Search Results (1,138)

Optical biosensors have emerged as a transformative technology for food safety monitoring. These devices combine biorecognition molecules with advanced optical transducers, enabling the detection of a wide array of food contaminants, including pathogens, toxins, pesticides, and antibiotic residues. This review comprehensively explores the principles, advancements, applications, and future trends of optical biosensors in ensuring food safety. The key advantages of optical biosensors, such as high sensitivity to trace contaminants, fast response times, and portability, make them an attractive alternative to traditional analytical methods. Types of optical biosensors discussed include surface plasmon resonance (SPR), interferometric, fluorescence and chemiluminescence, and colorimetric biosensors. SPR biosensors stand out for their real-time, label-free analysis of foodborne pathogens and contaminants, while fluorescence and chemiluminescence biosensors offer exceptional sensitivity for detecting low levels of toxins. Interferometric and colorimetric biosensors, characterized by their portability and visual signal output, are well-suited for field-based applications. Biosensors have proven invaluable in monitoring heavy metals, pesticide residues, and antibiotic contaminants, ensuring compliance with stringent food safety standards. The integration of nanotechnology has further enhanced the performance of optical biosensors, with nanomaterials such as quantum dots and nanoparticles enabling ultra-sensitive detection and signal amplification. Optical biosensors represent a vital advancement in the field of food safety, addressing critical public health concerns through their rapid and reliable detection capabilities. Continued interdisciplinary efforts in nanotechnology, material science, and device engineering are poised to further expand their applications, making them indispensable tools for safeguarding global food supply chains. Full article

The hepatitis D virus (HDV) is a small, defective RNA virus that induces the most severe form of viral hepatitis. Despite its severity, HDV infections are under-diagnosed due to non-standardized and costly diagnostic screening methods. However, limited research has been conducted on characterizing HDV-specific antibodies as alternative tools for diagnosis. Thus, we generated HDV-specific, polyclonal antibodies by immunizing rabbits with the HDV protein, small hepatitis delta antigen (SHDAg), in its oligomeric or denatured form. We identified SHDAg-specific linear epitopes by peptide array analysis and compared them to epitopes identified in HDV-infected patients. Using in silico structural analysis, we show that certain highly immunogenic domains in SHDAg, such as the coiled-coil domain, are masked in the oligomeric conformation of the protein; others, such as the second arginine-rich motif, are exposed. The nuclear localization signal is presumably exposed only by specific interaction of oligomeric HDAg with the HDV-RNA genome. Through surface plasmon resonance analysis, we identified two polyclonal antibodies derived from rabbit antisera with affinities in the lower nanomolar range. These antibodies were used to establish an ELISA that can quantitatively detect HDV virions in vitro and upon further optimization could be used as a promising alternative diagnostic screening method. Full article

The rapid and sensitive detection of regulatory elements within transgenic constructs of genetically modified organisms (GMOs) is essential for effective monitoring and control of their distribution. In this study, we present several innovative electrochemical biosensing platforms for the detection of regulatory sequences in genetically modified (GM) plants, combining the loop-mediated isothermal amplification (LAMP) method with electrodes functionalized by two-dimensional (2D) nanomaterials. The sensor design exploits the high surface area and excellent conductivity of reduced graphene oxide, Ti₃C₂T_x, and molybdenum disulfide (MoS₂) to enhance signal transduction. Furthermore, we used a “green synthesis” method for Ti₃C₂T_x preparation that eliminates the use of hazardous hydrofluoric acid (HF) and hydrochloric acid (HCl), providing a safer and more sustainable approach for nanomaterial production. Within this framework, the performance of various custom-fabricated electrodes, including laser-patterned gold leaf films, physical vapor deposition (PVD)-deposited gold electrodes, and screen-printed gold electrodes, is evaluated and compared with commercial screen-printed gold electrodes. Additionally, gold and carbon electrodes were electrochemically covered by gold nanoparticles (AuNPs), and their properties were compared. Several electrochemical methods were used during the DNA detection, and their importance and differences in excitation signal were highlighted. Electrochemical properties, sensitivity, selectivity, and reproducibility are characterized for each electrode type to assess the influence of fabrication methods and material composition on sensor performance. The developed biosensing systems exhibit high sensitivity, specificity, and rapid response, highlighting their potential as practical tools for on-site GMO screening and regulatory compliance monitoring. This work advances electrochemical nucleic acid detection by integrating environmentally-friendly nanomaterial synthesis with robust biosensing technology. Full article

Extremely high temperatures can severely impact urban livability and public health safety. However, risk assessments for high temperatures in cold-region cities remain inadequate. This study focuses on Dalian, a coastal city in northeastern China. Utilizing multi-source data, we established a population density prediction model based on the random forest algorithm and a heat vulnerability index (HVI) framework following the “Exposure-Sensitivity-Adaptability” paradigm constructed using an indicator system method, thereby building a high-temperature risk assessment system suited for more refined research. The results indicate the following: (1) Strong positive correlations exist between nighttime light brightness (NL), Road Density (RD), the proportion of flat area (SLP), the land surface temperature (LST), and the population distribution density, with correlation coefficients reaching 0.963, 0.963, 0.956, and 0.954, respectively. (2) Significant disparities exist in the spatial distribution of different criterion layers within the study area. Areas characterized by high exposure, high sensitivity, and low adaptability account for 13.04%, 8.05%, and 21.44% of the total area, respectively, with exposure being the primary contributing factor to high-temperature risk. (3) Areas classified as high-risk or extremely high-risk for high temperatures constitute 31.57% of the study area. The spatial distribution exhibits a distinct pattern, decreasing gradually from east to west and from the coast inland. This study provides a valuable tool for decision-makers to propose targeted adaptation strategies and measures based on the assessment results, thereby better addressing the challenges posed by climate change-induced high-temperature risks and promoting sustainable urban development. Full article

This study investigates the tribological behavior and wear mechanisms of Q235 steel components subjected to abrasive interaction with rice, a critical challenge in agricultural machinery performance and longevity. We employed a comprehensive multi-scale framework, integrating bench-top tribological testing, advanced Discrete Element Method (DEM) coupled with a wear model (DEM-Wear), and detailed surface characterization. Bench tests revealed a composite wear mechanism for the rice–steel tribo-pair, transitioning from mechanical polishing under mild conditions to significant soft abrasive micro-cutting driven by the silica particles inherent in rice during high-load, high-velocity interactions. This elucidated fundamental friction and wear phenomena at the micro-level. A novel, calibrated DEM-Wear model was developed and validated, accurately predicting macroscopic wear “hot spots” on full-scale combine harvester header platforms with excellent geometric similarity to real-world wear profiles. This provides a robust predictive tool for component lifespan and performance optimization. Furthermore, fractal analysis was successfully applied to quantitatively characterize worn surfaces, establishing fractal dimension (Ds) as a sensitive metric for wear severity, increasing from ~2.17 on unworn surfaces to ~2.3156 in severely worn regions, directly correlating with the dominant wear mechanisms. This study offers a valuable computational approach for understanding and mitigating wear in tribosystems involving complex particulate matter, contributing to improved machinery reliability and reduced operational costs. Full article

Floods represent one of the most significant climate-related hazards, particularly in regions with complex topographies and variable precipitation patterns. This study assesses flood-prone areas within the Mantaro watershed, Peru, using Geographic Information Systems (GISs) and morphometric analysis. The methodology integrates digital elevation models (DEMs) with hydrological parameters, applying weighted sum analysis to classify 18 sub-watersheds into different flood priority levels. Morphometric parameters, including basin relief, drainage density, and slope, were analyzed to establish correlations between watershed morphology and flood susceptibility. The results indicate that approximately 74.38% of the watershed exhibits high to very high flood risk, with the most vulnerable sub-watersheds characterized by steep slopes, high drainage densities, and compact morphometric configurations. The correlation matrix confirms that watershed topography significantly influences surface runoff behavior, underscoring the necessity of incorporating geospatial analysis into flood risk assessment frameworks. The classification of sub-watersheds into priority levels provides a scientific basis for optimizing resource allocation in flood mitigation strategies. This study highlights the importance of integrating advanced geospatial technologies, such as GISs and remote sensing, into hydrological risk assessments. The findings emphasize the need for proactive watershed management, including the use of real-time monitoring and digital tools for climate adaptation. Future research should explore the influence of land-use changes and climate variability on flood dynamics to enhance predictive modeling. These insights contribute to evidence-based decision-making for disaster risk reduction, reinforcing resilience in climate-sensitive regions. Full article

The discrete element computer simulation method is an effective tool that enables the study of the interaction mechanism between the fertilizer discharge device. However, the lack of accurate fertilizer models for hygroscopic fertilizer particles (HFP) has limited the application and development of the discrete element method in research precision fertilizer discharge device. Taking HFP as the research object, this research aims to establish the discrete element model of different moisture hygroscopic fertilizer particles, and to develop a method for predicting the discrete element parameters of HFP based on moisture content. The Hertz–Mindlin with JKR discrete element model was selected as the contact model for the HFP. The repose angle of HFP was used as the test index to select nine discrete element models for the HFP. Firstly, a mathematical model characterizing the relationship between fertilizer moisture content and the repose angle was established. Subsequently, the Plackett–Burman test identified the surface energy of hygroscopic fertilizer particles (HFP), the restitution coefficient between fertilizer and PC board, and the shear modulus as significant factors influencing the test index. The value range of the above parameters were determined by the steepest ascent test results. The Box–Behnken test obtained the regression model between the significant factors and the test index. The optimal combination of parameters of 2%, 4%, and 6% moisture contents of HFP were predicted based on the regression model and the HFP repose angle. The parameters were optimized using the repose angle error as the target. In order to further verify the accuracy of the HFP discrete element model, a fertilizer discharging simulation test was conducted. The results show that, compared with the actual fertilizer discharge amount, the simulation fertilizer discharge amount error of different moisture HFP was below 8.32%. The collective results indicated this method could reliably and precisely establish the discrete element model of various moisture content HFP. This model can be applied to the analysis of hygroscopic fertilizer discharging processes and the design of precision fertilizer discharge technology devices. Full article

This study investigates the optimization of hot-pressing parameters for ASP60 high-speed steel composites incorporating CuSn20 bronze alloy for use in diamond-reinforced tool applications. ASP60 and CuSn20 powders were characterized using XRD, XRF, DSC, SEM, and laser diffraction. The effects of CuSn20 addition at varying concentrations and compaction temperatures (950–1050 °C) on porosity, mechanical properties, and tribological performance were evaluated. Results showed that adding CuSn20 significantly reduced residual porosity due to its partial melting during compaction, which facilitated particle rearrangement and densification. Optimal conditions were identified at 1050 °C with 9.8 wt.% CuSn20, yielding minimal porosity (~3.7%) and the highest bending strength (374.51 ± 36.73 MPa). The optimized matrix was further reinforced with TiC-coated diamond particles at concentration c = 20, producing a composite material with excellent wear resistance, despite minor defects in the TiC coating observed on fracture surfaces. Tribological testing demonstrated that CuSn20 consistently lowered friction coefficients across all tested temperatures due to its self-lubricating properties and partial melting at elevated temperatures. Furthermore, ASP60 exhibited no measurable wear, making it a promising candidate for highly demanding applications. Overall, the study demonstrates that CuSn20 alloy enhances densification, mechanical performance, and tribological behavior of ASP60-based composites, indicating their strong potential for aggressive wire sawing and stone-cutting tool applications. Full article

Aluminum spot welding is extensively applied in automotive, aerospace, and rail sectors due to its favorable strength-to-weight ratio. While resistance spot welding (RSW) has been the traditional method, its high residual stresses, electrode wear, and limited performance with high-strength aluminum alloys have driven interest toward alternative techniques. Friction stir spot welding (FSSW) offers significant advantages over RSW, linear friction welding (LFW), and hybrid processes, including solid-state joining that minimizes porosity, lower energy consumption, and the elimination of consumable electrodes. Compared to LFW, FSSW requires simpler fixturing and is more adaptable for localized repairs, while offering superior joint surface quality over hybrid laser-assisted methods. Despite these advantages, gaps remain in understanding the influence of process parameters on heat generation, microstructural evolution, and mechanical performance. This review consolidates advancements in tool design, thermal characterization, and weld property for aluminum alloys. It presents comparative insights into temperature distribution, weld strength, hardness variation, and metallurgical transformations reported across studies. By critically synthesizing the earlier works, this work identifies knowledge gaps and potential design improvements, aiming to support the development of more efficient and robust FSSW processes for industrial application. Full article

In this paper, we develop a deep learning neural network characterized by feature fusion and physical guidance (denoted as FFPG-net) for reconstructing subsurface sea temperature (T) and salinity (S) from sea surface data. Designed with the idea of feature fusion, FFPG-net combines the deep learning algorithms of residual and channel attention with the physical constraints of vertical modes of T/S profiles decomposed by empirical orthogonal functions (EOFs). The results from a series of single point experiments show that FFPG-net outperforms the CNN or CNN-PG (without physical guidance or feature fusion) in the reconstruction of subsurface T/S in a region of the South China Sea (SCS), with monthly mean RMSEs of 0.31 °C (0.35 °C) and 0.06 psu (0.07 psu) for the reconstructed T/S profiles in winter (summer), averaged over the water depth of 1200 m and the study area. In addition, the performance of the FFPG-net can be improved significantly by incorporating full surface currents or geostrophic currents derived from SSH into the input variables for training the neural network. The preliminary application of FFPG-net in the SCS using satellite-derived sea surface observations indicates that FFPG-net is reliable and feasible for reconstructing subsurface ocean thermal fields in real situations. Our study highlights the advantages and necessity of combining deep learning algorithms with physical constraints in reconstructing subsurface T/S profiles. It provides an effective tool for reconstructing the subsurface global ocean from remote-sensing sea surface observations in the future. Full article

With the rapid development of advanced machining technologies such as high-speed cutting, dry cutting, and ultra-precision cutting, as well as the widespread application of various difficult-to-machine materials, the surface degradation problems such as wear, oxidation, and delamination faced by tools in the service process have become increasingly prominent, seriously restricting the performance and service life of tools. Nanocoatings, with their distinct nano-effects, provide superior hardness, thermal stability, and tribological properties, making them an effective solution for cutting tools in increasingly demanding working environments. For example, the hardness of the CrAlN/TiSiN nano-multilayer coating can reach 41.59 GPa, which is much higher than that of a single CrAlN coating (34.5–35.8 GPa). This paper summarizes the most common nanocoating material design, coating deposition technologies, performance evaluation indicators, and characterization methods currently used in cutting tools. It also discusses how to improve nanocoating performance using modulation analysis of element content, coating composition, geometric structure, and coating thickness. Finally, this paper considers the future development of nanocoatings for cutting tools in light of recent research hotspots. Full article

Despite notable advancements in lithium-ion battery (LIB) technology, growing industrialization, rising energy demands, and evolving consumer electronics continue to raise performance requirements. As the primary determinant of battery performance, cathode materials have become a central research focus. Among emerging candidates, iron-based fluorides show great promise due to their high theoretical specific capacities, elevated operating voltages, low cost (owing to abundant iron and fluorine), and structurally diverse crystalline forms such as pyrochlore and tungsten bronze types. These features make them strong contenders for next-generation high-energy, low-cost LIBs. This review highlights recent progress in iron-based fluoride cathode materials, with an emphasis on structural regulation and performance enhancement strategies. Using pyrochlore-type hydrated iron trifluoride (Fe₂F₅·H₂O), synthesized via ionic liquids like BmimBF₄, as a representative example, we discuss key methods for tuning physicochemical properties—such as electronic conductivity, ion diffusion, and structural stability—via doping, compositing, nanostructuring, and surface engineering. Advanced characterization tools (XRD, SEM/TEM, XPS, Raman, synchrotron radiation) and electrochemical analyses are used to reveal structure–property–performance relationships. Finally, we explore current challenges and future directions to guide the practical deployment of iron-based fluorides in LIBs. This review provides theoretical insights for designing high-performance, cost-effective cathode materials. Full article

The HYPEX^® process is a novel method for conditioning spent ion exchange resins from nuclear power plants, aiming to reduce final waste volume and carbon emissions by stabilizing the resins in metakaolin-based geopolymers. This study addresses the challenge posed by the natural variability of commercial metakaolin and defines a testing strategy to ensure consistent performance of the final matrix. The reactivity of two batches of metakaolin, characterized by comparable chemical composition and BET surface area, was evaluated by monitoring temperature evolution during geopolymerization at varying water-to-solid ratios. The resulting geopolymers were tested for compressive strength, water permeability, and strontium leachability to assess correlations between precursor properties and final matrix performance. Despite similar compositions, the two batches showed marked differences in compressive strength that could be linked to early thermal behavior. These findings demonstrate that conventional precursor characterization is insufficient to guarantee reproducibility and that thermal profiling is useful to predict mechanical performance. The results suggest the implementation of thermal response monitoring as a quality control tool to ensure the reliability of geopolymer wasteforms in nuclear applications. A simplified analytical model for the thermal evolution during geopolymerization was also developed, matching qualitatively the measured evolution, to suggest scale-up rules from laboratory specimens to full-scale drums, which should be achieved while preserving the thermal evolution. Full article

This study applied geostatistics to analyze thermal images of the back surface of pigs’ ears (TSO) to understand how spatial temperature variability influences thermoregulation. The objective was to assess TSO variability in pigs housed under two climate control systems, namely, pens without cooling (BTEST) and with an evaporative cooling system (BECS), using infrared thermography and geostatistical tools. A total of 432 thermal images were obtained from 18 finishing pigs at 08:00, 12:00, and 16:00. Semivariograms were modeled and validated, and kriging maps were developed to visualize the spatial temperature distribution. The pens were thermally characterized using reclassified Temperature and Humidity Index (THI) values. The Gaussian model (R² > 0.9) showed strong spatial dependence in temperature data. Pigs in the BECS system exhibited lower average TSO temperatures (28.2–38.6 °C) than those in the BTEST system, where temperatures exceeded 34 °C, highlighting the role of cooling in mitigating heat stress. In both systems, higher THI values were associated with increased TSO, indicating thermal discomfort under elevated environmental temperatures. Geostatistical analysis effectively revealed spatial patterns and variability in surface temperatures, providing key insights into how environmental conditions impact pigs’ thermal responses. Full article

Background: Detailed assessments, documentation, and evaluation of the wound characteristics in hard-to-heal wounds are essential for optimizing and individualizing wound care. However, the remaining challenge in clinical care includes the lack of high accuracy and precision tools for automated wound size (surface area and depth assessment) and a wound bed evaluation, i.e., a qualitative and quantification assessment of slough and necrosis. Objective/Methods: This study evaluates the accuracy and precision of the AI-powered technique, SeeWound© 2, compared to digital planimetry for a wound surface area and a wound bed characterization (slough and necrosis) in “in vitro” models and in patients, and a probe for depth, including diabetic foot ulcers, venous ulcers, pressure ulcers, and ischemic ulcers. Results: The data show that accuracy and precision (SeeWound© 2) for the wound surface area, the depth, and the wound bed characterization (slough and necrosis) were accuracy 96.28% and 90.00%, (CV 5.56%), respectively (wound size); 90.75% and 89.55%, (CV 3.07%), respectively (wound depth); 80.30% (slough) and 84.73% (necrosis) and 93.51% (slough) (CV 4.15%) and 82.35% (CV 8.34%) (necrosis). The precision for the digital planimetry was 88.61% (CV 7.00%) (slough) 85.74% (CV 7.54%) (necrosis). Conclusions: The overall accuracy and precision of the AI model in identifying wound size and depth were close to 90%, except for the accuracy and precision for slough and necrosis, where levels around 80% were achieved when compared to digital planimetry. The findings for the wound surface area and depth assessments, together with quantification of slough and necrosis, suggest that the SeeWound© 2 model can offer significant clinical benefits by improving documentation and supporting decision-making in wound management. Full article