The Role of Dynamic Imaging in Particle Size Analysis

Reliable and Reproducible Statements on Particle Shape and Size are Essential

The Role of Dynamic Imaging in Particle Size Analysis

Image Credit: FRITSCH GmbH - Milling and Sizing

Two common tasks in particle analysis are establishing the particle size distribution of a sample and questions about the shape of the particles. However, the choice of the appropriate technique to accomplish these tasks is dependent on a variety of parameters.

The world is complex, and there is not always a clear answer as to which is the best technique to encompass all aspects of the task at hand as efficiently and completely as possible. So, the essential characteristics of the material to be investigated must be considered, as well as what the analysis should yield.

The most crucial parameter is the size of the particles to be examined. If these have a size of only a few micrometers and less, one will resort to other techniques than with large chunks, which can also be a few millimeters in size.

This article will focus precisely on these ‘chunks’, ranging between a few micrometers and a few millimeters or even centimeters. This is the range in which images captured by light optics can exhibit their strengths.

Particle Sizer ANALYSETTE 28 ImageSizer ideal for production and quality control, research, development and laboratory.

Figure 1. Particle Sizer ANALYSETTE 28 ImageSizer ideal for production and quality control, research, development and laboratory. Image Credit: FRITSCH GmbH - Milling and Sizing

Dynamic Image Analysis – How Does it Work?

Produced by FRITSCH GmbH, the ANALYSETTE 28 ImageSizer provides up to about 75 images per second at an image resolution of 5 mega pixels.

These images are captured as the sample material passes the camera optics in the focal plane and is simultaneously illuminated using a backlight technique. The way in which the particles are transported past the camera can be realized in different ways.

In the simplest instance, the sample material ripples from a vibratory feeder and falls continuously past the camera. However, the sample can also be transferred to a suspension, which is then pumped through a measuring cell positioned in the focal plane of the camera optics.

Both of these techniques have benefits and drawbacks.

What Do You Do with the Captured Images?

The software must first detect each particle. To achieve this, the parts of the image which are darker than the background, the shadow of each particle, are searched for. It must be noted that the camera produces either black or white pixels, as well as various gray values in between.

The brightness transition from light to dark at the particle edges is not abrupt. So, it is necessary to establish starting from which gray value a pixel is identified as belonging to the particle, i.e., a threshold is introduced which divides all images into ‘background’ and ‘particle’.

This process is known as binarization. It is also helpful to see in the software where exactly this binarization threshold is located on captured images. This provides the user with a vital tool for judging how ‘reasonably’ the software captures individual particles.

Once the binarization has been performed, several parameters are established from the outlines gathered for each detected particle. The user has a choice, unlike a number of other techniques in particle size measurement.

The user must first consider what particle size definition to use for irregularly shaped particles. FRITSCH's ImageSizing Software (ISS) provides a large variety of possibilities.

Comparisons of distribution graphs that utilize different definitions of particle size can then be made quickly and flexibly, as seen in Figure 2.

Particle size distribution of a polymer granulate. The size parameter selected is the area-equivalent diameter, i.e. the diameter of a circle with an area equal to the particle cross-section.

Figure 2. Particle size distribution of a polymer granulate. The size parameter selected is the area-equivalent diameter, i.e. the diameter of a circle with an area equal to the particle cross-section. Image Credit: FRITSCH GmbH - Milling and Sizing

What can be Discovered About the Shape of the Individual Particles?

Further to establishing the particle size distribution, Dynamic Image Analysis has a strong focus on shape detection.

Do coarse particles differ fundamentally in shape from finer particles? How much do the particles of the examined sample deviate from the ideal spherical shape? What is the width-to-length ratio of the material? These are just some of the questions which an image acquisition system can address.

Two different options are now available in the ImageSizing-Software ISS when displaying the shape parameters of an examined material sample.

A distribution analogous to a particle size distribution can be generated: instead of particle size for a selected shape parameter, e.g., the Aspect Ratio (width-to-length), the Aspect Ratio is plotted on the x-axis.

Distribution of the aspect ratio plotted as a cumulative curve (solid line) and as a density distribution (bars). Note the index "0", which indicates a number distribution.

Figure 3. Distribution of the aspect ratio plotted as a cumulative curve (solid line) and as a density distribution (bars). Note the index "0", which indicates a number distribution. Image Credit: FRITSCH GmbH - Milling and Sizing

The y-axis then represents either the fraction within a certain Aspect Ratio interval (shown as a bar in Figure 3, right y-axis) or the relative fraction of particles with an Aspect Ratio smaller than the x-axis value (solid line in Figure 3, left y-axis).

This provides a fast, compact overview concerning the selected shape parameter.

Yet, this representation does not demonstrate for which particle sizes certain Aspect Ratio values may occur preferentially, for example, if large particles tend to deviate from the perfect spherical shape more than smaller ones. This is where demonstrating the data in a 2D, or even 3D Cloud is helpful.

2D Cloud of the Aspect Ratio over the Area Equivalent Diameter.

Figure 4. 2D Cloud of the Aspect Ratio over the Area Equivalent Diameter. Image Credit: FRITSCH GmbH - Milling and Sizing

For instance, Figure 4 exhibits the same measurement as a 2D Cloud. Each measured particle produces a data point where the y value gives the Aspect Ratio, and for the x value, the Area Equivalent Diameter is chosen. Any other size (or even shape) parameter can also be utilized for the x-axis.

For this example, it is obvious that the smaller particles deviate more frequently from the spherical shape than the bigger ones. Yet, a range around 750 µm particle size can also be found, where particles with a smaller aspect ratio also occur more regularly.

Conclusion

There is a great charm in this technique, and with many other methods for particle sizing, users are facing something like a black-box technique.

A sample is put in, data is produced in an almost miraculous way, and a result appears at the end in the output box. Usually, a deeper understanding is possible but involves a considerable amount of effort. However, by using this method, the user can always take a look at the images.

This information has been sourced, reviewed and adapted from materials provided by FRITSCH GmbH - Milling and Sizing.

For more information on this source, please visit FRITSCH GmbH - Milling and Sizing.

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