Search Results (4,046)

We report the development of highly luminescent, bovine serum albumin (BSA)-stabilized gold–silver bimetallic nanoclusters (Au-AgNCs@BSA) as a novel platform for high-sensitivity, ratiometric intracellular temperature sensing. Precise and non-invasive temperature sensing at the nanoscale is crucial for applications ranging from intracellular thermogenesis monitoring to localized hyperthermia therapies. Traditional luminescent thermometric platforms often suffer from limitations such as high cytotoxicity and low photostability. Here, we synthesized Au-AgNCs@BSA via a one-pot aqueous reaction, achieving significantly enhanced photoluminescence quantum yields (PL QYs, up to 18%) and superior thermal responsiveness compared to monometallic counterparts. The dual-emissive Au-AgNCs@BSA exhibit a linear ratiometric fluorescence response to temperature fluctuations within the physiological range (20–50 °C), enabling accurate and concentration-independent thermometry in live cells. Time-resolved PL and Arrhenius analyses reveal two distinct emissive states and a high thermal activation energy (E_a = 199 meV), indicating strong temperature dependence. Silver doping increases radiative decay rates while maintaining low non-radiative losses, thus amplifying fluorescence intensity and thermal sensitivity. Owing to their small size, excellent photostability, and low cytotoxicity, these nanoclusters were applied to non-invasive intracellular temperature mapping, presenting a promising luminescent nanothermometer for real-time cellular thermogenesis monitoring and advanced bioimaging applications. Full article

Object detection in remote sensing imagery is critical in environmental monitoring, urban planning, and land resource management. However, the task remains challenging due to significant scale variations, arbitrary object orientations, and complex background clutter. To address these issues, we propose a novel orientation module (SOAM Block) that jointly models object scale and directional features while exploiting geometric symmetry inherent in many remote sensing targets. The SOAM Block is constructed upon a lightweight and efficient Adaptive Multi-Scale (AMS) Module, which utilizes a symmetric arrangement of parallel depth-wise convolutional branches with varied kernel sizes to extract fine-grained multi-scale features without dilation, thereby preserving local context and enhancing scale adaptability. In addition, a Strip-based Context Attention (SCA) mechanism is introduced to model long-range spatial dependencies, leveraging horizontal and vertical 1D strip convolutions in a directionally symmetric fashion. This design captures spatial correlations between distant regions and reinforces semantic consistency in cluttered scenes. Importantly, this work is the first to explicitly analyze the coupling between object scale and orientation in remote sensing imagery. The proposed method addresses the limitations of fixed receptive fields in capturing symmetric directional cues of large-scale objects. Extensive experiments are conducted on two widely used benchmarks—DOTA and HRSC2016—both of which exhibit significant scale variations and orientation diversity. Results demonstrate that our approach achieves superior detection accuracy with fewer parameters and lower computational overhead compared to state-of-the-art methods. The proposed SOAM Block thus offers a robust, scalable, and symmetry-aware solution for high-precision object detection in complex aerial scenes. Full article

The growing use of remote-sensing technologies has placed greater demands on object-detection algorithms, which still face challenges. This study proposes a hierarchical adaptive feature aggregation network (HAF-YOLO) to improve detection precision in remote-sensing images. It addresses issues such as small object size, complex backgrounds, scale variation, and dense object distributions by incorporating three core modules: dynamic-cooperative multimodal fusion architecture (DyCoMF-Arch), multiscale wavelet-enhanced aggregation network (MWA-Net), and spatial-deformable dynamic enhancement module (SDDE-Module). DyCoMF-Arch builds a hierarchical feature pyramid using multistage spatial compression and expansion, with dynamic weight allocation to extract salient features. MWA-Net applies wavelet-transform-based convolution to decompose features, preserving high-frequency detail and enhancing representation of small-scale objects. SDDE-Module integrates spatial coordinate encoding and multidirectional convolution to reduce localization interference and overcome fixed sampling limitations for geometric deformations. Experiments on the NWPU VHR-10 and DIOR datasets show that HAF-YOLO achieved mAP50 scores of 85.0% and 78.1%, improving on YOLOv8 by 4.8% and 3.1%, respectively. HAF-YOLO also maintained a low computational cost of 11.8 GFLOPs, outperforming other YOLO models. Ablation studies validated the effectiveness of each module and their combined optimization. This study presents a novel approach for remote-sensing object detection, with theoretical and practical value. Full article

Semantic segmentation is one of the key tasks in the intelligent interpretation of remote sensing images with extensive potential applications. However, when ultra-high resolution (UHR) remote sensing images exhibit complex background intersections and significant variations in object sizes, existing multimodal fusion segmentation methods based on convolutional neural networks and Transformers face challenges such as limited receptive fields and high secondary complexity, leading to inadequate global context modeling and multimodal feature representation. Moreover, the lack of accurate boundary detail feature constraints in the final segmentation further limits segmentation accuracy. To address these challenges, we propose a novel boundary-enhanced multilevel multimodal fusion Mamba-Large Strip Convolution network (FMLSNet) for remote sensing image segmentation, which offers the advantages of a global receptive field and efficient linear complexity. Specifically, this paper introduces a new multistage Mamba multimodal fusion framework (FMB) for UHR remote sensing image segmentation. By employing an innovative multimodal scanning mechanism integrated with disentanglement strategies to deepen the fusion process, FMB promotes deep fusion of multimodal features and captures cross-modal contextual information at multiple levels, enabling robust and comprehensive feature integration with enriched global semantic context. Additionally, we propose a Large Strip Spatial Detail (LSSD) extraction module, which adaptively combines multi-directional large strip convolutions to capture more precise and fine-grained boundary features. This enables the network to learn detailed spatial features from shallow layers. A large number of experimental results on challenging remote sensing image datasets show that our method exhibits superior performance over state-of-the-art models. Full article

Metal–organic frameworks (MOFs) have characteristics such as a large specific surface area, distinct functional sites, and an adjustable pore size. However, the inherent low conductivity of MOFs significantly affects the charge transfer efficiency when they are used for electrocatalytic sensing. Combining MOFs with conductive materials can compensate for these deficiencies. For MOF/metal nanoparticle composites (e.g., composites with gold, silver, platinum, and bimetallic nanoparticles), the high electrical conductivity and catalytic activity of metal nanoparticles are utilized, and MOFs can inhibit the agglomeration of nanoparticles. MOF/carbon-based material composites integrate the high electrical conductivity and large specific surface area of carbon-based materials. MOF/conductive polymer composites offer good flexibility and tunability. MOF/multiple conductive material composites exhibit synergistic effects. Although MOF composites provide an ideal platform for electrocatalytic reactions, current research still suffers from several issues, including a lack of comparative studies, insufficient research on structure–property correlations, limited practical applications, and high synthesis costs. In the future, it is necessary to explore new synthetic pathways and seek; inexpensive alternative raw materials. Full article

Metal–Organic Frameworks (MOFs) have emerged as advanced porous crystalline materials due to their highly ordered structures, ultra-high surface areas, fine-tunable pore sizes, and massive chemical diversity. These features, arising from the coordination between an almost unlimited number of metal ions/clusters and organic linkers, have resulted in significant interest in MOFs for applications in gas storage, catalysis, sensing, energy, and biomedicine. Beyond their stand-alone properties and applications, recent research has increasingly explored the integration of MOFs with other substrates, particularly electrodes, polymeric thin films, and glass surfaces, to create synergistic effects that enhance material performance and broaden application potential. Coating MOFs onto these substrates can yield significant benefits, including, but not limited to, improved sensitivity and selectivity in electrochemical sensors, enhanced mechanical and separation properties in membranes, and multifunctional coatings for optical and environmental applications. This review provides a comprehensive and up-to-date summary of recent advances (primarily from the past 3–5 years) in MOF coating techniques, including layer-by-layer assembly, in situ growth, and electrochemical deposition. This is followed by a discussion of the representative applications arising from MOF-substrate coating and an outline of key challenges and future directions in this rapidly evolving field. This article aims to serve as a focused reference point for researchers interested in both fundamental strategies and applied developments in MOF surface coatings. Full article

Thermal management is an area of study in electronics focused on managing temperature to improve reliability and efficiency. When temperatures are too high, cooling systems are activated to prevent overheating, which can lead to reliability issues. To monitor the temperatures, sensors are often placed on-chip near hotspot locations. These sensors should be very small to allow them to be placed among compact, high-activity circuits. Often, they are connected to a central control circuit located far away from the hot spot locations where more area is available. This paper proposes sensing units for a novel temperature sensing architecture in the TSMC 180 nm process. This architecture functions by approximating the current through the sensing unit at a reference voltage, which is used to approximate the temperature in the digital back end using fitting functions. Sensing units are selected based on how well its temperature–current relationship can be modeled, sensing unit area, and power consumption. Many sensing units will be experimented with at different reference voltages. These temperature–current curves will be modeled with various fitting functions. The sensing unit selected is a diode-connected p-type MOSFET (Metal Oxide Semiconductor Field Effect Transistor) with a size of W = 400 nm, L = 180 nm. This sensing unit is exceptionally small compared to existing work because it does not rely on multiple devices at the sensing unit location to generate a PTAT or IPTAT signal like most work in this area. The temperature–current relationship of this device can also be modeled using a 2nd order polynomial, requiring a minimal number of trim temperatures. Its temperature error is small, and the power consumption is low. The range of currents for this sensing unit could be reasonably made on an IDAC. Full article

The multiscale interactive system composed of wind, leaves, and droplets serves as a critical dynamic unit in precision orchard spraying. Its coupling mechanisms fundamentally influence pesticide transport pathways, deposition patterns, and drift behavior within crop canopies, forming the foundational basis for achieving intelligent and site-specific spraying operations. This review systematically examines the synergistic dynamics across three hierarchical scales: Droplet–leaf surface wetting and adhesion at the microscale; leaf cluster motion responses at the mesoscale; and the modulation of airflow and spray plume diffusion by canopy architecture at the macroscale. Key variables affecting spray performance—such as wind speed and turbulence structure, leaf biomechanical properties, droplet size and electrostatic characteristics, and spatial canopy heterogeneity—are identified and analyzed. Furthermore, current advances in multiscale modeling approaches and their corresponding experimental validation techniques are critically evaluated, along with their practical boundaries of applicability. Results indicate that while substantial progress has been made at individual scales, significant bottlenecks remain in the integration of cross-scale models, real-time acquisition of critical parameters, and the establishment of high-fidelity experimental platforms. Future research should prioritize the development of unified coupling frameworks, the integration of physics-based and data-driven modeling strategies, and the deployment of multimodal sensing technologies for real-time intelligent spray decision-making. These efforts are expected to provide both theoretical foundations and technological support for advancing precision and intelligent orchard spraying systems. Full article

The increasing demand for noninvasive inspection (NII) of complex civil infrastructures requires overcoming the limitations of traditional ground-penetrating radar (GPR) systems in addressing diverse and large-scale applications. The solution proposed in this study focuses on an initial design that integrates a low-SWaP (Size, Weight, and Power) ultra-wideband (UWB) impulse radar with realistic electromagnetic modeling for deployment on unmanned aerial vehicles (UAVs). The system incorporates ultra-realistic antenna and propagation models, utilizing Finite Difference Time Domain (FDTD) solvers and multilayered media, to replicate realistic airborne sensing geometries. Verification and calibration are performed by comparing simulation outputs with laboratory measurements using varied material samples and target models. Custom signal processing algorithms are developed to extract meaningful features from complex electromagnetic environments and support anomaly detection. Additionally, machine learning (ML) techniques are trained on synthetic data to automate the identification of structural characteristics. The results demonstrate accurate agreement between simulations and measurements, as well as the potential for deploying this design in flight tests within realistic environments featuring complex electromagnetic interference. Full article

The mechanisms underlying reduced deformability of red blood cells (RBCs) in Plasmodium falciparum remain unclear. The decrease in RBC deformability associated with malarial infection was measured using ektacytometry, and only mean values were evaluated. In this study, we report the development of a microfluidic sensing device that can evaluate decreased RBC deformability at the single-cell level by measuring ionic current waveforms through micropores. Using an in vitro culture system, we found that when RBC deformability was reduced by P. falciparum infection, ionic current waveforms changed. As RBC deformability decreased, waveforms became asymmetric. Computer simulations suggested that these waveform parameters are largely independent of RBC size and may represent a reliable indicator of diminished deformability. This novel microfluidic RBC deformability sensor allows for detailed single-cell analysis of malaria-associated deformability reduction, potentially aiding in elucidating its pathology. Full article

Leaf loss caused by pest infestations poses a serious threat to forest health. The leaf loss rate (LLR) refers to the percentage of the overall tree-crown leaf loss per unit area and is an important indicator for evaluating forest health. Therefore, rapid and accurate acquisition of the LLR via remote sensing monitoring is crucial. This study is based on drone hyperspectral and LiDAR data as well as ground survey data, calculating hyperspectral indices (HSI), multispectral indices (MSI), and LiDAR indices (LI). It employs Savitzky–Golay (S–G) smoothing with different window sizes (W) and polynomial orders (P) combined with recursive feature elimination (RFE) to select sensitive features. Using Random Forest Regression (RFR) and Convolutional Neural Network Regression (CNNR) to construct a multidimensional (horizontal and vertical) estimation model for LLR, combined with LiDAR point cloud data, achieved a three-dimensional visualization of the leaf loss rate of trees. The results of the study showed: (1) The optimal combination of HSI and MSI was determined to be W11P3, and the LI was W5P2. (2) The optimal combination of the number of sensitive features extracted by the RFE algorithm was 13 HSI, 16 MSI, and hierarchical LI (2 in layer I, 9 in layer II, and 11 in layer III). (3) In terms of the horizontal estimation of the defoliation rate, the model performance index of the CNNR_HSI model (MPI = 0.9383) was significantly better than that of RFR_MSI (MPI = 0.8817), indicating that the continuous bands of hyperspectral could better monitor the subtle changes of LLR. (4) The I-CNNR_HSI+LI, II-CNNR_HSI+LI, and III-CNNR_HSI+LI vertical estimation models were constructed by combining the CNNR_HSI model with the best accuracy and the LI sensitive to different vertical levels, respectively, and their MPIs reached more than 0.8, indicating that the LLR estimation of different vertical levels had high accuracy. According to the model, the pixel-level LLR of the sample tree was estimated, and the three-dimensional display of the LLR for forest trees under the pest stress of larch caterpillars was generated, providing a high-precision research scheme for LLR estimation under pest stress. Full article

Selenium (Se) is an essential trace element involved in antioxidant redox regulation, thyroid hormone metabolism, and cancer prevention. Among its different forms, elemental selenium (Se⁰), particularly at the nanoscale, has gained growing attention in food, feed, and biomedical applications due to its lower toxicity and higher bioavailability compared to inorganic selenium species. However, the detection of Se⁰ in real samples remains challenging as current analytical methods are time-consuming, labour-intensive, and often unsuitable for rapid analysis. In this study, we developed a method for rapidly measuring Se⁰ using carbon nanodots (CNDs) produced from the Maillard reaction between glucose and glycine. The fabricated CNDs were water-dispersible and strongly fluorescent, with an average particle size of 3.90 ± 1.36 nm. Comprehensive characterisation by transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR), fluorescence spectroscopy, and Raman spectroscopy confirmed their structural and optical properties. The CNDs were employed as fluorescent probes for the selective detection of Se⁰. The sensor showed a wide linear detection range (0–12.665 mmol L⁻¹), with a low detection limit (LOD) of 0.381 mmol L⁻¹ and a quantification limit (LOQ) of 0.465 mmol L⁻¹. Validation with spiked real samples—including ultra-pure water, tap water, and soft drinks—yielded high recoveries (98.6–108.1%) and low relative standard deviations (<3.4%). These results highlight the potential of CNDs as a simple, reliable, and environmentally friendly sensing platform for trace-level Se⁰ detection in complex food and beverage matrices. Full article

In this study, we report the synthesis of Te and Ag₂Te micron-sized rods (MRs) via a controlled hot-injection-based quenching process, enabling the control of rod morphology and enhanced crystallinity. Structural analysis confirmed that the synthesized Te MRs exhibit a trigonal phase, growing along the (110) direction, while Ag₂Te MRs undergo a phase transformation into a monoclinic structure upon Ag doping. A simple and scalable photodetector (PD) was fabricated by drop-casting Te and Ag₂Te MRs onto PET plastic films, followed by the application of Ag paste electrodes. The PD demonstrated room-light-induced photocurrent responses, which increased significantly upon mechanical bending due to the formation of additional conductive pathways between MRs. The Ag₂Te-based bending sensor exhibited a fivefold enhancement in photocurrent compared to its Te counterpart and maintained high stability over 1000 bending cycles. These results highlight the potential of Te and Ag₂Te MRs for use in flexible and wearable motion-sensing technologies, offering a simple yet effective approach for integrating 1D telluride nanostructures into scalable optoelectronic applications. Full article

The Po valley (northern Italy) is the leading European region for processing tomato (Solanum lycopersicum L.) production. Although historically characterized by abundant water availability, this area is now increasingly affected by drought risk. This study presents a two-year evaluation of regulated deficit irrigation (RDI) on processing tomatoes in northern Italy. In 2019 (Parma) and 2022 (Piacenza), full irrigation (IRR, restoring 100% crop evapotranspiration) and RDI (100% IRR until the color-breaking stage, followed by 50% IRR) strategies were compared within a completely randomized block design. Overall, RDI resulted in a 25% reduction in water use without compromising yield, which was maintained through unchanged plant fertility and fruit size compared to IRR. Remote sensing data from PlanetScope imagery confirmed the absence of water stress in RDI-treated plants. Furthermore, increased soluble solids and dry matter contents under RDI suggest a physiological adaptation of processing tomatoes to late-season water deficit. Remarkably, environmental and economic sustainability indicators—namely water productivity and yield quality—were enhanced under RDI management. This study validates a simple, sustainable, and readily applicable irrigation approach for tomato cultivation in the Po valley. Future research should refine this method by investigating plant physiological responses to optimize water use in this key agricultural region. Full article

As lithium-ion batteries (LIBs) gain widespread use, detecting electrolyte–vapor emissions during early thermal runaway (TR) remains critical to ensuring battery safety; yet, it remains understudied. Gas sensors integrating oxide nanostructures offer a promising solution as they possess high sensitivity and fast response, enabling rapid detection of various gas-phase indicators of battery failure. Utilizing this approach, 3D ordered tin oxide (SnO₂) nanostructures were synthesized using polystyrene sphere (PS) templates of varied diameters (200–1500 nm) and precursor concentrations (0.2–0.6 mol/L) to detect key electrolyte–vapors, especially ethyl methyl carbonate (EMC), released in the early stages of TR. The 3D ordered SnO₂ nanostructures with ring- and nanonet-like morphologies, formed after PS template removal, were characterized, and the effects of template size and precursor concentration on their structure and sensing performance were investigated. Among various nanostructures of SnO₂, nanonets achieved by a 1000 nm PS template and 0.4 mol/L precursor showed higher mesoporosity (~28 nm) and optimal EMC detection. At 210 °C, it detected 10 ppm EMC with a response of ~7.95 and response/recovery times of 14/17 s, achieving a 500 ppb detection limit alongside excellent reproducibility/stability. This study demonstrates that precise structural control of SnO₂ nanostructures using templates enables sensitive EMC detection, providing an effective sensor-based strategy to enhance LIB safety. Full article