The interpretation of particle size data gathered by Dynamic Light Scattering can be fraught with confusion, especially when DLS results don’t appear to agree with orthogonal characterisation methods.
In this webinar we will discuss some common puzzles posed in interpreting DLS particle size data and discuss a number of factors that can skew our results. We will then introduce the Zetasizer Pro and Ultra’s new approach of assessing DLS data quality that uses machine learning artificial intelligence to identify sub optimal sample conditions and provide smart actionable advice for the user.
Summary
- Measurement type:
- Particle size
- Date:
- January 22 2019 - January 22 2019
- Time:
-
10:30 - 11:30
(GMT-05:00) Eastern [US & Canada] - Event type:
- Webinar - Live
- Language:
- English
- Products:
- Zetasizer range
- Technology:
- Light Scattering
Speakers
Alex Malm Ph.D -
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.
More information
Who should attend?
DLS users who would like guidance in interpreting and spotting artefacts in their data