Rapidly Measuring Scattered Polarization Parameters of the Individual Suspended Particle with Continuously Large Angular Range

Abstract

Suspended particles play a vital role in aquatic environments. We propose a method to rapidly measure the scattered polarization parameters of individual suspended particles with continuously large angular range (PCLAR), from 60° to 120° in one shot. A conceptual setup is built to measure PCLAR with 20 kHz; to verify the setup, 10 μm-diameter silica microspheres suspended in water, whose PCLAR are consistent with those simulated by Mie theory, are measured. PCLAR of 6 categories of particles are measured, which enables high-accuracy classification with the help of a convolutional neural network algorithm. PCLAR of different mixtures of Cyclotella stelligera and silica microspheres are measured to successfully identify particulate components. Furthermore, classification ability comparisons of different angular-selection strategies show that PCLAR enables the best classification beyond the single angle, discrete angles and small-ranged angles. Simulated PCLAR of particles with different size, refractive index, and structure show explicit discriminations between them. Inversely, the measured PCLAR are able to estimate the effective size and refractive index of individual Cyclotella cells. Results demonstrate the method's power, which intrinsically takes the advantage of the optical polarization and the angular coverage. Future prototypes based on this concept would be a promising biosensor for particles in environmental monitoring.

Keywords: continuously large angular range; individual suspended particle; optical scattering; polarization parameters.

Figure 1

(a) Schematic of experimental setup: S, light source; PSG, polarization state generator; HW, half-wave plate; QW, quarter-wave plate; DP, diaphragm; L1, L2, L3, L4, lens; SP, sample pool; PH, pinhole; CL, cylindrical lens. Inserted image in the dashed box: pixel arrangement of polarization line scan CCD; (b) Physical picture of the built experiment setup.

Figure 2

Uncalibrated (a–c) and calibrated (d–f) results of the scattered intensity I, DoLP, and AoP, and their normalized histograms (e–i).

Figure 3

(a) Scattered intensity with pixel of CCD and sampling time. (b) Temporal signal of averaged intensity $\overline{I}\left(t\right)$; red dotted line: threshold to select pulses.

Figure 4

Experiment and simulated results of scattered intensity I (a), polarization parameters q (b), and u (c) for SiO₂.

Figure 4

Experiment and simulated results of scattered intensity I (a), polarization parameters q (b), and u (c) for SiO₂.

Figure 5

Grayscale maps realigned from PCLAR for six categories of particles.

Figure 6

Flowchart of classification process.

Figure 7

Confusion matrix of the classifier on the testing dataset.

Figure 8

Group bar plot for particle number of SiO₂ and CY. Groups 1~5 corresponds to mixtures with total volumes of CY concentrate, 0, 50, 100, 150, and 250 μL, respectively.

Figure 9

Confusion matrixes for different angular selection strategies. (a) single angle: 120°; (b) discrete angles: 60°, 90°, 120°; (c) forward continuous angles range from 60° to 90°; (d) backward continuous angles range from 90° to 120°.

Figure 10

Geometric model of PSH for DDA simulation.

Figure 11

Simulated polarization parameters q (a) and u (b) of SiO₂, PS5, PS10, and PSH.

Figure 12

Polarization parameters q (a) and u (b). Experiment results of SiO₂ (red) and CY (green); Mie theory simulation results of SiO₂ (blue) and CY (yellow).