Search Results (263)

Extra-virgin olive oil (EVOO) is a chemically complex lipid matrix whose minor constituents—especially phenolic secoiridoids—drive sensory quality, oxidative stability, and health benefits. However, these bioactives are vulnerable to heat, light, oxygen, and pro-oxidant metals during processing and distribution, while the high cost of EVOO often makes it a target for adulteration and mislabeling. This review critically assesses nano-enabled, food-grade strategies that (i) preserve phenolics and aroma compounds through nanoencapsulation, inclusion complexes, Pickering stabilization, and structured lipid systems; (ii) control their release and bioaccessibility during digestion; and (iii) enhance authenticity verification via sensor-ready packaging, spectroscopy/chemometrics, and digital traceability systems (IoT, machine learning, blockchain). We align these innovations with the “product identity constraints” of the EVOO category and with official quality standards used in routine control (IOC/EU). Finally, we explore circular valorization of olive-mill by-products within food-centered biorefineries, outlining pathways to convert biomass into ingredients, materials, and energy, thus reducing environmental impacts. Research priorities are proposed to develop scalable, regulation-compliant nanotechnologies that extend shelf life and increase consumer trust without compromising EVOO category standards. Full article

Electrospinning is the most widely used technique for creating nanofibers. However, the low production rate and the usage of a high-voltage setup have become obstacles to its widespread application. One effective method for creating nanofibers from a variety of materials is centrifugal spinning. This review discusses centrifugal spinning (CS) as an effective and scalable nanofiber manufacturing technology, particularly in filtration systems, and presents it as a promising alternative to existing methods, such as electrospinning. The review highlights the advantages of CS, including its high production rate, cost efficiency, and the ability to process various materials to produce nano- and microfibers. Despite its high potential, the issues associated with CS technology include the unpredictability of fiber quality, the inability to control diameters, and the need for more robust mathematical models to predict fiber characteristics. To eliminate these shortcomings and further enhance the industrial utility of centrifugally spun nanofibers in filtration, future studies should focus on improving process control, exploring a broader range of polymers, optimizing melt electrospinning, and designing more advanced nozzle profiles. Full article

Foliar nanofertilization is increasingly being explored as a strategy to enhance crop nutritional quality; however, dose-dependent physiological and metabolic responses remain insufficiently defined. This study evaluated the effects of conventional NPK (20:20:20) and nano-formulated NPK combined with zinc (3 and 5 g/L) on the mineral composition, bioactive compounds, antioxidant capacity, and metabolic profile of sweet pepper (Capsicum annuum L., cv. ‘Dora’) grown under controlled conditions. Physicochemical characterization of the nanofertilizer by dynamic light scattering and transmission electron microscopy confirmed nanoscale primary particle size and revealed concentration-dependent aggregation behavior at higher Zn levels. Significant differences (p < 0.05) were observed among treatments in macro- and microelement content, total phenolics, flavonoids, carotenoids, ascorbic acid, and antioxidant activity. The application of nano NPK combined with 3 g/L Zn resulted in the highest accumulation of total phenolics, flavonoids, and vitamin C, accompanied by enhanced antioxidant capacity, suggesting stimulation of secondary metabolism. In contrast, the higher Zn concentration (5 g/L) further increased carotenoid content but was associated with elevated proline levels, indicating the onset of physiological stress. Multivariate analyses (PCA and ROC) supported dose-dependent metabolic modulation and confirmed that combinations of selected metabolites contributed to clearer differentiation between fertilization regimes. Overall, the results highlight the existence of an optimal nano-zinc application range that enhances fruit functional quality while avoiding stress-related metabolic imbalance, emphasizing the importance of physicochemical stability in nano-enabled fertilization strategies. While this study focused on a single sweet pepper cultivar, future research should explore other pepper species to evaluate whether similar dose-dependent nano Zn effects are observed. Full article

The integration of emerging technologies such as robotics and artificial intelligence (AI) has the potential to transform agricultural harvesting by improving efficiency, reducing waste, lowering labor dependency, and enhancing produce quality. This paper presents the development of an intelligent robotic berry harvesting system that combines deep learning–based perception with autonomous robotic manipulation for real-time strawberry harvesting. A computer vision pipeline based on the YOLOv11 segmentation model was developed and integrated into a Smart Mobile Manipulator (SMM) equipped with autonomous navigation, a 6-degree-of-freedom (6-DoF) xArm 6 robotic arm, and ROS middleware to enable real-time operation. Using a publicly available strawberry dataset comprising 2,800 images collected under ridge-planted cultivation conditions, the proposed YOLOv11-small segmentation model achieved 84.41% mAP@0.5, outperforming YOLOv11 object detection, Faster R-CNN, and RT-DETR in segmentation quality while maintaining real-time performance at 10 FPS on an NVIDIA Jetson Orin Nano edge GPU. A PCA-based fruit orientation and geometric analysis method achieved 86.5% localization accuracy on 200 test images. Controlled indoor harvesting experiments using synthetic strawberries demonstrated an overall harvesting success rate of 72% across 50 trials. The proposed system provides a general-purpose platform for berry harvesting in controlled environments, offering a scalable and efficient solution for autonomous harvesting. Full article

This paper proposes the use of laser-assisted cutting technology to control the brittle–plastic transition of single-crystal CaF₂ through local thermal softening, thereby suppressing its processing anisotropy. Nano-scratch experiments show that heating significantly increases the critical plastic cutting depth of each crystal plane and reduces the inter-plane differences. Based on this, laser-assisted ultra-precision turning was used to fabricate CaF₂ optical microcavities with a surface roughness below 10 nm, achieving a maximum quality factor of ~7.79 × 10⁷, and significantly reducing the performance differences among different crystal orientations. The research indicates that this method can effectively promote uniform plastic flow on each crystal plane, providing an effective approach for the high-performance and consistent fabrication of anisotropic brittle optical components. Full article

Sustainable nanotechnologies derived from renewable resources are increasingly being positioned at the interface of green chemistry, advanced drug delivery, and translational pharmaceutics. Over the past decade, lignocellulosic nanomaterials, chitin/chitosan platforms, polysaccharide-based nanogels and nano-enabled hydrogels, lignin- and polyphenol-derived nanostructures, and bio-based lipid nanocarriers have been engineered through progressively eco-efficient routes, including solvent-minimized self-assembly, nanoprecipitation, spray drying, hot-melt extrusion, and microfluidic-assisted fabrication. This work provides a structured evidence map of nano-enabled drug delivery and therapeutic platforms derived from renewable biological resources. Specifically, we aim to (i) identify and classify nanoplatform classes and renewable feedstocks; (ii) summarize reported pharmaceutical critical quality attributes (CQAs) and performance and safety endpoints; and (iii) appraise how “renewability” and “green” claims are evidenced (feedstock origin vs. process sustainability) and how frequently translational readiness factors (scalability, quality control, regulatory alignment) are addressed. We critically compare renewable and conventional nanomaterial platforms across key translational dimensions, including carbon footprint, batch consistency, biodegradability, functional tunability, safety/persistence, and scale-up maturity. Finally, we delineate a practical translational pathway—from biomass sourcing and fractionation to nanoformulation, characterization/stability, and GMP scale-up—highlighting cross-cutting enablers such as lifecycle assessment, EHS/toxicology risk assessment, quality-by-design, and regulatory alignment. Collectively, the evidence supports renewable nanomaterials as viable, scalable candidates for next-generation therapeutics, provided that variability control, standardized characterization, and safety-by-design principles are embedded early in development. Full article

In the present study, an amperometric aflatoxin B1 sensor was constructed via modifying a nano-graphite carbon paste microelectrode with a cationic surfactant of cetyltrimethylammonium bromide (CTAB) and a perfluorosulfonic acid resin of Nafion through a simple and controllable electrochemical scanning method. The experiment results show that CTAB–Nafion composite film has a good catalytic effect on the electrochemical response of aflatoxin B1. The electrocatalytic mechanism was investigated with the aid of different analytical techniques, including square wave voltammetry, electrochemical impedance spectroscopy, chronocoulometry, energy-dispersive spectroscopy and scanning electron microscopy. Under the optimal conditions, the linear range of the sensor is from 0.1 nM to 100 nM, and its detection limit and sensitivity are 20 pM (S/N = 3) and (24.9 ± 1.51) μA/nM, respectively. The accurate and rapid detection of aflatoxin B1, which has strong carcinogenicity, is of great significance for food quality monitoring and the protection of human health. Therefore, finally, the sensor was used to detect the concentration of aflatoxin B1 in milk and soy sauce samples, and the favorable recovery results indicated its good application prospects. Full article

This paper evaluates the current maturity of automatic code-generation workflows for deploying modern CNN-based object detectors on embedded GPU platforms. We compare a native pipeline against a code generation pipeline through a Model-Based Engineering (MBE) approach, using YOLOv8/YOLOv9 inference on NVIDIA Jetson Orin Nano and Jetson AGX Orin as representative edge-GPU workloads. We report detection-quality metrics (mAP, PR curves) and system-level metrics (latency distribution and initialization overhead) under a controlled single-class scenario based on a CARLA-generated sequence with frame-level annotations. Absolute accuracy and latency values are scenario-dependent and may vary under different camera optics, illumination, motion blur, sensor noise, occlusion patterns, and multi-class scene. Results quantify the performance gap between code generation and native pipelines and show that, for the evaluated workloads, the automated pipeline remains less competitive in both latency and accuracy. We discuss the implications of this gap for deployment workflows in safety-oriented domains, and we outline bottlenecks that should be addressed. The study is intended as a controlled traffic-light detection micro-benchmark and does not aim to validate full ADAS perception stacks. Full article

Background: Metastatic melanoma is an extremely aggressive malignancy with limited therapeutic options, despite advances in targeted and immunotherapy. MicroRNAs are key post-transcriptional regulators of gene expression and play a critical role in tumor adaptation, invasion, and metastasis. The aim of our study was to identify dysregulated miRNAs which may serve as novel biomarkers and therapeutic targets. Materials and Methods: The study was conducted on FFPE samples from metastatic melanoma (n = 15), compared to healthy skin tissue (n = 6). BRAF V600E/Ec mutation status was established by Real-Time qPCR. Expression miRNA analysis was performed, using digital counting of 827 miRNAs on the NanoString platform, with data normalization and fold change calculations. Results: Following normalization and quality control metrics, 58 differentially expressed miRNAs were identified in BRAFwt melanoma samples: 6 overexpressed and 52 inderexpressed miRNAs. In BRAFmut melanoma, 37 microRNAs were differentially expressed: 11 overexpressed and 26 underexpressed. Four miRNAs showed elevated expression in both melanoma groups. Among them, miR-146a-5p and miR-4286 demonstrated the highest elevation, especially in BRAFmut tumors. We focused further on their targeted genes. Conclusion: This study demonstrates significant alterations in the miRNA expression profile in metastatic melanoma and highlights the potential of miR-146a-5p and miR-4286 as key regulators of tumor biology. Full article

The global polymer waste burden has catalyzed a shift from linear “production–use–disposal” systems to circular models that prioritize recycling, reuse, and value retention. This article proposes an integrated, technology-ready roadmap for mechanical recycling and reuse of commodity and bio-based polymers via filament re-extrusion and Additive Manufacturing (AM). Building upon recent findings on performance envelopes of virgin vs. recycled Polylactic Acid (PLA) filaments processed by Fused Deposition Modeling (FDM), process parameter sensitivities (layer height, infill density) and their statistical optimization, and functional reinforcement routes (aluminum: Al, alumina: Al₂O₃, titanium: Ti, and Nano Boron Nitride: nano-BN), we articulate (1) a process–structure–property (PSP) mapping; (2) a low-defect, low-energy filament re-extrusion protocol; and (3) a graded-value strategy for upcycling mixed polymer streams. Across case analyses, we show that recycled PLA can achieve near-parity with virgin PLA when extrusion quality and printing parameters are controlled, and that ceramic/metal nanofillers enable thermal management and biocompatibility benefits crucial for durable reuse scenarios. Full article

The medical field now uses mRNA therapeutics to deliver fast programmable treatment options through versatile vaccination platforms. The worldwide adoption of mRNA therapeutics faces a major obstacle because these molecules require extreme cold storage and transportation systems. mRNA stability establishes a fundamental scientific and industrial challenge which requires researchers to unite formulation design with process control and material engineering for cold-chain independence. Current knowledge about RNA hydrolysis and lipid oxidation and water-mediated degradation is combined with new methods for solid-state stabilization through lyophilization and spray-freeze-drying and thin-film technologies. Mechanism such as vitrification, water replacement and excipient RNA interactions are assessed to establish the fundamental chemical properties needed for extended product stability. Advanced mRNA development strategies are also examined, including self-amplifying and circular RNA structures and nano-glass and metal–organic frameworks and artificial intelligence-based predictive design for creating stable mRNA formulations at room temperature. This review examines manufacturing and regulatory and logistical obstacles which affect real-world implementation of mRNA therapeutics through assessments of production scale and product quality tests and packaging strength and tropical environment testing. The combination of research findings presents a path to develop mRNA medicines which maintains their effectiveness when stored at 25 °C or above, thus enabling worldwide access to RNA-based treatments. The development of mRNA into a durable therapeutic platform requires scientists to merge molecular research with process development and regulatory standardization. Full article

Two approaches are described for construction of a screen-printed planar electrode (SPE) for potentiometric determination of iodide ion. The first, involves preparation and application of iron(II) bathophenanthroline tetraiodoplumbate complex ([Fe(bphen)₃][PbI₄]), as a sensitive and selective electroactive sensing material in a potentiometric electrode for iodide determination. The second is the use of a nano-sized poly(aniline-co-thiophene) (PANI-co-PT) as a solid-contact material in a planar miniaturized configuration. The SPE displays a Nernstian response for iodide ion with a calibration slope of −58.81 ± 0.69 mV/decade (R² = 0.9998) over a wide concentration range (9.17 × 10⁻⁷–6.94 × 10⁻³ mol/L), low detection limit (6.09 × 10⁻⁷ mol/L), rapid response time (5.0 ± 1.0 s) and long-life span (75 ± 3.0 d). The use of PANI-co-PT solid-contact layer significantly improves the ion-to-electron transduction, eliminates the formation of undesired thin water layer between the sensing membrane and the conducting substrate, prevents membrane delamination, enhances potential stability with a significantly reduced potential drift (8.32 ± 0.12 µV/min) and displays high redox capacitance (2.560 ± 0.040 mF). Water contact angle measurements confirm the increased hydrophobicity of the modified membrane electrode (from 44 ± 0.8° to 93 ± 1.4°) and demonstrate the membrane ability to repel moisture and further stabilize the sensor response. The proposed sensor is successfully integrated into a flow injection analysis (FIA) system to enable real-time and continuous iodide monitoring with high precision, high sample throughput and applicability for quality control of pharmaceuticals and environmental monitoring. Full article

Since the introduction of denoising diffusion probabilistic models (DDPM) in 2020, diffusion-based image generation has achieved remarkable quality but remains computationally demanding for resource-constrained environments. This survey systematically analyzes over 100 publications from 2020 to 2025, presenting a four-layer optimization stack that encompasses model architecture, controllable mechanisms, sampling algorithms, and model compression. We address the fundamental “quality–efficiency–control” trilemma through three research questions: (1) the architectural complexity gap between U-shaped network (UNet) and diffusion transformer (DiT) models, (2) the parameter overhead spectrum of control mechanisms from ControlNet (42%) to NanoControl (0.024%), and (3) the theoretical impact of quantization and bit-width reduction on information loss. Our analysis reveals that instant image generation is achievable through algorithmic innovations such as step distillation and architectural pruning, reducing the sampling steps from 50 to 4–8 (or even 1) and computational cost by over 90%. We utilize the floating point operations (FLOPs) efficiency ratio (FER) to highlight the discrepancy between theoretical FLOPs reduction and actual efficiency, pointing towards the need for system-level optimization. Key findings demonstrate that DiT architectures exhibit high computational density (FER > 1.6) and low-bit quantization such as 8-bit weight, and activation (W8A8) maintains an optimal balance between compression and quality (Fréchet inception distance degradation ΔFID < 1.0), and lightweight control mechanisms enable sophisticated image control with a negligible parameter overhead. This survey provides a comprehensive algorithmic optimization roadmap for practitioners targeting efficient on-device image generation. Full article

In clinical and preclinical magnetic resonance imaging (MRI), image quality is often limited by intrinsic tissue contrast, so paramagnetic agents are used to amplify relaxation differences and improve lesion detectability. Widely used gadolinium-based contrast agents present recognized drawbacks, stimulating interest in nanoscale platforms with tuneable magnetic and biological properties. This review provides a critical analysis on the use of metal-doped carbon nanodots (C-dots) as MRI contrast candidates. We briefly revisit MRI signal formation, spin–lattice ($T_1$) and spin–spin ($T_2$) relaxation, and relaxometric parameters $r_1$ and $r_2$ and outline how pulse-sequence choice favours $T_1$- or $T_2$-dominant agents. We compare approved small-molecule agents with nanostructured systems, highlighting unmet needs in safety, field-strength dependence, multimodality, and organ-specific imaging. A central focus is how nano- and molecular architectures of metal-doped carbon dots govern $r_1$ and $r_2$: the metal species and oxidation state, its location within the carbon matrix, surface chemistry and hydration, and the accessibility for proton and water exchange can shift performance toward $T_1$ or $T_2$. Engineered C-dots with controlled composition and metal dopants have proven to pair improved relaxivity with fluorescence, targeting ligands, or therapeutic payloads. Overall, metal-doped C-dots represent a flexible and potentially safer alternative to classical contrast agents; however, successful clinical translation and market uptake will still require standardized relaxometry at clinical field strengths, scalable and reproducible synthesis, and comprehensive in vivo safety and efficacy validation. Full article

Characterizing tight reservoirs is challenging due to the complex pore structure and strong heterogeneity at various scales. Current digital rock physics often struggles to reconcile high-resolution imaging with representative sample sizes, and 3D digital cores are frequently used primarily as visualization tools rather than predictive, computable platforms. Thus, a clear methodological gap persists: high-resolution models typically lack macroscopic geological features, while existing 3D digital models are seldom leveraged for quantitative, predictive analysis. This study, based on a full-diameter core sample of a single lithology (gray-black shale), aims to bridge this gap by developing an integrated workflow to construct a high-fidelity, computable 3D model that connects the micro–nano to the macroscopic scale. The core was scanned using high-resolution X-ray computed tomography (CT) at 0.4 μm resolution. The raw CT images were processed through a dedicated pipeline to mitigate artifacts and noise, followed by segmentation using Otsu’s algorithm and region-growing techniques in Avizo 9.0 to isolate minerals, pores, and the matrix. The segmented model was converted into an unstructured tetrahedral finite element mesh within ANSYS 2024 Workbench, with quality control (aspect ratio ≤ 3; skewness ≤ 0.4), enabling mechanical property assignment and simulation. The digital core model was rigorously validated against physical laboratory measurements, showing excellent agreement with relative errors below 5% for key properties, including porosity (4.52% vs. 4.615%), permeability (0.0186 mD vs. 0.0192 mD), and elastic modulus (38.2 GPa vs. 39.5 GPa). Pore network analysis quantified the poor connectivity of the tight reservoir, revealing an average coordination number of 2.8 and a pore throat radius distribution of 0.05–0.32 μm. The presented workflow successfully creates a quantitatively validated “digital twin” of a full-diameter core. It provides a tangible solution to the scale-representativeness trade-off and transitions digital core analysis from a visualization tool to a computable platform for predicting key reservoir properties, such as permeability and elastic modulus, through numerical simulation, offering a robust technical means for the accurate evaluation of tight reservoirs. Full article