American Association for Aerosol Research - Abstract Submission

AAAR 37th Annual Conference
October 14 - October 18, 2019
Oregon Convention Center
Portland, Oregon, USA

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Online Molecular Analysis of Secondary Organic Aerosol Using Droplet Assisted Ionization

DEVAN E. KERECMAN, Michael J. Apsokardu, Yao Zhang, Murray Johnston, University of Delaware

     Abstract Number: 137
     Working Group: Instrumentation and Methods

Abstract
Droplet Assisted Ionization (DAI) is an inlet ionization technique that allows for detection of intact molecular ions from preformed aerosol droplets. In our laboratory, DAI is performed by passing droplets through a temperature-controlled capillary tube that serves as the inlet to a Waters SYNAPT G2-S mass spectrometer. In this study, DAI is used to perform online molecular characterization of secondary organic aerosol (SOA) generated in a flow tube reactor by monoterpene ozonolysis. Previous work with DAI has determined the optimum operating conditions for a variety of test compounds. Ion formation depends strongly on droplet solvent composition and capillary temperature. Aqueous droplets give orders of magnitude higher signal intensity than dry particles or droplets consisting of other solvent compositions (Apsokardu, M. J. et al. Rapid Commun. Mass Spec. 2019). In the current study, SOA generated in the flow tube is size-selected with a differential mobility analyzer, aqueous droplets are formed by passing the aerosol through a condensation growth chamber, and sent into the DAI capillary inlet of the mass spectrometer to perform molecular analysis. In a previous study by our group using offline analysis to characterize β-pinene SOA, we noted a particle size dependence of the molecular composition, specifically an increasing oligomeric content with increasing particle size (Tu, P. and Johnston, M. V. Atoms. Chem. Phys. 2017). Presented here is an investigation of the size dependence on SOA composition using online analysis by DAI, as this method reduces the possible impact of artifacts associated with offline analysis. This work has implications for understanding the relative roles of gas- vs. particle- phase processes that cause particle growth.