holes, and undercutting defects in welds, with an average classification accuracy of over 90%. The detection and recognition rate of pit defects reaches 100%, and the detection accuracy of undercutting defects is 92.6%. And the overall missed detection rate is less than 3%, with both the missed detection rate and false detection rate for pit defects being 0%. The average detection time for each image is 0.24 s, meeting the real-time requirements of industrial automation. Compared with infrared and ultrasonic detection methods, the proposed machine-vision-based detection system has significant advantages in detection speed, surface defect recognition accuracy, and industrial adaptability. This provides an efficient and accurate solution for laser welding defect detection of automotive brake joints. Full article

Highly porous organosilicas were synthesized via direct co-condensation of two monomers, bis (triethoxysilyl) benzene and aminopropyltriethoxysilane, by adjusting the time between consecutive additions of the monomers and the ageing time of the as-obtained samples. The resulting organosilicas exhibited high porosities, with total pore volumes exceeding 2.2 cm³/g. Alongside detailed insights into the morphology, structure, and surface chemistry via a broad spectrum of various instrumental techniques, the obtained ultraporous amine-functionalized organosilicas were tested as adsorbents of diclofenac sodium, chosen here as a model drug. The results revealed remarkable differences in the physicochemical properties and adsorption efficiencies among the obtained samples, confirming that the time gap between the addition of the monomers and ageing time can be used to tune the morphological, structural, and chemical features of the obtained organosilicas and, as a consequence, their sorption efficiencies. Full article

The study of metabolic abnormalities regarding mitochondrial respiration and energy production has significantly advanced our understanding of cell biology and molecular mechanisms underlying cardiovascular diseases (CVDs). Mitochondria provide 90% of the energy required for maintaining normal cardiac function and are central to heart bioenergetics. During the initial phase of heart failure, mitochondrial number and function progressively decline, causing a decrease in oxidative metabolism and increased glucose uptake and glycolysis, leading to ATP depletion and bioenergetic starvation, finally contributing to overt heart failure. Compromised mitochondrial bioenergetics is associated with vascular damage in hypertension, vascular remodeling in pulmonary hypertension and acute cardiovascular events. Thus, mitochondrial dysfunction, leading to impaired ATP production, excessive ROS generation, the opening of mitochondrial permeability transition pores and the activation of apoptotic and necrotic pathways, is revealed as a typical feature of common CVDs. Molecules able to positively modulate cellular metabolism by improving mitochondrial bioenergetics and energy metabolism and inhibiting oxidative stress production are expected to exert beneficial protective effects in the heart and vasculature. This review discusses recent advances in cardiovascular research through the study of cellular bioenergetics in both chronic and acute CVDs. Emerging therapeutic strategies, specifically targeting metabolic modulators, mitochondrial function and quality control, are discussed. Full article

In this study, sodium hydroxide and calcium hydroxide are employed as activators to enhance the properties of foam concrete with hybrid alkali-activated cementitious material as the base mix. The effect of the activators on the properties of foam concrete is studied. The experimental results reveal that the presence of sodium hydroxide can also enhance the hydration rate of the base mix and increase the density of the pore wall in foamed concrete, thus enhancing the compressive strength of foamed concrete. Meanwhile, the addition of excessive sodium hydroxide may introduce too much water and increase the settlement of foamed concrete, thus resulting in a coarse and uneven pore structure. The settlement of the foam concrete with calcium hydroxide as an activator can be significantly increased, which is more related to the settlement of the matrix. The presence of calcium hydroxide could enhance dense pore walls, thus increasing the compressive strength and lowering water absorption. Full article

Shale and coal in the transitional marine–continental facies of the Ordos Basin serve as unconventional natural gas reservoirs, with their pore structures controlling gas adsorption characteristics and occurrence states. To quantitatively characterize the pore structure features and differences between these two reservoirs, this study takes the Shanxi Formation shale and coal in the Daning–Jixian area on the eastern margin of the Ordos Basin as examples. Field-emission scanning electron microscopy (FE-SEM), high-pressure mercury intrusion, low-temperature N₂ adsorption, and low-pressure CO₂ adsorption experiments were employed to analyze and compare the full-scale pore structures of the shale and coal reservoirs. Combined with methane isothermal adsorption experiments, the gas adsorption capacity and its differences in these reservoirs were investigated. The results indicate that the average total organic carbon (TOC) content of shale is 2.66%, with well-developed organic pores, inorganic pores, and microfractures. Organic pores are the most common, typically occurring densely and in clusters. The average TOC content of coal is 74.22%, with organic gas pores being the dominant pore type, significantly larger in diameter than those in transitional marine–continental facies shale and marine shale. In coal, micropores contribute the most to pore volume, while mesopores and macropores contribute less. In shale, mesopores dominate, followed by micropores, with macropores being underdeveloped. Both coal and shale exhibit a high SSA primarily contributed by micropores, with organic matter serving as the material basis for micropore development. The methane adsorption capacity of coal is 8–29 times higher than that of shale. Coal contains abundant organic micropores, providing a large SSA and numerous adsorption sites for methane, facilitating gas adsorption and storage. This study comprehensively reveals the similarities and differences in pore structures between transitional marine–continental facies shale and coal reservoirs in the Ordos Basin at the microscale, providing a scientific basis for the precise evaluation and development of unconventional oil and gas resources. Full article

The accurate prediction of carbon dioxide (CO₂) adsorption in metal–organic frameworks (MOFs) is critical for accelerating the discovery of high-performance materials for post-combustion carbon capture. Experimental screening of MOFs is often costly and time-consuming, creating a strong incentive to develop reliable data-driven models. Despite extensive research, most studies rely on standalone models or generic ensemble strategies that fall short in handling the complex, nonlinear relationships inherent in adsorption data. In this study, a novel ensemble learning framework is developed by integrating five distinct regression algorithms: Random Forest, XGBoost, LightGBM, Support Vector Regression, and Multi-Layer Perceptron. These algorithms are combined into four custom ensemble strategies: equal-weighted voting, performance-weighted voting, stacking, and manual blending. A dataset comprising 1212 experimentally validated MOF entries with input descriptors including BET surface area, pore volume, pressure, temperature, and metal center is used to train and evaluate the models. The stacking ensemble yields the highest performance, with an R² of 0.9833, an RMSE of 1.0016, and an MAE of 0.6630 on the test set. Model reliability is further confirmed through residual diagnostics, prediction intervals, and permutation importance, revealing pressure and temperature to be the most influential features. Ablation analysis highlights the complementary role of all base models, particularly Random Forest and LightGBM, in boosting ensemble performance. This study demonstrates that custom ensemble learning strategies not only improve predictive accuracy but also enhance model interpretability, offering a scalable and cost-effective tool for guiding experimental MOF design. Full article

Accumulation of ferrous sulfate residue (FSR) not only occupies land but also results in environmental pollution and waste of iron resource; thus, recycling of iron from FSR has attracted widespread concern. To this end, this article shows fabrication and system analysis of hematite (HM) nanoparticles from FSR via microwave-assisted reduction technology. Physicochemical properties of HM nanoparticles were investigated by multiple analytical techniques including X-ray diffraction (XRD), Fourier transform infrared spectrum (FTIR), Raman spectroscopy, scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), ultraviolet visible (UV-Vis) spectrum, vibrating sample magnetometer (VSM), and the Brunauer–Emmett–Teller (BET) method. Analytic results indicated that the special surface area, pore volume, and pore size of HM nanoparticles with the average particle size of 45 nm were evaluated to be ca. 20.999 m²/g, 0.111 cm³/g, and 0.892 nm, respectively. Magnetization curve indicated that saturation magnetization Ms for as-synthesized HM nanoparticles was calculated to be approximately 1.71 emu/g and revealed weakly ferromagnetic features at room temperature. In addition, HM nanoparticles exhibited noticeable light absorption performance for potential applications in many fields such as electronics, optics, and catalysis. Hence, synthesis of HM nanoparticles via microwave-assisted reduction technology provides an effective way for utilizing FSR and easing environmental burden. Full article

We developed biomimetic full-thickness artificial skin using stromal vascular fraction (SVF) cells and autologous keratinocytes for the dermal and epidermal layers of skin, respectively. Full-thickness artificial skin scaffolds were fabricated using 4% porcine collagen and/or elastin in a low-temperature three-dimensional printer. Two types of scaffolds with collagen-to-elastin ratios of 100:0 and 100:4 were printed and compared. The scaffolds were analyzed for collagenase degradation, tensile strength, and structural features using scanning electron microscopy. By 24 h, the collagen-only scaffolds showed gradual degradation, and the collagen-elastin scaffolds retained the highest structural integrity but were not degraded. In the tensile strength tests, the collagen-only scaffolds exhibited a tensile strength of 2.2 N, while the collagen-elastin scaffolds showed a tensile strength of 4.2 N. Cell viability tests for keratinocytes displayed an initial viability of 89.32 ± 3.01% on day 1, which gradually increased to 97.22 ± 4.99% by day 7. Similarly, SVF cells exhibited a viability of 93.68 ± 1.82% on day 1, which slightly improved to 97.12 ± 1.64% on day 7. This study presents a novel strategy for full-thickness artificial skin development, combining SVF and keratinocytes with an optimized single collagen scaffold and a gradient pore-density structure. Full article

Background/Objectives: Mesoporous silica materials, particularly KIT-6, offer promising features, such as large surface area, tunable pore structures, and biocompatibility, making them ideal candidates for advanced drug delivery systems. The aims of this study were to develop and evaluate an innovative modified-release platform for metformin hydrochloride (MTF), using KIT-6 mesoporous silica as a matrix, to enhance oral antidiabetic therapy. Methods: KIT-6 was synthesized using an ultrasound-assisted sol-gel method and subsequently loaded with MTF via adsorption from alkaline aqueous solutions at two concentrations (1 and 3 mg/mL). The structural and morphological characteristics of the matrices—before and after drug loading—were assessed using SEM-EDX, TEM, and nitrogen adsorption–desorption isotherms (the BET method). In vitro drug release profiles were recorded in simulated gastric and intestinal fluids over 12 h. Kinetic modeling was performed using seven classical models, and a multifractal theoretical framework was used to further interpret the complex release behavior. Results: The loading efficiency increased with increasing drug concentration but nonlinearly, reaching 56.43 mg/g for 1 mg/mL and 131.69 mg/g for 3 mg/mL. BET analysis confirmed significant reductions in the surface area and pore volume upon MTF incorporation. In vitro dissolution showed a biphasic release: a fast initial phase in an acidic medium followed by sustained release at a neutral pH. The Korsmeyer–Peppas and Weibull models best described the release profiles, indicating a predominantly diffusion-controlled mechanism. The multifractal model supported the experimental findings, capturing nonlinear dynamics, memory effects, and soliton-like transport behavior across resolution scales. Conclusions: The study confirms the potential of KIT-6 as a reliable and efficient carrier for the modified oral delivery of metformin. The combination of experimental and multifractal modeling provides a deeper understanding of drug release mechanisms in mesoporous systems and offers a predictive tool for future drug delivery design. This integrated approach can be extended to other active pharmaceutical ingredients with complex release requirements. Full article

Laser powder-bed fusion (L-PBF) additive manufacturing has been widely explored for fabricating intricate metallic parts such as lattice structures with thin struts. However, L-PBF-fabricated small parts (e.g., thin struts) exhibit different morphological and mechanical characteristics compared to bulk-sized parts due to distinct scan lengths, affecting the melt pool behavior between transient and quasi-steady states. This study investigates the keyhole porosity in Ti6Al4V thin struts fabricated by L-PBF, incorporating a range of strut sizes, along with various levels of linear energy densities. Micro-scaled computed tomography and image analysis were employed for porosity measurements and evaluations. Generally, keyhole porosity lessens with decreasing energy density, though with varying patterns across a higher energy density range. Keyhole porosity in struts predictably becomes severe at high laser powers and/or low scan speeds. However, a major finding reveals that the porosity is reduced with decreasing strut size (if less than 1.25 mm diameter), plausibly because the keyhole formed has not reached a stable state to produce pores in a permanent way. This implies that a higher linear energy density, greater than commonly formulated in making bulk components, could be utilized in making small-scale features to ensure not only full melting but also minimum keyhole porosity. Full article