Search Results (99)

Acute kidney injury (AKI) affects thousands of hospitalized patients annually, yet early detection remains challenging as serum creatinine elevation lags behind clinical deterioration. Decreased urine output (UO) represents a key diagnostic criterion of AKI, sometimes manifesting hours before biochemical changes; however, current manual monitoring methods are labor-intensive and prone to error. Here, we developed and validated a simple, cost-effective automated urine flow meter using non-contact optical sensors, a peristaltic pump, and microcontroller-based automation for precise, real-time monitoring of urine output in clinical settings, named P-meter. Three successive prototypes (V1, V2, V3) were validated against gold-standard gravimetric measurements over 285 h of testing during animal experiments that required bladder catheterization. Iterative refinement addressed miniaturization challenges, fluid dynamics optimization, and sensor positioning to achieve progressively improved accuracy. The optimized V3 prototype demonstrated further enhanced volumetric precision, stability, and flow accuracy with near-unity linearity vs. reference method (R² = 0.9889), minimal bias (mean error −0.1 mL), and 94.18% agreement within confidence limits (n = 86), outperforming the initial V1 prototype (R² = 0.9971, mean error −1.69 mL, n = 207) and intermediate V2 design (R² = 0.9941, mean error 3.63 mL, n = 390), primarily in terms of reduced bias and improved agreement. The P-meter offers accurate urine output monitoring at a lower cost than commercial systems, facilitating its use in early AKI detection and thereby improving patient outcomes. Full article

A novel capacitive interdigital electrode (IDE) sensor for the in-situ measurement of soil moisture is presented. Two planar electrode configurations, spiral and embracing, were designed and evaluated through modeling, simulation, fabrication, and experimental validation. Compared with conventional circular and square electrodes, the proposed structures exhibited higher sensitivity and greater electric field penetration, with the spiral configuration offering the advantage of easier fabrication. The experimental results demonstrated that the calibrated spiral IDE sensor achieved a coefficient of determination (R²) of 0.9976 and a mean squared error (MSE) of 0.859, indicating good stability and repeatability over the tested period. Furthermore, comparison with a commercial moisture sensor showed that the proposed sensor reached a higher R² value of 0.9995, exhibiting closer agreement with gravimetric measurements. These findings confirm that the developed sensor holds strong potential for in situ monitoring of soil moisture and can provide valuable technical support for landslide monitoring and prevention. Full article

This study aims to estimate the organic load of oily wastewater by using Chemical Oxygen Demand (COD) measurements, addressing the analytical challenges posed by the hydrophobic, nonpolar, and often emulsified nature of Fats, oil and grease (FOG). This study established a reproducible and practical methodology for measuring COD in wastewater containing FOG at a laboratory scale, utilizing the nonionic surfactant T80 as a solubilizing and emulsifying agent. Precise gravimetric methods were employed to measure the mass of T80 (indirectly from volume (100–1400 µL/L)) added, and its correlation with COD was established. A strong linear relationship (R² = 0.993–0.998) between T80 concentration and COD confirmed its stability and suitability as a calibration standard. Experiments with sunflower (1–4 mL/L) and rapeseed oils (1–3 mL/L) showed that COD increased linearly with oil concentration and stabilized after prolonged mixing (96–120 h), indicating complete emulsification and micellar equilibrium. Even under T80 overdose conditions, COD retained linearity (R² > 0.99), though absolute values were elevated due to excess surfactant oxidation. Temperature variation (5 and 20 °C) and mild heating of coconut fat (30–32 °C) showed no significant effect on COD reproducibility, indicating that mixing time and surfactant dosage are the dominant factors influencing measurement accuracy. Overall, the study establishes T80 as a reliable surfactant for solubilizing oily matrices, providing a consistent and repeatable approach for COD assessment of wastewater containing FOG. The proposed method offers a practical basis and a step towards environmental monitoring and process control in decentralized and industrial wastewater treatment systems. Full article

This study evaluates the corrosion behavior of N80 production tubing steel under high-temperature, high-pressure (HTHP) conditions representative of CO₂-based geothermal exploitation in depleted hydrocarbon reservoirs. We developed a staged laboratory protocol that simulates (i) an early multiphase production window (oil + formation brine + supercritical CO₂), (ii) the same environment with the originally developed non-commercial inhibitor (INH), and (iii) a later stabilized stage dominated by near-anhydrous supercritical CO₂ (scCO₂) with trace brine and oil. Corrosion was quantified by gravimetric mass-loss, complemented by multi-scale surface characterization (2D/3D optical profilometry) and microscopic cross-section analysis. In the early multiphase scenario unprotected N80 experienced severe attack (mass-loss rate ≈ 0.67 mm·year⁻¹) with both uniform corrosion and incipient pitting beneath ferrous-carbonate deposits. Addition of an inhibitor at 5000 ppmv reduced mass loss by more than an order of magnitude (to ≈0.09 mm·year⁻¹, ≈97% inhibition) and substantially limited pitting. Under stabilized, near-dry scCO₂ conditions, corrosion was negligible (≈0.0016 mm·year⁻¹). Multi-scale imaging linked observed morphologies (porous FeCO₃ scales, under-deposit pits) to measured rates and supported stage-specific mitigation recommendations. The novelty of this work lies in the integrated, staged HTHP experimental approach and in providing quantitative, actionable inputs for material selection, inhibitor deployment, and monitoring strategies for CCS–EGS projects that reuse depleted hydrocarbon reservoirs. Full article

Understanding suspended sediment transport in macrotidal embayments is crucial for assessing water quality, ecosystem function, and long-term morphological stability. This study provides a high-resolution, localized estimate of suspended sediment flux and examines the empirical relationship between turbidity (NTU, nephelometric turbidity unit) and total suspended matter (TSM, mg·L⁻¹) in the main tidal channel of Gomso Bay, a UNESCO-designated tidal flat on the west coast of Korea. A 13 h high-resolution fixed-point observation was conducted during a semi-diurnal tidal cycle using a multi-instrument platform, including an RCM, CTD profiler, tide gauge, and water sampling for gravimetric TSM analysis. Vertical measurements at the surface, mid, and bottom layers, taken every 15–30 min, revealed a strong linear correlation (R² = 0.94) between turbidity and TSM, empirically validating the use of optical sensors for real-time sediment monitoring under the highly dynamic conditions of Korean west-coast tidal channels. The net suspended sediment transport load was estimated at approximately 5503 kg·m⁻¹, with ebb-dominant residual currents indicating a net seaward sediment flux at the observation site. Residual flows over macrotidal channels are known to vary laterally, with landward fluxes often occurring over shoals. Importantly, the results from this single-station, short-duration observation indicate a predominantly seaward suspended sediment transport during the study period, which should be interpreted as a localized and time-specific estimate rather than a bay-wide characteristic. Nevertheless, these findings provide a baseline for assessing sediment flux and contribute to future applications in digital twin modeling and coastal management. Gomso Bay is part of the UNESCO-designated ‘Getbol, Korean Tidal Flats’, underscoring the global significance of preserving and monitoring this dynamic coastal system. Full article

Low-cost optical sensors for particulate matter (PM) monitoring, such as the SDS011, are widely used due to their affordability and ease of deployment. However, their accuracy strongly depends on aerosol properties and environmental conditions, necessitating reliable calibration. This study presents a theoretical and laboratory evaluation of a practical calibration method based on visibility sensors, which measure atmospheric light extinction and are readily available at many meteorological stations. Experiments were conducted in a controlled aerosol chamber, using SDS011 sensors, visibility sensors (FD70 and SWS250), and gravimetric samplers. The mass extinction coefficient was determined through parallel measurements of visibility and mass concentration, enabling conversion of optical signals into accurate PM values. The calibrated SDS011 sensors demonstrated consistent response with a stable normalization factor (dependent on aerosol type, wavelength, and particle size), allowing their deployment as a spatially distributed sensor network. Comparison with manufacturer calibration revealed substantial deviations due to differences in aerosol optical properties, highlighting the importance of application-specific calibration. The visibility-based approach enables real-time, continuous calibration of low-cost sensors with minimal equipment, offering a scalable solution for PM monitoring in resource-limited or remote environments. The method’s robustness under varying environmental conditions remains to be explored. Nevertheless, the results establish visibility-based calibration as a reliable and accessible framework for enhancing the accuracy of low-cost PM sensing technologies. The method enables scalable calibration with a single gravimetric reference and is suited for future field deployment in resource-limited settings, following additional validation under real atmospheric conditions. Full article

Monitoring moisture content in agricultural products during the drying process is critical for ensuring quality, preserving nutritional value, and optimizing energy consumption. This study presents the design and implementation of an optical fiber sensor based on multimode interference (MMI) for non-destructive detection of moisture content in mango (Mangifera indica L., cv. Ataulfo) and papaya (Carica papaya) slices during convective drying at 57 °C. Two sensors were designed and fabricated: one operates in the 975 nm range and the other in the 1414.25 nm range. These sensors detect variations in the refractive index caused by moisture loss, which directly affects the MMI spectral response. The sensor output was correlated with reference gravimetric measurements, demonstrating a dependence in tracking the output power as a function of the reduction in humidity over time. The results confirm the feasibility of the MMI-based optical fiber sensor as a reliable tool for in situ monitoring of drying dynamics in tropical fruits, offering potential applications in agri-food processing and quality control. Full article

Wearable, rectifiable aerosol photometers (WRAPs), instruments with combined nephelometer and on-board filter-based sampling capabilities, generally show strong correlations with reference instruments across a range of ambient and household PM_2.5 concentrations. However, limited data exist on their performance when challenged by mixed aerosol exposures, such as those found in dusty occupational environments. Understanding how these instruments perform across a spectrum of environments is critical, as they are increasingly used in human health studies, including those in which concurrent PM_2.5 and coarse dust exposures occur simultaneously. The authors collected co-located, ~24 h. breathing zone gravimetric and nephelometer PM_2.5 measures using the MicroPEM v3.2A (RTI International) and the UPAS v2.1 PLUS (Access Sensor Technologies). Samples were collected from adult brick workers (n = 93) in Nepal during work and non-work activities. Median gravimetric/arithmetic mean (AM) PM_2.5 concentrations for the MicroPEM and UPAS were 207.06 (interquartile range [IQR]: 216.24) and 737.74 (IQR: 1399.98) µg/m³, respectively (p < 0.0001), with a concordance correlation coefficient (CCC) of 0.26. The median stabilized inverse probability-weighted nephelometer PM_2.5 concentrations, after gravimetric correction, for the MicroPEM and UPAS were 169.16 (IQR: 204.98) and 594.08 (IQR: 1001.00) µg/m³, respectively (p-value < 0.0001), with a CCC of 0.31. Digital microscope photos and electron micrographs of filters confirmed large particle breakthrough for both instruments. A possible explanation is that the miniaturized pre-separators were overwhelmed by high dust exposures. This study was unique in that it evaluated personal PM_2.5 monitors in a high dust occupational environment using both gravimetric and nephelometer-based measures. Our findings suggest that WRAPs may substantially overestimate personal PM_2.5 exposures in environments with concurrently high PM_2.5 and coarse dust levels, likely due to large particle breakthrough. This overestimation may obscure associations between exposures and health outcomes. For personal PM_2.5 monitoring in dusty environments, the authors recommend traditional pump and cyclone or impaction-based sampling methods in the interim while miniaturized pre-separators for WRAPs are designed and validated for use in high dust environments. Full article

Meat juice leakage is a natural phenomenon, evident in culinary meat packaging, and is a key indicator of meat quality. This study aimed to evaluate the amount of meat juice leakage into the packaging during culinary pork, beef, and chicken storage in a refrigerated display case simulating retail conditions (2–4 °C, 12 days). The study included 1800 high-quality culinary meat samples, i.e., free of technological defects, packaged in vacuum (VAC) and modified atmosphere (MAP), with and without absorbent pads, obtained from meat processing plants. On the 12th day of storage, the leakage was determined in the meat portions using the gravimetric method, and pH and color (CIEL*a*b*) were measured using instrumental methods. It was found that the leakage level from culinary meat ranged from 2.10% for pork shoulder VAC to 10.70% for pork loin VAC, in slices, being influenced (p < 0.01) by each grouping factor: meat type, meat cut, and package type. Regardless of the packaging method, culinary chicken meat had a lower pH (p < 0.001) than pork and beef. The study also found significant negative correlations between pH and leakage in most culinary meat cuts, as shown by the results for ham VAC (r = −0.66), ham MAP (r = −0.59), and heel of round MAP (r = −0.50). Among meat color parameters, the most significant variability was observed for lightness (L*), whose mean value differed significantly depending on the type of meat (p < 0.001) and the meat cut (p < 0.001), and within the same culinary cut—except beef tenderloin and chicken breast fillet–also depending on the type of packaging (p < 0.05). Based on the results obtained in this study, covering a large number of culinary meat samples, it was suggested that recommended leakage levels, i.e., those that raise no concerns regarding meat quality, could range from 2% for shoulder and pork neck (both VAC) to just over 10% for tenderloin slices (packaged using the MAP and VAC methods). Our findings can be used by both meat producers and quality control authorities to monitor the quality of culinary meat, e.g., they may help determine maximum permissible leakage levels and design meat packaging methods to reduce leakage. Ultimately, these measures will enhance consumer confidence in meat production and quality. Additionally, the results systematize knowledge on meat leakage, providing valuable insights for scientists who support producers and retailers in their efforts to minimize this issue. Full article

The corrosion behavior of open-cell AlSn6Cu-based composites, one reinforced with SiC particles and the other with Al₂O₃ particles, was investigated. The composites were fabricated via liquid-state processing, employing both squeeze casting and the replication method, and they produced in two distinct pore size ranges (800–1000 µm and 1000–1200 µm). Corrosion performance was systematically evaluated through gravimetric (weight loss) measurements and electrochemical techniques, including open-circuit potential monitoring and potentiodynamic polarization tests. Comprehensive microstructural and phase analyses were conducted using X-ray diffraction, energy-dispersive X-ray spectroscopy, and scanning electron microscopy. The results revealed that both reinforcement type and pore architecture have a significant impact on corrosion resistance. Al₂O₃-reinforced composites consistently outperformed their SiC-containing counterparts, and pore enlargement generally improved performance for the unreinforced alloy and the Al₂O₃ composite but not for the SiC composite. Overall, the optimal corrosion resistance is achieved by pairing a coarser-pore architecture (1000–1200 µm) with Al₂O₃ reinforcement, which minimizes both instantaneous (electrochemical) and cumulative (gravimetric) corrosion metrics. This study addresses a gap in current research by providing the first detailed assessment of corrosion in open-cell AlSn6Cu-based composites with controlled pore architectures and different ceramic reinforcements, offering valuable insights for the development of advanced lightweight materials for harsh environments. Full article

This study presents a novel framework to assess the combined impact of soiling and thermal effects on rooftop PV systems through multi-seasonal, multi-site field campaigns in an industrial-urban environment. This work addresses key research gaps by providing a high-resolution, site-specific analysis that captures the synergistic effect of particulate accumulation and thermal stress on PV performance in an industrial-urban environment—a setting distinct from the well-studied arid climates. The study further bridges a gap by employing controlled pre- and post-cleaning performance tests across multiple sites to isolate and quantify soiling losses, offering insights crucial for developing targeted maintenance strategies in pollution-prone urban areas. Unlike previous work, it integrates gravimetric soiling measurements with high-resolution electrical (I–V), thermal, and environmental monitoring, complemented by PVSYST simulation benchmarking. Field data were collected from five rooftop plants in Bursa, Türkiye, during summer and winter, capturing seasonal variations in particulate deposition, module temperature, and PV output, alongside irradiance, wind speed, and airborne particulates. Soiling nearly doubled in winter (0.098 g/m²) compared to summer (0.051 g/m²), but lower winter temperatures (mean 19.8 °C) partially offset performance losses seen under hot summer conditions (mean 42.1 °C). Isc correlated negatively with both soiling (r = −0.68) and temperature (r = −0.72), with regression analysis showing soiling as the dominant factor (R² = 0.71). Energy yield analysis revealed that high summer irradiance did not always increase output due to thermal losses, while winter often yielded comparable or higher energy. Soiling-induced losses ranged 5–17%, with SPP-2 worst affected in winter, and seasonal PR declines averaged 10.8%. The results highlight the need for integrated strategies combining cleaning, thermal management, and environmental monitoring to maintain PV efficiency in particulate-prone regions, offering practical guidance for operators and supporting renewable energy goals in challenging environments. Full article

The instability of many volatile organic compounds (VOCs) limits their usage in different fragrance carriers and products. In scratch-and-sniff applications, VOCs are bound so strongly that release cannot happen without an external trigger. On the other hand, other fixatives like cyclodextrins release unstable volatile molecules too rapidly. We engineered biodegradable gelatin films whose release profile can be tuned by glycerol plasticization and alkaline protease degradation. Digitalized VOC release profiles acquired with the described near-real-time analysis toolkit are digital twins that replicate the behavior of the evaluated films in silico. Seven formulations were cast from 10% gelatin containing D-limonene, glycerol (5%, 20%), protease-C 30 kU mL⁻¹, and samples with additional water to establish a higher hydromodule for protease catalytic activity. Release profiles were monitored for nine days at 23 ± 2 °C in parallel by metal-oxide semiconductor (MOS) e-noses, gravimetric weight loss, and near-infrared measurements (NIR). These continuous measurements were cross-checked with gel electrophoresis, FTIR spectroscopy, hardness tests, and sensory intensity ratings. Results showed acceleration of VOC release by enzymatic treatment during the first days, as well as overall impact on the release profile. Differences in low and high glycerol films were observed, and principal component analysis of NIR spectra separated low and high glycerol groups, mirroring the MOS and FTIR data. Usability of MOS data was explored in comparison to more biased and subjective intensity results from sensory panel evaluation. Overall, the created toolkit showed good cross-checked results and enabled the possibility for close to real-time analysis for bio-based VOC carriers. Full article

Soil moisture or moisture content is a fundamental constituent of the hydrological system of the Earth and its ecological systems, playing a pivotal role in the productivity of agricultural produce, climate modeling, and water resource management. This review comprehensively examines conventional and advanced approaches for estimation or measuring of soil moisture, including in situ methods, remote sensing technologies, UAV-based monitoring, and machine learning-driven models. Emphasis is primarily on the evolution of soil moisture measurement from destructive gravimetric techniques to non-invasive, high-resolution sensing systems. The paper emphasizes how machine learning modules like Random Forest models, support vector machines, and AI-based neural networks are becoming more and more popular for modeling intricate soil moisture dynamics with data from several sources. A bibliometric analysis further underscores the research trends and identifies key contributors, regions, and technologies in this domain. The findings advocate for the integration of physics-based understanding, sensor technologies, and data-driven approaches to enhance prediction accuracy, spatiotemporal coverage, and decision-making capabilities. Full article

Effective water resource management plays a crucial role in achieving sustainability in agriculture, hydrology, and environmental protection, particularly under growing water scarcity and climate-related challenges. Soil moisture (θ), matric potential (h), and hydraulic conductivity (K) are critical parameters influencing water availability for crops and regulating hydrological, environmental, and ecological processes. To address the need for accurate, real-time soil monitoring in both laboratory and open-field conditions, we proposed an innovative IoT-based monitoring system called SHYPROM (Soil HYdraulic PROperties Meter), designed for the simultaneous estimation of parameters θ, h, and K at different soil depths. The system integrates capacitive soil moisture and matric potential sensors with wireless communication modules and a cloud-based data processing platform, providing continuous, high-resolution measurements. SHYPROM is intended for use in both environmental and agricultural contexts, where it can support precision irrigation management, optimize water resource allocation, and contribute to hydrological and environmental monitoring. This study presents recent technological upgrades to the proposed monitoring system. To improve the accuracy and robustness of θ estimates, the capacitive module was enhanced with an integrated oscillator circuit operating at 60 MHz, an upgrade from the previous version, which operated at 600 kHz. The new system was tested (i.e., calibrated and validated) through a series of laboratory experiments on soils with varying textures, demonstrating its improved ability to capture dynamic soil moisture changes with greater accuracy compared to the earlier SHYPROM version. During calibration and validation tests, soil water content data were collected across a θ range from 0 to 0.40 cm³/cm³. These measurements were compared to reference θ values obtained using the thermo-gravimetric method. The results show that the proposed monitoring system can be used to obtain predictions of θ values with acceptable accuracy (R² values range between 0.91 and 0.96). To further validate the performance of the upgraded SHYPROM system, evaporation experiments were also conducted, and the θ(h) and K(θ) relationships were determined among soils. Retention and conductivity data were fitted using the van Genuchten and van Genuchten–Mualem models, respectively, confirming that the device accurately captures the temporal evolution of soil water status (R² values range from 0.97 to 0.99). Full article

Monitoring soil moisture content (SMC) remains challenging due to its spatial and temporal variability. Accurate SMC prediction is essential for optimizing irrigation and enhancing water use efficiency. In this research, a Gradient Boosting Regressor (GBR) model was developed and validated to predict SMC in two soil textures, loam and silt loam, using meteorological data from Internet of Things (IoT) sensors and gravimetric SMC field measurements collected from five different depths. The statistical analysis revealed significant variation in SMC across depths in loam soil (p < 0.05), while silt loam exhibited more stable moisture distribution. The GBR model demonstrated high performance in both soil textures, achieving R² values of 0.98 and 0.94 for silt loam and loam soils, respectively, with low prediction errors (RMSE 0.85 and 0.97, respectively). Feature importance analysis showed that precipitation and humidity were the most influential features in loam soil, while solar radiation had the highest impact on prediction in silt loam soil. Soil depth also showed a significant contribution to SMC prediction in both soils. These results highlight the necessity for soil-specific modeling to enhance SMC prediction accuracy, optimize irrigation systems, and support water resources management approaches aligning with SDG6 objectives. Full article