The traditional approach for particle characterization is to use manual microscopy, but this technique is both labor-intensive and operator-dependent. Automated imaging is a more efficient alternative, a much faster way of gathering more statistically relevant data. Over the last decade, automated imaging has benefited hugely from advances in camera technology and data analysis software.
Nowadays, typical application areas for automated image analysis include pharmaceutical development, spray drying, energy storage/batteries, powder metallurgy, forensics, building materials and mining & minerals.
The technique involves capturing individual two-dimensional images of three-dimensional particles appropriately pre-dispersed on a plate. Statistically representative distributions are constructed by rapidly and automatically analyzing hundreds of thousands of particles per measurement.
Size versus shape
Various size and shape parameters are then determined from the dimensions of each image. Principal among these, for size, is circle equivalent (CE) diameter, which is calculated by converting the captured image into a circle of equivalent area to give a single number (diameter) representation of particle size (figure 4).
With respect to shape, an array of parameters may be developed from the defining dimensions of the particle (see figure 2) to build a complete picture. Parameters such as convexity, elongation and circularity, describe not only the overall shape of particles but also the regularity of the shape, whether the perimeter is smooth or more convoluted. Systems such as the Morphologi systems measure thousands of particles in just a few minutes to produce statistically relevant descriptors of size, shape, and transparency, that allow the identification and quantification of even very subtle differences.
When various size and shape parameters are combined, they provide a complete, detailed description of the morphological properties of particulate materials. By combining particle size measurements, with particle shape assessments, morphological imaging fully characterizes both spherical and irregularly-shaped particles. This enables deeper understanding of a sample’s characteristics through precise detection of agglomerates, foreign particles and other anomalous materials. It also delivers the data required to cross-validate other particle sizing methods which apply an equivalent-sphere approach to reporting particle size distributions.
The valuable information automated image analysis provides
Single particles or agglomerates
Particulate samples can be prone to agglomeration which may not be easily detected by other particle sizing techniques. Analysis of individual particles in the dispersion in terms of their outline shape enables you to determine if, and to what extent, agglomerates are present.
Regular or elongated
Milling can change particle shape and size, which affects a material’s processing behavior and final properties. By measuring shape parameters such as elongation or circularity, the overall sample form is monitored, and process changes can be made if required.
Rough or smooth
Powder flow and abrasive powder effectiveness are both influenced by particle surface texture. Particle shape parameters help assess if a powder is likely to stick in a hopper or if an abrasive powder has become worn.
Light or dark
Mineral samples often contain a mixture of different particle types. Using greyscale images to measure physical properties such as the amount of light passing through or being reflected from the particle’s surface helps to differentiate between these particles.
Get the best out of automated image analysis
Automated image analysis is a technique with an instant ‘wow’ factor. Knowing what pitfalls to avoid and following best practices makes the experience unforgettable. We’ll share the guidelines that can help improve the value and experience of this technique during the next session of our ‘Ask an Expert!‘ webinar on April 27th.