Adaptive Correlation: How to Get Better DLS Data with Less Time and Effort
In this webinar we introduce a new DLS data capture process called Adaptive Correlation which addresses issues to provide a more complete and accurate characterization of your sample.
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 as 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 and constitutes a financial burden, both in terms of additional sample preparation time and consumables costs.
Dr. Alex Malm
Alex has worked within the Nanomaterials R+D team since joining Malvern Panalytical in 2015. As part of the development team for the new Zetasizer Pro and Ultra, Alex has developed new algorithms as well as supporting the development of electronics and optical systems, and now also acts as an Intellectual Property Officer, helping to manage Malvern Panalytical’s patent portfolio. He has an MPhys from the University of Manchester, where he also completed a Doctorate in Enterprise supported by Malvern, where he developed light scattering techniques to characterize the structure and rheology of colloidal and polymer solutions.
- Who should attend?
Existing Zetasizer and competitor users who want to understand the benefits of Adaptive Correlation.
- Why attend?
To understand the benefits that Adaptive Correlation can offer over traditional correlation approvals.
- What will you learn?
How Adaptive Correlation works and the benefits it can offer such as faster measurements, more reliable data and less sample preparation.