When is a particle not a particle?

00:00:00 Welcome
00:00:15 Introduction
00:01:11 When is a particle not a particle?
00:01:30 Outline
00:02:26 Random number generator?
00:02:55 Random number generator?
00:03:20 When DLS is surprising
00:03:25 Some common DLS puzzles
00:04:50 What are these particles in my water?
00:05:30 How can DLS measurements go wrong?
00:05:35 How can it all go wrong?
00:05:51 How can it all go wrong?
00:06:00 How can it all go wrong?
00:06:43 DLS compared to Microscopy
00:06:57 DLS compared to Microscopy
00:07:24 DLS compared to Microscopy
00:07:45 DLS compared to Microscopy
00:08:05 DLS compared to Microscopy
00:08:33 DLS compared to Microscopy
00:08:54 Anscombe's quartet
00:10:02 One method- several answers
00:10:18 One method- several answers
00:10:39 One method- several answers
00:11:51 A recipe for a successful measurement
00:12:43 A recipe for a successful measurement
00:13:24 What does success look like?
00:13:43 How to be a DLS detective
00:13:49 Anatomy of a good correlation function
00:14:02 Anatomy of a good correlation function
00:14:35 Anatomy of a good correlation function
00:14:44 Anatomy of a good correlation function
00:14:59 When to take a closer look?
00:15:19 Dispersed Sample?
00:16:55 Capillary DLS
00:18:31 Enough light?
00:19:52 Stable count rate?
00:21:05 Single scattering?
00:22:34 Single scattering?
00:23:33 No optical noise?
00:24:57 Optional Optical Filters
00:25:53 Optional Optical Filters
00:26:19 Optional Optical Filters
00:26:27 Rotational/Translational diffusion
00:27:46 Rotational/Translational diffusion
00:28:04 Aggregated/Contaminated?
00:28:39 Adaptive Correlation
00:29:42 Monodispersed?
00:30:35 Monodispersed?
00:30:45 When fitting fails…
00:31:21 When fitting fails…
00:32:37 When fitting fails…
00:33:04 If you are not an expert…
00:34:44 Machine Learning and Data Quality Guidance
00:34:44 What is machine learning?
00:36:22 Machine learning with DLS data
00:37:18 How can Machine Learning help with DLS?
00:38:22 How is guidance given?
00:39:34 Following the Data Quality Guidance in practice
00:40:25 Following the Data Quality Guidance in practice
00:41:31 Following the Data Quality Guidance in practice
00:41:51 Some common DLS puzzles
00:44:18 What are these particles in my water?
00:44:27 What are these particles in my water?
00:44:46 Untitled
00:44:54 Untitled
00:45:13 Summary
00:45:59 Thank you for your attentionAny questions?alex.malm@malvernpanalytical.com
00:53:38 Thank you for your attentionQuestion & Answer SessionListening live:Ask your question by typing within the Q & A chat facility Listening on-demand:Send your questions toevents@malvernpanalytical.com

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.

Vortragende

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.

Weitere Informationen

Who should attend?
DLS users who would like guidance in interpreting and spotting artefacts in their data