Search Results (1,908)

MXenes, a rapidly emerging family of two-dimensional transition metal carbides and nitrides, have attracted considerable attention in recent years for their potential in next-generation energy storage technologies. In solid-state batteries (SSBs), they combine metallic-level conductivity (>10³ S cm⁻¹), adjustable surface terminations, and mechanical resilience, which makes them suitable for diverse functions within the cell architecture. Current studies have shown that MXene-based anodes can deliver reversible lithium storage with Coulombic efficiencies approaching ~98% over 500 cycles, while their use as conductive additives in cathodes significantly improves electron transport and rate capability. As interfacial layers or structural scaffolds, MXenes effectively buffer volume fluctuations and suppress lithium dendrite growth, contributing to extended cycle life. In solid polymer and composite electrolytes, MXene fillers have been reported to increase Li⁺ conductivity to the 10⁻³–10⁻² S cm⁻¹ range and enhance Li⁺ transference numbers (up to ~0.76), thereby improving both ionic transport and mechanical stability. Beyond established Ti-based systems, double transition metal MXenes (e.g., Mo₂TiC₂, Mo₂Ti₂C₃) and hybrid heterostructures offer expanded opportunities for tailoring interfacial chemistry and optimizing energy density. Despite these advances, large-scale deployment remains constrained by high synthesis costs (often exceeding USD 200–400 kg⁻¹ for Ti₃C₂T_x at lab scale), restacking effects, and stability concerns, highlighting the need for greener etching processes, robust quality control, and integration with existing gigafactory production lines. Addressing these challenges will be crucial for enabling MXene-based SSBs to transition from laboratory prototypes to commercially viable, safe, and high-performance energy storage systems. Beyond summarizing performance, this review elucidates the mechanistic roles of MXenes in SSBs—linking lithiophilicity, field homogenization, and interphase formation to dendrite suppression at Li|SSE interfaces, and termination-assisted salt dissociation, segmental-motion facilitation, and MWS polarization to enhanced electrolyte conductivity—thereby providing a clear design rationale for practical implementation. Full article

This study investigates the structural and magnetic properties of CoFe₂O₄ nanoparticles prepared by five different synthesis methods: coprecipitation, ultrasound-assisted coprecipitation, coprecipitation coupled with mechanochemical treatment, microemulsion and microwave-assisted hydrothermal synthesis. The produced powders were additionally functionalized with starch to improve biocompatibility and colloidal stability. The starch-coating procedure itself by sonication in starch solution, as well as its result, affects the structural and magnetic properties of functionalized nanoparticles. The resulting changes of properties in the process of ligand addition depend significantly on the starting nanoparticles, or rather, on the method of their synthesis. The structural, magnetic and spectroscopic properties of the resulting materials were systematically investigated using X-ray diffraction (XRD), Raman spectroscopy, Mössbauer spectroscopy and magnetic measurements. Taken together, XRD, Raman and Mössbauer spectroscopy show that starch deposition reduces structural disorder and internal stress, resulting in nanoparticles with a more uniform size distribution. These changes, in turn, affect all magnetic properties—magnetization, coercivity and magnetic anisotropy. Magnetic responses are preserved what is desirable for future biomedical applications. This work emphasizes the importance of surface modification for tailoring the properties of magnetic nanoparticles while maintaining their desired functionality. Full article

Diamond-like carbon (DLC) films are valued for their high hardness and wear resistance, but their application in harsh environments is limited by high internal stress and poor corrosion resistance. Co-doping with transition metals offers a promising route to overcome these drawbacks by tailoring microstructure and enhancing multifunctional performance. However, the synergistic effects of Ni and Cr co-doping in DLC remain underexplored. In this study, Ni and Cr co-doped DLC (NiCr-DLC) films were fabricated using filtered cathodic vacuum arc deposition (FCVAD). By varying the C₂H₂ flow rate, the carbon content and microstructure evolved from columnar to fine-grained and compact structures. The optimized film (F55) achieved an ultralow surface roughness (Sa = 0.26 nm), even smoother than the Si substrate. The Ni–Cr co-doping promoted a nanocomposite structure, yielding a maximum hardness of 15.56 GPa and excellent wear resistance (wear rate: 4.45 × 10⁻⁷ mm³/N·m). Electrochemical tests revealed significantly improved corrosion resistance compared to AISI 304L stainless steel, with F55 exhibiting the highest corrosion potential, the lowest current density, and the largest impedance modulus. This work demonstrates that Ni-Cr co-doping effectively enhances the mechanical and corrosion properties of DLC films while improving surface quality, providing a viable strategy for developing robust, multifunctional protective coatings for demanding applications in aerospace, automotive, and biomedical systems. Full article

This study investigated the effects of ultrasonic treatment of olive paste prior to malaxation on oil yield (Y), enzyme activity and virgin olive oil (VOO) quality in four Croatian olive varieties: Istarska Bjelica, Rosulja, Oblica and Levantinka. The oils were extracted using the Abencor system according to a central composite experiment design, with treatment durations of 3–17 min and power levels of 256–640 W. The parameters analyzed included Y, oxidative stability index (OSI), antioxidant capacity (AC), phenolic and α-tocopherol content, volatile compounds, fatty acid profile, and the activity of lipoxygenase, β-glucosidase, polyphenol oxidase, and peroxidase. Olive variety was the most influential factor in all variables. The response surface methodology showed that ultrasonic treatment at low-to-medium intensity improved several quality attributes. For example, Y increased by 4% in Oblica, phenolic content increased by up to 17% in Istarska Bjelica, and OSI and AC increased by 13–15% in Istarska Bjelica and Levantinka. In contrast, longer treatment and higher ultrasound power had a negative effect. No significant differences were found in other parameters examined. Overall, the application of ultrasound led to measurable, though moderate, improvements in Y and VOO quality, with results strongly dependent on olive variety and treatment conditions. These results underline the need for further optimization tailored to each variety. Full article

Background/Objectives: Crystallization is a key process in the manufacturing of active pharmaceutical ingredients (APIs), as it significantly affects the physical and chemical properties of the final product. Nicergoline, a clinically relevant ergot derivative, was chosen as a model compound to investigate how different crystallization strategies affect particle attributes. The objective of this study was to compare controlled and uncontrolled crystallization techniques and evaluate their impact on the physicochemical properties of nicergoline. Methods: Nicergoline was crystallized using controlled methods, including sonication-induced and seeding-induced crystallization, and uncontrolled methods, namely cubic and linear cooling, as well as acetone evaporation. The resulting powders were characterized by using a range of physicochemical techniques to assess particle morphology, size distribution, agglomeration behavior, and surface properties. Results: Uncontrolled crystallization methods produced particles prone to agglomeration, resulting in a broader particle size distribution ranging from 8 to 720 µm and heterogeneous surface characteristics. In contrast, controlled crystallization generated more uniform particles with reduced agglomeration and narrower particle size distributions. Among the evaluated methods, sonocrystallization provided the most effective control over particle size and morphology, demonstrated by a narrow size distribution ranging from 16 to 39 µm which correlated with improved flowability and surface energy. Conclusions: The study demonstrates that the choice of crystallization method significantly influences the structural and physicochemical properties of nicergoline. These findings highlight the importance of method selection for tailoring API properties to enhance downstream processing and product quality. Full article

In this work, the production of iron-containing powders by the electroplasma dispersion method was investigated under various discharge regimes and in electrolytes of different natures (NaCl and Na₂CO₃). The influence of technological parameters on particle morphology, phase composition, and elemental content was analyzed using X-ray diffraction (XRD), scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS), as well as laser particle size distribution analysis. It was found that the single-stage mode at 350 V in NaCl electrolyte led to the formation of predominantly irregularly shaped and fragmented particles, with a limited amount of spherical powders. The two-stage mode (350 V for 5 s followed by 250 V) in NaCl ensured a more stable formation of spherical particles with sizes of 60–80 μm; however, it was accompanied by intensive surface oxidation. The highest fraction of spherical powders was obtained in a Na₂CO₃ electrolyte under the two-stage mode, where homogeneous spheres with diameters of 20–75 μm and smooth surfaces were formed. According to EDS analysis, the powders consisted mainly of iron and oxygen, while in the samples synthesized in Na₂CO₃, the presence of sodium was detected, indicating the formation of mixed Na–Fe–O oxide phases. XRD confirmed the presence of a metallic α-Fe matrix along with oxide phases Fe₂O₃ and Fe₃O₄, while granulometric analysis (D50 ≈ 55 μm) revealed a relatively narrow particle size distribution. The obtained results demonstrate that variation in the discharge regime and electrolyte composition enables targeted control over the morphology and phase composition of the powders, making the electroplasma method a promising approach for producing metallic powders with tailored properties. Full article

Microfluidic methods are powerful platforms for synthesizing advanced functional materials because they allow for precise control of microscale reaction environments. Microfluidics manipulates reactants in lab-on-a-chip systems to enable the fabrication of highly uniform materials with tunable properties, which are crucial for drug delivery, diagnostics, catalysis, and nanomaterial design. This review emphasizes recent progress in microfluidic technologies for synthesizing functional materials, with a focus on polymeric, hydrogel, lipid-based, and inorganic particles. Microfluidics provides exceptional control over the size, morphology, composition, and surface chemistry of materials, thereby enhancing their performance through uniformity, tunability, hierarchical structuring, and on-chip functionalization. Our review provides novel insights by linking material design strategies with fabrication methods tailored to biomedical applications. We also discuss emerging trends, such as AI-driven optimization, automation, and sustainable microfluidic practices, offering a practical and forward-looking perspective. As the field advances toward robust, standardized, and user-friendly platforms, microfluidics has the potential to increase industrial adoption and enable on-demand solutions in nanotechnology and personalized medicine. Full article

Surface material recognition is a key component in robotic perception and physical interaction, particularly when leveraging both tactile and visual sensory inputs. In this work, we propose Surformer v1, a transformer-based architecture designed for surface classification using structured tactile features and Principal Component Analysis (PCA)-reduced visual embeddings extracted via ResNet 50. The model integrates modality-specific encoders with cross-modal attention layers, enabling rich interactions between vision and touch. Currently, state-of-the-art deep learning models for vision tasks have achieved remarkable performance. With this in mind, our first set of experiments focused exclusively on tactile-only surface classification. Using feature engineering, we trained and evaluated multiple machine learning models, assessing their accuracy and inference time. We then implemented an encoder-only Transformer model tailored for tactile features. This model not only achieves the highest accuracy, but also demonstrated significantly faster inference time compared to other evaluated models, highlighting its potential for real-time applications. To extend this investigation, we introduced a multimodal fusion setup by combining vision and tactile inputs. We trained both Surformer v1 (using structured features) and a Multimodal CNN (using raw images) to examine the impact of feature-based versus image-based multimodal learning on classification accuracy and computational efficiency. The results showed that Surformer v1 achieved 99.4% accuracy with an inference time of 0.7271 ms, while the Multimodal CNN achieved slightly higher accuracy but required significantly more inference time. These findings suggest that Surformer v1 offers a compelling balance between accuracy, efficiency, and computational cost for surface material recognition. The results also underscore the effectiveness of integrating feature learning, cross-modal attention and transformer-based fusion in capturing the complementary strengths of tactile and visual modalities. Full article

Handling delicate agricultural products, such as tomatoes, requires careful attention from workers during harvesting, sorting, and packaging processes. This labor-intensive approach is often inefficient and susceptible to human error. A potential solution to improve efficiency is the development of automated systems capable of replacing manual labor. However, such systems face significant challenges due to the irregular shapes and fragility of these products, requiring specialized adaptable and soft gripping mechanisms. In this context, this paper introduces a parametric design methodology for 3D-printed flexible fingers in soft grippers, tailored for agricultural applications. The approach was tested in a case study that targeted soft agricultural products with diameters between 45 and 75 mm. Three finger topologies were modeled and compared to identify an optimal configuration. A prototype was then developed using 3D printing with Z-SemiFlex. Experimental tests confirmed that the prototype could grasp different fruits reliably and without surface damage. It achieved an Average Precision (AP) of 87.5% for tomatoes and 92.5% for mandarins across 80 trials. These results validate the feasibility of the proposed design methodology for fingers in soft grippers. Full article

Traditional chemical-based sanitizers pose risks to health and the environment, highlighting the need for safer natural alternatives. We developed biocompatible hydrogels from carbohydrate-based biopolymers, chitosan (1.5% and 2.5%), and carboxymethylcellulose (CMC, 3% and 5%), each incorporating one of four antimicrobials: microcin J25, nisin Z, pediocin PA-1, or reuterin. Hydrogels were prepared by dissolving the polymers in aqueous solution and incorporating antimicrobials before gelation. The formulations were characterized using viscosity measurements, antimicrobial assays, and stability testing over 28 days of storage at room temperature (23–25 °C). Chitosan hydrogels with microcin J25 maintained strong activity against Salmonella enterica ATCC 6962, while nisin Z retained activity in gel and solution forms, though with some decline during storage. Pediocin PA-1 remained active in 1.5% and 2.5% chitosan hydrogels against Listeria monocytogenes ATCC 19115, but activity was lost in 3% and 5% CMC hydrogels. Reuterin preserved activity in CMC-based hydrogels throughout storage. In solution, microcin J25 and nisin Z consistently achieved ~7-log reductions, whereas pediocin PA-1 and reuterin reached up to ~5-log reductions. In gels, efficacy decreased at lower concentrations and shorter contact times, likely due to diffusion barriers. Overall, the hydrogels remained stable during storage, and CMC- and chitosan-based matrices with selected antimicrobials show promise as alternatives to chemical sanitizers. Their application should be tailored to specific needs, with formulations requiring longer contact times best suited for surfaces that allow prolonged exposure. Full article

Urban heat exposure, which intensifies with climate change, poses serious threats to public health in rapidly growing cities. Traditional assessments rely on static land surface temperature, often overlooking the role of human mobility in exposure frequency. This study introduces a travel-related heat exposure index (THEI) that combines ride-hailing trajectories and remote sensing data to capture dynamic human–environment thermal interactions. Using Chengdu, China, as a case study, the THEI is combined with local indicators of spatial association to outline high-exposure risk zones (HERZ). XGBoost with SHAP and partial dependence plot (PDP) methods is also applied to identify the nonlinear effects of built environment factors. Results showed the following: (1) distinct spatial clustering of high travel-related heat exposure in central urban districts and transit hubs; (2) city-wide exposure is primarily driven by transportation accessibility and urban form, such as intersection density and floor area ratio; (3) in contrast, HERZ are more strongly associated with demographic and socioeconomic factors, including population density, housing price and road density; and (4) vegetation, measured by the normalized difference vegetation index, demonstrates a consistent negative effect across scales, highlighting its critical role in mitigating thermal risks. These findings emphasize the necessity of incorporating mobility-based exposure metrics and spatial heterogeneity into climate-resilient urban planning, with differentiated strategies tailored for city-wide versus high-risk zones. Full article

This study presents the influence of the type of weave and relative fabric density on surface roughness and the coefficient of friction in raw cotton woven fabrics. Relative fabric density, which represents how full a fabric is compared to the maximum packing density allowed by its weave, provides a more accurate basis for comparison than absolute fabric density. Analysis revealed that both the type of weave and relative fabric density have a statistically significant effect on surface roughness, while neither factor significantly impacts the coefficient of friction. Notably, increasing relative fabric density consistently reduces surface roughness in plain, 2/2 twill, and, to some extent, 5-end satin fabrics, with plain fabrics showing the highest roughness overall. At high densities, 2/2 twill fabrics exhibit greater structural stability, yielding smoother surfaces than 5-end satin fabrics, reversing trends detected at lower densities. Furthermore, the relationship between surface roughness and friction was decoupled in plain and 2/2 twill fabrics—specifically, increased density leads to smoother surfaces and higher friction. 5-end satin fabrics were unique in showing a simultaneous reduction in both surface-mechanical properties as fabric density increased. These findings highlight that relative fabric density is a critical parameter for engineering fabrics with tailored performance properties. Full article

Ensuring the surface quality of fresh soybeans is critical for maintaining their commercial value and consumer confidence. However, traditional manual inspection remains labor-intensive, subjective, and inadequate for real-time, high-throughput sorting. In this study, we present a multi-scale attention-augmented You Only Look Once version 8 (YOLOv8) framework tailored for real-time surface defect detection in fresh soybeans. The proposed model integrates two complementary attention mechanisms—Squeeze-and-Excitation (SE) and Multi-Scale Dilated Attention (MSDA)—to enhance the detection of small, irregular, and low-contrast defects under complex backgrounds. Rather than relying on cross-model comparisons, we perform systematic ablation studies to evaluate the individual and combined contributions of SE and MSDA across diverse defect categories. Experimental results from a custom-labeled soybean dataset demonstrate that the integrated SE+MSDA model achieves superior performance in terms of precision, recall, and Mean Average Precision (mAP), particularly for challenging categories such as wormholes and speckles. The proposed framework provides a lightweight, interpretable, and deployment-ready solution for intelligent agricultural inspection, with potential applicability to broader food quality control tasks. Full article

This work presents the design and fabrication of microstructured hydrophobic surfaces via fused filament fabrication (FFF) 3D printing with polylactic acid (PLA). Three geometric patterns—triangular-based prisms (TG), truncated pyramids (TP), and truncated ellipsoidal cones (CET)—were developed to modify the surface wettability. Morphological analysis revealed that the printer resolution limits the accurate reproduction of sharp CAD-defined features. Despite this, TG structures exhibited superhydrophobic behavior evaluated through static water contact angles (WCAs), reaching up to 164° along the structured direction and so representing a 100% increase relative to flat PLA surfaces (WCA = 82°). To improve print fidelity, TP and CET geometries with enlarged features were introduced, resulting in contact angles up to 128°, corresponding to a 56% increase in hydrophobicity. The truncated shapes enable the fabrication of the smallest features achievable via the FFF technique, while maintaining good resolution and obtaining higher contact angles. In addition, surface functionalization with fluoropolymer-coated SiO₂ nanoparticles, confirmed by SEM and Raman spectroscopy, led to a further slight enhancement in wettability up to 18% on the structured surfaces. These findings highlight the potential of FFF-based microstructuring, combined with surface treatments, for tailoring the wetting properties of 3D-printed polymeric parts with promising applications in self-cleaning, de-icing, and anti-wetting surfaces. Full article

Automatic bone surface segmentation represents an advanced alternative for conventional patient registration methods in surgical navigation technologies. In pediatrics, such technologies require tailored approaches to ensure optimal performance—specifically in patients under the age of ten, whose immature bones have less distinct bone characteristics. In this study, we developed a segmentation model tailored for pediatric patients. We captured 4309 ultrasound images from the bones in the extremities, pelvis and thorax of 16 pediatric patients. The dataset was manually annotated by a technical physician and sample-wise validated by a pediatric radiologist. A UNeXt deep learning model was trained for automatic segmentation. The segmentation performance was evaluated using the mean centerline Dice score and the mean surface distance. A mean centerline Dice score of 0.85 (SD: 0.13) and a mean surface distance of 0.78 mm (SD: 1.15 mm) were achieved. No important differences in performance were observed for patients younger than the age of ten compared to older patients. Our results demonstrate that the segmentation model detects the bone surface with sufficient accuracy, enabling precise and effective patient registration. The model performs sufficiently across different pediatric age groups, making it a viable tool for integration into ultrasound-based patient registration in image-guided pediatric surgery. Full article