| 00:00:00 | To listen to the webinar use your computer headphones or speakersIf you prefer to use your telephone to listen to the audio, please click on request audio in the participant panel |
| 00:00:15 | Introduction |
| 00:01:09 | Adaptive CorrelationHow to get better DLS data with less time and effort |
| 00:01:27 | Outline |
| 00:02:11 | A quick overview of DLS |
| 00:03:55 | What is Adaptive Correlation? |
| 00:05:40 | Dust – causing headaches for DLS users |
| 00:06:12 | Why is DLS so sensitive to dust? |
| 00:08:32 | Traditional methods to handle ‘dust’ |
| 00:10:29 | What if we measure for less time? |
| 00:12:37 | What if we inspect the sub runs? |
| 00:14:32 | How much data should we record? |
| 00:16:01 | Better data with less preparation |
| 00:17:44 | What about the unusual data? |
| 00:19:00 | What about the unusual data? |
| 00:21:15 | Transient vs Steady State |
| 00:22:40 | How much data is classified as transient? |
| 00:24:31 | What about low scattering samples? |
| 00:26:38 | What about my data from the ZS Nano? |
| 00:26:45 | What does Adaptive Correlation give us? |
| 00:26:56 | Adaptive correlation solutions |
| 00:29:05 | Summary |
| 00:30:30 | Further information |
| 00:30:59 | Thank you for your attentionalex.malm@malvernpanalytical.com |
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. In this webinar we introduce a new DLS data capture process called Adaptive Correlation which addresses these issues to provide a more complete and accurate characterization of your sample.