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Proceedings Paper

Design and analysis of particles detecting system based on near forward light scattering
Author(s): Xiao-Jun Cui; Xiao Li; Jia-Xin Yu
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Paper Abstract

A novel design based on near forward light scattering detection system to measure size and concentration distribution of particles in liquids is reported. According to theory of Mie scattering, the influence of relative refractive index, particles size and wavelength on the detection results are discussed. A green optical fiber laser with 532nm was used as the excited light source. As a key part in the detection system, the focusing system using a lens structure to confine light sensitive area with Gauss distribution less than 80 μm2. The lateral size of the sample cell is limited to 100μm. In order to measure the particles in non-overlapping state and improve the accuracy and repeatability, a novel structure in the sample cell was used and particle velocity through the sample cell was controlled by high precision stepper motor control system of micro circulation pump. Particle light scattering signal acquisition was completed by the poly lens combination system, according to the receiving angle relative to the measured particle, which can adjust the light scattering direction to obtain better particles light scattering signal. Photoelectric signal conversion, amplification and acquisition are all the devices with high precision. The measurement results showed that the measurement system was accurate and stable when the particles size in the range of 0.5-5μm.

Paper Details

Date Published: 5 January 2017
PDF: 5 pages
Proc. SPIE 10244, International Conference on Optoelectronics and Microelectronics Technology and Application, 1024415 (5 January 2017); doi: 10.1117/12.2264571
Show Author Affiliations
Xiao-Jun Cui, Univ. of Jinan (China)
Xiao Li, Univ. of Jinan (China)
Jia-Xin Yu, Univ. of Jinan (China)


Published in SPIE Proceedings Vol. 10244:
International Conference on Optoelectronics and Microelectronics Technology and Application
Shaohua Yu; Jose Capmany; Yi Luo; Yikai Su; Songlin Zhuang; Yue Hao; Akihiko Yoshikawa; Chongjin Xie; Yoshiaki Nakano, Editor(s)

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