Key takeaways for error free analysis and reporting laser diffraction results

It is essential to know enough about laser diffraction to be able to answer questions in your Ph.D. viva, and ensure your data is of good enough quality to justify your conclusions. The technique can generate a lot of numbers, which parameter out of the many it can report is most appropriate for your sample? 

This webinar will give the essentials of how laser diffraction works, guidance on getting good quality data, and choosing the most appropriate parameters to track your analysis. The co-presenter speaks from experience on looking at their Ph.D. now, the parameters they tracked are not ideal!! 

Whether you have questions about samples or sample prep, setting up an experiment, or performing analysis, feel free to submit them so that we can deliver the information you require. There’s always more to learn, whether it’s setting up your measurements for quick batch processing, gaining confidence in the quality of your results, or presenting your data in the best format.  

For this webinar, we’ll assume you’ve watched the previous one on the same topic. Don't worry if you missed it or can't remember it – you can watch it back here. 

More information: 

This webinar is part of our ongoing ‘Ask an expert’ webinar series. These live webinars are meant for students, researchers, and professors alike who want to sharpen their analytical methods, deepen their knowledge, or find out how to improve their data. 

We’ll provide extensive materials analysis information and answer your most frequently asked questions. In other words, it’s the ultimate way to improve your materials science research and engineering knowledge. 

It’s free to attend any of the classes. For a full overview of the 2022 program, click here. 

To watch any of last year’s webinars, take a look at the full program recordings here.

Who should attend? 

• Anyone using a laser diffraction system in their research, especially Ph.D. students and regular authors of papers

What will you learn? 

• How to generate good quality data and the best metrics to describe it