Search Results (6,551)

Femtosecond laser has been widely utilized in functional microstructural surfaces for applications such as anti-reflection, radiative cooling, and self-cleaning. However, achieving high-efficiency manufacturing of high-consistency functional microstructures (with feature sizes ~1 μm) over large areas remains a challenge. Here, we report a femtosecond laser temporal and spatial modulation technique for fabricating large-area anti-reflective microholes on magnesium fluoride (MgF₂) windows. The beam was transformed into a Bessel beam to extend the Rayleigh length, enabling the fabrication of microhole arrays with sub-micron precision and surface roughness variations within 10 nm over a 6 μm focal position shift range (5–11 μm). By modulating MHz burst pulses, the aspect ratio of the microholes was increased from 0.3 to 0.7 without compromising a processing speed of 10,000 holes per second. As a proof of concept, large-area anti-reflective microholes were fabricated on a 20 mm × 20 mm surface of the MgF₂ window, forming a nanoscale refractive index gradient layer and achieving a transmittance increase to over 98%. This method provides a feasible solution for the efficient and high-consistency manufacturing of functional microstructures over large areas. Full article

Influenza viruses remain a major global public health concern, causing significant morbidity and mortality annually despite widespread vaccination efforts. The limitations of current seasonal vaccines, including strain-specific efficacy and manufacturing delays, have accelerated the development of next-generation candidates aiming for universal protection. This review comprehensively summarizes the recent progress in universal influenza vaccine research. We first outline the key conserved antigenic targets, such as the hemagglutinin (HA) stem, neuraminidase (NA), and matrix proteins (M2e, NP, and M1), which are crucial for eliciting broad cross-reactive immunity. We then delve into advanced antigen design strategies, including immunofocusing, multi-antigen combinations, computationally optimized broadly reactive antigens (COBRA), and nanoparticle-based platforms. Furthermore, we evaluate evolving vaccine delivery systems, from traditional inactivated and live-attenuated vaccines to modern mRNA and viral vector platforms, alongside the critical role of novel adjuvants in enhancing immune responses. The convergence of these disciplines—structural biology, computational design, and nanotechnology—is driving the field toward a transformative goal. We conclude that the successful development of a universal influenza vaccine will likely depend on the strategic integration of these innovative approaches to overcome existing immunological and logistical challenges, ultimately providing durable and broad-spectrum protection against diverse influenza virus strains. Full article

Porosity characterization in metallic alloys is a fundamental aspect of materials engineering due to its influence on mechanical and structural properties. This study presents a method based on digital image processing for detecting and analyzing porosity in the AlSi10Mg aluminum alloy, additively manufactured using laser powder bed fusion (L-PBF). X-ray computed tomography, segmentation algorithms and filtering techniques were employed to identify and quantify the porosity present in the material’s microstructure. The research demonstrates that combining numerical methods with qualitative analysis provides a comprehensive understanding of porosity characteristics. Notably, the effectiveness of the proposed image processing methods was validated by comparing the results with actual material density measurements. However, challenges such as the need for proper calibration and potential imaging artifacts affecting accuracy were identified. This study represents a significant advancement in materials engineering, offering a detailed methodology for porosity analysis in aluminum alloys that not only enhances quality control and process optimization, but also improves segmentation accuracy and facilitates the reliable detection of small and interconnected pores in complex additively manufactured geometries. Full article

The global challenge of food insecurity requires innovative approaches for sustainable food production and waste valorization. This study investigates the valorization of oat hulls, an abundant lignocellulosic by-product from oat manufacturing, by solid-state fermentation using edible filamentous fungi. Oat hulls sourced from oatmeal industrial side-streams were used as the sole substrate in co-cultures of Neurospora intermedia and Rhizopus oryzae. The fermentation process was optimized and upscaled, with fungal growth monitored via CO₂ efflux and modeled to assess substrate utilization. Comprehensive analyses revealed a significant increase in protein concentration (p < 0.05) in the fermented oat hulls compared to the non-fermented controls. The resulting product was successfully incorporated into granola bars, which underwent sensory evaluation and received positive feedback, demonstrating its potential as a value-added food ingredient. These findings highlight the feasibility of using edible fungi to upcycle cereal processing by-products into nutritionally enhanced alternative protein sources, supporting both food system sustainability and circular bioeconomy objectives. Full article

Background: Seasonal influenza vaccines must be reformulated annually due to the high genetic variability and antigenic drift of circulating influenza viruses. The annual update, guided by World Health Organization (WHO) recommendations, results in significant challenges, including compressed production time periods, elevated manufacturing costs, and global distribution pressures. Moreover, mismatches between vaccine strains and circulating viruses can severely reduce protective efficacy, underscoring the urgent need for broadly protective and long-lasting influenza vaccines. Methods: In this study, we developed an adjuvanted trivalent recombinant influenza virus-like particle vaccine (a-RIV) using the baculovirus–insect cell expression system and formulated it with an AS01-like adjuvant. The vaccine comprises full-length hemagglutinin (HA) proteins from WHO-recommended seasonal influenza strains: A/H1N1 (AH1), A/H3N2 (AH3), and B/Victoria (B/vic) lineages. The purified HA proteins were subsequently formulated with a liposomal adjuvant to enhance the immunogenicity. Results: In mouse immunization studies, the a-RIV vaccine elicited significantly stronger humoral and cellular immune responses than the licensed recombinant vaccine Flublok and the conventional inactivated influenza vaccine (IIV). High levels of functional anti-HA antibodies and antigen-specific T cell responses persisted for at least six months post-vaccination. Moreover, a-RIV induced broadly reactive antibodies capable of cross-binding to heterologous AH1 and AH3 influenza strains. Conclusions: Our data demonstrate that the a-RIV elicits enhanced, durable, and broadly cross-reactive immune responses against multiple influenza subtypes. These findings support the potential of adjuvanted recombinant HA-based vaccine as a promising candidate for the development of next-generation influenza vaccine. Full article

Paraffin-based phase change materials (PCMs) have emerged as promising candidates for thermal energy storage (TES) applications due to their high latent heat, chemical stability, and low cost. However, their inherently low thermal conductivity and the risk of leakage during melting–solidification cycles significantly limit their practical performance. To address these limitations, numerous studies have investigated composite PCMs in which paraffin is incorporated into porous supporting matrices. Among these, diatomite has garnered particular attention due to its high porosity, large specific surface area, and chemical compatibility with organic materials. Serving as both a carrier and stabilizing shell, diatomite effectively suppresses leakage and enhances thermal conductivity, thereby improving the overall efficiency and reliability of the PCM. This review synthesizes recent research on paraffin–diatomite composites, with a focus on impregnation methods, surface modification techniques, and the influence of synthesis parameters on thermal performance and cyclic stability. The mechanisms of heat and mass transport within the composite structure are examined, alongside comparative analyses of paraffin–diatomite systems and other inorganic or polymeric supports. Particular emphasis is placed on applications in energy-efficient buildings, passive heating and cooling, and hybrid thermal storage systems. The review concludes that paraffin–diatomite composites present a promising avenue for stable, efficient, and sustainable phase change materials (PCMs). However, challenges such as the optimization of pore structure, long-term durability, and large-scale manufacturing must be addressed to facilitate their broader implementation in next-generation energy storage technologies. Full article

Digital video surveillance systems are now common in the security infrastructure of modern times, but proprietary file systems provided by large manufacturers are a major challenge to the work of the forensic investigator. This paper proposes a forensic recovery methodology of Hikvision and Dahua surveillance systems by utilizing three major innovations: (1) adaptive temporal sequencing, which dynamically changes gap detection thresholds; (2) dual-signature validation with header–footer matching of DHFS frames; and (3) automatic manufacturer identification. The strategy puts into practice direct binary analysis of proprietary file systems, frame-based parsing and automatic video reconstruction. Testing on 27 surveillance hard drives showed a recovery rate of 91.8, a temporal accuracy of 96.7% and a false positive rate of 2.4%—the lowest of the tools tested with statistically significant improvements over commercial tools (p < 0.01). Better results with fragmented streams (87.2 vs. 82.4% with commercial tools) meet key forensic needs of determining valid evidence chronology. The open methodology offers the necessary algorithmic transparency to be court-admissible, and the automated MP4 conversion with metadata left intact makes the integration of forensic workflow possible. The study provides a scientifically validated approach to proprietary surveillance formats, which evidences technical innovativeness and practical usefulness to digital forensics investigations. Full article

Liquid crystal elastomers (LCEs), as a class of smart materials, have attracted significant attention across soft robotics, biomedical engineering, and intelligent devices because of their unique capabilities to undergo large, reversible, and anisotropic deformations under external stimuli. Over the years, fabrication methods have advanced from conventional molding and thin-film processing to additive manufacturing, with 4D printing emerging as a transformative approach by enabling time-dependent, programmable shape transformations. Among the available methods, direct ink writing (DIW) and vat photopolymerization are most widely adopted, with ink chemistry, rheology, curing, and printing parameters directly governing mesogen alignment and actuation performance. Recent advances in LCE actuators have demonstrated diverse functionalities in soft robotics, including bending, crawling, gripping, and sequential actuation, while biomedical applications span adaptive tissue scaffolds, wearable sensors, and patient-specific implants. This review discusses the conceptual distinction between 3D and 4D printing, compares different additive manufacturing techniques for LCE, and highlights emerging applications in the field of soft robotics and biomedical technologies. Despite rapid progress in LCE, challenges remain in biocompatibility, long-term durability and manufacturing scalability. Overall, innovations in 4D printing of LCEs underscores both the promise and the challenges of these materials, pointing toward their transformative role in enabling next-generation soft robotic and biomedical technologies. Full article

Despite its ultrahigh theoretical capacity, silicon anodes for lithium-ion batteries suffer from severe capacity decay caused by over 300% volume changes during cycling. While Si–Ge alloying and spherical nanostructuring have been demonstrated to improve ionic/electronic transport and mechanical resilience, scalable synthesis of homogeneous, sub-150 nm SiGe nanospheres from low-cost precursors remains challenging. Here, we report a hybrid plasma-spraying physical vapor deposition (PS-PVD) process that directly converts metallurgical-grade Si and Ge powders into phase-pure Si_0.8Ge_0.2 nanospheres (<100 nm) at a continuous rate of 1 g min⁻¹. The co-condensation mechanism during formation was elucidated through molecular dynamics (MD) simulations, which revealed a process initiated by inhomogeneous nucleation and followed by uniform cluster growth and spheroidization. Multiscale characterization confirmed the spherical morphology, compositional uniformity, and crystalline structure of the produced Si_0.8Ge_0.2 nanoparticles. The resulting anodes exhibited a stable capacity of ~1500 mAh g⁻¹ at 0.1C over 100 cycles (>80% retention) and a Coulombic efficiency of ~98%. This approach bridges the gap between high-performance design and industrial manufacturability, offering a practical route to next-generation anodes for electric vehicles. Full article

This paper innovatively proposes a high-precision fabrication strategy for silicon-based micro–nano-composite layered structures composed of micron-scale platforms and nanopillars, effectively addressing the challenges of alignment errors and material mismatch during manufacturing. By integrating electron beam lithography (EBL), inductively coupled plasma (ICP) etching, and ultraviolet nanoimprint lithography (NIL) into a unified multi-step workflow, the method achieves exceptional precision and efficiency in producing complex micro–nano-composite architectures. Comprehensive structural characterization is performed using scanning electron microscopy (SEM) and atomic force microscopy (AFM), with probe convolution effects carefully corrected to ensure accurate dimensional analysis. Experimental results confirm the outstanding stability and uniformity of the fabricated structures, exhibiting minimal deviations in both feature size and spatial layout. Nanopillars with diameters ranging from 50 to 200 nm are successfully integrated onto 1-µm square platforms, with the lateral deviation of 50 nm features maintained within 5% or less. Furthermore, the method effectively mitigates thermal stress-induced misalignment during the fabrication of multi-material layers, demonstrating strong potential for scalable production of advanced photonic devices and integrated nanophotonic systems. Overall, this work establishes a robust and versatile technical pathway for the precise manufacturing and quantitative characterization of micro–nano-composite structures, providing a key foundation for the next generation of photonic integration technologies. Full article

The manufacturing of freeform optical lenses, essential for advanced applications such as Earth observation and laser fusion, demands exceptional surface accuracy and lightweight designs. However, their complex, non-symmetrical geometries present significant manufacturing challenges. Conventional CNC machining strategies, which rely on fixed Cartesian step sizes, are inherently inefficient for surfaces with rapidly varying curvature. This inadequacy results in non-uniform material removal, prolonged machining times, and substandard surface quality. This study presents a novel curvature-adaptive machining strategy based on dynamic tangential toolpath optimization. The method continuously aligns the toolpath with the local surface geometry to maintain uniform cutting conditions. A dedicated computer-aided manufacturing (CAM) software environment was developed to generate the optimized toolpaths and corresponding G-code. Experimental validation on representative freeform optics demonstrated a substantial improvement in precision: a single error-compensation iteration achieved a reduction in peak-to-valley form error of up to 48.4%. The results confirm that the proposed strategy significantly outperforms conventional fixed-step methods, delivering superior surface finish, reduced machining time, and enhanced process flexibility without requiring specialized hardware. This work establishes a practical and high-precision advancement for the manufacture of high-performance freeform optical systems. Full article

Against the backdrop of intelligent manufacturing, the ball mill, as a key energy-consuming piece of equipment, requires an accurate perception of its load state, which is crucial for optimizing production efficiency and ensuring operational safety. However, its vibration signals exhibit typical nonlinear and non-stationary characteristics, intertwined with complex noise, posing significant challenges to high-precision identification. A core contradiction exists in existing diagnostic methods: convolution network-based methods excel at capturing local features but overlook global trends, while Transformer-type models, although capable of capturing long-range dependencies, tend to “average out” critical local transient information during modeling. To address this dilemma, this paper proposes a new paradigm for multi-scale feature collaborative perception. This paradigm is implemented through an innovative deep learning architecture—the Residual Block-Swin Transformer Network (RB-SwinT). This architecture subtly achieves hierarchical and in-depth integration of the powerful global context modeling capability of Swin Transformer and the excellent local detail refinement capability of the residual module (ResBlock), enabling synchronous and efficient representation of both the macro trends and micro mutations of signals. On the experimental dataset covering nine types of fine operating conditions, the overall recognition accuracy of the proposed method reaches as high as 96.20%, which is significantly superior to a variety of mainstream models. To further verify the model’s generalization ability, this study was tested on the CWRU public bearing fault dataset, achieving a recognition accuracy of 99.36%, which outperforms various comparative methods such as SAVMD-CNN. This study not only provides a reliable new technical approach for ball mill load identification but also demonstrates its practical application value in indicating critical operating conditions and optimizing production operations through an in-depth analysis of the physical connotations of each load level. More importantly, its “global-local” collaborative modeling concept opens up a promising technical path for processing a broader range of complex industrial time-series data. Full article

Background/Objectives: Conventional solidification methods for liquid self-nanoemulsifying drug delivery systems face significant limitations. This includes complex manufacturing processes, high costs, and environmental concerns. This study aimed to develop and optimize a thermoresponsive self-nanoemulsifying drug delivery system (T-SNEDDS) for dapagliflozin as a sustainable alternative solidification technique. Methods: Oil and surfactant were selected based on solubility and emulsification studies. The Box–Behnken approach was used to examine the impacts of three independent variables (pluronic F127, propylene glycol, and dapagliflozin concentrations) on liquefying temperature and time. Optimized T-SNEDDS was characterized in terms of particle size, zeta potential, and dissolution performance. Stability assessment included centrifugation testing and a six-month storage evaluation. The green pharmaceutical performance was comparatively evaluated against five conventional solidification methods using ten adapted parameters. Results: Imwitor 308 and Cremophor EL were selected as optimal excipients for SNEDDS formulation. In addition, Pluronic F127 and propylene glycol were used to induce solidification during storage. The optimized formulation (8.60% w/w Pluronic F127, 10% w/w propylene glycol, and 5% w/w dapagliflozin) exhibited a liquefying temperature of 33.5 °C with a liquefying time of 100.3 s and a particle size of 96.64 nm. T-SNEDDS significantly enhanced dissolution efficiency of dapagliflozin (95.7%) compared to raw drug (42.4%) and marketed formulation (91.3%). Green pharmaceutical evaluation revealed that T-SNEDDS achieved the highest score compared to conventional approaches. Conclusions: T-SNEDDS represents a superior sustainable approach for SNEDDS solidification that offers enhancement in drug dissolution while addressing manufacturing, environmental, and economic challenges through its solvent-free and single-step preparation process with excellent scalability potential. Full article

This study investigates the interplay of advanced manufacturing technologies (AMT), digital commerce, circular economy intensity, and digital maturity on innovation outcomes among Spanish manufacturing firms in the post-pandemic era. Drawing on resource orchestration theory and survey data from 1813 companies, the analysis employs regression and mediation techniques to assess direct and indirect effects on product and process innovation. Findings reveal that AMT adoption leads to modest, context-dependent improvements in process innovation, while effects on product innovation are limited or negative; e-commerce adoption alone does not predict substantial innovation gains, and jointly adopting these technologies rarely produces amplifying results. Greater circular economy intensity mediates a negative relationship with process innovation, indicating possible resource trade-offs between sustainability initiatives and innovation goals. Digital maturity inconsistently strengthens positive impacts and can further moderate innovation outcomes in interaction with circular economy practices. Notably, economic benefits from circular economy practices are concentrated in export-oriented firms and not widely distributed in the sample. These findings challenge assumptions that digital and green transformations universally enhance innovation, advocating for tailored policy and organizational strategies that account for sectoral and contextual differences. Full article

Variability across different calibration laboratories can impact the consistency of ocean color data; this study addresses that challenge through a coordinated comparison of spectral irradiance and radiance calibrations. As part of the Fiducial Reference Measurements for Satellite Ocean Color (FRM4SOC) Phase 2 project, the metrological consistency across six international laboratories was tested in the years 2022–2023. Each participant determined the responsivity for four transfer radiometers using their own SI-traceable radiometric standards and calibration procedures. This was among the first laboratory comparisons for Ocean Color Radiometry (OCR) using hyperspectral radiometers. The main objective was to verify that the instrument manufacturers and research laboratories can fulfill the updated International Ocean Color Coordination Group (IOCCG) protocols to perform SI traceable calibrations with an uncertainty of 1% (k = 1) for irradiance and slightly more for radiance. The comparison revealed biases among participants and provided an overview of the calibration capabilities of OCRs. The differences between the participants varied from ±1 … 2% up to ±5%. Biases due to different measurement conditions were corrected by the Pilot. Furthermore, biases due to traceability and different conditions revealed several data handling errors. However, after uniform data processing, the metrological compatibility between the participants was reached within ±3%. Full article