Angular dependence of multiangle dynamic light scattering for particle size distribution inversion using a self-adapting regularization algorithm. (April 2018)

• Record Type: Journal Article
• Authors: Li, Lei; Yu, Long; Yang, Kecheng; Li, Wei; Li, Kai; Xia, Min
• Is Part Of: Journal of quantitative spectroscopy & radiative transfer. Volume 209(2018)
• Page Start: 91
• Page End: 102
• Publication Date: 2018-04
• Subjects: Dynamic light scattering -- Inverse scattering -- Scattering measurements -- Particle size distribution -- Mie theory; Spectrum analysis -- Periodicals; Radiation -- Periodicals; Analyse spectrale -- Périodiques; Rayonnement -- Périodiques; Radiation; Spectrum analysis; Periodicals; 543.0858
• DOI: 10.1016/j.jqsrt.2018.01.022
• Languages: English
• ISSNs: 0022-4073

Abstract

Highlights: The algorithm self-adaptively optimizing the weighting coefficients, the inversion range and the inversion method is proposed. The algorithm combines a wavelet multiscale strategy with an iterative recursion inversion method. Even noise level is 10% or the inversion range is inappropriate the recovered results can fit the given distributions well. The proposed algorithm could discriminate the peaks of multimodal PSDs precisely. Six-angle analysis in the 30–130° range is an optimal angle set for recovering PSDs. Abstract: The multiangle dynamic light scattering (MDLS) technique can better estimate particle size distributions (PSDs) than single-angle dynamic light scattering. However, determining the inversion range, angular weighting coefficients, and scattering angle combination is difficult but fundamental to the reconstruction for both unimodal and multimodal distributions. In this paper, we propose a self-adapting regularization method called the wavelet iterative recursion nonnegative Tikhonov–Phillips–Twomey (WIRNNT-PT) algorithm. This algorithm combines a wavelet multiscale strategy with an appropriate inversion method and could self-adaptively optimize several noteworthy issues containing the choices of the weighting coefficients, the inversion range and the optimal inversion method from two regularization algorithms for estimating the PSD from MDLS measurements. In addition, the angular dependence of the MDLS for estimating the PSDs of polymeric latexes is thoroughly analyzed. The dependence of the results on the number and range of measurement angles was analyzed in depth to identify the optimal scattering angle combination. Numerical simulations and experimental results for unimodal and multimodal distributions are presented to demonstrate both the validity of the WIRNNT-PT algorithm and the angular dependence of MDLS and show that the proposed algorithm with a six-angle analysis in the 30–130° range can be satisfactorily applied to retrieve PSDs from MDLS measurements.