Item

Abstract

Errors in the experimental baseline used to normalize dynamic light scattering data can seriously affect the size distribution resulting from the data analysis. A revised method, which incorporates the characteristics of this error into the size distribution algorithm CONTIN (Ruf 1989), is tested with experimental data of high statistical accuracy obtained from a sample of phospholipid vesicles. It is shown that the various commonly used ways of accumulating and normalizing dynamic light scattering data are associated with rather different normalization errors. As a consequence a variety of solutions differing in modality, as well as in width, are obtained on carrying out data analysis in the common way. It is demonstrated that a single monomodal solution is retrieved from all these data sets when the new method is applied, which in addition provides the corresponding baseline errors quantitatively. Furthermore, stable solutions are obtainable with data of lower statistical accuracy which results from measurements of shorter duration. The use of an additional parameter in data inversion reduces the occurrence of spurious peaks. This stabilizing effect is accompanied by larger uncertainties in the width of the size distribution. It is demonstrated that these uncertainties are reduced by nearly a factor of two on using the normalization error function instead of the ‘dust term’ option for the analysis of noisy data sets