Search Results (3,282)

Concentration polarization (CP) is one of the inherent problems that lowers the operating performance of forward osmosis (FO) membranes. Therefore, a quantitative evaluation of CP is vital to understand its impact on the FO process. This study systematically investigated the influences of different CPs on the osmotic pressure drop across the membrane under different conditions by using the water transmission coefficient, η_WT, defined as the ratio of the measured water flux to the theoretical water flux. The results showed that η_WT decreased with an increase in the concentration gradient between the draw solution (DS) and the feed solution (FS) under different conditions. The proportions of osmotic pressure drop caused by dilutive internal concentration polarization (ICP) increased, while those caused by concentrative external concentration polarization (ECP) decreased, in different types of DSs in FO mode. Both ECP and ICP were found to be capable of reducing osmotic pressure. However, the internal CP had the dominant influence. To better understand the adverse effects of CP, using an organic FS provided greater insight than using deionized (DI) water as the FS. As the FS concentration increased, the water flux reduced, and the adverse effects of CP worsened. CaCl₂ led to a greater reduction in water transfer efficiency than NaCl when used as the DS. In comparison to FO mode, pressure-retarded osmosis (PRO) mode led to greater pure water flux and flux decline. In FO mode, both the proportion of dilutive ICP and the η_WT decreased, while the proportion of concentrative ECP increased over time. However, in PRO mode, the proportions of dilutive ECP and concentrative ICP increased, and η_WT gradually decreased. Dilutive ICP had a significant negative effect on osmotic pressure in the former, while dilutive ECP was dominant in the latter. Full article

The capability of U-Net methods and aerial orthoimagery to identify tree crown mortality in study areas in Bavaria, Germany was evaluated and aspects such as model transferability were investigated. We trained the models with imagery from May to September for the years 2019–2023. One goal was to differentiate between damaged crowns of deciduous, coniferous, and pine trees. The results from a validation area containing an independent dataset showed the best average F1-scores of 68%, 52%, and 66% for deciduous, coniferous, and pine trees, respectively. This study highlights the potential of U-Net methods for detecting tree mortality in large areas. Full article

Background/Objectives: Lactiplantibacillus plantarum is widely utilized in the fermentation industry and offers potential health benefits. However, large-scale comparative genomic analyses aimed at exploring its metabolic functions and conducting safety assessments are still lacking. Methods: In this study, we performed a comparative genomic analysis of 324 L. plantarum strains sourced from various origins and geographical locations. Results: The results revealed that L. plantarum possesses a total of 2403 core genes, of which 12.3% have an unknown function. The phylogenetic analysis revealed a mixed distribution from various origins, suggesting complex transmission pathways. The metabolic analysis demonstrated that L. plantarum strains can produce several beneficial metabolites, including lysine, acetate, and riboflavin. Furthermore, L. plantarum is highly capable of degrading various carbohydrates and proteins, increasing its adaptability. Further, we profiled the antimicrobial peptides (AMPs) in the genomes of L. plantarum. We identified a widely distributed AMP and its variants, presenting in a total of 280 genomes. In our biosafety assessment of L. plantarum, we identified several antibiotic resistance genes, such as Tet(M), ANT(6)-Ia, and mdeA, which may have potential for horizontal gene transfer within the Lactobacillaceae family. Conclusions: This study provides genomic insights into the genetic diversity, metabolic functions, antimicrobial properties, and biosafety of L. plantarum, underscoring its potential applications in biotechnology and environmental adaptation. Full article

Building-integrated photovoltaic (BIPV) windows present a viable path towards carbon neutrality in the building sector. However, conventional BIPV windows, such as semi-transparent photovoltaic (STPV) glazings, still suffer from inadequate thermal insulation, which limits their effectiveness across different climate conditions. To address this issue, the cadmium telluride-based vacuum PV glazing has been developed to enhance the thermal performance of BIPV applications. To fully understand the complex thermal behaviour under real-world operational scenarios, this study introduces a one-dimensional transient heat transfer model that can efficiently capture the time-dependent thermal dynamics of this novel glazing system. Based on the numerical solutions using the explicit finite difference method (FDM), the temperature profile of the vacuum PV glazing can be obtained dynamically. Consequently, the heat gain of the semi-transparent vacuum PV glazing can be calculated under time-varying outdoor and indoor conditions. The validated heat transfer model was applied under four different scenarios, viz. summer daytime, summer nighttime, winter daytime, and winter nighttime, to provide a detailed analysis of the dynamic thermal behaviour, including the temperature variation and the energy flow. The dynamic thermal characteristics of the vacuum PV glazing calculated by the transient heat transfer model demonstrate its excellent thermal insulation and solar control capabilities. Moreover, the thermal performance of vacuum PV glazing was compared with a standard double-pane window under various weather conditions of a typical summer day and a typical winter day. The results indicate that the vacuum PV glazing can effectively minimise both heat gain and heat loss. The fluctuation of the inner surface temperature can be controlled within a limited range away from the set point of the indoor room temperature. Therefore, the vacuum PV glazing contributes to stabilising the temperature of the indoor environment despite the fluctuating solar radiation and periodic outdoor temperature. It is suggested that the vacuum PV glazing has the potential to enhance the climate adaptability of BIPV windows under different climate backgrounds. Full article

The development of lightweight composites with superior mechanical properties and electromagnetic interference (EMI) shielding performance is essential for various structural and functional applications. This study investigates the effect of hybrid nanocarbon (graphene nanosheet (GNS) and carbon nanotube (CNT)) reinforcements on the properties of magnesium (Mg) matrix composites. Specifically, the GNS-CNT hybrid, which forms a three-dimensional interconnected network structure, was analyzed and compared to composites reinforced with only GNSs or CNTs. The objective was to determine the benefits of hybrid reinforcements on the mechanical strength and EMI shielding capability of the composites. The results indicated that the GNS-CNT/Mg composite, at a nanocarbon content of 0.5 wt.% and a GNS-CNT ratio of 1:2, achieved optimal performance, with a 55% increase in tensile strength and an EMI shielding effectiveness of 70 dB. The observed enhancements can be attributed to several key mechanisms: effective load transfer, which promotes tensile twinning, along with improved impedance matching and multiple internal reflections within the GNS-CNT network, which enhance absorption loss. These significant improvements position the composite as a promising candidate for advanced applications requiring high strength, toughness, and efficient electromagnetic shielding, providing valuable insights into the design of high-performance lightweight materials. Full article

Nanoparticle floating gate (NPFG) transistors have gained attention as synaptic devices due to their discrete charge storage capability, which minimizes leakage currents and enhances the memory window. In this study, we propose and evaluate a mesh-type floating gate transistor (Mesh-FGT) designed to emulate the characteristics of NPFG transistors. Individual floating gates with dimensions of 3 µm × 3 µm are arranged in an array configuration to form the floating gate structure. The Mesh-FGT is composed of an Al/Pt/Cr/HfO₂/Pt/Cr/HfO₂/SiO₂/SOI (silicon-on-insulator) stack. Threshold voltages (Vth) extracted from the transfer and output curves followed Gaussian distributions with means of 0.063 V (σ = 0.100 V) and 1.810 V (σ = 0.190 V) for the erase (ERS) and program (PGM) states, respectively. Synaptic potentiation and depression were successfully demonstrated in a multi-level implementation by varying the drain current (I_ds) and Vth. The Mesh-FGT exhibited high immunity to leakage current, excellent repeatability and retention, and a stable memory window that initially measured 2.4 V. These findings underscore the potential of the Mesh-FGT as a high-performance neuromorphic device, with promising applications in array device architectures and neuromorphic neural network implementations. Full article

Organic polymer introduction effectively enhances the toughness, bond strength, and durability of ordinary cement-based materials, and is often used for concrete repair and reinforcement. However, the entrained air effect simultaneously induced by polymer and the inhibitory action on cement hydration kinetics often lead to degradation in mechanical performances of polymer-modified cement-based composite (PMC). Nanomaterials provide unique advantages in enhancing the properties of PMC due to their characteristic ultrahigh specific surface area, quantum effects, and interface modulation capabilities. This review systematically examines recent advances in nano-reinforced PMC (NPMC), elucidating their synergistic optimization mechanisms. The synergistic effects of nanomaterials—nano-nucleation, pore-filling, and templating mechanisms—refine the microstructure, significantly enhancing the mechanical strength, impermeability, and erosion resistance of NPMC. Furthermore, nanomaterials establish interpenetrating network structures (A composite structure composed of polymer networks and other materials interwoven with each other) with polymer cured film (The film formed after the polymer loses water), enhancing load-transfer efficiency through physical and chemical action while optimizing dispersion and compatibility of nanomaterials and polymers. By systematically analyzing the synergy among nanomaterials, polymer, and cement matrix, this work provides valuable insights for advancing high-performance repair materials. Full article

Microbial contamination is a global concern with impacts on a variety of industries ranging from marine to biomedical applications. Recent research on hydrophilic polymer-based coatings is focused on combining antifouling polymers with nanomaterials to enhance mechanical, optical, and stimuli-responsive properties, yielding colour changing, self-healing, and super hydrophilic materials. This study combines the hydrophilic and antifouling properties of vitamin B5 analogous methacrylamide (B5AMA)-based polymers with stimuli-responsive anthocyanin-dye-loaded cellulose nanocrystals (CNCs) to develop antifouling materials with colour changing capabilities upon bacterial contamination. Poly(B5AMA)-grafted CNCs were prepared through surface-initiated photoiniferter reversible addition fragmentation chain transfer (SP-RAFT) polymerization and characterized through proton nuclear magnetic resonance (¹H-NMR), transmission electron microscopy (SEM/TEM), and X-ray photon spectroscopy (XPS) to confirm the formation of surface-grafted polymer chains. The bare CNCs and poly(B5AMA)-grafted CNCs were loaded with anthocyanin dye and evaluated for pH-dependent colour changing capabilities. Interestingly, anthocyanin-loaded CNCs demonstrated vibrant colour changes in both solution and dried film form upon bacterial contamination; however, limited colour changing capabilities of the composites, specifically in dried film form, were attributed to the enhanced dispersibility and antifouling capabilities of the polymer-coated CNCs. Full article

This paper presents the development of a compact nanopositioning stage with long-range capabilities and six-degree-of-freedom (DOF) closed-loop control. The system, referred to as NPS6D100, provides Ø100 mm planar and 10 mm vertical travel range while maintaining direct force transfer to the moving platform (or slider) in all DOFs. Based on an integrated planar direct drive concept, the system is enhanced by precise vertical actuation and full 6D output feedback control. The mechanical structure, drive architecture, guiding, and measurement subsystems are described in detail, along with experimental results that confirm sub-10 nm servo errors under constant setpoint operation and in synchronized multi-axis motion scenarios. With its scalable and low-disturbance design, the NPS6D100 is well suited as a nanopositioning platform for sub-10 nm applications in nanoscience and precision metrology. Full article

Unmanned aerial vehicles are increasingly used in civil Search and Rescue operations due to their rapid deployment and wide-area coverage capabilities. However, detecting missing persons from aerial imagery remains challenging due to small object sizes, cluttered backgrounds, and limited onboard computational resources, especially when managed by civil agencies. In this work, we present a comprehensive methodology for optimizing YOLO-based object detection models for real-time Search and Rescue scenarios. A two-stage transfer learning strategy was employed using VisDrone for general aerial object detection and Heridal for Search and Rescue-specific fine-tuning. We explored various architectural modifications, including enhanced feature fusion (FPN, BiFPN, PB-FPN), additional detection heads (P2), and modules such as CBAM, Transformers, and deconvolution, analyzing their impact on performance and computational efficiency. The best-performing configuration (YOLOv5s-PBfpn-Deconv) achieved a mAP@50 of 0.802 on the Heridal dataset while maintaining real-time inference on embedded hardware (Jetson Nano). Further tests at different flight altitudes and explainability analyses using EigenCAM confirmed the robustness and interpretability of the model in real-world conditions. The proposed solution offers a viable framework for deploying lightweight, interpretable AI systems for UAV-based Search and Rescue operations managed by civil protection authorities. Limitations and future directions include the integration of multimodal sensors and adaptation to broader environmental conditions. Full article

Glancing Angle Deposition (GLAD) has emerged as a versatile and powerful nanofabrication technique for developing next-generation gas sensors by enabling precise control over nanostructure geometry, porosity, and material composition. Through dynamic substrate tilting and rotation, GLAD facilitates the fabrication of highly porous, anisotropic nanostructures, such as aligned, tilted, zigzag, helical, and multilayered nanorods, with tunable surface area and diffusion pathways optimized for gas detection. This review provides a comprehensive synthesis of recent advances in GLAD-based gas sensor design, focusing on how structural engineering and material integration converge to enhance sensor performance. Key materials strategies include the construction of heterojunctions and core–shell architectures, controlled doping, and nanoparticle decoration using noble metals or metal oxides to amplify charge transfer, catalytic activity, and redox responsiveness. GLAD-fabricated nanostructures have been effectively deployed across multiple gas sensing modalities, including resistive, capacitive, piezoelectric, and optical platforms, where their high aspect ratios, tailored porosity, and defect-rich surfaces facilitate enhanced gas adsorption kinetics and efficient signal transduction. These devices exhibit high sensitivity and selectivity toward a range of analytes, including NO₂, CO, H₂S, and volatile organic compounds (VOCs), with detection limits often reaching the parts-per-billion level. Emerging innovations, such as photo-assisted sensing and integration with artificial intelligence for data analysis and pattern recognition, further extend the capabilities of GLAD-based systems for multifunctional, real-time, and adaptive sensing. Finally, current challenges and future research directions are discussed, emphasizing the promise of GLAD as a scalable platform for next-generation gas sensing technologies. Full article

The electrocatalytic CO₂ reduction reaction (CO₂RR) offers an energy-saving and environmentally friendly approach to producing hydrocarbon fuels. The use of a gas diffusion electrode (GDE) flow cell has generally improved the rate of CO₂RR, while the gas diffusion layer (GDL) remains a significant challenge. In this study, we successfully engineered a novel metal–organic framework (MOF) heterojunction through the controlled coating of zeolitic imidazolate framework (ZIF-L) on ultrathin nickel—metal–organic framework (Ni-MOF) nanosheets. This innovative architecture simultaneously integrates GDL functionality and exposes abundant solid–liquid–gas triple-phase boundaries. The resulting Ni-MOF@ZIF-L heterostructure demonstrates exceptional performance, achieving a formate Faradaic efficiency of 92.4% while suppressing the hydrogen evolution reaction (HER) to 6.7%. Through computational modeling of the optimized heterojunction configuration, we further elucidated its competitive adsorption behavior and electronic modulation effects. The experimental and theoretical results demonstrate an improvement in electrochemical CO₂ reduction activity with suppressed hydrogen evolution for the heterojunction because of its hydrophobic interface, good electron transfer capability, and high CO₂ adsorption at the catalyst interface. This work provides a new insight into the rational design of porous crystalline materials in electrocatalytic CO₂RR. Full article

The rapid spread of COVID-19 increased the need for speedy diagnostic tools, which led scientists to conduct extensive research on deep learning (DL) applications that use chest imaging, such as chest X-ray (CXR) and computed tomography (CT). This review examines the development and performance of DL architectures, notably convolutional neural networks (CNNs) and emerging vision transformers (ViTs), in identifying COVID-19-related lung abnormalities. Individual ResNet architectures, along with CNN models, demonstrate strong diagnostic performance through the transfer protocol; however, ViTs provide better performance, with improved readability and reduced data requirements. Multimodal diagnostic systems now incorporate alternative methods, in addition to imaging, which use lung ultrasounds, clinical data, and cough sound evaluation. Information fusion techniques, which operate at the data, feature, and decision levels, enhance diagnostic performance. However, progress in COVID-19 detection is hindered by ongoing issues stemming from restricted and non-uniform datasets, as well as domain differences in image standards and complications with both diagnostic overfitting and poor generalization capabilities. Recent developments in COVID-19 diagnosis involve constructing expansive multi-noise information sets while creating clinical process-oriented AI algorithms and implementing distributed learning protocols for securing information security and system stability. While deep learning-based COVID-19 detection systems show strong potential for clinical application, broader validation, regulatory approvals, and continuous adaptation remain essential for their successful deployment and for preparing future pandemic response strategies. Full article

In this study, a computational thermo-fluid dynamics simulation of a wide-slot jet impingement heating process is performed. The present configuration consists of a turbulent incompressible air jet impinging orthogonally on an isothermal cold plate at a Reynolds number of around 11,000. The two-dimensional mean turbulent flow field is numerically predicted by solving Reynolds-averaged Navier–Stokes (RANS) equations, where the two-equation eddy viscosity k-$\omega$ model is utilized for turbulence closure. As the commonly used shear stress transport variant overpredicts heat transfer at the plate due to excessive turbulent diffusion, the recently developed generalized k-$\omega$ (GEKO) model is considered for the present analysis, where the primary model coefficients are suitably tuned. Through a comparative analysis of the various solutions against one another, in addition to reference experimental and numerical data, the effectiveness of the generalized procedure in predicting both the jet flow characteristics and the heat transfer at the plate is thoroughly evaluated, while determining the optimal set of model parameters. By improving accuracy within the RANS framework, the importance of model adaptability and parameter tuning for this specific fluid engineering application is demonstrated. This study offers valuable insights for improving predictive capability in turbulent jet simulations with broad engineering implications, particularly for industrial heating or cooling systems relying on wide-slot jet impingement. Full article

Constructing a heterojunction is considered one of the most effective strategies for enhancing photocatalytic activity. Herein, we employ Ta₃N₅ and tubular graphitic carbon nitride (TCN) to construct a Ta₃N₅/TCN van der Waals heterojunction via electrostatic self-assembly for enhanced photocatalytic H₂ production. SEM and TEM results show that Ta₃N₅ particles (~300 nm in size) are successfully anchored onto the surface of TCN. The light absorption capability of the Ta₃N₅/TCN heterojunction is between those of Ta₃N₅ and TCN. The strong interaction between Ta₃N₅ and TCN with different energy structures (Fermi levels) by van der Waals force renders the formation of an interfacial electric field to drive the separation and transfer of photogenerated charge carriers in the Ta₃N₅/TCN heterojunction, as evidenced by the photoluminescence (PL) and photoelectrochemical (PEC) characterization results. Consequently, the optimal Ta₃N₅/TCN heterojunction exhibits a remarkable H₂ production rate of 12.73 mmol g⁻¹ h⁻¹ under visible light irradiation, which is 3.3 and 16.8 times those of TCN and Ta₃N₅, respectively. Meanwhile, the cyclic experiment demonstrates excellent stability of the Ta₃N₅/TCN heterojunction upon photocatalytic reaction. Notably, the photocatalytic performance of 15-TaN/TCN outperforms the most previously reported CN-based and Ta₃N₅-based heterojunctions for H₂ production. This work provides a new avenue for the rational design of CN-based van der Waals heterojunction photocatalysts with enhanced photocatalytic activity. Full article