Better DLS data with less time and effort with adaptive correlation

Abstract

By using statistical analysis and optimized data collection, Adaptive Correlation provides an improvement in the repeatability of DLS particle size measurements and the ability to measure primary particle sizes separately to the characterization of rare amounts of aggregated material.

Introduction

Whilst being able to measure particles of below 1 nm size, DLS is preferentially sensitive to larger particles due to the 6th power relationship between particle radius and scattering intensity.

This means that sample preparation typically needs to be scrupulous, especially for low scattering samples such a proteins and biological molecules. The contribution to contaminants such as dust and aggregates can be mitigated by filtering, however this may not always be practical or possible depending on the volume and fragility of the sample. Filtration of samples can also constitute a financial burden, both in terms of additional sample preparation time and consumables costs. A new DLS data capture process has been developed called Adaptive Correlation, which uses a statistically driven approach to produce the best correlation data, which in turn gives more reliable DLS size data, which, as we will see, can reduce the need for filtering and give added confidence in DLS results.

The algorithm is applicable to all samples suitable for measurement by DLS, although to demonstrate the approach, we will discuss measurements of Hen’s egg lysozyme which provides a challenging case as a sample which is small, low scattering and tends to aggregate.