Search Results (1,530)

Heavy-duty vehicles (HDVs) are major greenhouse gas emitters, and liquefied natural gas (LNG)-powered HDVs have emerged as a promising low-carbon alternative. However, their real-world emission performance and mitigation potential remain insufficiently studied, necessitating the characterization of LNG container trucks’ on-road CO₂ emissions via advanced sensing technologies. To characterize HDVs’ emission characteristics, real-driving emissions from China VI LNG and diesel-powered container trucks were measured employing portable emissions measurement systems (PEMS). The results reveal that high CO₂ emissions predominantly occur during low- to medium-speed acceleration and at speeds above 40 km/h with an acceleration exceeding 0.3 m/s² on highways, whereas emissions on port roads are more dispersed. A third-degree polynomial function fits emissions well with vehicle-specific power (VSP). Engine parameters mainly influence CO₂ emissions for LNG trucks, while VSP and acceleration significantly impact diesel trucks. The Random Forest model achieves superior prediction accuracy, particularly in highway scenarios, and significantly better CO₂ forecasting for LNG-powered trucks. These findings validate the effectiveness of PEMS-based sensing in characterizing low-carbon HDVs’ real-world emissions. The integration of multi-source sensor data and machine learning also provides a reference for intelligent sensing in transportation environmental monitoring. Full article

Timely and accurate soil property information is critical for sustainable agriculture and precision nutrient management. Conventional laboratory methods are accurate but costly and labor-intensive, restricting their feasibility for high-density soil mapping. Low-cost, portable near-infrared (NIR) spectroscopy presents a promising alternative for rapid, on-site, and non-destructive soil analysis. This study aimed to evaluate the potential of a low-cost, portable NIR sensor (NeoSpectra) for the quantitative prediction of key soil properties in paddy fields from Southeastern China. The target properties were soil organic matter (SOM), total nitrogen (TN), pH, and particle size fractions (clay, silt, and sand). A total of 995 soil samples were collected from representative paddy fields in the region and spectra measurements were conducted in the laboratory on air-dried samples. We developed and compared the performance of multiple machine learning algorithms, including partial least squares regression (PLSR), Cubist, random forest (RF) and memory-based learning (MBL), to build robust calibration models. The predictive models showed substantial performance for SOM and TN, indicating high accuracy (R² > 0.75, LCCC > 0.85, RPD > 2) for quantitative prediction. Predictions for pH, silt, sand, and clay were less accurate (R² of 0.48–0.53, LCCC of 0.67–0.71, RPD of 1.39–1.49), suggesting the sensor’s utility is limited to indicating general trends for these properties. Among the tested algorithms, MBL consistently provided the most accurate and robust predictions across the majority of soil properties. Our findings demonstrate that the low-cost portable NIR sensor, when coupled with appropriate machine learning algorithms, is a powerful and viable tool for the rapid and reliable estimation of critical paddy soil fertility properties (SOM and TN). This technology has significant potential to support field-level soil health monitoring, precision fertilization strategies, and sustainable land management in the agricultural systems of Southeastern China. Full article

The development of bioplastic films represents an alternative to conventional plastics and an opportunity for applications in intelligent packaging. The present study aimed to develop a smart material based on poly (vinyl alcohol) (PVA) incorporated with Clitoria ternatea L. extract, capable of monitoring shrimp freshness through colour changes associated with pH variations. The films were prepared using the casting method and characterised in terms of their physical, mechanical, structural, and functional properties. The incorporation of the anthocyanin extract (EAC) significantly intensified the colouration of the films, decreasing lightness (L*) from 88.7 to 37.1 and modifying the chromatic parameters (b from −0.16 to −22.34). Thickness increased from 109.5 μm to 184 μm as the extract concentration was raised, while water vapour permeability ranged from 0.77 to 1.79 g·m/m²·s·Pa, evidencing modifications in the structure of the polymeric matrix. From a mechanical standpoint, tensile strength decreased from 26.0 MPa to 15.2 MPa, and the elastic modulus was reduced by approximately 75.0 MPa, whereas the percentage elongation at break increased from 75.2% to 92.4%, confirming the plasticising effect of the extract. Functionally, the films exhibited a visible transition from blue to green during the refrigerated storage of shrimp, corresponding to increases in pH from 6.6 to 9.2 and total volatile basic nitrogen (TVB-N) values from 3.92 to 67.7 mg N/100 g. The formation of TVB-N followed first-order kinetics (R² = 0.997), confirming the sensitivity of the system as a freshness indicator. These results demonstrate the potential of PVA–anthocyanin films as intelligent colorimetric sensors for monitoring the freshness of protein-rich foods. Full article

In this study, a highly crystalline 2H (hexagonal)-phase MoS₂ sensing layer with a precisely controlled crystal structure was realized through a combination of DC sputtering and sulfurization annealing processes, and subsequently integrated with a D-shaped optical fiber to develop a highly sensitive carbon dioxide (CO₂) sensor. Conventionally sputtered MoS₂ thin films often suffer from the presence of unstable metallic 1T (tetragonal) phases and a high density of sulfur vacancies, which significantly degrade sensor reversibility and long-term stability. Here, high-temperature annealing under a sulfur-rich atmosphere was employed to induce a complete phase transition from the metastable 1T phase to the stable semiconducting 2H phase, while simultaneously healing sulfur vacancies. Enhanced crystallinity was confirmed by Raman spectroscopy. The fabricated sensor exhibited excellent linearity (R² > 0.99) and markedly improved repeatability over a CO₂ concentration range of 1000–10,000 ppm. This significant performance enhancement is attributed to reversible charge transfer induced by sulfur vacancy passivation, which modulates the complex refractive index of the MoS₂ layer and optimizes optical interaction with the evanescent field of the D-shaped fiber. The phase engineering and defect-healing strategy presented in this work effectively addresses the drift issues commonly observed in conventional electrical gas sensors and provides a crucial pathway toward the realization of high-performance optical gas sensors. Full article

In this paper, a study on the development of indium-doped Cu_xS heterojunction-based conductometry sensors is presented. To fabricate the sensors, thick films of In-Cu_xS heterojunctions were sprayed directly on the alumina sensing platform provided with interdigitated Pt electrodes. The effect of the doping level with different nominal amounts of InCl₃ additive (0%, 3%, and 5%) on the structural, morphological and optical properties of Cu_xS films was first studied by XRD, AFM, UV-Vis and Raman spectroscopy. Moreover, the electrical and sensing characteristics towards low concentrations of hydrogen sulfide (H₂S) in air were investigated. The tests carried out clearly demonstrated the positive effect of In doping on the H₂S sensing performance of Cu_xS. The 5%-doped Cu_xS sensor showed the highest sensitivity to the target gas compared to the other sensor, as well as good stability and selectivity properties. Full article

The physicochemical controls on gold precipitation in orogenic gold deposits remain poorly constrained, with traditional fluid inclusion and isotopic studies often yielding ambiguous results due to overprinting or incomplete records. This study addresses this challenge using chlorite—a sensitive mineral proxy for fluid conditions—as a quantitative sensor in the Jinshan orogenic gold deposit (>200 t Au) of the Jiangnan orogenic belt, South China. Hosted in Neoproterozoic phyllite within NE–NNE-trending ductile–brittle shear zones, Jinshan features auriferous quartz–polymetallic sulfide veins with prominent chlorite alteration. Integrating high-resolution SEM-EPMA analyses of multi-generational chlorite with thermodynamic modeling, we reconstruct the temporal evolution of temperature, oxygen fugacity (fO₂), pH and sulfur fugacity (fS₂) during ore formation. Four paragenetic stages are identified: Stage 1 (ankerite–quartz), Stage 2 (pyrite–arsenopyrite–quartz), Stage 3 (quartz–gold–polymetallic sulfide), and Stage 4 (chlorite–carbonate–quartz). Electron microprobe analysis reveals that the chlorite composition changes from Fe-rich chamosite (Stage 2) to Mg-rich clinochlore (Stage 3) and then to Fe-rich chamosite (Stage 4). Chlorite from Stage 2 (Chl-1) formed metasomatically at low fluid/rock ratios, while Stage 3 and 4 chlorites (Chl-2 and Chl-3) precipitated directly from higher fluid/rock ratio fluids. Chlorite compositions record a critical Stage 2–3 transition involving cooling from ~320 °C to ~260 °C, reduction (log fO₂ from −33.6 to −39.7), and alkalinization, and sulfur fugacity remained stable within a narrow range (log fS₂ = −13.6 to −8.0), followed in Stage 4 by minor reheating to ~280 °C, re-acidification, and a slight rebound in oxygen fugacity. Thermodynamic simulations reveal that the destabilization of Au(HS)₂⁻ complexes, primarily driven by the synergistic effects of cooling, pH increase, and decreasing oxygen fugacity, triggered gold precipitation during the main ore stage. Results demonstrate that abrupt cooling coupled with fluid alkalinization and reduction exerted the dominant control on gold precipitation in Jinshan, resolving long-standing debates on ore-forming mechanisms and highlighting chlorite as a robust quantitative sensor for fluid evolution. Full article

Mycelium-based composites (MBCs) have emerged as promising biofabricated materials that align with circular economy and clean technology goals by utilizing fungal networks to transform lignocellulosic residues into functional, biodegradable composites. Despite the MBC’s potentials, the intrinsic nature of the fungal strain, substrate physico-chemical composition and engineering property variability remain significant hurdles that should be critically surmounted. Substrate treatment is central to determining growth kinetics, microstructural uniformity, and mechanical performance in MBC production. This review highlights recent advancements in physical, chemical, biological, and hybrid pretreatment methods, including comminution, pasteurization, alkali hydrolysis, enzymatic conditioning, microwave-assisted hydrolysis, ultrasound pretreatment, steam explosion, plasma activation, and irradiation. These technologies collectively enhance substrate digestibility, aeration, and permeability while reducing contamination. Optimization parameters—temperature, pH, C:N ratio, moisture content, particle size, porosity, and aeration—are examined as critical process levers influencing hyphal density, bonding efficiency, and composite uniformity. Evidence suggests that properly engineered substrate treatments accelerate colonization, strengthen hyphal networks, and significantly improve compressive, tensile, and flexural material properties. The review discusses emerging process control tools such as AI-assisted modeling, micro-CT porosity analysis, and sensor-integrated bioreactors that enable reproducible and energy-efficient fabrication. Collectively, the findings position substrate engineering as a foundational technology for scaling high-performance mycelium composites and advancing sustainable material innovation. Full article

Leakage detection in water distribution networks plays an instrumental role in effectively addressing water loss, yet the scarcity of annotated leak events limits the applicability of supervised classification methods. While hydraulic simulation-generated datasets are often considered as an alternative, their generation is hindered by incomplete network topology and sparse sensor coverage in real-world settings. Consequently, many real-world solutions rely on unsupervised anomaly detection approaches but frequently struggle to balance sensitivity and accuracy. This study proposes a regression-ensemble framework that learns the district metered area (DMA)-specific demand–supply dynamics to detect emerging leaks using smart meter data, without requiring real or simulated labeled leak datasets for training. Regression models—Random Forest, Support Vector Regression, XGBoost, and Multi-Layer Perceptron—are trained on DMA-level consumption and supply data that are preprocessed to preserve background leakage while correcting emerging leaks. Deviations between predicted and observed supply are quantified through Pearson correlation, Kendall’s tau, and Z-score, whose anomaly indications are combined at metric and model levels using weights derived from model prediction accuracy. A leak is identified once the ensemble anomaly score crosses a threshold. The system detects leaks within 8–12 h of onset, achieving 90% and 98% accuracy on simulated and real leak scenarios, respectively, at an anomaly-score threshold of 0.5. Recall rates of 85% and 95% are observed for simulated and real leaks, respectively, whereas 95% and 100% recall are observed for no-leak events in both leak scenarios, respectively. Our proposed framework demonstrates the potential of smart meter-driven ensemble analytics for rapid and robust leak detection. Full article

This study compares a set of 43 AlphaSensor units produced by RadonTec GmbH, Wittislingen, Germany against the AlphaGUARD 1000PF (Bertin Technologies, Montigny-le-Bretonneux, France), which is used as a reference monitor. During this study, around 16 k integrated measurements were conducted. The concentration range varied between 10 and 20 k Bq/m³. Multiple key performance indicators, such as sensitivity, uncertainty, background, linearity, and temporal response, were evaluated using a variety of statistical approaches. The results confirm the manufacturer’s claim of 10% or lower uncertainty in comparison with AlphaGUARD. We tentatively suggest individual calibration factors and methodologies. Our conclusion is that the AlphaSensor and commercial devices based on it, such as AlphaTracer, are affordable and applicable for home use. With modest additional hardware, AlphaSensors are also a good option for scientific studies involving the deployment of large monitoring networks. Full article

Photoionization detector (PID) sensors are widely used for ambient Volatile organic compound (VOC) monitoring because they are inexpensive, flexible, and fast. However, PID outputs are strongly influenced by environmental conditions (especially temperature and relative humidity) and exhibit substantial inter-sensor variability, limiting their quantitative reliability. Here we present a rapid machine-learning calibration workflow that maps PID signals and meteorological covariates to a photochemically relevant reference metric, PTR-derived VOC OH reactivity (R_OH,PTR, s⁻¹), calculated from online PTR-ToF-MS VOC measurements weighted by OH reaction rate constants. Four MiniPID sensors were co-located with a PTR-ToF-MS and a thermohygrometer, and data were harmonized to 10-s resolution. Multiple regression models were evaluated, with ensemble methods (RF and XGBoost) providing the best overall performance. To ensure realistic generalization under temporal autocorrelation, validation used a time-aware split: models were trained on a contiguous 24-h co-location period and evaluated on subsequent days (out-of-time). In this out-of-time evaluation, XGBoost achieved strong agreement with R_OH,PTR across sensors (Pearson’s r = 0.85, R² = 0.64, RMSE = 1.74 s⁻¹), while substantially improving inter-sensor consistency. This short-duration calibration approach supports practical co-location-based harmonization of PID networks for high-temporal-resolution VOC reactivity monitoring in urban and industrial environments. Full article

Hypoxic pulmonary vasoconstriction (HPV) is a rapid and reversible constrictor response of the pulmonary vasculature, and especially its small muscular precapillary arteries, which is initiated by episodes of local alveolar hypoxia. Acting as a protective homeostatic vasomotor mechanism, HPV enables maximal gas exchange by diverting blood from poorly ventilated alveoli into those rich in oxygen, thereby optimizing oxygen uptake and the ventilation–perfusion (V/Q) ratio so as to maintain the arterial oxygen partial pressure (P_aO₂) within the physiological range. HPV is an intrinsic mechanism of pulmonary artery smooth muscle cells (PASMCs), and requires an O₂ sensor which acts through mediator(s) to trigger effector mechanisms within these cells to evoke constriction. Whereas HPV effector mechanisms are reasonably well defined, the nature of the O₂ sensor and mediators remains in dispute, and a number of proposals have been developed to account for these. Some (but not all) of these share a focus on the concept that hypoxia activates effector mechanisms by inducing a change in the PASMC cytoplasmic redox state. Of these, the Redox Theory, first proposed by Kenneth Weir and Stephen Archer in 1995, proposes that hypoxia inhibits mitochondrial production of reactive oxygen species (ROS), thereby causing the cytoplasm to become more reduced. This inhibits ongoing vasorelaxation maintained by the opening of voltage-gated K⁺ channels. In contrast, according to the Mitochondrial ROS hypothesis, introduced by Paul Schumacker and Naveen Chandel in 2001, hypoxia increases mitochondrial ROS production, causing an oxidizing shift in the cytoplasmic redox state that activates several vasoconstricting pathways. In a third redox-based scenario, developed by Michael Wolin and Sachin Gupte, hypoxia evokes contraction by causing a fall in H₂O₂ production by NADPH oxidase and by activating the pentose phosphate pathway. These effects inhibit basal vasorelaxation maintained by the guanylate cyclase and protein kinase G and also stimulate vasoconstricting mechanisms. In this comprehensive review, we first provide a detailed summary of the key studies contributing to the development of these proposals and then subject the evidence supporting them to a critical appraisal, based in part on how well they accord with the wider literature and recent developments in our understanding of how cells shape and deploy redox mechanisms in order to regulate cell function. Full article

Liquefied petroleum gas (LPG) is a widely used clean and efficient fuel across domestic and industrial sectors. However, the highly flammable nature of LPG poses serious safety risks. Therefore, the advancement of dependable and effective LPG sensors is vital. This work produced a cost-effective and extremely sensitive LPG thin film sensor that operates at room temperature using hydrothermally generated MoTe₂. The synthesized MoTe₂ was comprehensively characterized to investigate its phase purity, crystal structure, phase formation, and morphology employing powder X-ray diffraction (PXRD), field emission scanning electron microscopy (FE-SEM), and Raman spectroscopy. The PXRD and Raman results confirmed the formation of a single-phase hexagonal 2H-MoTe₂ structure, while FE-SEM analysis revealed elongated, sheet-like morphologies. The LPG sensing properties were evaluated across concentrations ranging from 0.5 to 2.0 vol%. The sensor exhibited a maximum response of 1.50 at 2.0 vol% LPG, while the fastest response and recovery times of 11 s and 23 s, respectively, were observed at 0.5 vol% LPG. Additionally, the sensor demonstrated excellent repeatability, reaching 99.55%. The mechanism involving the adsorption and desorption of LPG is also explained. Full article

Background: Research on vibrations induced by running has gained significant attention due to its implications for athletes’ performance, injury prevention, and overall well-being. Distance running exposes the body to repetitive impulsive forces, causing significant vibrations to travel through physiological systems and biomechanical structures. These vibrations increase fatigue and the risk of injury. Although it has gained importance, research on induced vibration during running and wearable equipment for monitoring is scarce. This study aims to evaluate the performance of a measurement system for monitoring the acceleration levels of induced vibrations during long-distance running, exploring the capability of non-invasive wearable devices to characterise vibration transmissibility and exposure. Moreover, a preliminary quantitative assessment of induced vibration levels for an indoor testing scenario is given. Methods: Metrological characterisation of Xsens Motion Trackers Awinda (MTw), off-the-shelf inertial magnetic motion trackers, was performed by measuring the sensors’ frequency bandwidth in a controlled environment, providing logarithmic sweep sine excitations at different levels (2 g, 5 g, 7 g, where g is meant to be the gravitational acceleration). A testing protocol for indoor testing was derived from the literature, allowing characterisation of the sensors’ behaviour in terms of vibration transmissibility and exposure detection in the intended application. Time domain and frequency domain analyses were conducted by following the ISO 2631 standard guideline for vibration exposure assessment, and measurement uncertainty was defined, either for the dynamic correction of the sensors’ frequency behaviour or for the computed time and frequency domain metrics. In this framework, a treadmill-based test was conducted. The aim was to evaluate the Xsens sensors’ performance in measuring vibration dose exposure and transmissibility. Three MTws were placed on the subject’s right tibia, back, and forehead using elastic bands. A 25-year-old female amateur runner completed a series of tests consisting of walking for 1 min at 3.5 km/h (instrumentation setup), followed by running at two speeds (8 km/h and 11 km/h) for 2–4 min per trial, with 5 min rest periods between tests. Conclusions: The tested measurement system showed promising results due to its capability to assess vibration exposure during sports activities, but dynamic correction was found to be mandatory for accurate vibration level assessment. The main outcome of this study is a method for characterising the accelerometers embedded in the proposed devices, along with an analysis strategy for future testing campaigns. Thanks to the portability of IMUs (inertial measurement units), this approach enables the evaluation of induced vibrations during in-field running measurements. Full article

A tunable diode laser absorption spectroscopy (TDLAS) sensor with a highly sensitive dual-component for methane (CH₄) and acetylene (C₂H₂) detection is reported in this paper for the first time. A multi-pass cell (MPC) design model was established employing a vector-based ray-tracing method. A dual-channel MPC with an interlaced dual hexagonal star pattern was designed to improve gas absorption and realize real-time synchronous detection of CH₄ and C₂H₂. During the simultaneous continuous monitoring of CH₄ and C₂H₂, the sensor exhibited an excellent linear response to concentration variations. The minimum detection limit (MDL) for CH₄ reached 132.08 ppb, improving to 77.32 ppb when the average time was increased to 300 s. In the case of C₂H₂, the MDL was measured at 20.19 ppb and further reduced to 3.50 ppb under the same extended average time. Full article

Accurate monitoring of intracranial pressure (ICP) is critical for the diagnosis and management of neurological disorders. Although various ICP sensors have been developed, their sensitivity is often limited, restricting their ability to detect subtle pressure variations. Therefore, there is a pressing need to develop ICP sensors with enhanced sensitivity to improve measurement accuracy and patient outcomes. In this paper, a highly sensitive and precise pressure sensor for intracranial pressure (ICP) monitoring was proposed. Theoretically, the beam-membrane-island structure was introduced and optimized to improve sensitivity and linearity compared to a flat membrane structure. The notches etched at beam end were designed for further improving sensitivity. Experimentally, the designed sensor achieved a sensitivity of 1.59 mV/V//kPa and a nonlinearity of −0.22% F.S. Additionally, the sensor can detect pressure with centimeter water column (cm H₂O) resolution, making it suitable for ICP monitoring. This technology holds broad application prospects in the field of medical devices. Full article