| 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.
Présentateur
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.
Pour en savoir plus
Who should attend?
DLS users who would like guidance in interpreting and spotting artefacts in their data