Search Results (1,421)

Water treadmill (WT) exercise is used for horses’ rehabilitation and training. Given that each training needs to be individualized for each horse, the goal is to assess whether infrared thermography (IRT) can serve as a non-invasive tool for daily monitoring of individual training and rehabilitation progress in horses undergoing WT exercise. Fifteen Polish Warmblood school horses were subjected to five WT sessions: dry treadmill, fetlock-depth water, fetlock-depth water with artificial river (AR), carpal-depth water, and carpal-depth water with AR. IRT images, collected pre- and post-exercise, were analyzed for the mean temperature (Tmean) and maximal temperature (Tmax) across 14 regions of interest (ROIs) representing the body surface overlying specific superficial muscles. While on a dry treadmill, Tmean and Tmax increased post-exercise in all ROIs; wetting of the hair coat limited surface temperature analysis in ROIs annotated on limbs. Tmax over the m. brachiocephalicus, m. trapezius pars cervicalis, m. triceps brachii, and m. semitendinosus increased during walking in carpal-depth water, which therefore may be suggested as an indirect indicator of increased activity related to forelimb protraction and flexion–extension of the limb joints. Tmax over the m. latissimus dorsi and m. longissimus increased during carpal-depth WT exercise with active AR mode, which may be suggested as an indicator of increased workload including vertical displacement of the trunk. Full article

The detection of trace chemicals at low and ultra-low concentrations is critical for applications in environmental monitoring, medical diagnostics, food safety and other fields. Conventional detection techniques often lack the required sensitivity, specificity, or cost-effectiveness, making real-time, in situ analysis challenging. Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical tool, offering improved sensitivity through the enhancement of Raman scattering by plasmonic nanostructures. While noble metals such as Ag and Au are currently the reference choices for SERS substrates, fabrication methods should balance enhancement efficiency, reproducibility and scalability. In this study, we propose a novel approach for SERS substrate fabrication using reactive Aerosol Jet Printing (r-AJP) as an innovative additive manufacturing technique. The r-AJP process enables in-flight Ag seed reduction and nucleation of Ag nanoparticles (NPs) by mixing silver nitrate and ascorbic acid aerosols before deposition, as suggested by computational fluid dynamics (CFD) simulations. The resulting coatings were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses, revealing the formation of nanoporous crystalline Ag agglomerates partially covered by residual matter. The as-prepared SERS substrates exhibited remarkable SERS activity, demonstrating a high enhancement factor (10⁶) for rhodamine (R6G) detection. Our findings highlight the potential of r-AJP as a scalable and cost-effective fabrication strategy for next-generation SERS sensors, paving the way for the development of a new additive manufacturing tool for noble metal material deposition. Full article

Current work investigates the fabrication and performance of nanocomposite membranes, modified with varying concentrations of hybrid nanostructures comprising titanium nanowires coated with iron nanoparticles (TiO₂ NWs-Fe₂O₃), for the removal of Naphthol Blue Black (NBB) dye from industrial wastewater. A series of analytical tools were employed to confirm the successful modification including scanning electron microscopy and EDX analysis, porosity and hydrophilicity measurements, Fourier-transform infrared spectroscopy, and X-Ray Diffraction. The incorporation of TiO₂ NWs-Fe₂O₃ has enhanced membrane performance significantly by increasing the PWF and improving dye retention rates of nanocomposite membranes. At 0.7 g of nanostructure content, the modified membrane (M8) achieved a PWF of 93 L/m²·h and NBB dye rejection of over 98%. The flux recovery ratio (FRR) analysis disclosed improved antifouling properties, with the M8 membrane demonstrating a 73.4% FRR. This study confirms the potential of TiO₂ NWs-Fe₂O₃-modified membranes in enhancing water treatment processes, offering a promising solution for industrial wastewater treatment. These outstanding results highlight the potential of the novel PES-TiO₂ NWs-Fe₂O₃ membranes for dye removal and present adequate guidance for the modification of membrane physical properties in the field of wastewater treatment. Full article

This research develops a dual physics-based machine learning system to forecast fuel consumption and CO₂ emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte Carlo simulations provides a solid foundation for training machine learning models, particularly in cases where dataset restrictions are present. The XGBoost model demonstrated superior performance compared to Support Vector Regression, Gaussian Process Regression, Random Forest, and Shallow Neural Network models, achieving near-zero prediction errors that closely matched physics-based calculations. The physics-based analysis demonstrated that the Combined scenario, which combines hull coatings with bulbous bow modifications, produced the largest fuel consumption reduction (5.37% at 15 knots), followed by the Advanced Propeller scenario. The results demonstrate that user inputs (e.g., engine power: 870 kW, speed: 12.7 knots) match the Advanced Propeller scenario, followed by Paint, which indicates that advanced propellers or hull coatings would optimize efficiency. The obtained insights help ship operators modify their operational parameters and designers select essential modifications for sustainable operations. The model maintains its strength at low speeds, where fuel consumption is minimal, making it applicable to other oil tankers. The hybrid approach provides a new tool for maritime efficiency analysis, yielding interpretable results that support International Maritime Organization objectives, despite starting with a limited dataset. The model requires additional research to enhance its predictive accuracy using larger datasets and real-time data collection, which will aid in achieving global environmental stewardship. Full article

Multivalent lectin–glycan interactions (MLGIs) are vital for viral infection, cell-cell communication and regulation of immune responses. Their structural and biophysical data are thus important, not only for providing insights into their underlying mechanisms but also for designing potent glycoconjugate therapeutics against target MLGIs. However, such information remains to be limited for some important MLGIs, significantly restricting the research progress. We have recently demonstrated that functional nanoparticles, including ∼4 nm quantum dots and varying sized gold nanoparticles (GNPs), densely glycosylated with various natural mono- and oligo- saccharides, are powerful biophysical probes for MLGIs. Using two important viral receptors, DC-SIGN and DC-SIGNR (together denoted as DC-SIGN/R hereafter), as model multimeric lectins, we have shown that α-mannose and α-manno-α-1,2-biose (abbreviated as Man and DiMan, respectively) coated GNPs not only can provide sensitive measurement of MLGI affinities but also reveal critical structural information (e.g., binding site orientation and mode) which are important for MLGI targeting. In this study, we produced mannuronic acid (ManA) coated GNPs (GNP-ManA) of two different sizes to probe the effect of glycan modification on their MLGI affinity and antiviral property. Using our recently developed GNP fluorescence quenching assay, we find that GNP-ManA binds effectively to both DC-SIGN/R and increasing the size of GNP significantly enhances their MLGI affinity. Consistent with this, increasing the GNP size also significantly enhances their ability to block DC-SIGN/R-augmented virus entry into host cells. Particularly, ManA coated 13 nm GNP potently block Ebola virus glycoprotein-driven entry into DC-SIGN/R-expressing cells with sub-nM levels of EC₅₀. Our findings suggest that GNP-ManA probes can act as a useful tool to quantify the characteristics of MLGIs, where increasing the GNP scaffold size substantially enhances their MLGI affinity and antiviral potency. Full article

Brain ischemia is a severe condition caused by reduced cerebral blood flow, leading to the disruption of ion gradients in brain tissue. This imbalance triggers spreading depolarizations, which are waves of neuronal and glial depolarization propagating through the gray matter. Microelectrode arrays (MEAs) are essential for real-time monitoring of these electrophysiological processes both in vivo and in vitro, but their sensitivity and signal quality are critical for accurate detection of extracellular brain activity. In this study, we evaluate the performance of a flexible microelectrode array based on gold-coated zinc oxide nanorods (ZnO NRs), referred to as nano-fMEA, specifically for high-fidelity electrophysiological recording under pathological conditions. Acute mouse brain slices were tested under two ischemic models: oxygen–glucose deprivation (OGD) and hyperkalemia. The nano-fMEA demonstrated significant improvements in event detection rates and in capturing subtle fluctuations in neural signals compared to flat fMEAs. This enhanced performance is primarily attributed to an optimized electrode–tissue interface that reduces impedance and improves charge transfer. These features enabled the nano-fMEA to detect weak or transient electrophysiological events more effectively, making it a valuable platform for investigating neural dynamics during metabolic stress. Overall, the results underscore the promise of ZnO NRs in advancing electrophysiological tools for neuroscience research. Full article

Rosemary (Salvia rosmarinus) is renowned for its antioxidant, anti-inflammatory, and antihyperglycemic properties, largely attributed to its rich phytochemical profile. This study evaluates the potential of metabolites from Pseudomonas shirazensis NFV3, formulated in silver nanoparticles (AgNPs), to enhance the bioactivity of rosemary extracts in postharvest applications. Rosemary stems were treated with AgNPs coated with bacterial metabolites (NP), bacterial cells, or metabolites (LM), and the extracts’ phytochemical composition and bioactivities were assessed. HPLC and HPLC–MS analyses revealed that the NP treatment induced significant metabolic remodeling, particularly upregulating rosmarinic acid and selected triterpenes (ursolic and betulinic acids), while reducing carnosic acid levels. NP-treated extracts exhibited significantly enhanced inhibition of cyclooxygenase (COX-1 and COX-2), indicating improved anti-inflammatory potential. The α-glucosidase inhibition and antioxidant activity (DPPH assay) of the extracts were not substantially altered, suggesting the selective enhancement of pharmacological functions. These findings demonstrate that nanoparticle-based elicitation selectively remodels secondary metabolism in rosemary, improving extract quality and bioactivity. This strategy offers a novel, sustainable tool for optimizing plant-based therapeutics in the phytopharmaceutical industry. Full article

65 Mn is a high-quality carbon structural steel that exhibits excellent mechanical properties and machinability. It finds broad applications in machinery manufacturing, agricultural tools, and mining equipment, and is commonly used for producing mechanical parts, springs, and cutting tools. Fe901 is an iron-based alloy that exhibits excellent hardness, structural stability, and wear resistance. It is widely used in surface engineering applications, especially laser cladding, due to its ability to form dense and crack-free metallurgical coatings. To enhance the surface hardness and wear resistance of 65 Mn steel, this study employs a laser melting process to deposit a multi-layer Fe901 alloy coating. The phase composition, microstructure, microhardness, and wear resistance of the coatings are investigated using X-ray diffraction (XRD), optical microscopy, scanning electron microscopy (SEM), Vickers hardness testing, and friction-wear testing. The results show that the coatings are dense and uniform, without visible defects. The main phases in the coating include solid solution, carbides, and α-phase. The microstructure comprises dendritic, columnar, and equiaxed crystals. The microhardness of the cladding layer increases significantly, with the multilayer coating reaching 3.59 times the hardness of the 65 Mn substrate. The coatings exhibit stable and relatively low friction coefficients ranging from 0.38 to 0.58. Under identical testing conditions, the wear resistance of the coating surpasses that of the substrate, and the multilayer coating shows better wear performance than the single-layer one. Full article

In this study, a magnet-integrated fabric phase sorptive extraction (MI-FPSE) protocol was developed in combination with high pressure liquid chromatography—diode array detection (HPLC-DAD) for the simultaneous determination of five phenylurea pesticides (i.e., chlorbromuron, diuron, linuron, metoxuron, monuron) in environmental water samples. To produce the MI-FPSE device, two individual sol-gel coated carbowax 20 M (CW 20 M) cellulose membranes were fabricated and stitched to each other, while a magnetic rod was inserted between them to give the resulting device the ability to spin and serve as a stand-alone microextraction platform. The adsorption and desorption step of the MI-FPSE protocol was optimized to achieve high extraction efficiency and the MI-FPSE-HPLC-DAD method was validated in terms of linearity, sensitivity, selectivity, accuracy, and precision. The limits of detection (LODs) were found to be 0.3 μg L⁻¹. The relative recoveries were 85.2–110.0% for the intra-day and 87.7–103.2% for the inter-day study. The relative standard deviations were better than 13% in all cases. The green character and the practicality of the developed procedure were assessed using ComplexGAPI and Blue Analytical Grade Index metric tools, showing good method performance. Finally, the developed method was successfully used for the analysis of tap, river, and lake water samples. Full article

As manufacturing demands for challenging-to-machine metallic materials continue to evolve, the performance of cutting tools has emerged as a critical limiting factor. The synergistic application of micro-texture and coating in cutting tools can improve various properties. For the processing of existing micro-texture, because of the fast cooling and heating processing method of laser, there are defects such as remelted layer stacking and micro-cracks on the surface after processing. This study introduces a preheating-assisted technology aimed at optimizing the milling performance of textured coated tools. A milling test platform was established to evaluate the performance of these tools on titanium alloys under thermally assisted conditions. The face-centered cubic response surface methodology, as part of the central composite design (CCD) experimental framework, was employed to investigate the interaction effects of micro-texture preparation parameters and thermal assistance temperature on milling performance. The findings indicate a significant correlation between thermal assistance temperature and tool milling performance, suggesting that an appropriately selected thermal assistance temperature can enhance both the milling efficiency of the tool and the surface quality of the titanium alloy. Utilizing the response surface methodology, a multi-objective optimization of the textured coating tool-preparation process was conducted, resulting in the following optimized parameters: laser power of 45 W, scanning speed of 1576 mm/s, the number of scans was 7, micro-texture spacing of 130 μm, micro-texture diameter of 30 μm, and a heat-assisted temperature of 675.15 K. Finally, the experimental platform of optimization results is built, which proves that the optimization results are accurate and reliable, and provides theoretical basis and technical support for the preparation process of textured coating tools. It is of great significance to realize high-precision and high-quality machining of difficult-to-machine materials such as titanium alloy. Full article

The goal of this study is to develop an efficient machine learning framework for designing high-hardness multi-metal nitride coatings, overcoming the limitations of traditional trial-and-error methods. The development of multicomponent metal nitride hard coatings via multi-arc ion plating remains a significant challenge due to the vast compositional search space. Although theoretical studies in macroscopic, mesoscopic, and microscopic domains exist, these often focus on idealized models and lack effective coupling across scales, leading to time-consuming and labor-intensive traditional methods. With advancements in materials genomics and data mining, machine learning has become a powerful tool in material discovery. In this work, we construct a compositional search space for multicomponent nitrides based on electronic configuration, valence electron count, electronegativity, and oxidation states of metal elements in unary nitrides. The search space is further constrained by FCC crystal structure and hardness theory. By incorporating a feature library with micro-, meso-, and macro-structural characteristics and using clustering analysis with theoretical intermediate variables, the model enriches dataset information and enhances predictive accuracy by reducing experimental errors. This model is successfully applied to design multicomponent metal nitride coatings using a literature-derived database of 233 entries. Experimental validation confirms the model’s predictions, and clustering is used to minimize experimental and data errors, yielding a strong agreement between predicted optimal molar ratios of metal elements and nitrogen and measured hardness performance. Of the 100 Vickers hardness (HV) predictions made by the model using input features like molar ratios of metal elements (e.g., Ti, Al, Cr, Zr) and atomic size mismatch, 82 exceeded the dataset’s maximum hardness, with the best sample achieving a prediction accuracy of 91.6% validated against experimental measurements. This approach offers a robust strategy for designing high-performance coatings with optimized hardness. Full article

In recent years, interest in HEAs has increased exponentially due to their extraordinary properties, especially for wear- and corrosion-resistant applications. However, the main problem involves correctly selecting the HEA composition required for a specific application, as most of the data are scattered throughout the literature, and only a limited number of models accurately predict the properties. Therefore, a database of 415 HEA alloys (bulk) and coatings obtained using thermal spray (TS) techniques has been created, compiled from scientific studies over the past 20 years. This tool collects information on physical, mechanical, and chemical properties, with a particular emphasis on corrosion and wear resistance. This facilitates material comparison and selection according to the needs of a specific application. In particular, the database highlights how composition and deposition technique also affect performance, identifying CoCrFeNi (CGS and in bulk) as a promising candidate for simultaneous wear and corrosion resistance. Full article

Nickel-based superalloy Hastelloy C276 is widely used in high-performance industries due to its strength, corrosion resistance, and thermal stability. However, these same properties pose substantial challenges in machining, resulting in high tool wear, surface defects, and dimensional inaccuracies. This study investigates methods to enhance machining performance and surface quality by evaluating the tribological behavior of TiSiN/TiAlN-coated carbide inserts under six cooling and lubrication conditions: dry, MQL with coconut oil, Cryo-LN₂, Cryo-LCO₂, MQL–Cryo-LN₂, and MQL–Cryo-LCO₂. Open-slot finishing was performed at constant cutting parameters, and key indicators such as cutting zone temperature, tool wear, surface roughness, chip morphology, and microhardness were analyzed. The hybrid MQL–Cryo-LN₂ approach significantly outperformed other methods, reducing cutting zone temperature, tool wear, and surface roughness by 116.4%, 94.34%, and 76.11%, respectively, compared to dry machining. SEM and EDS analyses confirmed abrasive, oxidative, and adhesive wear as the dominant mechanisms. The MQL–Cryo-LN₂ strategy also lowered microhardness, in contrast to a 39.7% increase observed under dry conditions. These findings highlight the superior performance of hybrid MQL–Cryo-LN₂ in improving machinability, offering a promising solution for precision-driven applications. Full article

Nanowear-resistant coatings are critical for extending the service life of mechanical components, yet their performance optimization remains challenging due to the complex interplay between atomic-scale defects and macroscopic wear behavior. While experimental characterization struggles to resolve transient interfacial phenomena, multiscale simulations, integrating ab initio calculations, molecular dynamics, and continuum mechanics, have emerged as a powerful tool to decode structure–property relationships. This review systematically compares mainstream computational methods and analyzes their coupling strategies. Through case studies on metal alloy nanocoatings, we demonstrate how machine learning-accelerated simulations enable the targeted design of layered architectures with 30% improved wear resistance. Finally, we propose a protocol combining high-throughput simulation and topology optimization to guide future coating development. Full article

This study investigates the fabrication, nanomechanical behavior, and tribological performance of nanostructured superlattice coatings (NSCs) composed of alternating TiAlSiNb-N/TiCr-CN bilayers. Deposited via High-Power Ion-Plasma Magnetron Sputtering (HiPIPMS) onto 100Cr6 steel substrates, the coatings achieved nanohardness values of ~25 GPa and elastic moduli up to ~415 GPa. A novel empirical method was applied to extract stress–strain field (SSF) gradient and divergence profiles from nanoindentation load–displacement data. These profiles revealed complex, depth-dependent oscillations attributed to alternating strain-hardening and strain-softening mechanisms. Fourier analysis identified dominant spatial wavelengths, DWL, ranging from 4.3 to 42.7 nm. Characteristic wavelengths WL1 and WL2, representing fine and coarse oscillatory modes, were 8.2–9.2 nm and 16.8–22.1 nm, respectively, aligning with the superlattice period and grain-scale features. The hyperfine structure exhibited non-stationary behavior, with dominant wavelengths decreasing from ~5 nm to ~1.5 nm as the indentation depth increased. We attribute the SSF gradient and divergence spatial oscillations to alternating strain-hardening and strain-softening deformation mechanisms within the near-surface layer during progressive loading. This cyclic hardening–softening behavior was consistently observed across all NSC samples, suggesting it represents a general phenomenon in thin film/substrate systems under incremental nanoindentation loading. The proposed SSF gradient–divergence framework enhances nanoindentation analytical capabilities, offering a tool for characterizing thin-film coatings and guiding advanced tribological material design. Full article