Search Results (259)

Recent developments in particle physics require cost-effective pixel detectors capable of operating under increased energy and luminosity conditions foreseen in future collider experiments. In response, monolithic CMOS pixel sensors incorporating modern readout architectures have emerged, combining high-rate capability with substantial radiation tolerance. To optimize the performance of these sensors for application in tracking detectors, a comprehensive characterization has been done focusing on threshold and noise behavior as a function of front-end DAC tuning parameters. The effect of radiation damage has been investigated using high-intensity X-ray irradiation, followed by a detailed comparison of sensor performance before and after irradiation. The threshold distribution is observed to be uniform across the pixel matrix. Irradiation introduces a systematic shift in the threshold, with a larger impact at low-threshold configurations, while overall uniformity is preserved. In contrast, the noise remains largely stable across the parameter space. The correlation between threshold and noise is used to identify optimal operating regions, demonstrating that stable, efficient performance can be achieved across quite a wide range of configurations. These results confirm the robustness of the sensor under irradiation and its suitability for operation in radiation environments relevant to future high-energy physics experiments. Full article

Mass concentrations of particulate matter are a fundamental metric for air quality and health impact assessment; however, they are insufficient for accurately characterizing exposure. They do not capture particle size distribution or number concentration. Therefore, aerosol assessment should include particle number concentration (PNC), which better represents toxicologically relevant fractions and enables more precise source identification. The aim of this study was to conduct a comprehensive evaluation of particle number concentration (PNC) measurements in the 0.3–10 µm size range obtained using three low-cost Sensirion SPS30 particle sensors under field conditions in an urban environment. The analyses included an assessment of agreement between the SPS30 sensors, an evaluation of their measurement performance against the OPS 3330 optical particle spectrometer, and the development of calibration models. The SPS30 sensors showed high inter-device repeatability for PNC in the 0.3–1.0 µm range (CV_d < 2%). However, measurement performance declined with increasing particle size, with the index of agreement (IOA) decreasing from 0.8 (0.3–0.5 µm) to −0.5 (2.5–10 µm). Sensor accuracy was influenced by meteorological conditions: relative humidity primarily affected short-term variability (precision and dynamic agreement), while temperature controlled systematic bias. Although incorporating these variables into advanced calibration models improved performance, SPS30 sensors remained unsuitable for PNC measurements in the 2.5–10 µm range, exhibiting systematic errors of ~25% even after nonlinear correction. The findings support the responsible use of low-cost particle sensors for supplementary air quality monitoring, contributing to accessible environmental data and sustainable urban air quality management. Full article

Unauthorized unmanned aerial vehicle (UAV) intrusions in sensitive areas such as airports have made accurate UAV detection and localization a pressing need. Acoustic sensing is passive and weather-independent, but conventional microphone arrays require many elements and a large aperture. This paper proposes an acoustic vector sensor (AVS)-based method, termed Covariance Enhancement and Confidence-guided Tracking for 3D Acoustic Localization (CECT-3DAL). A single AVS measures the sound pressure and three-axis particle velocity at one point. Adaptive diagonal loading improves the robustness of the covariance matrix at a low signal-to-noise ratio (SNR). An exponential spectral enhancement strategy sharpens the spatial spectrum peaks for direction estimation, and an eigenvalue-ratio-based confidence drives confidence-weighted smoothing of the angle sequences. Meanwhile, a dual-sensor geometric model provides a closed-form three-dimensional solution. In simulations, the azimuth and elevation root-mean-square errors (RMSEs) were below 1.5° for SNR above 4 dB. In an anechoic chamber, confidence-weighted smoothing reduced the azimuth and elevation standard deviations from 4.34° and 2.63° to 1.46° and 0.86°. In field experiments, the hovering azimuth stayed within a 90% span of 2–3.5°, with an average horizontal RMSE of 0.209 m against a GPS reference, and trajectories under various flight modes remained continuous and smooth. The proposed method offers a compact, passive, and low-cost solution for counter-UAV acoustic surveillance. Full article

Broadband direction-of-arrival (DOA) estimation using a single acoustic vector sensor (AVS) is an important problem in passive underwater source localization and underwater acoustic signal processing, especially for compact underwater platforms and passive acoustic monitoring applications. However, conventional grid-based sparse Bayesian learning (SBL) may suffer from grid mismatch when the true bearing lies between adjacent predefined grid points. Although a dense grid can reduce this mismatch, it increases computational cost and dictionary coherence. To address this problem, this paper proposes an acoustic-intensity-guided local grid-refinement SBL method, termed AI-LGR-SBL. The pressure and particle-velocity channels are first used to construct acoustic intensity information and detect candidate source regions. The coarse bearing results then guide target-related spectral peak selection during SBL iterations, and local grid refinement is performed only around the selected directions. Simulations involving single-source and two-source scenarios show that AI-LGR-SBL yields sharper spatial spectra and lower estimation errors than conventional grid-based SBL. Compared with basic SBL, AI-LGR-SBL reduces the RMSE by approximately 10% in the low-SNR region and by 4–7% at relatively high SNRs. Compared with globally dense-grid SBL, it reduces the average runtime by approximately 47.3% and 43.6% in the single-source and equal-power two-source scenarios, respectively. Lake-trial data further demonstrate clear bearing–time trajectories and effective sub-grid peak refinement, supporting the feasibility of the proposed method for broadband underwater DOA estimation and passive source localization using a single AVS. Full article

Recent demand for reliable, low-power, and cost-effective gas sensors has spurred research into chemiresistive materials that operate under ambient conditions. PEDOT and PEDOT:PSS combined with semiconductor metal oxides (SMOs) have attracted attention due to their complementary properties: polymer flexibility and stability, alongside oxide reactivity and robustness. This review highlights the integration of PEDOT and PEDOT:PSS with n- and p-type SMOs, concentrating on fabrication techniques, sensing mechanisms, and performance indicators, such as sensitivity, selectivity, and response time. Emphasis is placed on heterojunction engineering, morphology control, and the influence of particle size and environmental factors. Despite notable progress, challenges persist in long-term stability, selectivity in mixed gases, and performance under varying conditions. Interface engineering and composite optimisation show promise, with potential applications in environmental monitoring, industrial safety, and wearable diagnostics. Full article

Introduction: International studies have demonstrated a positive impact on air quality associated with the presence of green areas in urban conglomerates. However, in Brazil, studies addressing the impacts of urban green areas on air quality are still incipient and are predominantly focused on large urban centers. The objective of this study was to investigate the relationship between urban green areas, surface temperature (LST), and air quality across 15 medium-sized Brazilian cities. Methods: Concentrations of particulate matter fractions (PM₁, PM_2.5, and PM₁₀) were monitored from January 2023 to May 2024 using second data from low-cost sensors. The NDVI and both daytime and nighttime LST profiles were extracted via Google Earth Engine within a 1 km buffer zone surrounding each station via the Sentinel-2 and MODIS 11A1 satellite data, respectively. Spatial–temporal co-variation patterns were explored using principal component analysis (PCA). To model these dynamics while controlling for spatial dependencies, a multi-criteria framework compared linear models (simple linear regression (LM) and linear mixed (LMM)) and generalized models (generalized additive (GAM) and generalized additive mixed (GAMM)). Results: The results revealed a positive relationship between NDVI and PM_2.5 and PM₁₀ fractions in specific regions, while surface temperatures showed a direct association with finer particles (PM₁ and PM_2.5). The regression coefficient showed the significant association of PM2.5 with NDVI and nighttime LST (β = 1.330; IC 95%: [0.397; 2.270]; p = 0.005). The GAMM was the best-fitting model for all particle fractions, demonstrating that incorporating monitoring stations as random intercepts successfully controls for unmeasured local heterogeneity, while penalized splines accurately capture non-linear environmental factors. Conclusions: Although many studies have shown that green areas in temperate regions typically act as consistent sinks for particulate matter, our study revealed localized and seasonal responses in tropical urban landscapes. It should be noted that our study is conducted on a national scale and that the use of low-cost sensors and remote sensing does not allow us to distinguish between the localized microclimatic benefits of vegetation and the long-range transport of regional pollutants. Full article

The pervasive contamination of aquatic environments by microplastic particles necessitates the development of rapid, cost-effective and field-deployable detection methodologies to complement established but laboratory-bound spectroscopic techniques such as Fourier-transform infrared and Raman microscopy. The demand for field-suitable methods with a broad accessibility comes from researchers themselves. In this review we systematically examine recent advances in optical methods for microplastics identification with a particular emphasis on birefringence as a key diagnostic feature of partially crystalline synthetic polymers. In particular, we analyze three complementary technological directions: liquid crystal-based sensors that exploit orientational order disruptions at interfaces for label-free microplastics detection; polarization holographic imaging combined with machine learning for high-throughput particle classification; and on-chip polarization light microscopy enabling compact and portable analyzing systems. Liquid crystal platforms demonstrate exceptional sensitivity to submicron particles and enable real-time visualization of microplastics aggregation at aqueous interfaces, though they currently lack polymer-specific chemical identification. Conversely, smart polarization holography integrated with Stokes polarimetry and deep learning algorithms achieves over 90% accuracy in distinguishing microplastics from natural particles while processing up to 10,000 particles per minute. Emerging on-chip polarized light microscopy offers a pathway toward miniaturized, low-cost devices suitable for field applications. By synthesizing insights from foundational studies, this review identifies convergent interdisciplinary trends—particularly the integration of artificial intelligence with multimodal optical imaging—and outlines persistent challenges including standardization, interference from natural organic matter, and the transition from laboratory prototypes to robust field-deployable instruments. The systematization of birefringence-based approaches aims to guide future research towards integrated monitoring systems capable of addressing water quality concerns. Full article

Urban construction activities are recognized as significant contributors to particulate matter (PM) emissions; however, the accurate real-time monitoring of size-resolved PM fractions presents a formidable challenge. Traditional low-cost PM sensors predominantly report cumulative concentrations, which obscures the distinct health and regulatory significance of PM1, PM2.5, and PM10. This study systematically evaluates the performance of two low-cost sensors—PMS5003 and Sniffer4D—utilizing non-cumulative measurements obtained under controlled laboratory conditions designed to simulate construction PM generated from concrete slab drilling. Sensor performance was rigorously analyzed using Pearson correlation coefficients, standard deviation, and mean percentage differences. Six correction models—linear regression, polynomial regression, Random Forest (RF), XGBoost, Artificial Neural Network (ANN), and Kalman filter—were independently developed for each PM size fraction to enhance measurement precision. Findings reveal that RF and ANN consistently provided the most accurate corrections, particularly for PM1 and PM2.5, with RF achieving a coefficient of determination (R²) > 0.89 for PM1 and R² > 0.87 for PM2.5 at the 50 s duration. This investigation introduces a size-resolved correction framework specifically designed for construction environments, thereby advancing the capability of low-cost sensors to enable accurate particle-specific exposure assessments. Full article

The low signal-to-noise ratio (SNR) of existing magnetic sensors limits the detection of magnetic nanoparticles (MNPs) in microfluidic biosensing. We present a novel microfluidic coil-based impedance detection system for quantifying magnetic particles, including Fe filings and citrate-coated Fe₃O₄ MNPs, with potential applications in magnetically guided biosensing. Unlike conventional approaches that directly measure the magnetic properties of dispersed particles, our method employs an external collector magnet to concentrate particles within a copper coil detector. The accumulated particles alter the coil’s electromagnetic response through changes in the sample’s dielectric properties, producing an amplified impedance signal proportional to sample volume. We evaluated detection performance for 1–10 mg of ferromagnetic Fe filings and citrate-coated Fe₃O₄ MNPs across a broad frequency range. Results show a strong linear correlation between particle mass and impedance change, with SNR values from 25 dB to over 45 dB, demonstrating high sensitivity and precision. Coil sensitivity was further optimized by varying the number of turns (5, 10, and 15), enabling frequency-specific customization. This approach provides a scalable, low-cost platform adaptable to polymer-coated MNPs targeting biological analytes. Full article

The expansion of paper-based and wearable microwave electronics demands conductors that are highly conductive, finely patterned, mechanically robust, and compatible with low-cost, biodegradable substrates. This study reports a laser-scribing strategy for high-performance copper–liquid metal (Cu–LM) conductors on paper based on laser sintering of Cu–LM composite particles, with an auxiliary adhesive transfer step to facilitate integration on flexible substrates. Laser-induced reactive sintering creates a network wherein sintered liquid metal and CuGa₂ acts as a conductive bridge, interconnecting the dispersed Cu particles. This provides efficient electron transport pathways, achieving a high conductivity of 4.2 × 10⁶ S/m under optimal laser conditions, surpassing that of pure eutectic gallium–indium (EGaIn) alloys. The self-healing nature of LM enables exceptional mechanical flexibility and stable electrical performance under severe deformation. The utility of this platform is demonstrated by a miniaturized microwave liquid level sensor that provides multi-parameter water-level detection and sensor calibration. These results establish laser-scribed Cu–LM on paper as a low-cost and disposable option for high-performance microwave sensors and flexible wireless electronics. Full article

Air pollution associated with public transport systems constitutes a critical yet highly heterogeneous component of urban exposure and represents an important challenge for sustainable urban mobility and environmental health governance. Commuters and transport workers are frequently subjected to pollutant concentrations that exceed those reported by ambient background monitoring networks. This review provides a comprehensive synthesis of the global scientific literature on air quality in public transport microenvironments—including buses, bus stops, terminals, and underground stations—through a multidimensional analytical framework that considers climatic classification, socio-economic context, meteorological drivers, transport microenvironment typology, sampling strategies, analytical techniques, and exposure metrics. A large body of peer-reviewed studies published worldwide was examined to identify dominant patterns, methodological trends, and persistent knowledge gaps. Across regions, the evidence consistently reports elevated concentrations of particulate matter (PM_2.5, PM₁₀, and ultrafine particles) and traffic-related gaseous pollutants, particularly within confined or poorly ventilated environments and during peak traffic periods. Marked geographical, climatic, and socio-economic imbalances are evident, with most studies conducted in temperate and tropical climates and in countries with very high or high Human Development Index, whereas arid, continental, and low-HDI regions remain substantially underrepresented. From a methodological perspective, the literature is dominated by short- to intermediate-term monitoring campaigns relying on active sampling, mobile measurements, and increasingly calibrated low-cost sensors, while long-term stationary observations and standardized integrative monitoring frameworks remain scarce. Although advanced analytical approaches—such as chemical characterization, environmental magnetism, receptor modeling, computational fluid dynamics, and inhaled dose assessment—are increasingly applied, their systematic integration remains limited. Overall, this review reveals persistent methodological, geographical, and conceptual gaps and highlights the urgent need for standardized, interdisciplinary, and long-term monitoring strategies to improve exposure assessment and support evidence-based mitigation policies and sustainable urban transport planning aimed at reducing health risks associated with public transport-related air pollution. Full article

Classification of vehicle engines using the chemical composition of the exhaust from these engines can be used to identify the engine’s design and verify compliance with environmental regulations through the vehicle’s emissions. This paper describes a method to identify the type of vehicles using machine learning (ML), where low-cost MQ series sensors measure the gases and particle emissions from a vehicle exhaust system, while simultaneously collecting and measuring the vehicle’s temperature and humidity levels. A custom-designed multi-sensor exhaust sensing module is employed to capture real-time exhaust emissions prior to entering the atmosphere. Exhaust samples are collected from vehicles representing three major engine categories: petrol, diesel, and compressed natural gas (CNG). In addition, fresh air samples are collected as a baseline environmental reference for comparison. All exhaust measurements are collected under controlled and consistent engine operating conditions to ensure comparable emission profiling across vehicle classes. To ensure consistent combustion-based emission profiling, this study focuses on conventional fuel-powered vehicles. MQ-series gas sensors are sensitive to combustion by-products emitted during engine operation, such as carbon monoxide (CO), hydrocarbons (HC), while also exhibiting cross-sensitivity to other gaseous components present in exhaust mixtures. Nevertheless, the proposed system performs pattern-based classification using relative sensor response signatures. Standardization of data is achieved through z-score normalization. The best models developed (based on three separate experimental designs) are trained and validated using six supervised machine learning algorithms such as Logistic Regression, Support Vector Machine (RBF), k-Nearest Neighbors, Random Forest, Gradient Boosting Decision Tree, and XGBoost and are compared against one another. Evaluation of the tested algorithms using various evaluation metrics demonstrated that ensemble models outperformed all other algorithms, achieving the highest accuracy of 99.5%. Furthermore, noise analysis confirms that the proposed solution maintains high classification accuracy (more than 89%) even under substantial sensor perturbations mimicking the real-world deployment. The solution proposed below illustrates how using gas sensors and advanced algorithms can provide accurate exhaust identification and identify engines in real-time. Full article

Printed biosensors have attracted increasing attention as platforms for rapid, low-cost, and portable diagnostics because they can be fabricated on flexible or rigid substrates using scalable printing techniques. Their performance is strongly influenced by both the printing process and the materials employed, since factors such as ink rheology, particle dispersion, interfacial behavior, and post-processing conditions directly affect device architecture, sensing performance, and manufacturing reliability. This review summarizes recent advances in printed biosensors from the combined perspectives of printing technologies and functional materials. Commonly employed printing techniques, including inkjet, screen, aerosol jet, and roll-to-roll gravure printing, are discussed with emphasis on their processing characteristics and material requirements. The review also examines key material platforms used in printed biosensors, including carbon-based nanomaterials, metal oxides, metal nanoparticles, conductive polymers, dielectric materials, and hybrid composites, highlighting their roles in electrical conductivity, catalytic activity, biomolecule immobilization, mechanical flexibility, and overall analytical performance. Finally, current challenges and emerging research directions are outlined with respect to ink stability, post-processing strategies, sensor reliability, manufacturability, and practical translation. Overall, this review emphasizes that the development of high-performance printed biosensors depends on the synergistic integration of rational material design with optimized printing strategies. Full article

This two-week pilot study within the Horizon Europe EDIAQI project evaluated the real-life performance of portable air filtration units in two office environments (a small office and a shared kitchen) under continuous device operation and daily filter replacement. Indoor particle concentrations were monitored continuously using low-cost sensors (LCS) from three providers and supported by gravimetric measurements, while daily activity logs documented occupancy patterns, printing, cooking, and other source events together with purifier ON/OFF status. Particulate matter (PM) mass concentrations showed no systematic improvement during purifier ON periods; instead, temporal variability was dominated by indoor activities and episodic emissions, with occasional short-term peaks around filter replacement suggestive of minor resuspension. Chemical analysis provided a clearer picture: polycyclic aromatic hydrocarbons (PAHs) responded differently across fractions and compositions. Across monitored locations, high-molecular-weight PAHs in the PM₁ fraction decreased during purifier ON periods (approximately 30% lower on average), whereas low-molecular-weight PAHs measured in total suspended particles (TSP) were higher during ON periods, indicating that semi-volatile fractions and activity/ventilation dynamics can outweigh simple filtration effects. Overall, the findings highlight a gap between laboratory-derived filtration performance metrics and outcomes in occupied, mixed-source indoor environments and emphasise the importance of device sizing, placement, airflow mixing, and complementary source control and ventilation strategies when deploying filtration-based IAQ interventions. Full article

This paper presents the design of a sensor system for remote measurements of exhaust emissions from automotive combustion engines. The system’s purpose is to determine the likelihood of a given vehicle’s potential harmfulness to the environment. This system, if implemented, could detect vehicles posing a threat to the environment in road traffic. A remote measurement system can be installed in the front of a measuring vehicle driving behind the vehicle being diagnosed. This approach allows for rapid road testing of multiple vehicles while they are operating in real-world conditions where engines can emit the highest levels of undesirable pollutants. Exceeding emission standards may be related to modifications made to the vehicle’s exhaust gas aftertreatment systems, engine wear, or malfunctions of engine-related systems such as the diesel particulate filter (DPF) or catalytic converter. Toxic and undesirable substances include carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NO_x), carbon dioxide (${\mathrm{C}\mathrm{O}}_2$), and particulate matter (PM) particles. The main goal of the measurements is to identify vehicles that potentially pose a threat to the environment during normal operation. The sensor system consists of several types of sensors utilizing various physical and chemical phenomena, with particular emphasis on their low cost and easy availability. The measurement unit utilizes MEMS technology, photoacoustic spectroscopy, electrochemical methods, light absorption and scattering, spectrophotometry, and electro-optical detection. Full article