Towards an accurate size distribution of emulsion droplets by merging distributions estimated from different measuring methods

It is well known that the use of different methods for measuring particle size distributions may lead to very different results. This is completely understood in the case of solid particles which may differ in several other aspects beyond their size (e.g. shape). However, the explanation is more difficult for the case of small size emulsion droplets where the shape of all droplets is spherical. Here, the idea that the error distribution with respect to droplet size is the origin of different results from different measuring method, is proposed. Each measuring method relies on a physical principle and so the error is relatively uniformly distributed through the values of the variable related to this physical principle. Any attempt to transform this variable leads to severe error maldistribution. The present work not only explains the reason of deviations of droplet size distribution between measuring methods but also suggests the compilation of information from two measuring methods to find a particle size distribution (PSD) with increased accuracy compared to the two individual methods. Several approaches in this direction are proposed and tested.

Automated summary

It is commonly known that different methods for measuring particle size distributions can yield significantly different results. While this can be explained for solid particles due to differences in aspects beyond just size (such as shape), it is more challenging for small size emulsion droplets which are spherical in shape. This study proposes that the error distribution in droplet size is the reason for different results from different measuring methods. Each method relies on a physical principle, causing the error to be uniformly distributed across the values of the corresponding variable. Attempts to transform this variable result in severe error maldistribution. The study not only explains the deviations in droplet size distribution between measuring methods but also suggests the combination of information from two methods to improve accuracy compared to individual methods.