Search Results (1,126)

The configurational stability and mobilizable oil release behavior of a multiscale gel–particle cooperative nested system within tight sandstone pore structures were systematically investigated. Scanning electron microscopy (SEM), atomic force microscopy (AFM), and μCT-based three-dimensional reconstruction were employed to characterize the multiscale structural features of the system. Interfacial regulation behavior was analyzed using contact angle measurements, oil–water interfacial tension (IFT), and zeta potential tests, while core flooding experiments were conducted to evaluate seepage response and oil displacement performance. The results indicate that particle reinforcement transforms the gel pore walls from a weakly rough interface into a strongly rough and mechanically interlocked structure, with the root-mean-square surface roughness increasing from 23.6 nm to 71.4 nm. μCT quantitative analysis shows that the pore volume fraction increases from 38.6% to 52.4%, and the connectivity ratio rises from 41.2% to 68.5, leading to the formation of a more continuous pore–throat network. Interfacial property measurements reveal that the rock surface contact angle decreases from 116.3° to 60.5°, and the oil–water interfacial tension is reduced from 27 mN·m⁻¹ to 3–5 mN·m⁻¹. Meanwhile, the system–rock interface exhibits a stronger overall negative surface charge. During displacement experiments, the pressure differential at 3.0 pore volumes (PV) is only 17.0 kPa, significantly lower than that of the control gel (26.2 kPa). The oil recovery is increased to 44.8%, while the residual oil saturation decreases from 0.46 to 0.32, and the displacement efficiency improves from 36.1% to 55.6%. These results demonstrate that the multiscale gel–particle cooperative nested system establishes a stable, regulated seepage configuration in tight sandstone and enables sustained mobilization of trapped oil under relatively low-pressure gradients through the coupled regulation of wettability, interfacial tension, and interfacial electrostatics. This study elucidates a coupled mechanism of configurational stability–flow channel redistribution–continuous oil mobilization and provides a new material design and regulation strategy for efficient recovery of residual oil in tight reservoirs. Full article

This comprehensive review examines the application of fractal theory and technology in the study of cementitious materials, with a focus on cement and concrete. We begin by introducing the fundamental concepts of fractal geometry and the various fractal dimensions used to quantify material features. We then explore how fractal analysis has been applied to key aspects of cementitious materials, including pore structure, particle size distribution, fracture surfaces, and crack propagation. Each section highlights the methodologies employed, the insights gained, and the implications for material design and performance. Additionally, we discuss the use of fractal-based techniques in the non-destructive testing and monitoring of structures. Finally, we address the challenges and limitations of fractal approaches and propose future directions for research in this interdisciplinary field. Fractal theory can become a useful tool in the study of cementitious materials, aiding a deeper understanding of their physical properties and long-term durability, and guiding the design of more durable and efficient construction materials by giving engineers the required knowledge on the technology and its limitations. Full article

The Sichuan Basin serves as a key arena for ultra-deep natural gas exploration. The Nanhuan System Gucheng Formation, characterized by its ancient geological age and great burial depth, lacks almost any drilling data within the basin interior, and its sedimentary features and natural gas potential remain unstudied. Based on outcrop sections of the Nanhuan Gucheng Formation along the northern margin of the Sichuan Basin, sedimentological and hydrocarbon reservoir characteristics were analyzed. The study reveals: ① The lower Gucheng Formation at the Chenkou Yuyang section comprises three lithofacies: deformed-bedding conglomeratic sandstone, massive-bedded medium sandstone, and dark-gray horizontally-bedded mudstone, interpreted as deposits of turbidity channels, turbidite fan lobes, and deep-water shelf mud, respectively; ② The turbidity channel and fan sandstones exhibit dissolution pores, with porosities ranging from 8% to 12%, representing favorable reservoirs with a cumulative thickness exceeding 40 m. The deep-water shelf mud shows TOC values between 0.8% and 1.5%, serving as favorable source rocks with a cumulative thickness over 30 m. These two units are interbedded, forming an effective source-reservoir assemblage; ③ Based on the west–east outcrop transect (Zhenba Xiaoyangba, Chenkou Yuyang, and Mahuang Gou sections), the thickness of the Gucheng Formation displays a thin–thick–thin variation, interpreted as reflecting a sedimentary transition from shallow-water shelf delta to deep-water shelf/turbidite systems and back to shallow-water shelf deposits. A rift depositional model with a gentle western slope and steep eastern slope is proposed. In deep-water shelf areas, turbidite sandstone reservoirs are vertically interbedded with shelf mudstone source rocks, while in shallow-water shelf areas, deltaic sandstone reservoirs are laterally connected to source rocks. Spatially, this constitutes a hydrocarbon distribution pattern characterized by “vertical stacking and lateral connectivity,” providing valuable insights for ultra-deep natural gas exploration in the Sichuan Basin. Full article

The flexible foam piezoresistive sensor demonstrates significant potential for wearable strain-sensing applications due to its substantial deformation capacity, excellent flexibility, and cost effectiveness. However, conventional flexible foam piezoresistive sensors often struggle to simultaneously achieve high sensitivity, a wide pressure detection range, fast response and long-term stability. This paper employed a glucose-based sugar-templating method to fabricate a fine-pore (50 μm) foam structure complemented by a dual-filler strategy to enhance overall performance. A robust porous conductive network was constructed by embedding zinc oxide (ZnO) and multi-walled carbon nanotubes (MWCNTs) into a polydimethylsiloxane (PDMS) matrix. The resulting sensor exhibits outstanding piezoresistive properties, featuring a wide linear detection range (0–80% strain) and a high sensitivity of 9.02 kPa⁻¹ within the 0–10 kPa pressure range. It demonstrates rapid response/recovery times of 50/70 ms and maintains stable output performance even after 5000 compression cycles at 300 kPa. The sensor also exhibits negligible environmental interference and excellent long-term stability. When attached to finger joints, feet soles, or the throat, the sensor enables functions such as finger bending recognition, race-walking violation discrimination, gait analysis, and vocal fold vibration recognition, thereby demonstrating its considerable potential for application in human–computer interaction and human motion detection. Full article

Metal–organic frameworks (MOFs) possess ordered pore structure, high surface area, tunable composition and tailorable functionality, and thus present promising prospect in many applications. Among them, titanium-based MOFs (Ti-MOFs) composed of organic ligands and titanium–oxygen clusters exhibit great potential in photocatalysis, owing to their diverse topological configurations, outstanding photocatalytic activity, low toxicity, and easy production. The latest developments in Ti-MOFs, including the synthetic strategies, structural features, methods for enhancing catalytic performance, and typical applications, were reviewed in this paper. The application in CO₂ reduction, hydrogen evolution, organic pollutant removal, and photocatalytic sensing were emphasized. Moreover, we present a distinctive perspective on the relationship between the structure and their applications of Ti-MOFs, and provide new information in the design and construction of advanced Ti-MOFs for high-efficiency photocatalytic conversion. Full article

Airframe noise generated at wing trailing edges and high-lift devices, such as flaps, remains a major challenge during landing, with significant contributions in the low-frequency band of 500–1500 Hz. While solid surfaces reflect this acoustic energy, metallic porous materials can effectively absorb it through viscous and thermal dissipation within their internal pore structure. To address this, the present study examines the acoustic absorption characteristics of open-cell AlSi porous cylinders featuring controlled pore diameters between 0.3 mm and 2.25 mm. Measurements were conducted in an acoustic impedance tube according to the ISO 10534-2:2023 standard, using six cylindrical samples (28 mm diameter, 70 mm length). Two sets of measurements were performed for each sample (front and rear faces), and the average values were used. The findings indicate that the normal-incidence sound absorption coefficient α rises as pore size increases, reaching 0.93–0.97 at low frequencies of 500–700 Hz for the samples with the largest pores (1.8–2.25 mm). These results indicate that open-cell AlSi alloys offer strong low-frequencies sound absorption, positioning them as promising options for aeroacoustic noise mitigation, including applications such as porous trailing edge and hybrid flap designs. Full article

Humidification conditioning has been increasingly applied in brown rice milling to improve processing performance. However, the underlying mechanisms by which humidification alters the mechanical behavior and microstructure of the bran layer remain insufficiently understood. In this study, the effects of humidification on the mechanical properties and surface microstructure of the brown rice bran layer were investigated, and the optimal conditioning parameters were further determined based on milling performance. Brown rice samples were conditioned to different moisture levels, and the corresponding changes in bran layer tensile strength, surface roughness, and microstructural features were analyzed using tensile testing, three-dimensional surface profilometry, and scanning electron microscopy. The results show that humidification significantly disrupts the continuity of the fibrous matrix in the bran layer, leading to reduced tensile strength and wear resistance. Moderate humidification (around 16% moisture content) promotes the formation of micro-pores and weakens structural integrity, facilitating bran removal during milling and improving head rice yield (HRY), whereas excessive humidification results in over-softening and increased kernel breakage. On this basis, a quadratic orthogonal rotatable composite design was employed to optimize the combined effects of moisture content, humidification time, and equilibration time on HRY and specific energy consumption. The optimal conditioning parameters were identified as 16% moisture content, 30 s humidification time, and 36 min equilibration time. This study provides the mechanistic insights into the humidification-induced structural and mechanical evolution of the brown rice bran layer, through experimental optimization of humidification operating parameters, offering practical guidance for improving milling quality and energy efficiency. Full article

The solubilization of poorly water-soluble drugs remains a critical challenge in pharmaceutical research. The formulation of solid dispersions employing mesoporous silica nanoparticles (MSN) constitutes a key strategy for enhancing the hydrophilicity and oral bioavailability of Biopharmaceutics Classification System (BCS) Class II drugs. Although several commercial mesoporous silica excipients have been approved for pharmaceutical use, there remains room for improvement regarding drug loading capacity, stability, and controllability of drug release. Methods: for this purpose, dendritic mesoporous silica nanoparticles (DMSN) with a radial dendritic structure and pH-responsive degradation properties were designed and synthesized using celecoxib (CEL) as the model drug, featuring a pore size of 21.51 nm. CEL was loaded onto DMSN and seven commercial solid dispersion excipients using the solvent evaporation method. Results: owing to its high surface area, pore volume, and radial structure, DMSN achieved 39.72% drug loading in an amorphous state, markedly improving wettability, dissolution, and physical stability. Accelerated stability tests showed that DMSN inhibited recrystallization, outperforming traditional solid dispersions. Pharmacokinetic studies in rats demonstrated that the oral bioavailability of CEL-DMSN was 1.29-fold higher than that of commercial celecoxib capsules. Conclusions: in conclusion, these results confirmed the potential of DMSN in enhancing the stability, promoting oral absorption, and reducing gastrointestinal irritation of poorly soluble drugs. Full article

Shale pore volume is a critical parameter for reservoir evaluation. Accurate and rapid determination of this parameter is essential for identifying sweet spots and performing reliable reserve estimations. Currently, laboratory experiments remain the standard for determining pore volume; however, these methods are typically time-consuming, costly, and labor-intensive. To complement traditional experimental approaches, we developed a Gaussian Process Regression (GPR) model to estimate shale pore volume based on mineralogical compositions. The model is specifically tailored for the Longmaxi shale, utilizing six input features: the contents of Total Organic Carbon (TOC), clay, quartz, feldspar, carbonate, and pyrite. The GPR model achieved a mean absolute percentage error (MAPE) of 9.97% on the testing dataset, while it yielded an MAPE of 17.66% when applied to an additional independent validation set. Finally, a sensitivity analysis using the Shapley additive explanations was conducted to elucidate the influence of mineralogical constituents on shale pore volume. Full article

This study focuses on porous polyimide (PPI) lubricating materials for high-speed aerospace bearings. Based on their real microstructure, three-dimensional digital model reconstruction and mesoscale seepage characteristics were investigated. First, a sequence of two-dimensional slice images of PPI was obtained using micro-focus X-ray computed tomography (CT). Through image filtering, threshold segmentation, and three-dimensional reconstruction, a highly faithful digital model of the pore structure was constructed, and a quantified pore-network model was further extracted. Second, a multiple-relaxation-time lattice Boltzmann model based on the D3Q27 discrete scheme was established, and its accuracy and stability in complex boundaries and pressure-driven flows were verified using classic benchmark cases. Subsequently, the validated numerical model was applied to the reconstructed PPI pore structure to simulate and systematically analyze the single-phase seepage behavior of lubricating oil. The results show that the lubricant seepage exhibits a strong “preferential flow path” effect, with most of the flow transported through a small number of large-size throats. A clear quantitative relationship exists between the microscopic flow field structure—including velocity distribution, flow paths, and pressure gradient—and the pore-topology features, such as throat-size distribution, connectivity, and tortuosity. This verifies the mesoscale mechanism that “structure governs flow.” The complete technical chain established in this work—“real-structure reconstruction–numerical model validation–seepage mechanism analysis”—provides a reliable theoretical and numerical tool for gaining deeper insight into the lubricant transport behavior in porous polyimide and offers guidance for the microstructural design and optimization of this material. Full article

Rapid vascularization is essential for bone regeneration in oral and maxillofacial surgery. This systematic review synthesised in vivo evidence on 3D-printed composite scaffolds in rodent critical-size calvarial defects quantified by Microfil perfusion and micro-CT. “Composite” was defined as an organic–inorganic construct within the printed scaffold (not a single-phase scaffold with a surface coating). PubMed, MEDLINE, and Web of Science Core Collection were searched for studies published from January 2014 to December 2025. Eligible studies compared composite scaffolds with non-composite (single-phase) scaffolds and/or empty controls and reported vascular outcomes (vessel number, vascularized area) together with bone outcomes (new bone area, bone volume fraction [BV/TV], and bone mineral density). Ten studies met the inclusion criteria. In outcome-specific exploratory analyses, composite scaffolds were associated with higher new bone area than comparators (p = 0.031). Functional modifications were associated with higher vascularized area (p = 0.025) and higher new bone area (p = 0.038), while dual-factor modifications showed the largest gain in new bone area (p = 0.002). Pore sizes ≥ 400 μm were associated with higher BV/TV (p = 0.029). Heterogeneity in designs, follow-up, and reporting, together with small sample sizes, precluded meta-analysis. Composite scaffolds appear promising, but standardised methodologies and improved reporting are needed to define optimal design features and support translation. Full article

To address the limited detection accuracy of casting defects—including pores, inclusions, and looseness—in digital radiography (DR) images, which stems from their small scale, high morphological variability, and interference from complex background textures, we propose MTS-YOLOv11: an edge–discrepancy collaborative defect detection framework tailored for casting DR imagery. Built upon YOLOv11, MTS-YOLOv11 incorporates three key innovations: (1) a Multi-Scale Edge Information Enhancement System (MSEES), integrated into the C3K2 module of the backbone network, to strengthen discriminative feature extraction for minute defects; (2) a TripletAttention mechanism embedded in high-level backbone stages to jointly calibrate channel–spatial dependencies and suppress texture-induced spurious responses under complex backgrounds; (3) a Scale-Discrepancy-Aware Gated Fusion (SDAGFusion) module positioned immediately before the detection head, enabling scale-discrepancy-aware gated fusion of multi-scale features, emphasizing defect regions while suppressing background interference. Experimental results show that on the casting DR dataset, MTS-YOLOv11 achieves mAP@0.5 = 96.5% and mAP@0.5:0.95 = 68.5%—improvements of 1.3 and 1.2 percentage points over the baseline YOLOv11—across all three defect categories. Moreover, on the same platform, MTS-YOLOv11 achieves an inference speed of 359.07 FPS, compared with 346.86 FPS for the baseline. Meanwhile, the model has 2.72M parameters and 7.8G FLOPs. These results indicate a consistent improvement in detection accuracy while maintaining a practical balance between precision and computational efficiency. Moreover, cross-dataset generalization tests on newly acquired industrial DR data show that MTS-YOLOv11 consistently outperforms the baseline across evaluation metrics, suggesting improved robustness to unseen imaging conditions and supporting its potential for real-world foundry inspection. Full article

Ammonia nitrogen stands as a pivotal water quality indicator within the frameworks of aquatic ecological quality assessment and aquatic ecological governance systems. This study focuses on the adsorption method, selecting four inorganic adsorbents—clinoptilolite, volcanic rock, bentonite, and fly ash—as research subjects, and introduces rare earth modifiers for rare earth-loading modification. Various modifications were applied to the adsorbents to enhance their ammonia nitrogen adsorption efficacy. Combined with material characterization, the microscopic features and adsorption behaviors of the adsorbents were elucidated, aiming to provide a theoretical foundation for addressing practical engineering challenges and to screen out the optimal inorganic adsorbent and the most effective modification protocol. Based on the experimental findings, cerium chloride modification can significantly enhance the ammonia nitrogen adsorption performance of clinoptilolite. Under the optimal preparation conditions (cerium chloride concentration: 1.0%, solid–liquid ratio: 1:40, pH = 9), the ammonia nitrogen removal efficiency reaches 85.45%. This modification process leads to the formation of new substances: a large amount of cerium oxide and cerium hydroxide are loaded onto the surface of clinoptilolite, which contributes to the increases in specific surface area (21.92 m²/g), average pore diameter (12.27 nm), and total pore volume (0.07 cm³/g). Furthermore, during the modification, cerium hydroxide undergoes hydroxylation, rendering the clinoptilolite surface negatively charged—this facilitates the adsorption of ammonia nitrogen via electrostatic interaction. Notably, the characteristic structural peaks of clinoptilolite remain unchanged before and after modification, indicating that the modification primarily acts on the material surface. This not only improves the ammonia nitrogen adsorption efficiency but also preserves the structural stability of clinoptilolite. Full article

Mesoporous bioactive glasses (MBGs) represent a significant advancement in bioactive glass technology, combining the well-established osteoconductive and osteoinductive properties of traditional bioactive glasses with the structural precision provided by highly ordered mesoporosity. Their characteristic architecture, defined by uniform pores typically ranging from a few to several tens of nanometers and exceptionally high surface areas reaching several hundred m²/g, enables enhanced drug-loading capacity, controlled therapeutic ion release, and accelerated tissue regeneration. In this work, we emphasize how the synthesis of these materials is predominantly governed by structure-directing agents, which critically influence the pore size, mesophase ordering, surface area, and structural stability. Additionally, we discuss how compositional tailoring, particularly through therapeutic ion doping with elements such as Sr, Cu, Zn, or B, can impart osteogenic, angiogenic, antibacterial, or antioxidant functionalities. Moreover, we illustrate how these functionalities can be further expanded and enhanced by employing a comprehensive suite of characterization tools to establish robust correlations between synthesis parameters, mesostructural features, and biological performance. Improving the above functionalities enables the MBGs to exhibit exceptional versatility across biomedical applications, notably in bone tissue engineering (as hierarchical or composite scaffolds), controlled drug delivery (anticancer, antibiotic, and anti-inflammatory agents), wound healing, dental therapy, and bioactive implant coatings. Finally, we acknowledge that despite their broad potential, several associated challenges remain, including the synthesis scalability, batch-to-batch reproducibility, mechanical fragility of pure MBGs, and the complexity of predicting in vivo degradation and ion-release behaviors. We believe that emerging research directions, including eco-friendly synthesis routes, stimuli-responsive smart MBGs, multifunctional theranostic platforms, and patient-specific additive manufacturing, are poised to overcome current limitations and drive the next generation of MBG-based biomedical technologies. Full article

In the context of Industry 4.0, reliable automatic inspection of weld surface defects is critical for structural safety, yet current deep learning-based detectors struggle with the extreme scale variation and anisotropic shapes characteristic of weld flaws such as pores, cracks, and lack of fusion. Existing YOLO-family models, although effective on general-purpose datasets, often fail to robustly localize tiny defects and long, slender discontinuities while remaining lightweight enough for industrial edge deployment. A critical research gap lies in the lack of task-specific optimization for weld defects: standard attention mechanisms are isotropic and cannot capture linear defect continuity, while existing loss functions ignore scale disparity between tiny pores (area < 100 pixels²) and large incomplete fusion defects (area > 5000 pixels²), leading to unstable regression. Here, we propose a dual-optimized lightweight YOLOv11 framework tailored for weld defect detection that addresses both feature representation and bounding-box regression. Here, we propose a dual-optimized lightweight YOLOv11 framework tailored for weld defect detection that addresses both feature representation and bounding-box regression. First, we introduce WeldSimAM, an enhanced attention module that augments parameter-free SimAM with directional (horizontal/vertical) and channel-wise enhancement to better capture the directional texture of linear weld defects. Second, we develop an Enhanced Normalized Wasserstein Distance (EnNWD) loss, which incorporates scale-disparity penalties and relative-area-based weighting to mitigate sample imbalance and improve regression accuracy for tiny and large-aspect-ratio targets. Validated via 10-fold cross-validation on three datasets (self-built + two public), the method achieves 99.48% mAP@0.5 and 73.29% mAP@0.5:0.95, outperforming YOLOv11 by 0.13 and 3.76 percentage points (p < 0.01, two-tailed t-test), with 5.21 MB and 132 FPS on NVIDIA RTX 4090. It also surpasses non-YOLO SOTA methods (e.g., EfficientDet-Lite3) by 3.8–5.5 percentage points in mAP@0.5 (p < 0.05), offering a practical real-time solution for industrial inspection. Full article