Many nanoparticle analysis techniques focus on size at the expense of shape. Advanced particle size measurements, such as dynamic light scattering (DLS), often assume a uniformity of shape to statistically generate performance predictions based on the hydrodynamic diameters of particles in solution. This is suitable for many applications. However, manufacturers are increasingly becoming aware of the limitations of such techniques. Particle shape analysis has become more and more prevalent within the industry for more accurate predictions of raw material performance in processing, packaging, transit, or end-use conditions.
Outlining Dynamic Imaging Particle Analysis (DIPA)
Image analysis is the ideal solution for particle shape analysis. In terms of classical measurement techniques, imaging particle analysis is closer to conventional microscopy than the likes of DLS. Just like optical microscopy, dynamic image analysis relies on visible light to illuminate samples and resolve the minute geometric differences between discrete particles and agglomerated structures. The primary difference between microscopy and dynamic imaging is the use of high-speed digital cameras that can capture live imagery of thousands of particles, significantly improving workflows and eliminating the risk of inaccuracies due to in situ human error.
Dynamic imaging particle shape analysis subsequently uses statistical measurements based on tens of different shape parameters. These include – but are by no means limited to:
- Circularity, rectangularity, ellipticity, convexity, and surface uniformity
- Compactness and smoothness
- Equivalent circular area / perimeter diameter
- Bounding rectangle length / aspect ratio
- Fiber length / curl
- Polygon order
This list is just a small sample of parameters that dynamic imaging particle shape analyzers may consider as part of an overall analytical run. Some of these factors are closely interrelated. Circularity can be construed as a measure of perimeter smoothness against particle form, for example. These interconnecting properties are part of the reason why many dynamic image particle shape analyzers use histogram overlays for image comparisons, and why some measurements are usually run before others. Area (number of pixels) is the simplest, and therefore the first, measurement usually conducted but the data acquired may then inform further statistical analysis (i.e. circular equivalent diameter).
There is a complex interplay between distinct particle shape parameters, and it is important to accurately characterize raw materials using as many data points as possible to provide quantitative insights for accurate quality and process control.
Particle Shape Analysis with Particulate Systems
Particulate Systems specializes in particle size and shape analysis solutions that can help innovate and improve workflows in various markets. For particle shape analysis via dynamic imaging, we offer the SentinelProTM; the best-in-class DIPA system for improving flowability, dispersibility, packing density, and stability of your raw materials.Posted on