Influence of initial model on regularized inversion of noisy dynamic light scattering data

Regularization algorithm is a common method to recover the particle size distributions (PSDs) from dynamic light scattering (DLS) data. The initial regularization model has important influence on the inversion results. In this paper, the narrow and wide simulation distributions of 90 nm and 250 nm particles were inversed by the smallest, the flattest and the smoothest initial model respectively. The inversion results show that the initial model has almost no influence on the inversion results under the noise level of 0. With the increase of noise level, although the errors of peak and PSD value inversed by the three initial models are all increased, the increases by using flattest model and smoothest model are obviously less than that using the smallest model. When the noise level is greater than 0.01, the better particle size distribution results can be obtained by using the flattest model than the smallest and the smoothest models, and more accurate particle peak values can be got by using the smoothest model than the flattest and the smallest models. For obtaining the optimal PSDs inversed by regularization from noisy DLS data, the flattest model is recommended, and for getting the optimal peak value, the smoothest model is the best choice.