Differentiation of soot particulates in air using polarized light scattering method

Polarization Characterization of Porous Particles Based on DDA Simulation and Multi-Angle Polarization Measurements

Porous suspended particles are hazardous to human health due to their strong absorption capacity for toxic substances. A fast, accurate, in situ and high-throughput method to characterize the microporous structure of porous particles has extensive application value. The polarization changes during the light scattering of aerosol particles are highly sensitive to their microstructural properties, such as pore size and porosity. In this study, we propose an overlapping sphere model based on the discrete dipole approximation (DDA) to calculate the polarization scattering characteristics of porous particles. By combining scattering calculations with multi-dimensional polarization indexes measured by a multi-angle polarized scattering vector detection system, we achieve the identification and classification of pore-type components in suspended particles. The maximum deviation based on multiple indexes is less than 0.16% for the proportion analysis of mixed particles. Simultaneously, we develop a quantitative inversion algorithm on pore size and porosity. The inversion results of the three porous polymer particles support the validity and feasibility of our method, where the inversion error of partial particles is less than 4% for pore size and less than 6% for porosity. The study demonstrates the potential of polarization measurements and index systems applied in characterizing the micropore structure of suspended particles.

Total retardance measurements based on the complex Fourier coefficients for the rotating polarizer analyzer system

We propose a demodulation algorithm based on the calculus of the complex Fourier coefficients; we used a dual rotating polarizer-analyzer polarimeter to show the feasibility of our proposal. Our demodulation algorithm considers the frequency response obtained by the system, and its possible to calculate the total retardation, fast axis orientation and ellipticity of a sample. Our proposal does not require recovering the full Mueller matrix from getting those parameters. In addition, as the proposal does not use retarders for the measurement, the system presents potential applications for multi-wavelength measurements on phase retardation samples. We show experimental results showing the capabilities of our proposal in characterizing a polarization retardance sample.

Tri-zone flame spatial structure imaging combined with endogenic polarized scattering

We propose a multi-mode optical imaging method to retrieve the 2D and 3D spatial structures of the preheating, reaction, and recombination zones of an axisymmetric steady flame. In the proposed method, an infrared camera, a visible light monochromatic camera, and a polarization camera are triggered synchronously to capture 2D flame images, and their corresponding 3D images are reconstructed by combining different projection position images. The results of the experiments conducted indicate that the infrared and visible light images represent the flame preheating and flame reaction zones, respectively. The polarized image can be obtained by computing the degree of linear polarization (DOLP) of raw images captured by the polarization camera. We discover that the highlighted regions in the DOLP images lie outside the infrared and visible light zones; they are insensitive to the flame reaction and have different spatial structures for different fuels. We deduce that the combustion product particles cause endogenic polarized scattering, and that the DOLP images represent the flame recombination zone. This study focuses on the combustion mechanisms, such as the formation of combustion products and quantitative flame composition and structure.

Simultaneously Acquiring Optical and Acoustic Properties of Individual Microalgae Cells Suspended in Water

Microalgae play a vital role in aquatic ecological research, but the fine classification of these tiny and various microalgae cells is still challenging for the community. In this paper, we propose a multimodality technique to simultaneously acquire the polarized light scattering, fluorescence and laser-induced acoustic wave signals originated from individual microalgae cells in water. Experiments of different species of Spirulina and different states of Microcystis have been conducted to test our experiment setup, and the results demonstrate that this method can well discriminate microalgae cells with pigment or microstructural differences. Moreover, with these modalities, the consumption of absorbed energy is evaluated quantitively, and a possible way to assess photosynthesis on a single-cell level is presented. This work is expected to be a powerful technique to probe the biophysical states of microalgae in the aquatic ecosystem.

Monitoring particulate composition changes during the flocculation process using polarized light scattering

Monitoring the particulate composition changes during the flocculation process is still challenging for the research community. We use an experimental setup based on polarized light scattering to measure the polarization states of the scattered light of the individual particles. We build a classifier based on the support vector machine and feed it with the measured parameters. Results show that the classifier can effectively classify the particulate compositions, such as the sediment particles, flocculants, and flocs, which can be used to monitor the particulate composition changes during the flocculation process. Discussions on the intensity and polarization parameters find that the polarization parameters play a vital role in the classification of the particulate compositions in the flocculation suspensions. Additionally, the further analysis of the experimental data and the related simulations show that the degree of polarization can be an indicator of the flocculation process. We prove that the method based on polarized light scattering may be a potential in situ monitoring tool in the future for the study of the flocculation process.

Polarization Measurements and Evaluation Based on Multidimensional Polarization Indices Applied in Analyzing Atmospheric Particulates

Online identification and characterization of suspended aerosols can provide a scientific basis for understanding aerosol transformations, quantitatively evaluating the impacts on air quality, public health, and the source apportionment of different atmospheric particulate matters. In this study, we confirm the validity of our developed high-throughput multi-angle polarized scattering vector detection of aerosols and multidimensional polarization scattering index systems. By observation of the mean values, variance, and Wilk’s Lambda of multidimensional polarization indices for different aerosol types, the polarization index shows unique characterization abilities for aerosol properties, and the optimal combination of polarization indices can always be found for a specific aerosol category with a high resolution and discrimination. Clearly, the multidimensional polarization indices of individual aerosols are more suitable for online and real-time aerosol identification and even help to explain the in situ microphysical characteristics of aerosols or evaluate the dynamic evolution of aerosols.

Real time and online aerosol identification based on deep learning of multi-angle synchronous polarization scattering indexes

In this study, we employ our developed instrument to obtain high-throughput multi-angle single-particle polarization scattering signals. Based on experimental results of a variety of samples with different chemical composition, particle size, morphology, and microstructure, we trained a deep convolutional network to identify the polarization signal characteristics during aerosol scattering processes, and then investigate the feasibility of multi-dimensional polarization characterization applied in the online and real-time fine and accurate aerosol recognition. Our model shows a high classification accuracy rate (>98%) and can achieve aerosol recognition at a very low proportion (<0.1%), and shows well generalization ability in the test set and the sample types not included in the training set. The above results indicate that that the time series pulses from multi-angle polarization scattering contain enough information related with microscopic characteristics of an individual particle, and the deep learning model shows its capability to extract features from these synchronous multi-dimensional polarization signals. Our investigations confirm a good prospect of aerosol attribute retrieval and identifying and classifying individual aerosols one by one by the combination of multi-dimensional polarization scattering indexes with deep learning method.

Estimation and extraction of the aerosol complex refractive index based on Stokes vector measurements

We present a way to estimate the aerosol complex refractive indices (ACRI) based on a real-time Stokes vector measurement technology. First, we introduce how to simultaneously get the multi-polarization signals of multi-scattering angles. Then we demonstrate the numerical inversion to retrieve an ACRI based on an iterative Mie algorithm. Meanwhile, we show the experimental results of several types of aerosol samples. Our optimal estimation of ACRI inversion shows a good agreement with the references, which confirms the feasibility and then implies a good prospect of multidimensional polarization characterization applied in the future aerosol recognition, especially suitable for near-spherical scatters.

Study on polarization scattering applied in aerosol recognition in the air

In this work, we present an in situ online aerosol recognition scheme by synchronized parallel polarization scattering analysis. By theoretical simulations, we select the feasible scattering angles and evaluate the potential of Stokes parameters to identify aerosols. Correspondingly, we develop a measurement system based on multi-angle optical scattering and multidimensional polarization analyzing technique. We construct two index groups based on non-normalized and normalized polarization parameters respectively, and employ their frequency distribution histograms instead of the simple average values to identify and classify different types of aerosols. The experimental verification confirms a future way of a multi-dimensional polarization parameter group applied in a fast and effective air pollutants monitoring.

Differentiation of suspended particles by polarized light scattering at 120°

Probing suspended particles in seawater, such as microalgae, microplastics and silts, is very important for environmental monitoring and ecological research. We propose a method based on polarized light scattering to differentiate different suspended particles massively and rapidly. The optical path follows a similar design of a commonly used marine instrument, BB9, which records backscattering of non-polarized light at 120°. In addition, polarization elements are added to the incident and scattering path for taking polarization measurements. Experiments with polystyrene microspheres, porous polystyrene microspheres, silicon dioxide microspheres, and different marine microalgae show that by carefully choosing the incident polarization state and analyzing the polarization features of the scattered light at 120°, these particles can be effectively differentiated. Simulations based on the Mie scattering theory and discrete dipole approximation (DDA) have also been conducted for particles of different sizes, shapes and refractive indices, which help to understand the relationship between the polarization features and the physical properties of the particles. The laboratory system may serve as a prove-of-concept prototype of new instrumentations for applications on board or even with submersibles.