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In this Letter, we report a dual division of focal plane (DoFP) polarimeters-based full Mueller matrix microscope (DoFPs-MMM) for fast polarization imaging. Both acquisition speed and measurement accuracy are improved compared with those of a Mueller matrix microscope based on dual rotating retarders. Then, the system is applied to probe the polarization properties of a red blood cells smear. The experimental results show that a DoFPs-MMM has the potential to be a powerful tool for probing dynamic processes in living cells in future studies.

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.

A graphene-assisted hBN/SiO 2 hybrid structure is proposed and demonstrated to enhance near-field thermal radiation (NFTR). Due to the complementarity between the hyperbolic phonon polaritons of hBN and the surface phonon polaritons of SiO 2 at mid-infrared frequencies, coupling modes can remarkably improve the photon tunneling probability over a broad frequency band, especially when assisted by the surface plasmon polaritons of graphene sheets. Thus, the heat flux can exceed the blackbody limit by 4 orders of magnitude at a separation distance of 10 nm and reach 97% of the infinite limit of graphene-hBN multilayers using only two layers with a thickness of 20 nm each. The first graphene layer controls most of the heat flux, while the other layers can be used to regulate and optimize. The dynamic relationship between the chemical potential μ and the gap distance d are thoroughly discussed. Optimal heat flux of our graphene-assisted hBN/SiO 2 hybrid structure with proper choices of (μ 1 , μ 2 , μ 3 ) for different d (from 10 nm to 1000 nm) is further increased by 28.2% on average in comparison with the existing graphene-hBN triple-layer structure.

Polarimetric measurements are becoming increasingly accurate and fast to perform in modern applications. However, analysis on the polarimetric data usually suffers from its high-dimensional nature spatially, temporally, or spectrally. This paper associates polarimetric techniques with metric learning algorithms, namely, polarimetric learning, by introducing a distance metric learning method called Siamese network that aims to learn good distance metrics of algal Mueller matrix images in low-dimensional feature spaces. As an experimental example, 12,162 Mueller matrix images of eight algal species are measured via a forward Mueller matrix microscope. Eight classical metric learning algorithms, including principle component analysis, multidimensional scaling, isometric feature mapping, t-distributed stochastic neighbor embedding, Laplacian eigenmaps, locally linear embedding, linear discriminant analysis, and metric learning to rank, are considered, by which the algal Mueller matrix images are mapped to two-dimensional (2D) feature spaces with different distance metrics. Support-vector-machine-based holdout sample classification accuracies of the 2D feature vectors are provided in a supervised manner for quantitative comparisons of the low-dimensional distance metrics, including the results of the eight metric learning algorithms and 16 Siamese architectures with varying convolution, inception, and full connection modules. This study shows that the Siamese approach is an effective metric learning algorithm that can adaptively extract features exhibiting empirical correlations with the fast-axis-orientation-dependent and spatially variant algal retardance induced by the algal microstructures.

The development of highly sensitive and low-cost methods for detecting DNA is of critical importance. Here, we describe a strategy for the highly sensitive and low-cost digital detection of target DNA. Individual DNA molecules were encoded with a single gold nanorod (Au NR), which was then separated and enriched using the magnetic immune-separation process, followed by dehybridization and dispersion into a buffer solution and immobilization on glass slides for polarized dark-field microscopic imaging. With the imaging we can get the first three data sets of the Stokes vector, and the experimental degree of the linear polarization of the light scattered by the Au NR was obtained. Using the Monte Carlo simulation program, the Muller matrix of the Au NRs was simulated and the simulated degree of the linear polarization was calculated to be 0.58. Based on the experimental and simulated degree of the linear polarization, the Au NRs were identified and quantified with an in-house Matlab program, and the concentration of the target DNA at the femtomolar level was therefore achieved.

We present the Mueller matrix imaging system to classify morphologically similar algae based on convolutional neural networks (CNNs). The algae and cyanobacteria data set contains 10,463 Mueller matrices from eight species of algae and one species of cyanobacteria, belonging to four phyla, the shapes of which are mostly randomly oriented spheres, ovals, wheels, or rods. The CNN serves as an automatic machine with learning ability to help in extracting features from the Mueller matrix, and trains a classifier to achieve a 97% classification accuracy. We compare the performance in two ways. One way is to compare the performance of five CNNs that differ in the number of convolution layers as well as the classical principle component analysis (PCA) plus the support vector machine (SVM) method; the other way is to quantify the differences of scores between full Mueller matrix and the first matrix element m11, which does not contain polarization information under the same conditions. As the results show, deeper CNNs perform better, the best of which outperforms the conventional PCA plus SVM method by 19.66% in accuracy, and using the full Mueller matrix earns 6.56% increase of accuracy than using m11. It demonstrates that the coupling of Mueller matrix imaging and CNN may be a promising and efficient solution for the automatic classification of morphologically similar algae.

As submicron particles play an important role in a variety of ecosystems that include aqueous, terrestrial, and atmospheric, a measurement system to quantify them is highly desirable. In pursuit of formulating and fabricating a system to size them using visible light, a system that collects multi-directional scattered light from individual particles is proposed. A prototype of the system was simulated, built, and tested via calibration with a set of polystyrene spheres in water with known sizes. Results indicate that the system can accurately resolve the size of these particles in the 0.1 to 0.8 μm range. The system incorporates a design that uses oblique illumination to collect scattered light over a large range of both forward and backward scatter angles. This is then followed by the calculation of a ratio of forward to backscattered light, integrated over a suitably defined range. The monotonic dependence of this ratio upon particle size leads to an accurate estimate of particle size. The method was explored first, using simulations, and followed with a working version. The sensitivity of the method to a range of relative refractive index was tested using simulations. The results indicate that the technique is relatively insensitive to this parameter and thus of potential use in the analysis of particles from a variety of ecosystems. The paper concludes with a discussion of a variety of pragmatic issues, including the required dynamic range as well as further research needed with environmentally relevant specimens to create a pragmatic instrument.

In a recent study we reported on the gradient index (GRIN) lens Stokes polarimeter (GLP) [Opt. Lett.39, 2656 (2014)10.1364/OL.39.002656OPLEDP0146-9592]. With a simple architecture, this polarimeter can measure the state of polarization in a single shot. In this article, we present further studies for improving the performance of the GLP. Detailed discussions are presented on the optimization process of the GLP based on different choices of data from the CCD images. It is pointed out that many optimization techniques, although developed for other types of Stokes polarimeters, can also be applied to the GLP because the GRIN lens can traverse all possible retardance and fast axis modulations.

A four-quadrant detector (4QD) consists of four well-balanced detectors. We report on a Stokes polarimeter with optimal linear polarization measurements based on a 4QD. We turned the four intensity-detection channels into four polarization-analyzing channels by placing four polarizers and one quarter-wave plate in front of the individual detectors. An optimization method for the four polarization-analyzing channels is proposed to improve measurement accuracy. Considering applications in favor of linear polarization measurements instead of global optimization for all the possible states of polarization (SOP), we optimize the polarimeter first for the linear polarization components and then for the circular polarization component. The polarimeter is capable of simultaneous measurements of fast varying SOP with improved performance for the linear polarizations.

In this work, we apply Mueller matrix polar decomposition (MMPD) method in a forward scattering configuration on anisotropic scattering samples and look for the physics origin of depolarization and retardance. Using Monte Carlo simulations on the sphere-cylinder birefringence model (SCBM), and forward scattering experiments on samples containing polystyrene microspheres, well-aligned glass fibers and polyacrylamide, we examine in detail the relationship between the MMPD parameters and the microscopic structure of the samples. The results show that the spherical scatterers and birefringent medium contribute to depolarization and retardance respectively, but the cylindrical scatterers contribute to both. Retardance due to the cylindrical scatterers changes with their density, size and order of alignment. Total retardance is a simple sum of both contributions when cylinders are in parallel to the extraordinary axis of birefringence.

Polarization techniques can suppress multiply scattering light and have been demonstrated as an effective tool to improve image quality of superficial tissues where many cancers start to develop. Learning the penetration depth behavior of different polarization imaging techniques is important for their clinical applications in diagnosis of skin abnormalities. In the present paper, we construct a two-layer sample consisting of isotropic and anisotropic media and examine quantitatively using both experiments and Monte Carlo simulations the penetration depths of three different polarization imaging methods, i.e., linear differential polarization imaging (LDPI), degree of linear polarization imaging (DOLPI), and rotating linear polarization imaging (RLPI). The results show that the contrast curves of the three techniques are distinctively different, but their characteristic depths are all of the order of the transport mean free path length of the top layer. Penetration depths of LDPI and DOLPI depend on the incident polarization angle. The characteristic depth of DOLPI, and approximately of LDPI at small g, scales with the transport mean free path length. The characteristic depth of RLPI is almost twice as big as that of DOLPI and LDPI, and increases significantly as g increases.

We present the experimental results for the two-dimensional backscattering Mueller matrix of a scattering medium containing polystyrene microspheres and silk fibers and simulate the same Mueller matrix using a polarization-sensitive Monte Carlo program with both layered and homogeneous sphere-cylinder scattering models. We discuss the characteristic features in each Mueller matrix element and their relations with the parameters of the spherical and cylindrical scatterers in the medium. Both experiments and simulations suggest that the Mueller matrix elements can be used to characterize the structural and optical properties of anisotropic scattering media.

By comparing the spatially resolved unpolarized, polarized reflectance and Mueller matrix elements of skeletal muscle with a scattering medium containing polystyrene microspheres and silk fibers, we demonstrate that the sphere-cylinder scattering model (SCSM) can reproduce the characteristic features of skeletal muscle. Both experiments and polarization sensitive Monte Carlo simulation provide evidences that SCSM may be used to characterize the structural and optical properties of skeletal muscle.

Nondestructive reconstruction of the location and width of a thin-strip defect in a strongly confined planar waveguide is considered. Explicit reconstruction formulas are given for a quick reconstruction of a thin-strip defect whose width is small by measuring the scattered fields at the two end faces of the planar waveguide for two frequencies. Numerical results are given, and the analytical reconstruction method is shown to be reliable regardless of the location of the defect.