What is a shape model and which should I choose?

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Courtesy of Sequoia Scientific, Inc.

For the LISST-100X and LISST-Portable particle sizers, the user has an option to choose between two different shape models for inverting the scattering pattern into a size distribution. The two models are called Spherical and Randomly shaped particles.

The Spherical particle model performs the mathematical inversion under the assumption that the particles that scattered light are all spheres. Sequoia employs the so-called full Mie solution when using this approach. The Mie solution is a generalized solution to the scattering of light from spheres and is being used as the standard inversion method by all laser manufacturers.

However, sediment particles in aquatic and industrial environments are rarely, if ever, perfect spheres. Consequently Sequoia provides an option to invert the scattering pattern under the assumption that the particles are randomly (or irregularly) shaped particles. The exact details of how this inversion takes place is described in a scientific paper, which can be downloaded from the library section. A brief version of the method is described in this News article on Sequoia’s website: http://www.sequoiasci.com/article/random-shaped-particles-lissts/

So what method should you use? Unless you know for a fact that your particles are spherical you should choose the randomly shaped model. The only exception to this would be if you have to do a lot of comparisons with outputs from other laser particle sizers, for example a laboratory particle sizer. The reason for this is that no other laser manufacturer provides a randomly shaped particle model for inversion, so there will be nothing to compare to if you select the randomly shaped model.

If you choose the randomly shaped model, the LISST-Portable will return a size distribution that is directly comparable to a sieve size distribution. Please note that the size ranges changes a little, depending on what inversion model you choose to use. For details about why this is so, please consult the paper.

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