Search Results (2,179)

Fine particulate matter PM_2.5 continues to pose a critical public health risk in Northern Thailand, particularly in Chiang Mai, where traditional filtration methods often face limitations in cost and efficiency for large-scale applications. This study introduces a novel “Evaporator Condensation Growth Particle Scrubber (ECGP)” designed to enhance the collection efficiency of sub-micron particles by enlarging their physical size through a pressure-driven growth mechanism. The ECGP system utilizes synergistic effects between solid nuclei, high relative humidity, and mechanical pressure modulation. The ECGP system integrates solid nuclei, ~95% relative humidity and mechanical pressure modulation within a single chamber. Using incense smoke as a PM surrogate, the process utilizes controlled adiabatic cycles to induce stable heterogeneous condensation. The results indicate that the integrated process effectively shifts particle size distribution, reducing the PM_2.5/PM₁₀ mass ratio from 1.00 to 0.83. This indicates that approximately 17.5% (with a standard deviation < 1% across 10 trials, p < 0.05) of the fine mass successfully transitioned into the larger, more filterable PM₁₀ fraction and exhibited high physical stability and resistance to re-evaporation, effectively overcoming the low-efficiency threshold (typically <10%) of standard mechanical scrubbers and cyclones for sub-micron dust. This study concludes that ECGP technology offers a promising, cost-effective alternative for improving indoor air quality in large public infrastructures by leveraging particle inertia for enhanced removal, providing a scalable solution to the persistent smog crisis. Full article

Surface Acoustic Wave (SAW) technology, long central to analog signal processing and RF filtering, is undergoing a major renewal. Driven by advances that decouple SAWs from traditional piezoelectric materials and fixed-function devices, the field is gaining unprecedented control over acoustic, optical, and electronic interactions at the micro and nanoscale. This review synthesizes these developments across four fronts: new physical mechanisms for SAW manipulation, emerging material platforms, ranging from thin films to 2D systems, along with reconfigurable device architectures and circuits, and the expanding landscape of applications they enable. Optical methods are reshaping how SAWs are generated and controlled, bypassing the limits of conventional electromechanical coupling. Coherent optical excitation of high-Q SAW cavities via Brillouin-like optomechanical interactions now grants access to modes in non-piezoelectric substrates such as diamond and silicon, while on-chip SAW excitation in photonic waveguides through backward stimulated Brillouin scattering opens new integrated sensing routes. In parallel, magneto-acoustic experiments have revealed nonreciprocal SAW diffraction from resonant scattering in magnetoelastic gratings. On the device side, ZnO thin-film transistors integrated on LiNbO₃ exploit acoustoelectric coupling to realize voltage-tunable phase shifters; UHF Z-shaped delay lines achieve high sensitivity in a compact footprint; and parametric synthesis of wideband, multi-stage lattice filters targets 5G-class performance. Atomistic simulations show that SAW propagation in 2D MXene films can be engineered via surface terminations, while aerosol jet printing and SAW-assisted particle patterning provide agile, cleanroom-light fabrication of microfluidic and magnetic components. These advances enable applications ranging from hybrid quantum systems and quantum links to lab-on-a-chip particle control, SBS-based and UHF sensing, reconfigurable RF front-ends, and soft robotic actuators based on patterned magnetic composites. At the same time, optical techniques offer non-contact probes of dissipation, and MXenes and other emerging materials open new regimes of acoustic control. Conclusively, they are transforming SAW technology into a versatile, programmable platform for mediating complex interactions in next-generation electronic, photonic, and quantum systems. Full article

The accurate quantification of microplastics (MPs) in aquatic environments remains challenging due to the heterogeneous distribution of MPs in different environments, making representative sampling difficult, as well as methodological variabilities in sampling, sample processing, and detection. This study examined measurement fluctuations for MP analysis across four distinct water matrices: wastewater treatment plant (WWTP) effluent, tap water (TW), combined sewer overflow (CSO), and surface water (SW). Two sampling strategies were compared: grab sampling (0.5 L, n = 5) and large-volume filtration using a particle sampling unit (PSU; 100 L, 10 µm mesh, subsampled). Samples were processed through oxidative digestion, stained with fluorescent dye, and analyzed via fluorescence microscopy with automated particle counting. Recovery experiments using polyamide (PA) reference particles (357 ± 60 µm) were conducted to assess method accuracy. PSU sampling demonstrated higher precision (mean R.S.D. 41 ± 17%) compared to grab sampling (mean R.S.D. 64 ± 19%), despite additional variability introduced by subsampling. Recovery rates reached 93 ± 7% for grab samples and 88 ± 23% for PSU samples with complete filter analysis. Statistical modeling revealed that achieving a ±25% margin of error (95% CI) required 21 PSU samples versus 51 grab samples. The quadratic relationship between the margin of error and required sample size underscores the importance of methodological optimization for cost-effective monitoring. These findings provide practical guidance for designing MP monitoring campaigns and demonstrate that fluorescent labeling combined with large-volume sampling offers a reliable approach for MP quantification in diverse aquatic environments. Full article

Precise manipulation and directed transport of micro- and nano-particles are cornerstones of emerging lab-on-a-chip technologies. Traditional optofluidic systems that combine optical tweezers with microfluidic channels enable long-range transport. However, they rely on fixed physical boundaries that lack reconfigurability. To bridge this gap, we propose a reconfigurable virtual optical waveguide (VOW) based on a discretized beam-shaping strategy. By superposing two orthogonally polarized shaped beams, we construct interference-free optical channels without physical boundaries. This platform enables programmable transport along complex trajectories, including space-filling Hilbert curves that maximize interaction path length, and shields the transport channel from perturbations induced by surrounding particles. Crucially, the VOW offers multi-dimensional sorting capabilities: (i) it performs precise size-dependent sieving via tunable channel widths, and (ii) it functions as an intrinsic material filter by stably guiding scattering-dominated particles (e.g., gold) while rejecting gradient-dominated dielectric ones. This work establishes a versatile, contactless strategy for adaptive optical logistics and on-chip material purification. Full article

The enzymatic saccharification of cellulose remains a key step in biomass conversion processes, often influenced by enzyme stability, distribution, and accessibility at solid–liquid interfaces. Immobilization of cellulolytic enzymes on nanostructured supports has been proposed as a strategy to modulate catalytic behavior; however, the relationship between enzyme loading and catalytic response remains insufficiently understood. In this study, CuO-based nanobiocatalysts were prepared through controlled cellulase immobilization and systematically evaluated under defined experimental conditions. Structural and physicochemical characterization was performed using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and integrated thermal analysis (TGA–DTG–DSC), enabling a comparative assessment of the analyzed systems. SEM analysis showed that the average particle diameter increased from 39.5 ± 14.8 nm (CuO nanoparticles) to 95.6 ± 21.8 nm (NPI10), 106.6 ± 27.7 nm (NPI15), and 113.5 ± 23.1 nm (NPI20), indicating progressive variations in particle organization with increasing enzyme loading. Catalytic performance was evaluated through enzymatic hydrolysis of cellulose filter paper as a model substrate, with products quantified by HPLC at a representative reaction time. The system prepared at lower enzyme loading (NPI10) exhibited product formation comparable to that of the free enzyme, with apparent average glucose formation values of 1.054 and 1.047 mg·mL⁻¹·h⁻¹, respectively. In contrast, higher immobilization levels were associated with reduced catalytic output. Across all systems, glucose was the predominant product, with negligible accumulation of intermediate oligomers under the evaluated conditions. These results indicate that increasing enzyme loading does not correspond to proportional increases in product formation and highlight the influence of enzyme distribution and accessibility within the system. The combined structural and catalytic observations provide a controlled framework for evaluating how immobilization conditions influence system behavior in nanobiocatalytic systems. Full article

Fluorapatite is a typical phosphate rock resource. Fluorapatite tends to generate fine mud agglomeration, which induces dehydration challenges owing to its inherently fine particle size and negative surface charge. In this paper, phosphate tailings slurries from a phosphate mine in Hubei Province, China, were selected as the research object, and flocculation–dehydration experiments were conducted using anionic, cationic, and nonionic polyacrylamide (PAM) flocculants. The results show that the maximum settling velocity is 51 mm/s and the moisture content of filter cake is 41.54%, which were obtained when the unit consumption of cationic flocculant with molecular weight 12 million was 1000 g/t. The mechanism of sedimentation and dehydration was studied by infrared spectroscopy and a particle size analyzer. The results showed that polyacrylamide was effectively adsorbed on the mineral surface, and the size of flocs increased significantly. Finally, the mechanism of sedimentation and dehydration was proposed. It has important guiding significance for the efficient solid–liquid separation and water circulation of fluorapatite mineral processing wastewater. Full article

Airborne particulate matter (PM) poses a major health risk, yet species selection for vertical greening systems (VGS) is poorly quantified. We evaluated PM retention by seven commercially available shade-tolerant species grown in a modular living wall system (LWS) on a north-facing façade at Sapienza University of Rome. After 3 months of in situ exposure, leaves were analyzed via SEM (1000×), collecting 210 images, 30 per species. An automated FIJI/ImageJ pipeline segmented particles, computed equivalent circular diameters, and classified them into (PM < 0.5, PM [0.5, 1), PM [1, 2.5), PM [2.5, 10), and PM ≥ 10 µm). Across species, ultrafine and fine fractions dominated deposits, with the <0.5 µm class typically comprising 60–70% of counts. Vinca minor cv. albomarginata exhibited the highest densities in ultrafine and fine classes, closely followed by Fatsia japonica; Hedera helix captured more coarse particles (2.5–10 µm and >10 µm). Heuchera sanguinea consistently displayed the lowest densities across all size classes. Performance patterns aligned with leaf surface traits: wax-coated, moderately rough or gently structured cuticles favored adhesion, whereas highly irregular microrelief did not consistently enhance retention. Methodological considerations include thresholding sensitivity, use of equivalent circular diameter for irregular particles, and an upper area filter that may undercount large aggregates. The findings identify Vinca minor cv. albomarginata and Fatsia japonica as priority species for PM mitigation in shaded VGS, with Hedera helix complementing coarse PM capture. The results provide trait-based, design-oriented guidance for living wall species selection in Mediterranean urban and indoor contexts. 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

Ammonia (NH₃) emissions from concentrated animal feeding operations (CAFOs) are recognized contributors to secondary fine particulate matter (PM_2.5) formation, yet empirically derived secondary PM_2.5 emission factors applicable to livestock operations remain limited. This study investigated NH₃-derived secondary PM_2.5 formation under controlled laboratory conditions using a PTFE flow reactor in which NH₃ was reacted with sulfur dioxide (SO₂) across ammonia-rich NH₃:SO₂ ratios, with and without zero air. The resulting aerosols were characterized using gravimetric analysis, elemental analysis, Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM/EDS), and particle size distribution (PSD) measurements. The recovered particles were dominated by inorganic ammonium–sulfur species, with FTIR and elemental trends indicating sulfite-related intermediates under no-zero-air conditions and more oxidized ammonium–sulfur products under oxygenated conditions. Accounting for both filter-collected and wall-deposited particles, unit particulate emission factors normalized to ammonia input were derived. Size-based apportionment using PSD data indicated that approximately 76.6% of the recovered particulate mass was within the PM_2.5 size range. Scaling the experimentally derived unit emission factors using literature-based ammonia emission rates yielded an estimated secondary PM_2.5 emission factor of 0.351 ± 0.084 g PM_2.5 per animal head per day for cattle feedlots, corresponding to approximately 3–4% of reported total PM_2.5 emissions. Because the experimental system isolates NH₃–SO₂ interactions under idealized conditions and does not represent full atmospheric chemistry, the derived values should be interpreted as screening-level estimates of NH₃-derived secondary PM_2.5 formation potential intended to support comparative air quality assessments of CAFOs rather than direct predictions of ambient PM_2.5 concentrations. Full article

Filtration systems are essential for drip irrigation using sediment-laden water sources such as the Yellow River. This study focused on a sand filter (filtration accuracy: 150 μm), a disc filter (filtration accuracy: 125 μm), and their combined multi-stage filtration system (flow rate: 30–50 m³/h). In situ tests were conducted under Yellow River water conditions in the Hetao Irrigation District, Inner Mongolia, China, to evaluate the response of filtration performance to sediment characteristics, flow rate, and operating time. On this basis, Differential Evolution-optimized Random Forest Regression (DE/RFR) was further established to predict filtration performance. The results showed that: (1) Under sediment concentrations of 0.62–3.6 g/L and median particle sizes of 4.70–16.03 μm, the head loss of the sand filter (ΔH_si) remained stable over the operating time. Conversely, the head loss of the disc filter (ΔH_di) increased with the operating time; the magnitude of this increase grew with higher flow rates, sediment concentrations, and median particle sizes, reaching 0.07 MPa after 16–235 min of operation. The head loss of the multi-stage filtration system (ΔH_i) was primarily generated by the disc filter. (2) The filtration efficiency of the filters and the filtration system was 2.5–6.4%. The outlet sediment concentration and particle size distribution were linearly correlated with the inlet values, and the outlet sediment particle size distribution remained below the clogging risk threshold for emitters. (3) Prediction models for ΔH_si, ΔH_di, and ΔH_i were developed based on MLR, RFR, and DE/RFR. Among these, DE/RFR exhibited the highest accuracy in predicting these variables, with R² values ranging from 0.71 to 0.93 and RMSE values from 0.0017 to 0.0104 MPa. (4) Results from Pearson correlation and feature importance analysis indicated that ΔH_si, ΔH_di, and ΔH_i were primarily influenced by flow rate, sediment concentration and operating time, and flow rate and operating time, respectively. (5) Building upon the DE/RFR model, a Filtration Cycle Prediction Model (FCPM) was developed to determine the operational duration required for the head loss across both the filters and the filtration system to reach 0.07 MPa. The two models developed in this study provide technical support for the configuration and operation of drip irrigation filtration systems using sediment-laden water. Full article

To address the issues of uneven grayscale distribution, weak defect features, and small target scales on the coating surface of LCD polarizers during manufacturing, an improved YOLOv10-N-based method is proposed for surface defect detection. First, a polarizer coating defect dataset is constructed based on the LCD polarizer coating process and the characteristics of coating defects. Adaptive median filtering is then employed for image denoising, while a particle-swarm-optimization-based improved histogram equalization method is adopted for image enhancement. Next, the Scale-aware Pyramid Pooling (SCPP) module is introduced into the C2f module of the backbone network to construct the C2f_SCPP feature extraction module, thereby improving the model’s ability to detect coating defects with different morphologies through multi-scale semantic feature fusion. In addition, rotation-equivariant convolution PreCM is incorporated into the SPPF module of the backbone network to build the SPPF_PreCM module, which effectively suppresses feature redundancy and scale conflicts while strengthening the representation of tiny defects. Finally, while retaining the original Distribution Focal Loss (DFL) branch of YOLOv10, WIoU is used to replace CIoU as the IoU loss term in bounding box regression, thereby improving localization accuracy and accelerating model convergence during training. Experimental results show that, compared with YOLOv10-N, the proposed method improves mAP@0.5 and mAP@0.5:0.95 by 1.8 and 2.8 percentage points, respectively, demonstrating its effectiveness for polarizer coating defect detection. However, its generalization capability under diverse production environments, varying illumination conditions, and complex noise scenarios still requires further investigation. Full article

Ballistic missiles exhibit high velocities and rapid maneuverability after atmospheric reentry, posing substantial challenges for anti-ballistic missile (ABM) interception. This paper presents an integrated interception framework that combines an input estimation method with an improved particle swarm optimization-based guidance law (IPSOG). The input estimation approach processes noisy radar measurements to estimate target states in the presence of unknown system inputs and measurement noise. Its performance is evaluated through simulations and compared with the extended Kalman filter (EKF), demonstrating improved estimation accuracy and robustness under highly maneuvering conditions. An improved particle swarm optimization algorithm is employed to design the interceptor guidance law. Compared with conventional proportional navigation guidance (PNG), the proposed guidance method provides enhanced adaptability to target maneuvers. Numerical simulations are conducted to evaluate interception performance against maneuvering ballistic missile targets. Results show reductions in miss distance and interception time while maintaining lower average lateral acceleration and a larger effective interception region. These results indicate that the proposed framework improves both target state estimation and interceptor guidance performance for highly maneuvering ballistic missile targets. Full article

The increasing detection of micro-nanoplastics (MNPs) in environmental settings and in human biological samples has raised growing concern about their potential implications for human health. Exposure to plastic particles may cause oxidative stress, inflammation, and metabolic reactions, according to previous studies. It is unidentified, consequently, to what extent these basic processes result in identifiable clinical illness patterns. This narrative review investigated whether a structured symptom-mapping approach might be used to identify recurrent multisystem symptom patterns that might be consistent with environmental exposure. The Chicago Cluster System (CCS) is a conceptual framework that includes four environmental exposure indicators in addition to 26 clinical signs and symptoms. The CCS framework was used for filtering clinical descriptions of 375 disorders in 21 disease groups using conventional medical references. Using exploratory thresholds of at least eight compatible symptoms, the aim of this study was to identify scenarios exhibiting convergence with the CCS symptom pattern. Forty disorders (11%) exhibited eight or more CCS-compatible symptoms out of the 375 diseases that were evaluated. Neurodegenerative, metabolic, inflammatory, and gastrointestinal illnesses were among the various clinical categories in which these symptoms emerged. Fatigue (65%), upper gastrointestinal problems (58%), and increased inflammatory markers (55%) were the most commonly reported symptoms. These results imply that certain types of chronic disorders may exhibit recurrent multisystem symptom patterns. The observed overlap should be regarded cautiously because many CCS signs are non-specific and widely spread across medical conditions. The CCS framework may organize multisystem symptom patterns in environmental health research. Full article

Ice accretion poses a threat to fixed-wing aerial vehicles as it alters the wings’ shape and thus degrades the aerodynamic performance. In manned aircraft, the icing detection system assists the pilot and utilises dedicated sensors. However, in unmanned aerial vehicles (UAVs), onboard icing detection can generally only be achieved using standard sensors in conjunction with dynamical models, because dedicated sensors are rarely available. In this paper, we propose two approaches based on the particle filter for both icing detection and accurate state and aerodynamic parameter estimation in the presence of icing, with different levels of severity. The first approach uses the observation likelihood for icing hypothesis testing with a complement of the Gaussian kernel to compute icing probability. The second approach uses a discrete jump approach based on a Bernoulli process and a subset of particles to test the icing hypothesis for faster icing detection by estimating changes in icing-related aerodynamic parameters. Using both approaches, the simulation results demonstrate improved estimation accuracy compared to an extended Kalman filter (EKF), under both moderate and severe icing conditions. With adequate tuning, the proposed approaches show potential for indirect icing detection in UAVs. They also enable the computation of icing severity and provide a more accurate and reliable estimate of the icing probability compared to the EKF. Full article

High-temperature particulate matter (PM) filtration remains a fundamental challenge, because most fiber filters not only face the challenge of high temperatures but also suffer from an inherent trade-off between capture efficiency, pressure drop, and service life. This paper reports a hierarchical layered zirconia (ZrO₂) ceramic fiber aerogel featuring a continuous multiscale gradient. The aerogel was prepared by gradient air-blown spinning, and the resulting structure has directional order, with the fiber diameter gradually decreasing from upstream to downstream, thus forming a pore size gradient and achieving hierarchical particle interception across multiple scales. This rational design simultaneously suppresses surface clogging and reduces flow resistance, resolving the longstanding trade-off between efficiency and permeability. Consequently, this aerogel achieves an ultra-high filtration efficiency of 99.96%, a low pressure drop of 156 Pa, and a high dust-holding capacity of 101 g m⁻². The material also exhibits outstanding mechanical toughness (80% compressive strain elasticity and 25.75% tensile fracture strain) and thermal stability up to 1000 °C. Moreover, it maintains over 99.95% filtration efficiency at high temperatures and can be fully regenerated through 800 °C heat treatment. This work establishes a structure-based design paradigm for high-temperature filtration media and provides a scalable pathway for next-generation industrial flue gas purification. Full article