The multiangle dynamic light scattering (MDLS) technique provides more robust, reproducible, and accurate particle size distributions (PSDs) than single-angle dynamic light scattering. However, in MDLS, the determination of peak locations is difficult but significant, particularly for multimodal distributions. In this paper, a self-adaptive algorithm, the iterative recursion nonnegative Tikhonov–Phillips–Twomey (IRNNT-PT) algorithm, is proposed for the estimation of the PSD from MDLS measurements. This algorithm optimizes the weighting coefficients, distinguishes features of PSDs and chooses the optimal inversion method from two regularization algorithms self-adaptively. Numerical simulations and experimental results for unimodal and multimodal distributions are presented to demonstrate both the validity and noise immunity of the IRNNT-PT algorithm, and demonstrate that the proposed algorithm can be well applied to reconstruct PSDs from MDLS measurements.

与单角度动态光散射相比，多角度动态光散射（MDLS）技术提供了更健壮，可重现和准确的粒度分布（PSD）。但是，在MDLS中，确定峰位置非常困难，但意义重大，尤其是对于多峰分布而言。本文提出了一种自适应算法，即迭代递归非负Tikhonov–Phillips–Twomey（IRNNT-PT）算法，用于从MDLS测量值估计PSD。该算法优化了加权系数，区分了PSD的特征，并自适应地从两种正则化算法中选择了最佳的反演方法。