Search Results (471)

Micro-nano bubbles (MNBs), typically characterized by diameters ranging from tens of micrometers to hundreds of nanometers, have gained significant attention in recent years due to advancements in nanotechnology and related characterization methods. This technology has shown great promise in the field of petroleum engineering. Among the various applications, the integration of MNBs with gel technology plays a critical role in enhancing drilling safety. This paper aims to systematically review the current status, challenges, and optimization strategies for the application of MNBs in petroleum engineering, with a particular focus on their combined use with gel technology in oilfield applications. The paper first introduces the preparation methods and physicochemical properties of MNBs tailored for oilfield applications. It then systematically reviews the use of MNBs in the following three key areas of petroleum engineering: drilling, enhanced oil recovery (EOR), and oil–water separation. The paper also compares domestic and international technological approaches, highlighting the challenges associated with the large-scale application of MNBs in China. Notably, in the areas of drilling and enhanced oil recovery, the synergistic use of MNBs and gel technology has demonstrated significant potential. The gel–MNB combined technology demonstrates particular promise for China’s special reservoirs, as gel’s high molecular weight compensates for MNBs’ sedimentation defects, while their synergistic effects on interfacial tension reduction and drilling fluid stabilization provide an eco-efficient approach for extreme conditions. Additionally, focusing on the combined application of gel and MNB technology, along with adjustments in gel stability and MNB size, could offer a promising solution for the efficient and sustainable development of special reservoirs (such as those with high temperature, pressure, and salinity) in China. Full article

Fused silica optical components with antireflection (AR) coatings are key components in high-power laser systems. However, their reliability is severely challenged by multi-wavelength irradiation and the presence of unavoidable matrix surface defects. To investigate the coupling effects of electric field modulation between multi-wavelength irradiation, AR coating layers, and defects in AR-coated fused silica, this paper uses the finite-difference time-domain (FDTD) method to simulate the nanoscale electric field intensity distribution in fused silica coated with a double-layer AR coating at three different design wavelengths using multi-wavelength lasers. The effects of electric field coupling between the coating layers and defects are analyzed for three representative scratch geometries. The results show that when the incident wavelength matches the AR design wavelength, the interface field is effectively suppressed, resulting in a smoother field distribution and localized hot spots. Conversely, mismatched wavelengths induce severe field distortion, producing multiple hot spots and lateral interference fringes. Wide, shallow scratches are particularly sensitive to wavelength mismatch, with a 532 nm AR coating exhibiting a global maximum enhancement factor of 1.63442 for 355 nm incident light. These findings highlight the coupling effects of scratch geometry, AR coating dispersion, and laser wavelength on electric field modulation. This research provides valuable insights for optimizing antireflection coatings and improving defect tolerance in multi-wavelength laser applications, helping to improve the reliability of high-power laser systems. Full article

Drones offer a promising solution for automating distribution tower inspection, but real-time defect detection remains challenging due to limited computational resources and the small size of critical defects. This paper proposes TDD-YOLO, an optimized model based on YOLOv11n, which enhances small defect detection through four key improvements: (1) SPD-Conv preserves fine-grained details, (2) CBAM amplifies defect salience, (3) BiFPN enables efficient multi-scale fusion, and (4) a dedicated high-resolution detection head improves localization precision. Evaluated on a custom dataset, TDD-YOLO achieves an mAP@0.5 of 0.873, outperforming the baseline by 3.9%. When deployed on a Jetson Orin Nano at 640 × 640 resolution, the system achieves an average frame rate of 28 FPS, demonstrating its practical viability for real-time autonomous inspection. Full article

This study investigates the effects of Ar ion irradiation on the mechanical properties and microstructure of SA508 Grade 3 Class 1 and Class 2 reactor pressure vessel steels. Three different fluence levels of Ar ion irradiation were applied to simulate accelerated irradiation damage conditions. Charpy impact and tensile tests conducted before and after irradiation showed no significant changes in bulk mechanical properties. Stopping and Range of Ions in Matter (SRIM) and Transport of Ions in Matter (TRIM) simulations revealed that Ar ion irradiation produces a shallow penetration depth of approximately 2.5 µm, highlighting the limitations of conventional macro-mechanical testing for evaluating irradiation effects in such a thin surface layer. To overcome this limitation, nano-indentation tests were performed, revealing a clear increase in indentation hardness after irradiation. Transmission electron microscopy (TEM) analysis using STEM–BF imaging confirmed a higher density of irradiation-induced defects in the irradiated specimens. The findings demonstrate that while macro-mechanical properties remain largely unaffected, micro-scale testing methods such as nano-indentation are essential for assessing irradiation-induced hardening in shallowly damaged layers, providing insight into the behavior of SA508 reactor pressure vessel steels under accelerated irradiation conditions. Full article

The high material cost has restricted the development of concentrated solar power (CSP) systems. In this study, a low-cost alternative material was developed by adding aluminum to 304 stainless steel to form a protective oxide film, thereby enhancing its corrosion resistance to molten salt. Three material variants were tested: untreated hot-rolled plates after solution treatment and cold-rolled high-aluminum 304 stainless steel (High-Al304SS) after solution treatment and annealing treatment. After all samples were immersed in a NaNO₃-KNO₃ mixed salt at 600 °C for 480 h, corrosion products including NaFeO₂, CrO₂, Mn₂O₄, and NiCr₂O₄ were formed. The phase composition was determined by XRD, and the surface and cross-section of the corrosion layer were analyzed by SEM and EDS surface and point analysis. The corrosion rate of the samples was calculated by the weight loss method. Notably, an Al₂O₃-Cr₂O₃ composite oxide film was formed on the sample surface, effectively inhibiting corrosion. The high defect density and grain boundary energy introduced by the cold-rolling process, as well as the precipitation of the second phase during annealing, accelerated the corrosion process of the samples. However, the hot-rolled samples after solution treatment exhibited excellent corrosion resistance (64.43 μm/year) and, through further process optimization, are expected to become an ideal low-cost alternative material for 347H stainless steel (23 μm/year) in CSP systems. Full article

Background/Objectives: Polymeric barrier membranes (BMs) are usually used in guided bone regeneration to isolate the bone defect from the surrounding tissue, favoring bone apposition. This study proposes a third-generation BM made of polycaprolactone (PCL), loaded with different concentrations of nano-hidroxyapatite (nHAP) and gentamicin (GEN), and fabricated by electrospinning. Methods: The mechanical properties of the polymer, together with the fabrication procedure, offer porosity with interconnectivity to permit cell adhesion and proliferation. Bacterial contamination of the BM can induce infection at the bone level, leading to unfavorable clinical outcomes of the regeneration procedure. Results: Therefore, BMs have been proposed as carriers for local GEN antibiotic therapy, demonstrating antibacterial properties against S. aureus, S. mutans, and P. aeruginosa, depending on the drug concentration, while being negligibly affected by the nHAP content. X-ray diffraction, FTIR-ATR, and SEM allowed for BM structural characterization, demonstrating the presence of GEN/nHAP and establishing the fiber diameter, which influences the mechanical properties in dry and wet conditions and the drug release behaviorA BM cytotoxicity assessment, performed over 1 and 5 days, revealed that a high nHAP concentration provided protection against cytotoxicity, in contrast to GEN, and that cell proliferation and cell adhesion increased in the presence of nHAP. The BM’s bioactivity was demonstrated by mineralization after 21 days in simulated body fluid in an SEM/EDX analysis. Conclusions: The electrospun 15 wt.% nHAP and 2 wt.% GEN-loaded third-generation BM could be a promising alternative for guided bone regeneration. Full article

Artificial intelligence (AI), particularly machine learning (ML), is equipping laser micro/nano processing with significant intelligent capabilities, demonstrating exceptional performance in areas such as manufacturing process modeling, process parameter optimization, and real-time anomaly detection. This transformative potential is driving the development of next-generation laser micro/nano processing technologies. The key challenges confronting traditional laser manufacturing stem from the complexity of laser–matter interactions, resulting in difficult-to-control processing outcomes and the accumulation of micro/nano defects across multi-step processes, ultimately triggering catastrophic process failures. This review provides an in-depth exploration of how machine learning effectively addresses these challenges through the integration of data-driven modeling with physics-driven modeling, coupled with intelligent in situ monitoring and adaptive control techniques. Systematically, we summarize current representative breakthroughs and frontier advances at the intersection of machine learning and laser micro/nano processing research. Furthermore, we outline potential future research directions and promising application prospects within this interdisciplinary field. Full article

In this study, we present the development of a high-average-power, exceptionally stable extreme-ultraviolet (EUV) laser source based on a high-order harmonic generation (HHG) technique. The spectrum of an ytterbium-doped laser is broadened through self-phase modulation (SPM) in a gas-filled hollow fiber and compressed down to 25.3 fs for efficient harmonic generation. The high harmonics are generated in a krypton (Kr) gas cell, delivering a total power of 241 μW within the 30–60 nm spectral range, corresponding to a single harmonic output of 166 μW at a central wavelength of 46.8 nm. Notably, the system demonstrates good power stability with a root-mean-square (RMS) deviation of only 1.95% over 12 h of continuous operation. This advanced light source holds great potential for applications in nano- and quantum-material development and in semiconductor wafer defect detection. Future work aims to further enhance the output power in the 30–60 nm band to the milliwatt level, which would significantly bolster scientific research and technological development in related fields. Full article

This study targets insulation challenges in high-frequency power transformers (HFPTs), which are an integral part of the high-voltage, high-capacity isolated DC/DC converter under development for offshore renewable energy systems. We propose a nano-silicon carbide (SiC)-doped polyimide (PI) winding insulation strategy to enhance discharge resistance and thermal stability under high-frequency electric stress. Experimental results show that 10 wt% SiC doping significantly improves insulation performance, extending failure time from 17 to 50 min and reducing maximum discharge amplitude by 76%, owing to enhanced charge trapping and interfacial polarization suppression. Surface and volume resistivity measurements further confirmed the improvement; at 120 °C, the 10 wt% SiC composite maintained high surface resistivity 3.30 × 10¹⁴ Ω and volume resistivity 1.41 × 10¹⁵ Ω·cm, significantly outperforming pure PI. In contrast, 20 wt% SiC, though still resistive, showed reduced stability due to agglomeration and interfacial defects, with a surface resistivity of 2.07 × 10¹⁴ Ω and degraded dielectric performance. Dielectric analysis revealed that 10 wt% SiC suppressed dielectric constant and loss across the frequency range, while 20 wt% SiC exhibited increased values at high frequency. These results highlight 10 wt% SiC as an optimal formulation for HFPT winding insulation. Full article

Fabric defect detection is essential for quality assurance in textile manufacturing, where manual inspection is inefficient and error-prone. This paper presents a real-time deep learning-based system leveraging YOLOv11 for detecting defects such as holes, color bleeding and creases on solid-colored, patternless cotton and linen fabrics using edge computing. The system runs on an NVIDIA Jetson Orin Nano platform and supports real-time inference, Message Queuing Telemetry (MQTT)-based defect reporting, and optional Real-Time Messaging Protocol (RTMP) video streaming or local recording storage. Each detected defect is logged with class, confidence score, location and unique ID in a Comma Separated Values (CSV) file for further analysis. The proposed solution operates with two RealSense cameras placed approximately 1 m from the fabric under controlled lighting conditions, tested in a real industrial setting. The system achieves a mean Average Precision (mAP@0.5) exceeding 82% across multiple synchronized video sources while maintaining low latency and consistent performance. The architecture is designed to be modular and scalable, supporting plug-and-play deployment in industrial environments. Its flexibility in integrating different camera sources, deep learning models, and output configurations makes it a robust platform for further enhancements, such as adaptive learning mechanisms, real-time alerts, or integration with Manufacturing Execution System/Enterprise Resource Planning (MES/ERP) pipelines. This approach advances automated textile inspection and reduces dependency on manual processes. Full article

It is challenging to manufacture complex and intricate shapes and geometries with desired surface characteristics using a single manufacturing process. Parts often need to undergo post-processing and must be transported from one machine into another between steps. This makes the whole process cumbersome, time-consuming, and inaccurate. These shortcomings play a major role during the manufacturing of micro and nano products. Hybrid manufacturing (HM) has emerged as a favorable solution for these issues. It is a flexible process that combines two or more manufacturing processes, such as additive manufacturing (AM) and subtractive manufacturing (SM), into a single setup. HM works synergistically to produce complex, composite, and customized components. It makes the process more time efficient and accurate and can prevent unnecessary transportation of parts. There are still challenges ahead regarding implementing and integrating sensors that allow the machine to detect defects and repair or customize parts according to needs. Even though modern hybrid machines forecast an exciting future in the manufacturing world, they still lack features such as real-time adaptive manufacturing based on sensors and artificial intelligence (AI). Earlier reviews do not profoundly elaborate on the types of laser HM machines available. Laser technology resolutely handles additive and subtractive manufacturing and is capable of producing groundbreaking parts using a wide scope of materials. This review focuses on HM and presents a compendious overview of the types of hybrid machines and setups used in the scientific community and industry. The study is unique in the sense that it covers different HM setups based on machine axes, materials, and processing parameters. We hope this study proves helpful to process, plan, and impart productivity to HM processes for the betterment of material utilization and efficiency. Full article

Reporter viruses are valuable tools for studying infections at the cellular level and in living animals. They also enable rapid, high-throughput antiviral drug screening and serological studies. We previously developed a bioluminescence-based reporter virus, rTN09-PA-Nluc, derived from influenza A/Tennessee/1-560/2009 (TN09, pH1N1) in which a NanoLuc (Nluc) reporter protein was fused to the PA protein. Reduced growth of rTN09-PA-Nluc in MDCK cells and mice was restored by mutations arising from mouse adaptation. Here, to test the hypothesis that the growth defect resulted from the PA-Nluc protein fusion, we generated the luciferase reporter virus rTN09-PA-Nluc/SG, which undergoes StopGo translation to yield separate PA and NLuc proteins along with a proportion of the PA-Nluc fusion. The rTN09-PA-Nluc/SG virus had greater protein expression and increased replication in MDCK cells compared to rTN09-PA-Nluc. The reporter virus encoding StopGo translation was superior to the virus without it in bioluminescence-based virus neutralization assays in vitro, providing results in 24 h as opposed to 3 days using unmodified influenza virus and standard neutralization assay protocols. However, the reporter virus encoding StopGo translation remained attenuated in mice. Mouse-adaptive mutations were needed for full virulence and efficient non-invasive imaging in mice. Overall, these findings demonstrate the benefit of incorporating StopGo translation into influenza reporter viruses for in vitro assays, yet mouse-adapted mutations appeared superior in mice. Full article

To address the problems of low coverage rate and low detection accuracy in UAV-based aircraft skin defect detection under complex real-world conditions, this paper proposes a method combining a Greedy-based Breadth-First Search Coverage Path Planning (GB-CPP) approach with an improved YOLOv11 architecture (INN-YOLO). GB-CPP generates collision-free, near-optimal flight paths on the 3D aircraft surface using a discrete grid map. INN-YOLO enhances detection capability by reconstructing the neck with the BiFPN (Bidirectional Feature Pyramid Network) for better feature fusion, integrating the SimAM (Simple Attention Mechanism) with convolution for efficient small-target extraction, as well as employing RepVGG within the C3k2 layer to improve feature learning and speed. The model is deployed on a Jetson Nano for real-time edge inference. Results show that GB-CPP achieves 100% surface coverage with a redundancy rate not exceeding 6.74%. INN-YOLO was experimentally validated on three public datasets (10,937 images) and a self-collected dataset (1559 images), achieving mAP@0.5 scores of 42.30%, 84.10%, 56.40%, and 80.30%, representing improvements of 10.70%, 2.50%, 3.20%, and 6.70% over the baseline models, respectively. The proposed GB-CPP and INN-YOLO framework enables efficient, high-precision, and real-time UAV-based aircraft skin defect detection. Full article

Low-temperature plasma cleaning technology has been widely used to clean various optical components precisely. After the complete removal of organic contaminants from fused silica surfaces through plasma cleaning, continuous plasma irradiation can lead to nano-defects on the fused silica surface, resulting in the degradation of optical performance. Thus, the microscale processes underlying plasma-induced surface damage on fused silica were investigated through molecular dynamics simulations, aiming to analyze the mechanisms of surface damage on optical components during plasma cleaning. Oxygen plasma bombardment disrupted fused silica bonds, leading to the successive sputtering of silicon–oxygen atoms. The quantity of sputtered silicon atoms demonstrated a linear correlation with irradiation time. The emergence of pit defects and distinctive interface damage patterns elucidated the impact of neutral oxygen atoms. Critical findings underscore the onset of significant damage beyond 33 eV, underlining plasma’s role in thinning fused silica. Temperature is a crucial factor affecting surface damage during plasma cleaning. Ultimately, investigating the surface damage mechanism of fused silica during plasma cleaning establishes a groundwork for achieving non-destructive optics cleaning. Full article