Study On The Inversion Algorithms Of Particle Size Distribution In Multiangle Dynamic Light Scattering

Dynamic light scattering (DLS) is a widely used technique for estimating the particle size distribution (PSD) with particles in the submicrometer range through the inversion of intensity autocorrelation function. However, the technique has limitations due to the relatively low information content inherent in the measured signal and, consequently, poor PSD resolution can be expected. Multiangle dynamic light scattering (MDLS) compensates for the low information in a single-angle correlation function by including the correlation functions of the other measurement angles, which are influenced by the different scattering characteristics and dynamics of the particles for different scattering angles. So MDLS could provide more accurate PSD using more information for the estimation of the PSD. However, MDLS needs more complex experiment device and has more difficulty in inversing the PSD. Based on the angular dependence of dynamic light scattering, the influence of the angle calibration noise to the PSD, the estimation of the weighting coefficients and the inversion methods of MDLS, the main work of this paper consists of,1. The theory of MDLS was analysed. Appropriated weighting coefficients, which could be estimated by baseline values or other methods, were used to combine each intensity autocorrelation function into a data analysis. And noise on these coefficients may seriously compromise the estimate of PSDs.2. The angular dependence of dynamic light scattering was studied. Particles with different diameters have different scattering characteristics at different scattering angles and more information could be obtained at different scattering angles to give better PSDs. However, the improvement of PSD becomes less obvious as the increase of number of angles. In some cases, the PSD may become worse as much noise was added with the increase of angle numbers. And bimodal distributions were more affected by the number of angles than unimodal distributions.3. The influence of angle calibration noise to the PSD was studied. The intensity autocorrelation function at each scattering angle must be measured in MDLS, and the PSD was affected by the angle calibration noise. The results show that small particles in unimodal distribution was greater affected by angle calibration noise than large particles and the bimodal distribution containing small particles was more effected by the bimodal distribution without small particles.4. The MDLS inversion using angular intensity weighting determined by iterative recursion was studied. The weighting coefficients play an important role in the estimation of PSD. The iterative recursion method could give better weighting coefficients than other methods. The simulated unimodal distribution and bimodal distribution and experimental bimodal distribution all verified the feasibility of the method.5. The MDLS inversion using a modified Chahine method was studied. The modified Chahine method was insensitive to the noise of the weighting coefficients and could give good PSD, even though the weighting coefficients have10%noise. The modified Chahine method could give better PSDs than Tikhonov method using the baselines of the autocorrelation functions to obtain the weighting coefficients.6. An iterative algorithm of MDLS inversion was studied. The iterative algorithm avoided the accurate calibration of scattering angles. The inversion results show that the PSD of small particles was better than the large particles in the unimodal distributions and the PSD of large particles was better than the small particles in the bimodal distributions because of the relationship between the particle size and the scattering intensity.MDLS could give better PSD than single-angle DLS. However, the inversion of PSD is still the important and difficult points. The PSD inversion of MDLS was studied in this work and seeks to obtain more accurate PSD to meet the increasingly social requirements of complicated PSDs.