Simulation-guided regression approach for estimating the size distribution of nanoparticles with dynamic light scattering data
提出一种模拟引导的回归方法，将动态光散射测量中的粒径分布估计转化为正则化回归问题，避免传统数值反演的低效，在模拟和真实数据中均优于CONTIN方法。
This article presents a simulation-guided regression approach for estimating the size distribution of nanoparticles from Dynamic Light Scattering (DLS) measurements. The properties and functionalities exhibited by nanoparticles often depend on their sizes, so the precise quantification of the sizes is important for characterizing and monitoring the quality of a nanoparticle synthesis process. The state-of-the-art method used in the size quantification from DLS measurements is the CONTIN, which is based on a computationally ineffective numerical inversion. We propose a new approach that avoids the numerical inversion by reformulating the problem into a regularized regression problem, with the basis functions being generated by a computer simulation of DLS measurements. For many simulation studies and one real data study, our method outperformed the CONTIN in terms of estimation accuracy and computational efficiency.