Search Results (5,027)

A compact electrochemical sensor module for pH detection was developed for potential integration into specialized devices used for live cell or tissue incubation, for applications in highly parallelized cell culture analysis, by incorporating Organ-on-Chip devices. This research focuses on the deposition, structural and chemical analysis, and functional characterization of different titanium-oxide layers with various compositions as potentially sensitive materials for pH sensing applications. The titanium-oxide layers were deposited using vacuum sputtering and atomic layer deposition at 100 °C and 300 °C, respectively. Transmission electron microscopy and X-ray photoelectron spectroscopy were utilized to determine the specific composition and structure of different titanium-oxide layers. These TiO_x-functionalized electrodes were connected to the application-specific analog front-end chip of the low-power readout circuit for precise evaluation. The pH sensitivity of the differently modified electrodes, employing various TiO_x materials, was evaluated using pH calibration solutions ranging from pH 6 to 8. Among the various deposition solutions, such as sputtering or high-temperature atomic layer deposition, the TiO_x layer deposited using low-temperature atomic layer deposition proved more suitable for pH sensing applications, with a sensitivity of 54.8–56.7 mV/pH, which closely approximates the Nernstian response. Full article

Sintered nano-silver paste is widely used in electronic packaging due to its excellent thermal and electrical conductivity. A phenomenological variable-order fractional constitutive model has been developed to characterize the evolution of its mechanical properties, incorporating dependencies on both temperature and strain rate. Based on the Weissenberg number and classical Arrhenius equation, a formulation for relaxation time with temperature and strain rate dependence has been proposed. A temperature- and rate-sensitive fractional order is introduced to capture the coupled influences of thermal and strain rate effects. Furthermore, the effects of temperature and the strain rate on the elastic modulus and relaxation time are quantitatively described through established coupling criteria. Simulation results demonstrate that the proposed model offers high accuracy and strong predictive capability. Comparisons with the classical Anand model highlight the effectiveness of the variable-order fractional model, particularly at lower temperatures. Full article

Background: Acute exacerbation (AE) of idiopathic inflammatory myopathy-associated interstitial lung disease (IIM-ILD) is fatal. Infection is one of the most important triggers of the AE of IIM-ILD. We evaluated the clinical features and prognosis of idiopathic (I-AE) and infection-triggered (iT-AE) acute exacerbation in IIM-ILD patients. Methods: We retrospectively reviewed 278 consecutive patients with IIM admitted to our hospital between January 2014 and December 2020. Among them, 69 patients experienced AE of IIM-ILD, including 34 with I-AE and 35 with iT-AE. Clinical features and short- and long-term outcomes were analyzed in this preliminary study. Results: Compared with I-AE, patients with iT-AE presented with lower hemoglobin and PaO₂/FiO₂ ratios but higher pulse, body temperature, white blood cell count, neutrophil percentage (NEU), C-reactive protein, erythrocyte sedimentation rates, lactate dehydrogenase, and hydroxybutyrate dehydrogenase levels. They also had more extensive ground-glass opacities (GGOs) on high-resolution computed tomography (all p < 0.05). Mortality was significantly higher in iT-AE than that in I-AE at 30 days (28.6% vs. 5.9%), 90 days (34.3% vs. 14.9%), and 1 year (54.3% vs. 17.6%; log-rank test, p = 0.002). Multivariate logistic regression showed that the combination of NEU and GGO extent could help discriminate iT-AE from I-AE (area under the receiver operating characteristic curve: 0.812; 95% confidence interval: 0.711–0.913; sensitivity: 71.4%, specificity: 73.5%, accuracy: 72.5%). Conclusion: This study found that iT-AE patients exhibited more severe hyperinflammation and markedly worse survival than I-AE patients. Combining NEU and GGO extent may assist in differentiating AE subtypes. Larger prospective studies are required to validate these findings. Full article

Electromagnetic Railguns Face Severe Ablation and Melting Risks Due to Extremely High Transient Thermal Loads During High-Speed Launching, Directly Impacting Launch Reliability and Service Life. To address this thermal management challenge, this study proposes and validates the effectiveness of spray cooling technology. Leveraging its high heat transfer coefficient, exceptional critical heat flux (CHF) carrying capacity, and strong transient cooling characteristics, it is particularly suitable for the unsteady thermal control during the initial launch phase. An experimental platform was established, and a three-dimensional numerical model was developed to systematically analyze the dynamic influence mechanisms of nozzle inlet pressure, flow rate, spray angle, and spray distance on cooling performance. Experimental results indicate that the system achieves maximum critical heat flux (CHF) and rail temperature drop at an inlet pressure of 0.5 MPa and a spray angle of 0°. Numerical simulations further reveal that a 45° spray cone angle simultaneously achieves the maximum temperature drop and optimal wall temperature uniformity. Key parameter sensitivity analysis demonstrates that while increasing spray distance leads to larger droplet diameters, the minimal droplet velocity decay combined with a significant increase in overall momentum markedly enhances convective heat transfer efficiency. Concurrently, increasing spray distance effectively improves rail surface temperature uniformity by optimizing the spatial distribution of droplet size and velocity. Full article

Chlorpyrifos CPF (O,O-diethyl O-(3,5,6-trichloro-2-pyridyl) phosphorothioate), known also as Chlorpyrifos-ethyl, is one of the most utilized organophosphorus pesticides worldwide. Additionally, CPF could be used as a chemical warfare agent surrogate. Although its acute toxicity is not high, it is responsible for both a large number of intoxications and chronic, delayed neurological effects. In this work, it is reported for the first time the qualitative and quantitative response produced by CPF vapors, using a pocket-held Time-of-Flight Ion Mobility Spectrometer (ToF IMS) with a non-radioactive ionization source and ammonia doping, model LCD-3.2E (Smiths Detection Ltd.), operated near ambient temperature (below 30 °C). Spectra of CPF in positive ion mode included two distinct product ion peaks; thus, identification of CPF vapors by IMS relies on these peaks—the monomer M·NH₄⁺ with reduced ion mobility K₀ = ca. 1.76 cm²V⁻¹s⁻¹ and the dimer M₂·NH₄⁺ with K₀ = ca. 1.47 cm²V⁻¹s⁻¹ (where M may be assignable to CPF molecule)—and positive reactant ions (Pos RIP) have K₀ = ca. 2.25 cm²V⁻¹s⁻¹. Excellent sensitivity, with a limit of detection LOD of 0.72 ppb_v (10.5 μg m⁻³) and a limit of quantification LOQ of 2.41 ppb_v (35.1 μg m⁻³), has been noticed; linear response was up to 100 ppb_v, while saturation occurs over ca. 1000 ppb_v (14.6 mg m⁻³). Our results demonstrate that this method provides a robust tool for both off-site and on-site detecting and quantifying CPF vapors at trace levels, which has strong implications for either industrial hygiene or forensic investigations concerning the pesticide Chlorpyrifos, as well as for monitoring of environmental contamination by organophosphorus pesticides. Full article

The growing need for efficient and safe high-energy lithium-ion batteries (LIBs) in electric vehicles and grid storage necessitates advanced internal monitoring solutions. This work presents a comprehensive simulation model of a novel integrated optical sensor based on ethylene carbonate-filled photonic crystal fiber (EC-PCF). The proposed design synergistically combines a chirped fiber Bragg grating (FBG) and an extrinsic Fabry–Pérot interferometer (EFPI) on a multiplexed platform for the multifunctional sensing of refractive index (RI), temperature, strain, and pressure (via strain coupling) within LIBs. By matching the RI of the PCF cladding to the battery electrolyte using ethylene carbonate, the design maximizes light–matter interaction for exceptional RI sensitivity, while the cascaded EFPI enhances mechanical deformation detection beyond conventional FBG arrays. The simulation framework employs the Transfer Matrix Method with Gaussian apodization to model FBG reflectivity and the Airy formula for high-fidelity EFPI spectra, incorporating critical effects like stress-induced birefringence, Transverse Electric (TE)/Transverse Magnetic (TM) polarization modes, and wavelength dispersion across the 1540–1560 nm range. Robustness against fabrication variations and environmental noise is rigorously quantified through Monte Carlo simulations with Sobol sequences, predicting temperature sensitivities of ∼12 pm/°C, strain sensitivities of ∼1.10 pm/$\mathsf{\mu }$$\mathsf{\epsilon }$, and a remarkable RI sensitivity of ∼1200 nm/RIU. Validated against independent experimental data from instrumented battery cells, this model establishes a robust computational foundation for real-time battery monitoring and provides a critical design blueprint for future experimental realization and integration into advanced battery management systems. Full article

Arthritis and arthrosis are prevalent joint diseases that cause pain and disability, and their early diagnosis is crucial for preventing irreversible damage. Conventional diagnostic methods such as X-ray, ultrasound, and MRI have limitations in early detection, prompting interest in alternative techniques. This work presents the design and clinical evaluation of a prototype device for non-invasive early diagnosis of arthritis (inflammatory joint disease) and arthrosis (osteoarthritis) using infrared thermography and deep neural networks. The portable prototype integrates a Raspberry Pi 4 microcomputer, an infrared thermal camera, and a touchscreen interface, all housed in a 3D-printed PLA enclosure. A custom Flask-based application enables two operational modes: (1) thermal image acquisition for training data collection, and (2) automated diagnosis using a pre-trained ResNet50 deep learning model. A clinical study was conducted at a university clinic in a temperature-controlled environment with 100 subjects (70% with arthritic conditions and 30% healthy). Thermal images of both hands (four images per hand) were captured for each participant, and all patients provided informed consent. The ResNet50 model was trained to classify three classes (healthy, arthritis, and arthrosis) from these images. Results show that the system can effectively distinguish healthy individuals from those with joint pathologies, achieving an overall test accuracy of approximately 64%. The model identified healthy hands with high confidence (100% sensitivity for the healthy class), but it struggled to differentiate between arthritis and arthrosis, often misclassifying one as the other. The prototype’s multiclass ROC (Receiver Operating Characteristic) analysis further showed excellent discrimination between healthy vs. diseased groups (AUC, Area Under the Curve ~1.00), but lower performance between arthrosis and arthritis classes (AUC ~0.60–0.68). Despite these challenges, the device demonstrates the feasibility of AI-assisted thermographic screening: it is completely non-invasive, radiation-free, and low-cost, providing results in real-time. In the discussion, we compare this thermography-based approach with conventional diagnostic modalities and highlight its advantages, such as early detection of physiological changes, portability, and patient comfort. While not intended to replace established methods, this technology can serve as an early warning and triage tool in clinical settings. In conclusion, the proposed prototype represents an innovative application of infrared thermography and deep learning for joint disease screening. With further improvements in classification accuracy and broader validation, such systems could significantly augment current clinical practice by enabling rapid and non-invasive early diagnosis of arthritis and arthrosis. Full article

Coating plays an important role in advancing sensing technology by significantly enhancing sensitivity, stability, and response time. The unique properties of nanostructures, including high surface-to-volume ratio and tunable porosity, make them suitable candidates for improving sensor performance. By optimizing nanostructure coatings, advancements in high-precision humidity sensing devices are achievable, enabling a wide range of industrial applications, especially in humidity-controlled industries. In this study, the effects of annealing time, annealing temperature, and the number of coating layers on the properties of additive-enhanced SnO₂ nanostructure were investigated. The experiment was carried out by subjecting the additive-enhanced SnO₂ nanostructure to different annealing times and annealing temperatures to analyze its impact on crystallinity, porosity, and moisture adsorption properties. Upon optimizing the annealing parameters, multilayer coatings were carried out to assess the effect of the total number of coating layers on hygroscopic behavior. A hygroscopicity test was carried out on each sample to evaluate its moisture adsorption and desorption capabilities. The results demonstrated that controlled annealing conditions significantly improve the nanostructure’s hygroscopic properties, and the optimized coating layers further enhanced the moisture retention, making the developed SnO₂ nanostructure a promising candidate for advanced sensing applications. Full article

The detection of allergens is essential for ensuring food safety, protecting public health, and providing accurate information to consumers. Wheat (Triticum aestivum L.) and maize (Zea mays L.) are recognized as important food allergens. In this study, novel PCR methods were developed for the reliable detection of wheat and maize allergens, including wheat high-molecular-weight glutenin subunit (HMW-GS) and low-molecular-weight glutenin subunit (LMW-GS), as well as three maize allergens, namely, Zea m 14, Zea m 8, and zein. Wheat and maize genomic DNA, as well as allergen genes, were examined during 60 min of baking at 180 °C and 220 °C. Agarose gel electrophoresis revealed degradation of genomic DNA and amplified PCR fragments in correlation with increasing baking temperature and time. For each target gene, the best primers were identified that could detect HMW-GS and LMW-GS genes in wheat samples and Zea m 14, Zea m 8, and zein genes in maize samples after baking at 220 °C for 60 min and 40 min, respectively. The results indicate that these PCR methods can be used for the reliable and sensitive detection of wheat and maize allergens in processed foods. Full article

This study investigates the structural, morphological, and gas-sensing properties of pure and strontium-doped lanthanum cobaltite (La_1−xSr_xCoO₃) perovskite thin films obtained by Pulsed Electron Deposition (PED). This sustainable ablative technique successfully produced high-quality, dense, nanocrystalline films on Si and MgO substrates, demonstrating excellent stoichiometric transfer from the source targets. A comprehensive analysis using XRD, SEM, TEM, AFM, and XPS was conducted to characterize the films. The results show that Sr-doping significantly refines the microstructure, leading to smaller crystallites and a more uniform surface topography. Gas sensing measurements, performed in a temperature range of 100–450 °C, revealed that all films exhibit a characteristic p-type semiconductor response to nitrogen dioxide (NO₂). The La_0.8Sr_0.2CoO₃ composition, in particular, demonstrated the most promising performance, with enhanced sensitivity and excellent operational stability at temperatures up to 350 °C. These findings validate that PED is a reliable and precise method for fabricating complex oxide films and confirm that Sr-doped LaCoO₃ is a highly promising material for developing high-temperature NO₂ gas sensors. Full article

Gallium nitride high electron mobility transistors (GaN HEMTs), characterized by their extremely high switching speeds and superior high-frequency performance, have demonstrated significant advantages, and gained extensive applications in fields such as aerospace and high-power-density power supplies. However, their unique internal architecture renders these devices highly sensitive to circuit parasitic parameters. Conventional circuit design methodologies often induce severe issues such as overshoot and high-frequency oscillations, which significantly constrain the realization of their high-frequency performance. To solve this problem, this paper investigates the nonlinear dynamic behavior of GaN HEMTs during switching transients by establishing an equivalent impedance model. Based on this model, a detailed analysis is implemented to elucidate the mechanism by which RC Snubber circuits influence the system’s resonance frequency and the amplitude at the resonant frequency. Through this analysis, an optimal RC Snubber circuit parameter is derived, enabling effective suppression of high-frequency oscillations during the switching transient of GaN HEMT. Experimental results demonstrate that the proposed design achieves a maximum reduction of 40% in voltage overshoot, shortens the ringing time to one-twentieth of the original value, and suppresses noise by 20 dB in the high-frequency range of 20 MHz to 30 MHz, thereby significantly enhancing the stability and reliability of circuit operation. Additionally, considering the heat dissipation requirements in high power density scenarios, this work optimizes the layout of devices, and heat sinks to maintain operational temperatures within safe limits, further mitigating the impact of parasitic parameters on overall system performance. Full article

Glazed façades play a decisive role in building energy performance, particularly in high-radiation equatorial climates. This study examines the thermal behavior and economic feasibility of three glazing systems—10 mm monolithic clear glass, laminated solar-control glass, and selective double glazing—applied to the Plataforma Gubernamental Norte, the largest institutional building in Ecuador. Dynamic simulations using DesignBuilder with the EnergyPlus engine assessed solar gains, HVAC demand, and operative temperatures, complemented by a sensitivity analysis of SHGC, U-value, and Tvis. Results indicate that selective double glazing reduced annual HVAC consumption by 78.21% (110.6 MWh), while laminated glazing achieved a 55.40% reduction. SHGC and U-value emerged as the most influential parameters, whereas Tvis had no impact on energy loads. Despite strong technical performance, the economic analysis revealed payback periods exceeding 235 years under Ecuador’s subsidized tariff (USD 0.10/kWh), compared to the 18–25 years commonly observed in Europe. This highlights the “efficiency paradox”: advanced glazing solutions deliver significant energy savings but remain financially unfeasible in subsidy-driven contexts. The findings underscore the need for policy reforms to better align façade design strategies with energy resilience, an issue particularly relevant after Ecuador’s 2024 electricity crisis and ongoing debates on subsidy elimination. Full article

Geothermal energy has been recognized as a promising renewable resource for sustainable power generation; however, the efficiency of conventional geothermal power plants has remained relatively low, and high investment costs have limited their competitiveness with other renewable technologies. In this context, the present study introduced an innovative geothermal electricity generation system aimed at enhancing energy efficiency, cost-effectiveness, and sustainability. Unlike traditional configurations, the system raised the geothermal source temperature passively by employing advanced heat transfer mechanisms, eliminating the need for additional energy input. Comprehensive energy, exergy, and exergoeconomic analyses were carried out, revealing a net power output of 43,210 kW and an energy efficiency of 30.03%, notably surpassing the conventional Kalina cycle’s typical 10.30–19.48% range. The system’s annual electricity generation was 11,138.53 MWh, with an initial investment of USD 3.04 million and a short payback period of 3.20 years. A comparative assessment confirmed its superior thermoeconomic performance. In addition to its technoeconomic advantages, the environmental performance of the proposed configuration was quantified. A streamlined life cycle assessment (LCA) was performed with a functional unit of 1 MWh of net electricity. The proposed system exhibited a carbon footprint of 20–60 kg CO₂ eq MWh⁻¹ (baseline: 45 kg CO₂ eq MWh⁻¹), corresponding to annual emissions of 0.22–0.67 kt CO₂ eq for the simulated output of 11,138.53 MWh. Compared with coal- and gas-fired plants of the same capacity, avoided emissions of approximately 8.6 kt and 5.0 kt CO₂ eq per year were achieved. The water footprint was determined as ≈0.10 m³ MWh⁻¹ (≈1114 m³ yr⁻¹), which was substantially lower than the values reported for fossil technologies. These findings confirmed that the proposed system offered a sustainable alternative to conventional geothermal and fossil-based electricity generation. Multi-objective optimization using NSGA-II was carried out to maximize energy and exergy efficiencies while minimizing total cost. Key parameters such as turbine inlet temperature (459–460 K) and ammonia concentration were tuned for performance stability. A sensitivity analysis identified the heat exchanger, the first condenser (Condenser 1), and two separators (Separator 1, Separator 2) as influential on both performance and cost. The exergoeconomic results indicated Separator 1, Separator 2, and the turbine as primary locations of exergy destruction. With an LCOE of 0.026 USD/kWh, the system emerged as a cost-effective and scalable solution for sustainable geothermal power production without auxiliary energy demand. Full article

Cold-air pooling (CAP) and frost risk represent significant climate-related hazards in karstic and agricultural environments, where local topography and surface cover strongly modulate microclimatic conditions. This study focuses on the Mohos sinkhole, Hungary’s cold pole, situated on the Bükk Plateau, to investigate the formation, structure, and persistence of CAPs in a Central European karst depression. High-resolution terrain-based modeling was conducted using UAV-derived digital surface models combined with multiple GIS tools (Sky-View Factor, Wind Exposition Index, Cold Air Flow, and Diurnal Anisotropic Heat). These models were validated and enriched by multi-level temperature measurements and thermal imaging under various synoptic conditions. Results reveal that temperature inversions frequently form during clear, calm nights, leading to extreme near-surface cold accumulation within the sinkhole. Inversions may persist into the day due to topographic shading and density stratification. Vegetation and basin geometry influence radiative and turbulent fluxes, shaping the spatial extent and intensity of cold-air layers. The CAP is interpreted as part of a broader interconnected multi-sinkhole system. This integrated approach offers a transferable, cost-effective framework for terrain-driven frost hazard assessment, with direct relevance to precision agriculture, mesoscale model refinement, and site-specific climate adaptation in mountainous or frost-sensitive regions. Full article

Climate change and urbanization are intensifying the urban heat island effect and negatively impacting outdoor thermal comfort in cities. Innovative planning strategies are required to design more livable and resilient urban spaces. Building on a state of the art of current Urban Digital Twins (UDTs) for outdoor thermal comfort analysis, this paper presents the design and implementation of a functional UDT prototype. Developed for a pilot area in Huelva, Spain, the system integrates real-time environmental data, spatial modeling, and simulation tools within an open-source architecture. The literature reveals that while UDTs are increasingly used in urban management, their application to outdoor thermal comfort remains limited and technically challenging, especially in terms of real-time data, modeling accuracy, and user interaction. The case study demonstrates the feasibility of a modular, open-source UDT capable of simulating mean radiant temperature and outdoor thermal comfort indexes at high resolution and visualizing the results in a 3D interactive environment. UDTs have strong potential for supporting microclimate-sensitive planning and improving outdoor thermal comfort. However, important challenges remain, particularly in simulation efficiency, model detail, and stakeholder accessibility. The proposed prototype addresses several of these gaps and provides a basis for future improvements. Full article