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Research on the Inversion Algorithm of PCS in Nano Particle Sizing Measurement Based on Regularization and Particle Swarm Algorithm

Author: WangYuanLei
Tutor: ShenJin
School: Shandong University of Technology
Course: Detection Technology and Automation
Keywords: Photon correlation spectroscopy Iterative regularization Particle Swarm L - curve criterion
CLC: O433.1
Type: Master's thesis
Year: 2010
Downloads: 35
Quote: 0
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Nanoparticles because of their special electrical, magnetic, power, light, heat and other characteristics, play an increasingly important role in the national economy. Related to the characteristics and the particle size of the nanoparticles, the nanoparticles measurement into one of the priorities of the particle studies. PCS method is an effective method for measuring nano-particle size and its distribution, wherein the particle size of the inversion is one of the priorities of the PCS method, and is also one of the difficulties. The inversion algorithm granularity PCS nanoparticle measurement techniques were studied, the main work is as follows: a light intensity correlation function inversion nano particle size needs to solving first class Freholm integral equations, the equation is a pathological problem. In this paper, the iterative regularization method monodisperse and dual-dispersed particles under different noise levels inversion, inversion results show that the noise level of less than 0.05, the regularized inversion error of 0-10%, iterative regularization of the inversion error for the O-7%; noise level of 0.05, regularization has been unable to inverse the particle size distribution, the the iterative regularization monodisperse inversion peak error of not more than 8%. The double dispersion peak inversion error is less than 12%;, iterative regularization number of iterations general requirements: hours in the noise, the number of iterations; noise is large, the number of iterations. Second, the inversion of the nano-particle size from another angle, can be seen as an optimization problem, this paper using particle swarm nanoparticles inversion. Unimodal and bimodal distribution of particles inversion results show that: the noise level is less than 0.05, less than 10%; noise level is much higher than the theoretical value when the ratio of the peak at 0.05 and 0.1, so that the inversion of the resulting particle size distribution produce a deviation from the true distribution. Particle swarm optimization objective function constraints affect the speed and accuracy of particle size inversion, this paper is the method of flattening functional as the objective function and feasible constraints using particle swarm particle size inversion. The inversion results show that the smoothness constraint objective function, the particle swarm algorithm, the particle size distribution of smooth, solved without smoothness constraint inversion results of the objective function in the presence of the particle size distribution in the peak too focused. Fourth, in the L-curve criterion based on the unimodal distribution and a bimodal distribution of particles using particle swarm inversion. The inversion results show that the noise level is small, the objective function L curve export proceeds inversion result given smooth solution to address the phenomenon of the particle size distribution of centralized functional and flattening as compared to the objective function of the particle swarm algorithm no need to strike the regularization parameter, reduce the amount of calculation. The PCS particle measurement technology, the inversion of the particle size is the main reason affecting particle measurement accuracy. Currently, the particle size distribution of nanoparticles measured accurately measure still restricts the wide application of this technology, this paper work done to contribute to the development of the PCS technology.

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CLC: > Mathematical sciences and chemical > Physics > Optics > Spectroscopy > Spectral measurements
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