Poster Number : 4

A new model-based two-stage particle classifier (PACLA) for airborne particles analyzed with automated SEM/EDX

Main Author : Eva Bieler
Title/Position : SEM Specialist
Affiliation : Nano Imaging Lab /SNI
City, Country : Basel, Switzerland
Other Authors : M. Meier1,2, J. Rausch1,2, D. Jaramillo Vogel1,2, R. Locher3, T. Zünd1, E. Bieler4, B. Grobety2 1Particle Vision GmbH, c/o Fri Up, Annexe 2, Passage du Cardinal 11, Fribourg, 1700, Switzerland 2Department of Earth Sciences, University of Fribourg, Fribourg, 1700, Switzerland 3Institute of Data Analysis and Process Design (IDP), Zurich University of Applied Sciences (ZHAW), Winterthur, 8400, Switzerland 4Swiss Nanoscience Institute (SNI), Nano Imaging Lab, University of Basel, 4056 Basel, Switzerland

Abstract :

Keywords: particle classifier, SEM/EDX, single particle analysis, PM, source apportionment
Presenting author email: mario.meier@particle-vision.ch

Particulate matter (PM) severely impacts environment and health. Automated Scanning Electron Microscopy (SEM) coupled with Energy Dispersive X-ray spectroscopy (EDX) measures elemental composition and morphological properties of hundreds of single particles per sample.
For interpreting these large data sets, we developed a two-stage classifier (PACLA). Particles are classified rule-based into main classes according to the elements present. The particles within these main classes are further subdivided into subclasses, based on the concentration of these elements using a model-based algorithm.