FeatureExpress – Shifting Particle Analysis Up a Gear
24th January 2024 | Author: Dr Matt Hiscock
Particle analysis is, at least for me, one of the most interesting ways of performing analysis on an electron microscope as it involves the automation of many of the tasks that we would otherwise perform manually – identifying what to analyse, performing the analysis and interpreting the results.
When we perform particle analysis in AZtecFeature, we often have multiple requirements of our task – our analysis must be accurate, reliable, repeatable and, more often than not, fast. The last of these aims, for our analysis to be fast, comes about as we try to increase our efficiency by analysing more samples in less time – potentially giving us the opportunity to understand more about our area of study by looking at more samples, or to cover an ever-wider breadth of samples in a fixed amount of time – e.g. in a service lab addressing the needs of multiple customers.
The need for high throughput is often particularly strong when we’re doing the sort of particle analysis that involves looking at a high number of particles – perhaps tasks like trying to understand the relative proportions within a population of particles or searching through thousands of particles to find if a critical one is present. In these cases, the time to perform the EDS analysis of each of the particles is the limiting factor for the overall speed of the analysis.
In a recent release of AZtecLive, we released an update to AZtecFeature called FeatureExpress. This update to the way that we perform particle analysis has massively increased the speed with which we communicate where to acquire EDS data from. This has had the impact that the maximum rates at which we can perform particle analysis with EDS has increased to more than 120,000 particles per hour (not including image acquisition, stage moves etc.). Numbers like this are, of course, relatively hard to imagine on their own, so I’d recommend having a look at the video below which explains it far better than words can.
As you can see in the comparison video, the difference in speed is huge, while the data collected is the same – you can see this from the colouring of the particles, which represents their classification (i.e. which compositional group they belong to).
Just to repeat, the data collected from each particle is the same in both the fast and slow cases – we’re using the same analytical conditions and the same EDS acquisition time – it’s just the communication that has changed. Because the way that we detect particles has not changed, this means that existing users can simply upgrade and see a speed improvement without changing anything in their analytical recipe. To put it into numbers, even when we take imaging and stage moves into account, it’s possible to save in excess of 80% of a run time, depending on the settings and hardware used by moving to FeatureExpress.
This improvement is, of course, applicable to a wide range of particle analysis applications – from geology (as in the example here) to forensics; from technical cleanliness, to semiconductor manufacturing, or steel making, and many more. All of these applications will see a step change in particle analysis throughput.
We’d love to show you the benefits that AZtecFeature with FeatureExpress can bring to your analysis. Why not get in touch and let us run a demonstration on your sample?
Dr Matt Hiscock,
MAG Head of Product Science, Oxford Instruments
Dr Matt Hiscock is the MAG Head of Product Science at Oxford Instruments and holds an MSci in geology from the University of Bristol and a PhD in geochemistry from the University of Edinburgh. He has also worked in the mining industry in Australia and been involved in the running of an academic SEM facility. He joined Oxford Instruments NanoAnalysis in 2013 and works with customers across a wide range of applications to understand their needs and how Oxford Instruments products can address them, as well as overseeing the development of products designed for specific applications and industries, particularly in the field of feature analysis.