Automated Tandem Aerosol Classifier Experiments

JULIE PONGETTI, Timothy Sipkens, Morteza Kiasadegh, Jason S. Olfert, Jonathan Symonds, Cambustion Ltd

     Abstract Number: 308
     Working Group: Exhibitor and Instrument Application Showcase

Abstract
Aerosol classifiers are instruments which can select suspended particles based upon a particular physical characteristic over a narrow range of that characteristic. Examples include the Differential Mobility Analyzer (DMA), which selects particles by their electrical mobility (diameter); the Centrifugal Particle Mass Analyzer (CPMA) or Aerosol Particle Mass analyzer (APM), which select particles by their mass to charge ratio; and the Aerodynamic Aerosol Classifier (AAC), which selects particles by their relaxation time (or aerodynamic diameter). Often two of these classifiers will be used in series in so called “tandem” experiments to give yet more specific information about the nature of aerosol particles. For example, a tandem DMA (mobility diameter) and CPMA (mass) arrangement can measure the particle effective density. More generally, two-variable distributions (e.g. mass-mobility count distributions) can be built up by scanning both classifiers over a certain range of a two-dimensional matrix of setpoints and placing a particle counter downstream of the tandem pair. Such data can, for example, yield important insight into the transport properties of atmospheric or pharmaceutical aerosols

These experiments are however complicated to set up, and somewhat lengthy to execute. There is now an ongoing research collaboration between the University of Alberta, the National Research Council Canada, and Cambustion to automate such experiments using a variety of commercially available classifiers, by creating open-source Python software to control the classifiers and take readings from detectors, and to process (invert) the data to produce three-dimensional plots of the desired properties.

In this showcase we introduce the concept of tandem classifier experiments, and talk about the challenges. We also present the current state of the above automation project including a hands-on demonstration of part of such an experiment, augmented with video footage of a more complete experiment.

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