Search Results (203)

Particle size distribution (PSD) is a key determinant of aerosol optical properties and plays an important role in optical sensing and environmental monitoring. However, estimating PSD from light scattering measurements remains a challenging inverse problem due to its ill-posed nature and sensitivity to noise. To achieve the objective, this study proposed a physics-informed, data-driven inversion framework for PSD reconstruction using multi-angle light scattering signals generated from Mie scattering simulations. Synthetic datasets were generated using Johnson–SB, lognormal, and bimodal lognormal PSDs under various optical conditions, and the resulting scattering intensities were used to train machine learning models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR). The proposed framework was evaluated using both point-wise error metrics and distribution-based metrics, including Kullback–Leibler divergence and Wasserstein distance. The results showed that RF and XGBoost consistently achieved the highest reconstruction accuracy, with R² values exceeding 0.98 across different PSDs, and significantly outperformed conventional linear baseline methods, including Ridge regression (representing Tikhonov regularization) and Non-negative Least Squares (NNLS). Additional experiments using lognormal and bimodal lognormal PSDs further confirmed the distributional generalization capability of the proposed model. The reconstructed PSDs also showed strong agreement with the reference distributions and remained robust under Gaussian, lognormal, and combined noise perturbations of up to 20%. Therefore, integrating physics-based scattering simulations with machine learning provided an accurate and robust solution for the inverse Mie scattering problem in optical particle characterization. Full article

An experimental setup has been developed that enables the conversion of a complex stream of polydisperse droplets generated by an ultrasonic dispenser into a stream of nearly identical droplets falling through a vertical channel. The fall of droplets of an aqueous NaCl solution in this channel, filled with heated dry air, is studied. Water from the droplets evaporates quickly, and crystals of a solid salt crust form on their surface. At a later stage of the process, the remaining solution is removed from the droplet using a jet of water vapor that passes through the pores of the polycrystalline crust. It was first observed that some of the drying droplets suddenly shifted to one side under the influence of the reactive force generated by the vapor jet. Images obtained using a scanning electron microscope show that the salt particles formed have a diameter of around 25 µm, are slightly porous, and consist of numerous crystals. It has been proven that these particles do not have a central cavity. The use of seawater and the role of salt particles in protecting against thermal radiation from fires are briefly discussed. Calculations based on Mie theory have shown that the contribution of light scattering by thin-walled hollow sea salt particles formed above the ocean surface during relatively slow evaporation of seawater droplets can be significant to the ocean’s heat balance. Full article

The interaction of electromagnetic waves with atmospheric aerosols plays a significant role in communication systems and multimedia information transmission. Understanding the interaction of vortex light beams with an aerosol-laden atmosphere is indispensable for establishing a framework of the environmental channel. During the interaction, different optical effects such as absorption and scattering will result in energy attenuation, and this yields the deterioration of the transmission feature of the vortex beam signal. In this study, we present a theoretical analysis of Gaussian vortex beams (GVBs) scattering by diverse aerosol (unformed carbon, dust, sulphate, silicate, soot, and nitrate) particles in the atmosphere on the basis of the well-established generalized Lorenz–Mie theory (GLMT). Combined with the lognormal distribution model for aerosol particles, the attenuation and transmission characteristics of GVBs for different aerosol particles are analyzed. The extinction efficiency $\left(Q_{ext}\right)$ factor of GVB, caused by the absorption and scattering of various aerosols, becomes smaller compared to that of a basic Gaussian beam (GB). Increasing the OAM mode index, the energy attenuation and transmission caused by aerosol absorption and scattering further decrease. Moreover, this research provides a basis to analyze the optical characteristics of the twisted beams in different atmospheric channels, such as wireless communication networks over aerosol-laden systems and material interactions. Full article

To address the limited constraints of ground-based lidar with few channels in retrieving aerosol microphysical parameters in urban atmospheres, this study developed a method to retrieve aerosol volume size distribution and effective radius from a 355/532/1064 nm triple-wavelength elastic-scattering, single-polarization lidar system. The method uses 3β + 2α optical quantities as input constraints, applies Mie scattering theory as the forward model, parameterizes the volume size distribution with B-spline functions, and achieves stable solutions through Tikhonov regularization and cross-validation. To reduce uncertainties in prior parameters, including the complex refractive index, particle size range, and lidar ratio, an optimization strategy based on parameter search, retrieval reconstruction, and error minimization was introduced. Numerical simulations showed that the method reproduced the main features of a bimodal lognormal aerosol volume size distribution with good feasibility and stability. Two case studies further showed fine-mode dominance and decreasing extinction coefficient, depolarization ratio, and effective radius with height under good air quality conditions, but enhanced coarse-mode contribution and effective radius in the upper cloud-influenced layer under lightly polluted conditions, as inferred from the combined variations in RSCS, extinction coefficient, depolarization ratio, and effective radius. Full article

Baltic amber exhibits a wide range of colors and has attracted considerable attention in materials science. Previous studies have mainly focused on the origin and formation characteristics of beeswax-amber, while the relationship between beeswax-amber color and the microstructural characteristics of internal bubbles remains poorly understood. Ten beeswax-amber specimens exhibiting a color gradient from yellow to white were selected. Scanning electron microscopy (SEM) was used to examine and analyze their internal structures, with a focus on documenting bubble size, number, and density characteristics. Ultraviolet (UV) illumination was employed for qualitative optical observation, and Fourier-transform infrared (FTIR) spectroscopy was conducted to identify component phase and spectra. The correlation between bubbles and color was analyzed to infer the origin of white beeswax-amber’s coloration and explore the mechanisms underlying beeswax-amber’s color variation. Results indicate that beeswax-amber coloration is closely linked to its microscopic bubble characteristics. The microstructure satisfies conditions for Mie scattering, some white beeswax-amber samples contain abundant nanoscale bubbles, triggering a combined effect of Rayleigh and Mie scattering. These results demonstrate that the color of Baltic amber is governed not only by its intrinsic body color but also by the synergistic optical effects arising from internal bubble microstructures, providing a physically grounded explanation for its diverse appearances. Full article

Using an in situ method of time-resolved Mie scattering indicatrix registration, the dynamics of micro- and nanoparticle formation during the explosive boiling of a solution of ibuprofen in subcritical carbon dioxide (T₀ = 302 K, P₀ = 71 bar) were investigated. The process is found to exhibit multistage behavior. At the jet front, ibuprofen microaggregates with a mean radius of 1.4 ± 0.2 μm are formed, maintaining a stable size over the initial ~100 ms. Subsequent reduction in boiling intensity results in a decrease in the particle radius to 650 ± 100 nm. In the following stage, nanoscale CO₂ clusters (20–50 nm) are detected by the Mie scattering technique. The findings indicate that the final size of the resulting ibuprofen particles is governed not only by the initial thermodynamic conditions but also by the boiling dynamics of the ibuprofen-saturated CO₂ solution during the pulsed ejection process. Full article

In this study, a low-cost AS7265x-based multispectral electronic tongue system was developed for estimating milk composition and adulteration indicators and supported with an explainable artificial intelligence (XAI) framework. Experimental analyses were conducted on 190 augmented commercial milk samples, where fat, protein, solids-not-fat (SNF), density, freezing point, and added water ratio were treated as target variables. Sensor data were modeled as RAW, DERIVED, and FUSION feature sets, and regression performance was compared using Random Forest, Gradient Boosting, AdaBoost, KNN, and XGBoost. Model validation was carried out with both five-fold cross-validation and Leave-One-Out (LOO) strategies to assess field-level generalizability. Results showed that a narrow-band, low-cost optical sensor platform can estimate not only fat and protein but also SNF, density, and freezing point with high accuracy. Within the XAI framework, permutation-based importance analysis and SHAP were used to identify critical spectral bands for each target parameter, enabling data-driven recommendations for band-oriented sensor design optimization. The study presents a scalable methodology that integrates low-cost sensor design, multi-parameter quality estimation, and explainable modeling beyond traditional fat–protein-focused approaches. Across all six targets, the XAI analysis consistently identified the near-infrared channel at 860 nm (asIR_3) as the most informative band, reflecting the combined effect of water absorption and Mie scattering by fat globules; the visible channel at 680 nm (asVIS_4) emerged as a secondary band, reflecting dissolved-matter scattering. These bands are therefore the natural starting point for cost-reduced versions of the sensor. Among the compared feature sets (RAW, DERIVED, FUSION), the 18-band RAW configuration provided the most balanced performance across all six targets. Full article

Reliable detection of personnel is critical for situational awareness and life-saving interventions during indoor fire rescue operations, where dense smoke rapidly obscures visibility and compromises conventional vision systems. Visible-light cameras fail under such conditions due to severe Mie scattering, while thermal infrared (TIR) imaging—though capable of penetrating smoke—often lacks the fine-grained texture needed to distinguish human forms from background clutter. Furthermore, practical deployment of multimodal sensors is hindered by spatial misalignment between modalities, which degrades fusion efficacy and detection accuracy. To address these challenges, this paper proposes GLAFC-YOLO (Global-Local Alignment and Frequency-aware Cross-attention Fusion), a dual-stream multimodal detection framework specifically designed for personnel localization in smoke-obscured indoor fires. GLAFC-YOLO fuses near-infrared (NIR) and TIR imagery through three novel components: (1) a Global-Local Feature Alignment Subnet (GL-FAS) that rectifies geometric misalignment across modalities; (2) a Modality-Adaptive Frequency Channel Attention (MA-FCA) module that enhances complementary smoke-penetrating thermal signatures and NIR texture cues in the frequency domain; and (3) a Confidence-Aware Transposed Cross-Attention (CA-TCA) mechanism that suppresses smoke-induced artifacts and restores weakened human-centric features. Evaluated on a newly collected multimodal dataset of indoor fire scenarios with annotated personnel, GLAFC-YOLO achieves substantial improvements over the baseline YOLOv11 architecture. Specifically, it achieves Recall improvements of 43.2% and 0.5% compared to unimodal NIR and TIR baselines, respectively. In addition, it achieves improvements of 37.4% and 3.9% in $mA{P}_{50}$ and 17.3% and 17.0% in $mA{P}_{50–95}$. Experimental results indicate that GLAFC-YOLO outperforms competitive models and reduces personnel miss rates, demonstrating its robustness and readiness for real-world fire-rescue assistance. Full article

Multispectral LiDAR can simultaneously obtain distance and spectral information and shows great potential for underwater detection. However, absorption and scattering caused by suspended particles in water lead to energy attenuation and multiple scattering, which affect echo intensity and ranging accuracy, while the propagation characteristics under multi-wavelength conditions remain insufficiently studied. In this study, a simplified underwater propagation simulation model for multispectral LiDAR is established based on the equivalent spherical-particle assumption, combining Mie scattering theory with a semi-analytical Monte Carlo method. The effects of particle size on echo intensity and ranging error are analyzed under fixed concentration conditions. Based on this model, a detection-threshold-constrained optimal wavelength selection criterion is formulated. Multi-distance analysis (3, 5, 8, and 15 m) confirms that the preferred wavelength is primarily governed by particle size and remains stable across depths. The results show that the optimal detection wavelength shifts with particle size, being about 560 nm for fine particles and gradually moving toward the 400–480 nm blue–green band for larger particles. Experimental validation shows that the simulation-based ranging correction reduces RMSE by 9.4–25.9% (average 18.1%) and MAE by 11.8–29.7% (average 22.0%) across five experimental distances. The results provide a preliminary reference for wavelength selection in multispectral LiDAR systems under simplified conditions. Full article

Atmospheric laser beam propagation is typically perturbed by the dual influences of aerosol particle systems and atmospheric turbulence. This joint perturbation induces intensity fluctuations in the transmitted optical field, which significantly degrades the performance of laser-based systems. This study integrates and improves upon existing simulation algorithms, establishing a coupled model that combines the Monte Carlo method and multi-phase screens. The model accurately characterizes optical field evolution and reveals that the impacts of scattering and turbulence on the scintillation index (SI) are not simply additive: turbulence perturbation enhances intensity fluctuations, leading to an increase in SI; however, as the energy proportion of scattered light rises, its statistical stationarity begins to dominate the optical field characteristics, stabilizing SI. Based on radiative transfer and Mie scattering theories, an analytical formula for single-scattering SI is derived, enabling direct calculation from fundamental parameters. Furthermore, a composite SI expression is established using the scattered-to-transmitted light intensity ratio. To address model deviations along the dimensions of visibility and turbulence strength, a sinusoidal compensation model and a logarithmic compensation model are proposed, respectively. Validation results verify the complementary and competitive mechanisms of scattering and turbulence in modulating intensity fluctuations. This research provides efficient theoretical tools and practical references for simulating and optimizing laser transmission in complex atmospheric environments. Full article

The vertical distribution of atmospheric aerosol layers plays a fundamental role in understanding their climatic and environmental effects. Using one year of lidar observations in Jinhua, together with ground-based meteorological measurements and ERA5 reanalysis data, this study develops an integrated analytical framework to investigate the structural characteristics of aerosol layers in Mie-scattering profiles and their meteorological driving factors. K-means clustering identifies three representative aerosol layer structure types: single-layer concave, single-layer convex, and multi-layer profiles. By combining the Boruta algorithm with a random forest model, the dominant meteorological factors associated with each structure type are quantified across four boundary-layer stages (00–06, 06–12, 12–18, 18–24 LT). Temperature, humidity, wind speed, wind direction, divergence, and vertical velocity exhibit distinct influences across different boundary-layer conditions, revealing differentiated regulatory mechanisms governing aerosol layer structure change. The proposed framework establishes a coupled perspective between atmospheric dynamic/thermodynamic processes and aerosol layer structure formation, providing a basis for refined modeling of aerosol evolution and improved understanding of aerosol–meteorology interactions. Full article

Numerical simulations of (1) two aerosol types such as organic carbon (i.e., spherical) and dust (i.e., non-spherical) particles, and (2) their mixtures are carried out. Optical and microphysical parameters of these aerosols in our simulations are provided by MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2). The inversion routine is performed with TiARA (Tikhonov Advanced Regularization Algorithm) using the Lorenz–Mie (i.e., spherical) light-scattering model in unsupervised and automated, i.e., autonomous mode. The results of our numerical simulations show that the accuracy of the inversion results for the aerosol mixtures from synthetic optical data perturbed by ±10% random error is comparable to the accuracy observed for the inversion results of the “pure” spherical particles. In particular, the retrieval uncertainties of effective radius, and number, surface-area, and volume concentrations of these mixtures are ±30%, ±10%, between −50% and +100% and ±30%, respectively. However, we need to apply a modified version of the gradient correlation method (GCM) to stabilize the inversion results. The results of this study will form the baseline for future work, where we plan to apply TiARA to optical data products obtained from real lidar observations in the framework of the SCC (Single Calculus Chain) of EARLINET (European Aerosol Research Lidar Network). Full article

Silica nanoparticles (SNPs) are pivotal in designing functional optical films, but accurately modeling their properties is hindered by the limitations of classical effective medium theories, which break down for larger particles and complex morphologies. We introduce a robust, effective medium theory that overcomes these limitations by incorporating full Mie scattering solutions, thereby accounting for size-dependent and multipolar effects. Our model is comprehensively developed for unshelled, shelled, mixed, and hollow SNPs randomly dispersed in a host medium. Its accuracy is rigorously benchmarked against 3D finite-element method simulations. This work establishes a practical and reliable framework for predicting the optical response of SNP composites, significantly facilitating the rational design of high-performance coatings, such as anti-glare layers, with minimal computational cost. Full article

Building-integrated photovoltaics (BIPVs) can benefit not only from transparent but also from opaque modules that maximize light capture. We present haze-assisted luminescent solar concentrators (HALSCs) that integrate scattering and luminescence in multilayer designs. Polymer–liquid crystal composites with embedded dyes form micron-scale domains that act as broadband Mie scattering centers, while the dye provides spectral conversion. Monte Carlo ray-tracing simulations and experiments reveal that edge-emitted intensity increases with haze thickness but saturates beyond a threshold; segmenting the same thickness into multiple thinner layers enables repeated scattering, markedly enhancing side-guided emission. When coupled with crystalline silicon solar cells, multilayer HALSCs converted this optical advantage into enhanced photocurrent, with triple-layer devices nearly doubling output relative to transparent controls. These findings establish opacity–luminescence coupling and multilayer haze engineering as effective design principles, positioning HALSCs as practical platforms for advanced BIPVs and optical energy-management systems. Full article

A photovoltaic-integrated broadband photodetector based on vertically-stacked lateral-aligned III–V nanowire arrays is proposed and investigated. The staggered arrangement configuration drastically reduces the competition between solar cell and photodetector that is difficult to avoid in vertically-stacked planar structures, which enables broadband strong absorption. The lower GaAs nanowires (NWs) act as Mie scattering centers, which scatter the incident light passing through the gaps back to the upper layer, enhancing the absorption of InAs NWs over a wide wavelength range from the ultraviolet to the infrared. Meanwhile, the light trapping effect of the upper InAs nanowires improves the absorption of lower GaAs NWs. At a near-infrared wavelength of 1400 nm, the photovoltaic-integrated InAs nanowire photodetector exhibits a photocurrent density of 168.83 mA/cm² and responsivity of 0.168 A/W, 90% and 93% higher than the single layer InAs nanowires. The conversion efficiency of the GaAs nanowire solar cell is also improved after integration. This work may pave the way for the development of self-powered miniaturized broadband photodetectors. Full article