Search Results (509)

In deep-space exploration and remote sensing, characterizing radiative transfer in complex planetary atmospheres is fundamental for robust target detection and optical navigation. On the Martian surface, intense scattering and attenuation by dust aerosols pose severe environmental interference, challenging star sensors used for high-precision navigation. To address this, this study develops a spectral radiative transfer model based on the Null Collision Monte Carlo Method to characterize the optical background of the dusty Martian atmosphere. Mie scattering theory is employed for dust particles, while gas molecular absorption is modeled via line-by-line integration. The simulated sky radiance is validated against Mars rover Navcam observations, yielding an average relative error of 7.83% between the modeled and observed radiance values across scattering angles greater than 5°. Building on this, an imaging link model evaluates surface-based detection performance, including signal-to-noise ratio, detection success probability, and star count. Optical parameters—aperture, field of view, and integration time—are optimized for nighttime and dawn-dusk modes. Spatio-temporal assessments are conducted globally across Martian years, focusing on the Zhurong landing site and Tianwen-3 candidates. Finally, an Earth-environment equivalence experiment using a 60% transmittance filter verifies the design’s robustness. This work confirms the feasibility of star-sensor-based attitude determination on Mars. Full article

High-performance hydrogen sensors are crucial for the safe operation of lithium-ion batteries with regard to thermal runaway monitoring, which has driven extensive research. In this work, in order to satisfy the urgent requirement of H₂ sensors for lithium-battery safety monitoring, a polystyrene sacrificial phase was introduced to fabricate porous WO₃-based sensing films with tunable oxygen stoichiometry. Through PAD (Powder Aerosol Deposition) followed by sintering in air at 500 °C, the pore structure and defect chemistry of WO_3-x films were simultaneously regulated. Structural characterizations show that polystyrene can be completely removed during sintering without changing the main γ-WO₃ phase, while interconnected pores are generated and the films evolve from near-stoichiometric WO₃ toward oxygen-deficient WO_3-x with increased oxygen-vacancy concentration. Meanwhile, the PAD-derived amorphous matrix encapsulated nanocrystalline is largely preserved, providing stable charge-transport pathways. The porous structure improves hydrogen diffusion and reaction accessibility, while bulk reduction and oxygen-vacancy enrichment enhance surface reactivity and charge transfer, together leading to improved H₂ sensing performance. Among all samples, WP5 exhibits the best overall performance, achieving stable H₂ detection over a wide range of 10 ppb to 20,000 ppm at 120 °C, together with extraordinary response. These results demonstrate that this strategy is an effective route for simultaneously engineering porosity and defect chemistry in WO_3-x-based sensing films, and highlight the potential of Pd/PS–WO_3-x sensors for lithium-ion battery thermal runaway warning. Full article

Hydrogen is a clean energy carrier essential for carbon neutrality, but its invisible and odorless nature poses significant safety risks, particularly during low-concentration leaks. Although metal oxide semiconductor (MOS) sensors offer fast response and high sensitivity, their ability to detect ppb-level hydrogen remains limited. In this work, we present a high-performance hydrogen gas sensor based on nickel-doped tin dioxide (Ni-SnO₂) nanorods, directly grown on planar electrodes via aerosol-assisted chemical vapor deposition (AACVD). By optimizing the Ni doping ratio and nanorod morphology, the 3 wt% Ni-SnO₂ sensor achieves a low detection limit of 100 ppb for H₂, demonstrating promising potential for low-concentration hydrogen detection. Moreover, the sensor exhibits outstanding selectivity, with a response to 100 ppm H₂ nearly six times higher than that to the next most responsive interfering gas (NH₃). Comprehensive XPS and Raman analyses reveal that Ni doping introduces abundant oxygen vacancies and lattice defects, which are the key origins of the enhanced sensing performance. Notably, the 3 wt% Ni-SnO₂ sensor strikes an optimal balance between lattice defects and structural stability, delivering both high sensitivity and good moisture resistance with minimal baseline drift over weeks of operation. This work establishes a facile and scalable AACVD strategy for engineering defect-rich SnO₂ nanostructures, enabling sub-ppm hydrogen detection with high selectivity and long-term stability—addressing a critical gap in practical hydrogen safety monitoring. Full article

Fluorescent aerosol particles (FAPs) are widely used as a real-time proxy for primary biological aerosol particles; however, their seasonal characteristics and size-resolved distributions in office environments remain poorly understood. In this study, FAPs were measured in ten office spaces located in four distinct regions of Japan during summer and winter using a real-time Bioaerosol Sensor. Indoor and outdoor FAP concentrations, indoor/outdoor ratios, and the size-resolved FAP fraction were evaluated. Indoor FAP concentrations were generally below 100 particles per liter (p/L), although peak concentrations of 140 p/L in summer and 195 p/L in winter were observed. Significant seasonal differences were detected in most offices, with several buildings showing higher concentrations in winter. Many offices exhibited relative humidity levels below 40% during winter, suggesting that dry indoor conditions may have promoted particle resuspension and contributed to elevated FAP concentrations. Indoor–outdoor comparisons suggested contributions from both indoor sources and outdoor infiltration. The size-resolved FAP fraction increased markedly with particle size, with median indoor values reaching 40–74% for 2.0–5.0 μm particles and 96–100% for particles > 5.0 μm. These findings indicate that FAPs in office environments are strongly associated with coarse particles and exhibit substantial seasonal and building-dependent variability. Full article

Transition metal oxides constitute an important materials platform for chromic phenomena because their optical response is strongly coupled to the changes in electronic structure, phase state, carrier concentration, and defect chemistry. This review discusses selected transition metal oxide thin films, with emphasis on VO₂ and other vanadium oxides, WO₃, NiO, and TiO₂. The review summarizes the structural and electronic characteristics of these representative oxide systems and highlights the role of phase composition, crystal structure, oxygen non-stoichiometry, and defect chemistry in determining their optical response. The main thin film preparation routes, including pulsed laser deposition, magnetron sputtering, sol–gel and aerosol spray methods, atomic layer deposition, chemical vapor deposition, electrochemical routes, and molecular beam epitaxy, are reviewed with respect their influence on obtained thin films. Particular attention is given to applications in thermochromic VO₂-and electrochromic WO₃/NiO-based smart windows, and transition metal oxide-based gasochromic hydrogen sensors. Key challenges related to transition temperature tuning, luminous transmittance, solar modulation, optical contrast, cycling stability, ion transport and large-area integration are also discussed. Overall this review provides a comparative overview of selected transition metal oxide thin films by connecting material chemistry and physics, thin film preparation technology and functionality. Full article

Maternal exposure to polycyclic aromatic hydrocarbons (PAHs) during pregnancy has been associated with adverse obstetric and perinatal outcomes, including miscarriage, low birth weight, intrauterine growth restriction, and spontaneous abortion. Exposure occurs through multiple pathways, including dietary intake and inhalation, which ultimately determine the final body burden. PAHs may reach relevant levels in the blood, representing the initial step in their internal distribution to the placenta, umbilical cord, and breast milk, thereby compromising maternal–fetal health. In this exploratory study, maternal blood samples were collected from pregnant women residing in different regions of Antioquia, Colombia. Serum was isolated from whole blood, subsequently extracted using freezing-assisted liquid–liquid extraction, purified by solid-phase extraction, and analyzed by GC–MS. Method performance showed PAH recoveries between 60 and 120%, limits of detection (LOD) ranging from 0.5 to 3.3 ng·mL⁻¹, and limits of quantification (LOQ) ranging from 1.4 to 9.9 ng·mL⁻¹. Airborne PAH concentrations were measured using a photoelectric aerosol sensor, and higher levels were observed in municipalities intersected by major highways, indicating a strong vehicular contribution, with an average concentration of 72.6 ± 39.2 ng·m⁻³. Low and medium-molecular weight PAHs were detected in serum samples at an average concentration of 43.8 ± 8.8 ng·g⁻¹ of lipid (mean of ∑ individual congeners). In contrast, a high-molecular-weight PAH, benzo[a]pyrene (BaP), was detected in one participant. Pyrene (PYR) and fluoranthene (FLU) were the predominant congeners, suggesting combustion-related sources, primarily vehicular emissions. Serum PAH levels showed a correlation with the frequency of consumption of canned fish and meat, but not with short-term airborne PAH measurements. These exploratory findings suggest that dietary intake is a primary pathway of bioaccumulation during acute exposure and plays a key role in determining the parental PAHs burden during pregnancy in polluted environments. However, additional data on parent PAHs and their metabolites are needed to provide a more comprehensive assessment of cumulative exposure arising from dietary sources and chronic inhalation of airborne PAHs. Full article

Aerobiology, the science that studies atmospheric biological particles (including pollen, fungal spores, bacteria, and viruses), has undergone a profound transformation from a descriptive, observational discipline into a predictive, data-driven field, thanks to advances in statistical methods and environmental sensing technologies. Early research, based on classical statistical methods such as descriptive analysis, correlation analysis, and linear regression, established a fundamental understanding of seasonal dynamics and environmental relationships. However, the inherent complexity of aerosol biological systems—characterized by nonlinear interactions, spatiotemporal variability, and multiscale processes—has spurred the adoption of modern statistical techniques. These techniques include time-series analysis, generalized linear and additive models, spatial statistics, Bayesian inference, machine learning, and data assimilation, often combined with high-resolution environmental monitoring and sensor networks. In recent years, hybrid modeling approaches have emerged, combining mechanistic understanding of atmospheric transport and biological emissions processes with data-driven learning to improve the accuracy, robustness, and interpretability of predictions. This review comprehensively compares classical, modern, and hybrid statistical methods in air biology, exploring their theoretical foundations, practical applications, and inherent limitations. Furthermore, this review highlights emerging paradigms such as uncertainty quantification, causal inference, digital twins, and AI-driven real-time prediction systems. It also discusses challenges, including data heterogeneity, model interpretability, and cross-regional portability. By treating aerobiology as a complex adaptive environmental–biological system, this study highlights statistical methods that link observations to mechanisms and advance scalable, reliable, systems-oriented prediction frameworks for future research and applications. Full article

As the initial link in the dust cycle, sand saltation directly determines the release of dust aerosols into the atmosphere. Suspended dust can modify meteorological conditions, potentially altering sand saltation characteristics, though this relationship requires further investigation. The recent deployment of automatic visibility sensors has provided new observational support for this study. Using hourly observations from five meteorological stations in the Taklamakan Desert during March–August 2016–2024, together with reanalysis data, this study estimated threshold wind speeds under different dust-intensity conditions, as indicated by horizontal visibility. We analyzed sand saltation frequency and horizontal dust flux across the region under varying visibility conditions, and reassessed recent trends and drivers of dust emission. Results indicate that threshold wind speeds range between 4.67 and 4.71 m·s⁻¹, with a notable increase in wind speed when horizontal visibility falls below 5 km. Based on these thresholds, our analysis reveals significant regional differences in both dust emission frequency and flux under varying visibility conditions, with clear skies also identified as an important contributor to dust emission. Specifically, horizontal dust flux in the Taklamakan Desert showed a decreasing trend during 2016–2021 and a slight increase during 2021–2024, and this recent change in dust-emission trends may be linked to changes in atmospheric circulation and meteorological conditions. These findings provide a scientific basis for dust forecasting, early warning, disaster prevention, and mitigation in the Taklamakan Desert and its surrounding areas. Full article

This work evaluates a potassium ion-selective electrode (K-ISE) fabricated using Aerosol Jet Printing (AJP) and compares its performance with that of a commercial K⁺-selective electrode (KION). Both sensors exhibit near-Nernstian behavior, with average sensitivities of 57.91 ± 5.07 mV/dec for the AJP device and 57.28 ± 5.07 mV/dec for the commercial electrode, confirming a near-Nernstian K⁺ response over the tested concentration range. Single-interferent response tests demonstrate that AJP-printed electrodes provide a more stable and less sensitive response to sodium interference (24.37 ± 1.39 mV/dec) compared to KION (33.95 ± 8.95 mV/dec), while showing comparable NH₄⁺ response and a slightly higher response toward urea than KION. Morphological analysis (OM and SEM) reveals that AJP enables smoother, more homogeneous films and improved control over the transducer/membrane interface. Unlike previous studies, this work presents a direct experimental comparison between AJP-fabricated and commercial ISEs under controlled interference conditions relevant to agricultural and environmental matrices. Although the AJP sensors exhibited near-Nernstian behavior and fast response times, their reproducibility was lower than that of the commercial electrodes (RSD = 30.12% vs. 18.45%), indicating that further optimization of the printing and membrane deposition processes is required. Full article

Post-monsoon open-field stubble burning in northwestern (NW) India—a key agricultural region known as the “breadbasket”—is a longstanding practice used to clear fields. Satellite observations spanning over two decades have revealed significant upward trends in crop production, vegetative greenness, and the frequency of post-harvest fires, with this last contributing to hazardous air quality during the peak burning season (mid-October to mid-November). Since 2022, thermal anomaly data from Aqua-MODIS and SNPP-VIIRS sensors have shown a sharp decline in reported fire events—an observation that contrasts starkly with the concurrent rise in regional aerosol loading detected from space. This apparent discrepancy became particularly pronounced in 2024–2025, prompting a closer examination using high-temporal-resolution imagery from the Advanced Meteorological Imager (AMI) on the geostationary satellite GEO-KOMPSAT-2A. These observations revealed a clear spike in fire-related signals occurring around and after 4:00 p.m. local time, i.e., outside the typical noon to 2:00 p.m. detection window of the MODIS and VIIRS. A fire detection algorithm exploiting the fire-sensitive shortwave-infrared 3.8 μm signal and its contrast to 11.2 μm infrared observations is designed to adopt AMI observations and applied to its multi-year observations (2019–2025). The resulting fire dataset unambiguously shows a gradual shift in stubble burning activity toward the late afternoon hours beginning in 2022 which is underreported by polar-orbiting satellites. The orbital drift of NASA’s MODIS sensor on the Aqua platform allows detection of some of the gradually shifting fires during afternoon hours, but the MODIS still misses a large number of fires occurring around and after 4 p.m. The AMI’s relatively coarse spatial resolution (~4 km), a consequence of its slant viewing geometry over NW India, imposes inherent limitations on quantifying the full extent of fire occurrences. The operational air quality forecasting models currently assimilate satellite fire detections predominantly captured during early afternoon overpasses of the MODIS and VIIRS. The temporal shift in fire activity complicates such forecast, leading to a substantial underestimation of emissions. Intense stubble burning and the resulting air pollution highlight the need for effective crop residue management practices for mitigating the frequency of open biomass burning and thereby reducing episodic degradation of air quality and its associated public health and economic impacts. Full article

In this study, six serpentine resistive strain sensors are manufactured on two cantilevers made of FR-4 (Flame Retardant 4) with dimensions of 25 mm × 140 mm. Three strain sensors are printed on each substrate using particle-free EI 615 silver ink. The method of fabrication is hybrid in nature and consists of aerosol jet (AJ) printing a layer of conductive material and selectively sintering certain regions before removing the non-sintered material with 1-dodecene solvent. The gauges on one cantilever are coated with a 10 µm dielectric layer using Norland Electronic Adhesives (NEA) 121, which serves as the passivation layer, while the three gauges on the other cantilever are left exposed. The samples are subjected to two standard thermal–mechanical loading conditions: namely, a vibration test according to the MIL-STD-883 method 2007 Cond A and a high-temperature soak test according to the Mil-Std-883 method 1008 Cond B. The reliability of the devices is quantified by assessing the percent change in their resistances and gauge factors (GF) between tests. The percent change is then used to ascribe a reliability metric to the gauges. 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

Adolescent vaping has become a persistent health and behavioural challenge in schools, yet many institutions lack reliable tools to detect and respond to concealed e-cigarette use. This study addresses this problem by evaluating the real-world performance of a low-cost “Internet of Things” (IoT) vape detection system deployed across 37 high-risk restroom and change-room locations at a large Australian Independent school. The aim was to determine whether an IoT-based environmental monitoring platform could accurately identify vaping events, support timely staff intervention, and provide actionable insights into student behaviour patterns. A longitudinal case study design was used, collecting continuous particulate matter (PM_2.5 and PM₁₀) data at one-minute intervals over an 18-month period, where PM_2.5 and PM₁₀ refer to particulate matter with aerodynamic diameters ≤ 2.5 µm and ≤10 µm, respectively, reported in micrograms per cubic metre (µg/m³. Threshold-based alerting, cloud-based data processing, and school-led Closed-circuit television (CCTV) verification were combined to assess detection accuracy, temporal trends, and operational responses. The system recorded more than 300 vaping-related incidents, with clusters aligned to predictable times of day and higher prevalence among senior students. Operational detection performance was high, with alert events characterised by rapid, concurrent PM_2.5 and PM₁₀ excursions consistent with vaping-related aerosol profiles, although staff responsiveness declined over time due to alert fatigue and competing priorities. A major environmental smoke event demonstrated the need for context-aware logic to reduce false positives. The findings demonstrate that real-time aerosol monitoring is not only technically reliable but also highly effective in detecting vaping within school environments. These perspectives help explain why user engagement, alert fatigue, and institutional follow-through are as critical as sensor accuracy itself. Ultimately, the effectiveness of vape detection relies on strong organisational commitment, well-defined response workflows, and alignment with broader wellbeing and policy strategies. When these elements are in place, such systems can evolve from simple detection tools into intelligent, integrated components of school health governance. Full article

The Pushbroom Imager for Cloud and Aerosol Research and Development (PICARD) visible through shortwave infrared imaging spectrometer was developed to carry a calibration laboratory environment to high altitudes, while also providing high-dynamic-range bright cloud-top radiance measurements across a field of view just under 50 degrees. The in-flight performance of this new spectroradiometer was validated in comparison to multiple reference data sources and targets using imagery collected aboard NASA’s ER-2 high-altitude aircraft during the Western Diversity Time Series (WDTS) airborne science campaign in April 2023 and the September 2024 Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) Postlaunch Airborne eXperiment (PACE-PAX), both operating out of southern California. PICARD measurements from flights over Railroad Valley Playa, Nevada, USA, were compared to high-resolution radiance spectra of the dry lakebed provided by the Radiometric Calibration Network (RadCalNet) Working Group. Direct comparison to satellite cloud radiometry was enabled by the ER-2 flying in coordination with simultaneous overpasses of the Terra, Aqua, and NOAA-20 Earth-observing satellites during WDTS and with the PACE observatory during PACE-PAX. To account for large spectral differences between incandescent laboratory sources and solar illumination, PICARD calibration relies on measurements using the Goddard Laser for Absolute Measurements of Radiance (GLAMR) to characterize and minimize spectral stray light from the instrument’s twin Offner grating spectrometers. Good agreement in comparison to reference measurements demonstrates PICARD’s ability to provide imagery for environmental science or for testing new sensor designs and retrieval algorithms for cloud and aerosol research with verified laboratory calibrations at high altitudes. Full article

The Advanced Meteorological Imager (AMI) on GEO-KOMPSAT-2A (GK-2A) lacks a 2.1 μm shortwave infrared channel, precluding the dark target surface reflectance estimation that other geostationary aerosol retrievals rely on. We propose an improved land aerosol optical depth (AOD) retrieval in which background surface reflectance (BSR) is derived entirely from pixel-level bidirectional reflectance distribution function (BRDF) inversion using the scaled Ross-Thick Li-Sparse (sRTLS) kernel model fitted to geostationary time-series observations. Unlike existing approaches, the algorithm inverts the BRDF independently at each retrieval channel without relying on spectral reflectance relationships or external surface reflectance products; it assumes a low-background AOD during an initial accumulation period and then iteratively refines both BRDF coefficients and AOD. Two aerosol models—generic and dust—are supported, with a geographic dust-zone mask activating two-model selection during spring. Validation against 74 Aerosol Robotic Network sites over 2023 yields R = 0.86, RMSE = 0.15, and bias = −0.02, compared with R = 0.59, RMSE = 0.25, and bias = −0.04 for the National Meteorological Satellite Center (NMSC) GK-2A AOD product. The largest improvements appear at AOD ≤ 0.1 (bias: +0.03 versus +0.11) and AOD > 0.8 (bias: −0.12 versus −0.85). The full March–May (MAM) evaluation yields bias = −0.06 across all 74 sites. As a separate parallel retrieval restricted to matchups inside the geographic dust-zone mask, the proposed algorithm (dust model included) gives bias = −0.03, which worsens to −0.11 when only the generic model is applied—nearly a fourfold increase. A comparison against Himawari-9/Advanced Himawari Imager (AHI)—a co-located geostationary sensor carrying a 2.3 μm shortwave infrared (SWIR) channel—shows that the proposed algorithm (R = 0.897) outperforms Himawari-9/AHI (R = 0.855) across all metrics, demonstrating competitive accuracy without relying on a SWIR channel. Full article