Dynamic Light Scattering Data Processing: User Independent or User Driven Algorithms

Dynamic light scattering is beyond competition when measuring particle size in dispersions ranging from nanometers to micrometers. This method has a serious problem in processing experimental data. Instrument noise and polydispersity of the sample lead to the situation when one autocorrelation function corresponds to several different particle size distributions. Often a scientist may not be suspicious of possible other solutions, having one result from a program. I demonstrate the problem on model and experimental data using three known programs: CONTIN, Malvern Zetasizer and DynaLS. I also use my own free program Autocor, which allows to test our own hypotheses about the modality and shape of the particle size distribution by optimizing parameters of the model with a small number of adjustable parameters. The results show the feasibility of a "model based" or "user driven" approach when the raw DLS data are ambiguous or the particle size distribution seems questionable.

动态光散射（Dynamic Light Scattering, DLS）在纳米至微米级分散体系的粒径测量中具备无可比拟的优势。但该方法在实验数据处理环节存在显著缺陷：仪器噪声与样品的多分散性（polydispersity）会导致单条自相关函数（autocorrelation function）对应多种不同粒径分布的情况。研究人员往往仅依赖分析软件输出的单一结果，而未意识到可能存在其他可行解。本文基于模型数据与实验数据，借助三款主流分析软件——CONTIN、马尔文Zetasizer以及DynaLS，展示了这一问题。此外本文还使用了自主开发的免费软件Autocor，该工具可通过优化少量可调参数的模型参数，对粒径分布的模态（modality）与形态开展自定义假设验证。实验结果表明，当原始动态光散射数据存在歧义或粒径分布结果存疑时，采用"基于模型"或"用户主导"的分析方法具备可行性。