Search Results (1,865)

To meet the demand for online monitoring of acetylene (C₂H₂) in transformer oil, a high-sensitivity detection system based on photoacoustic spectroscopy (PAS) is presented. The system integrates custom-designed modules for signal acquisition, phase-sensitive detection, and data processing, centered around a high-performance microcontroller. A full-wave lock-in amplification-based phase-sensitive detection circuit enables precise extraction of nV-level photoacoustic signals. Finite element simulations of the resonant photoacoustic cell in COMSOL 6.2 were conducted to optimize the structural configuration and sensor placement, achieving maximum acoustic response. Calibration experiments confirmed excellent system performance, demonstrating a linear response (R² > 0.99) over the 0.5–20 ppm range and a practical detection limit of 0.1 ppm. Comparative evaluations against conventional dissolved gas analysis (DGA) equipment verify the system’s sensitivity, stability, and temporal resolution, demonstrating its potential as a high-sensitivity and reliable solution for transformer fault gas diagnostics. Full article

To achieve a substantial enhancement in thermodynamic efficiency, Generation IV nuclear reactors are designed to operate at significantly elevated temperatures compared to conventional reactors. Moreover, they typically employ a fast neutron spectrum, characterized by higher neutron energy and flux. This combination results in a considerably more intense radiation environment within the core relative to traditional thermal neutron reactors. Therefore, the measurement of neutron flux in the core of Generation IV nuclear reactors faces the challenge of a high-temperature and high-radiation environment. Conventional neutron flux monitoring equipment—including fission chambers, gas ionization chambers, scintillator detectors, and silicon or germanium semiconductor detectors—faces considerable challenges in Generation IV reactor conditions. Under high temperatures and intense radiation, these sensors often experience severe performance degradation, significant signal distortion, or complete obliteration of the output signal by noise. This inherent limitation renders them unsuitable for the aforementioned applications. Consequently, significant global research efforts are focused on developing neutron detectors capable of withstanding high-temperature and high-irradiation environments. The objective is to enable accurate neutron flux measurements both inside and outside the reactor core, which are essential for obtaining key operational parameters. In summary, the four different types of neutron detectors have different performance characteristics and are suitable for different operating environments. This review focuses on 4H-SiC, diamond detectors, high-temperature fission chambers, and self-powered neutron detectors. It surveys recent research progress in high-temperature neutron flux monitoring, analyzing key technological aspects such as their high-temperature and radiation resistance, compact size, and high sensitivity. The article also examines their application areas, current development status, and offers perspectives on future research directions. Full article

To address the lack of compact and high-performance gas sensors in the literature, a miniaturized photoacoustic sensor has been developed using a resonant capacitive MEMS specifically designed for gas detection. Its performance is enhanced by coupling it to a T-shaped acoustic cavity, which confines and directs the acoustic waves toward the transducer. Electrical and photoacoustic characterizations were carried out to determine the nominal capacitance and resonance frequency of the device. The acoustic coupling resulted in a significant improvement in the transducer’s mechanical response, while the linearity of the sensor was confirmed over a broad concentration range. This improvement led to a reduction in the limit of detection (LOD) from 186 ppmv to 16 ppmv. In parallel, the Normalized Noise-Equivalent Absorption ($NNEA$) metric improved from $1.49\times {10}^{-7}\mathrm{W}·{\mathrm{cm}}^{-1}·{\mathrm{Hz}}^{-1/2}$ to $1.28\times {10}^{-8}\mathrm{W}·{\mathrm{cm}}^{-1}·{\mathrm{Hz}}^{-1/2}$, representing a 11-fold increase in sensitivity. Stability over time is confirmed through Allan–Werle deviation analysis, confirming the reliability of the signal over extended measurement periods. These results demonstrate that coupling a resonant MEMS transducer to a well-designed acoustic cavity is an efficient strategy to significantly improve the sensitivity of photoacoustic gas detection systems. Full article

Gas sensors are vital tools in areas such as environmental monitoring, industrial safety, and personal healthcare. Among various sensing materials, semiconductor metal oxides (SMOs) are widely studied owing to their high sensitivity, good stability, and notable catalytic activity. To overcome inherent drawbacks of pure SMOs—such as high operating temperatures, limited selectivity, sluggish response/recovery behavior, and inadequate long-term stability—functionalization with noble metals has emerged as a powerful modification strategy. This review systematically outlines the primary mechanisms through which noble metals enhance gas sensing performance and analyzes the key factors influencing sensor behavior. Finally, we discuss the current challenges and future directions in the development of noble metal-modified SMO gas sensors. Full article

Riverine–lacustrine ecosystems in river–lake continua face increasing threats, yet conventional vulnerability maps often overlook local degradation drivers. This study presents an advanced satellite-based mapping framework using Deep Attention Networks (DANets) for accurate, interpretable vulnerability assessment. In the Ebinur Lake Basin, a representative dryland river system, we first built a satellite-derived evidence map of ecosystem stress aligned with the IPCC’s vulnerability definition. We then optimized DANets via two nature-inspired algorithms: Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO). The optimized models demonstrated strong predictive capacity, explaining a large share of vulnerability variance (R² = 0.78 for GA-DANets; R² = 0.76 for GWO-DANets). For high/low-vulnerability discrimination, GWO-DANets was most effective and stable, with a mean AUC = 0.960 ± 0.044. Factor importance analysis identified soil organic carbon (SOC; 0.29), precipitation seasonality (0.24), and aridity (0.22) as dominant drivers. Two distinct pathways emerged: chronic degradation in arid plains, driven by low SOC and poor water retention; and acute hydrological stress in wetlands, where carbon-rich soils are sensitive to drying. This insight shifts management from uniform to targeted approaches: soil restoration in plains and water-flow protection in wetlands. By integrating metaheuristically optimized deep learning with multi-sensor satellite data, the framework offers a scalable decision-support tool for safeguarding water-dependent ecosystems. The study confirms that vulnerability in the basin follows two predictable, process-based trajectories, which can be directly linked to measurable soil and hydrological conditions. These clear patterns allow managers to prioritize interventions where they will have the greatest effect under ongoing climate pressure. Full article

Photoacoustic spectroscopy (PAS) is a powerful technique for selective gas detection; however, its performance in non-resonant configurations is fundamentally constrained by the poor low-frequency response of conventional acoustic detectors. Commercial MEMS microphones, although compact and cost effective, exhibit limited infrasound sensitivity, which restricts the development of truly miniaturised and broadband PAS systems. To address this limitation, we present a novel MEMS fluidic microphone (f-mic) that operates on a thermal sensing principle and is explicitly optimised for the infrasound regime. The sensor demonstrates a constant sensitivity of 32 μV/Pa for frequencies below 20 Hz. A detailed analytical model incorporating frequency-dependent effects is developed to identify and investigate the critical design parameters that influence system performance. The overall system exhibits a band-pass frequency response, enabling broadband operation. Based on these insights, a miniaturised photoacoustic cell is fabricated, ensuring efficient optical coupling and f-mic integration. Experimental validation using a CO₂-targeted laser system demonstrates a linear response up to 5000 ppm, a sensitivity of 6 nV/ppm, and a theoretical detection limit of 300 ppb over 100 s, resulting in an NNEA of $6\times {10}^{-6}$ W cm⁻¹ Hz^−0.5. These results establish the f-mic as a robust, scalable solution for non-resonant PAS, effectively overcoming a significant bottleneck in compact gas sensing technologies. Full article

Anaerobic digestion (AD) is a nonlinear and disturbance-sensitive process in which instability is often induced by feedstock variability and biological fluctuations. To address this challenge, this study develops an entropy-guided machine learning framework that integrates parameter prediction, uncertainty quantification, and entropy-based evaluation of AD operation. Using six months of industrial data (~10,000 samples), three models—support vector machine (SVM), random forest (RF), and artificial neural network (ANN)—were compared for predicting biogas yield, fermentation temperature, and volatile fatty acid (VFA) concentration. The ANN achieved the highest performance (accuracy = 96%, F1 = 0.95, root mean square error (RMSE) = 1.2 m³/t) and also exhibited the lowest prediction error entropy, indicating reduced uncertainty compared to RF and SVM. Feature entropy and permutation analysis consistently identified feed solids, organic matter, and feed rate as the most influential variables (>85% contribution), in agreement with the RF importance ranking. When applied as a real-time prediction and decision-support tool in the plant (“sensor → prediction → programmable logic controller (PLC)/operation → feedback”), the ANN model was associated with a reduction in gas-yield fluctuation from approximately ±18% to ±5%, a decrease in process entropy, and an improvement in operational stability of about 23%. Techno-economic and life-cycle assessments further indicated a 12–15 USD/t lower operating cost, 8–10% energy savings, and 5–7% CO₂ reduction compared with baseline operation. Overall, this study demonstrates that combining machine learning with entropy-based uncertainty analysis offers a reliable and interpretable pathway for more stable and low-carbon AD operation. Full article

Partial discharge (PD) is not only the primary manifestation of insulation deterioration in gas-insulated switchgear (GIS) but also a critical indicator of the equipment’s insulation condition. PD in GIS typically occurs at media interfaces such as the surface of the basin insulator and is characterized by high randomness and low amplitude. Conventional built-in ultra-high frequency sensors exhibit limitations in early warning and detection performance. This study proposes and demonstrates a self-sensing shielding ring embedded within the basin insulator, functioning as a novel UHF sensor. Finite-difference time-domain (FDTD) is a numerical method used to solve problems involving electromagnetic fields. Based on actual GIS structural parameters, a FDTD simulation platform is constructed and a built-in sensor is used as a control to evaluate the receiving performance of the self-sensing shielding ring for PD signals. Time-domain array simulations are conducted to investigate the influence of radial, angular and axial positions on the observed performance. The results show that the proposed shielding ring exhibits broadband and low-reflection characteristics, achieving an average S₁₁ of −6.347 dB, which is significantly lower than those of the built-in sensors (−1.270 dB and −1.274 dB). The results demonstrate that the self-sensing shielding ring enables high sensitivity and the wideband detection of partial discharge, providing a new design approach and technical foundation for online early-warning systems in GIS. Full article

Gas sensors provide versatile opportunities for detecting volatile organic compounds (VOCs) such as acetone, methanol, ethanol, propanol, isoprene, and aldehydes in exhaled breath (EB) associated with COVID-19 respiratory infections. These VOCs provide valuable information about metabolic markers linked with COVID-19. They have opened opportunities to develop sensors for COVID-19 screening based on breath analysis. These sensors have the potential to provide the rapid detection of viruses in healthcare settings. RT-PCR, as a conventionally adopted diagnostic method, has a detection limit around 10–100 RNA copies/mL, with an accuracy of around 95%. Gas sensors have demonstrated VOC detection limits at the ppm level in COVID-19 EB and have displayed a sensitivity and specificity of 98.2% and 74.3%, respectively. Multiple gas sensors combined with machine learning algorithms have the potential to enhance the specificity of VOC detection. In addition to having an accuracy similar to that of the PCR method, the VOC-based diagnosis of COVID-19 offers unique advantages in terms of non-invasive and rapid detection. This review provides an overview of state-of-the-art gas sensors developed for COVID-19 detection. Despite there being significant developments in this field, there are certain challenges that still need to be addressed—these include the impact of environmental factors, the specificity of detection, the sensing range, and precision limitations, leading to accuracy issues. Despite these existing challenges, the integration of gas sensors with machine learning methods can enhance the accuracy of the detection of COVID-19. Future research directions are proposed to validate and standardize the application of gas sensors for COVID-19 in clinical settings. Full article

A mechanical structure of a fiber Fabry–Perot interferometric vibration sensor for monitoring oil and gas pipelines has been proposed, and design analysis research on performance improvement has been carried out. By designing a serpentine beam structure, the mechanical sensitivity of the sensor is enhanced. Meanwhile, by designing a vertically symmetrical gravity-sensing structure, the cross-axis sensitivity of the sensor is reduced. The results of simulation analysis show that the mechanical sensitivity of the proposed design structure is 89.20 μm/g, which is 32.44 times that of the conventional structure. Moreover, due to the design of low cross-axis sensitivity, the optical sensitivity of the vibration sensor will not be degraded because of its installation status on the pipeline. The proposed mechanical structure provides a design reference for the application of the fiber Fabry–Perot interferometric vibration sensor on oil and gas pipelines, and offers potential for the development of a high-performance comprehensive safety risk monitoring system for oil and gas pipelines. Full article

A novel optical fiber sensor, based on a composed-type Sagnac loop for gas pressure sensing, has been introduced and experimentally validated. This sensor consists of a centimeter-scale twin-hole and dual-core fiber (THDCF) sandwiched by two segments of polarization-maintaining fibers (PMFs) via splicing. Given that the pure quartz PMF is insensitive to the variations in gas pressure, it is unsuitable for gas pressure sensing. To improve the sensitivity, a short piece of THDCF is added to the PMF-based Sagnac loop. Theoretical analysis has demonstrated that the presence of THDCF could significantly amplify the impact of air pressure on birefringence. Experimental results reveal that as the ambient gas pressure rises from 0 to 1.2 MPa, the interference spectrum exhibits an obvious red shift with a high sensitivity of 8.381 nm/MPa. The sensor’s reliability has undergone repeated verification by increasing and decreasing the pressure. Attributed to its simple structure, easy fabrication, low cost, and high sensitivity, the proposed sensor is particularly suited for development in harsh environments. Full article

The current research presents the synthesis, characterization, and application of a novel gas sensor based on polypyrrole/strontium titanate (PPy/STO) nanocomposites for the selective detection of CO₂. Utilizing chemical oxidative polymerization, PPy and PPy/STO nanocomposites with varying STO (10–50) wt.% were synthesized and characterized. The structural and morphological analysis confirms the formation of spherical structure and well-dispersed PPy nanoparticles with increasing crystallinity and interaction of STO in PPy chain particle compactness as the STO content increases. The integration of perovskite STO within the conducting polymer matrix enhances the electronic structure, porosity, and surface area of the composite, promoting improved gas sensing performance. Electrical impedance spectroscopy reveals that the composites exhibit a frequency-dependent dielectric response and conduction attributed to charge carrier mobility and interfacial polarization effects. PPy/STO 20% exhibits highest conductivity and dielectric constants of 0.03604 Scm⁻¹ and 1.074 × 10⁴, respectively. Real-time CO₂ sensing experiments conducted at 50 °C demonstrate good sensitivity, stability, and rapid response/recovery characteristics, particularly for the PPy/STO 10% and 40% composites. These findings highlight the potential of PPy/STO nanocomposites as flexible, lightweight, and efficient materials for portable CO₂ gas sensors, addressing the growing needs for environmental and health monitoring. Full article

Designing metal–semiconductor-based core–shell nanostructures with strong interactions is emerging as a unique component for chemiresistor applications. Here, we have developed an effective hydrogen (H₂) gas sensor based on Au-In₂O₃ core–shell nanostructures, which were synthesized through a short-time microwave hydrothermal process. At an optimal temperature of 375 °C, the device based on Au-In₂O₃ displays a high sensitivity of ~42, which is five times greater than that of the In₂O₃ toward 100 ppm of H₂ gas. Moreover, the Au-In₂O₃ sensor showed higher selectivity toward H₂ gas and stability over a long period. The excellent H₂ gas-sensing performance of Au-In₂O₃ core–shell nanoparticles can be credited to the sensitization and Au catalytic effect core, and their strong interaction with the In₂O₃ component. Our work not only accounts for a facile synthesis approach for Au-In₂O₃ core–shell nanoparticles by synergistic properties of Schottky heterojunctions, but also offers a new insight into how strong metal–semiconductor interactions (SMSIs) play a dynamic role in developing high-performance gas-sensing devices. Full article

Anaerobic reactors are often used to control emissions and capture greenhouse gas (GHG) (biogas, a mixture of carbon dioxide and methane) from waste such as dairy manure. However, real-time monitoring of biogas production during in vitro anaerobic experiments is often challenging mainly due to the unpredictable and low levels of biogas production in a lab reactor system. The application of Internet of Things (IoT) technologies can enhance real-time monitoring of biogas production and GHG emissions from livestock waste. Integration of IoT to anaerobic reactors provides transformative solutions for low-cost monitoring. In this study, an IoT based sensor system that included a highly sensitive Renesas mass flow sensor module for biogas monitoring, Adafruit ported pressure sensor for monitoring of reactor pressure, and ultra-small DROK temperature probe for temperature monitoring was built and implemented for determining the biogas production in anaerobic reactors. Further, impacts of anaerobic process on the reduction of pathogenic organisms such as E. coli were determined using the conventional culture-based method. Results showed that the application of the IoT based system was able to monitor biogas production in real-time, and transmit the data to mobile phone using the ThingSpeak IoT platform offered by MathWorks (MATLAB) (Natick, MA, USA). The difference between the sensor’s biogas volume readings and actual observations over a 30-day time interval was 5–6% indicating the high level of accuracy and low error levels of the system. Further, results showed 1.6–4.8 log reductions of E. coli in effluent of anaerobic reactors indicating substantial impacts of the anaerobic process on pathogen indicator reduction. We anticipate that the system we used in this study has a substantial potential to enhance monitoring of anaerobic reactors and GHG emissions from livestock waste. Full article

Monitoring the freshness of perishable foods remains a challenge due to the lack of simple and reliable indicators that can visually reflect chemical and microbial changes. In this study, a colorimetric freshness indicator was developed using bromocresol green (BCG) and bromocresol purple (BCP), two pH-sensitive dyes with complementary transition ranges, to provide a visible and quantitative response corresponding to beef quality during cold storage. Cellulose acetate (CA) films were prepared by incorporating the dyes with different plasticizers—glycerol and polyethylene glycol (PEG 200 and PEG 400)—at varying ratios, resulting in 24 formulations. Based on color stability and sensitivity to trimethylamine (TMA) vapor, two optimized indicators were selected for further packaging tests with beef samples stored at 4 °C. Beef freshness was evaluated by total bacterial count (TBC), total volatile basic nitrogen (TVB-N), and pH, while volatile amines in the headspace were quantified using solid-phase microextraction–gas chromatography–flame ionization detection (SPME–GC–FID). The color difference (ΔE) of the indicators showed strong correlations with TBC and TVB-N, and a threshold of ΔE ≈ 12 provided a practical visual cue corresponding to the microbiological safety limit. The two indicators exhibited complementary functions, with G100-1 acting as an early-warning sensor and G100-2 maintaining contrast at later stages. These findings demonstrate the potential of this dual-indicator system as a simple, non-destructive tool for intelligent packaging applications. Full article