2018 Volume 55 Issue 12 Pages 631-637
This paper presents a new iterative algorithm to solve linear inverse problem for static light scattering (SLS) particle size distribution measurement.
The problem is written by Mv = w, where v is a particle size distribution (unknown), w is intensities of the scattered light of particles (known), and M is a kernel matrix based on Mie scattering theorem.
This study was made to propose new iterative algorithm which copes both high peak detection and robustness to measure mixture sample, especially narrow or monodisperse distribution mixture sample (picket fences).
First, we introduced two known algorithms. Then we propose new algorithm which was united of the two algorithms with mixture coefficient Cm. After that, we evaluated the optimal Cm and the capability of new algorithm by simulation which contained 6 peaks picket fences sample. Finally, we discussed the superiority of the algorithm especially robustness for complex particle size distributions.
