[PAST EVENT] Cheminar: Atmospheric Aerosol Particle Liquid Phase Transitions + Ice Nucleation Using Microfluidics
LocationIntegrated Science Center (ISC), 1127 or Virtual
540 Landrum Dr
Williamsburg, VA 23185Map this location
Atmospheric aerosols are one of the major contributing factors to our climate, yet are a leading source of uncertainty in climate modeling. Part of this uncertainty arises from the complex nature of individual aerosol particles: the composition and phase of aerosol particles evolve dramatically with changes in the ambient environmental conditions. The resultant composition and phase inform the particle’s optical properties, species uptake and partitioning, and activation to cloud condensation or ice nuclei. In this work, recent advancements using analytic thermodynamic modeling and laboratory microscale flows will be highlighted for aerosol droplet systems, towards improved understanding of the properties and phase of aerosols in our atmosphere. First, statistical thermodynamic approaches will be presented for determining particle surface tension, composition, and surface-to-bulk partitioning in aerosols across the full range of relative humidity. Second, novel microfluidic methods for measuring droplet phase transitions, including liquid-liquid phase separation (LLPS) and ice nucleation (IN), will be introduced. Microfluidics offers the advantage of rapid and monodisperse droplet generation with precise temperature control and minimal solid-surface contact during experiments. Temperature and relative humidity dependence of LLPS and crystallization for model aerosol droplets with varying composition is explored. It is observed that temperature has a significant effect on some systems while having no effect on others depending on the organic to inorganic ratio (OIR) as well as the identity of the organic and inorganic phases. IN results of model biological ice nucleating particles, Snomax, along with bulk and sea surface microlayer sea water samples obtained from a simulated waveflume experiment (SeaSCAPE) are studied in static and high-throughput droplet microfluidic platforms. Automated detection and classification of frozen droplets from liquid drops was implemented through machine learning with a deep neural network.
Cari S. Dutcher is an Associate Professor of Mechanical Engineering (ME) and Chemical Engineering and Materials Science (CEMS) at the University of Minnesota, Twin Cities, with research interests in aerosol science and multiphase fluids. Cari has served on the American Association for Aerosol Research (AAAR) board of directors, AAAR aerosol physics working group chair, and currently serves as secretary on the AAAR Executive Board. She has received a number of early faculty awards, including the 3M Non-Tenured Faculty Award, NSF CAREER and AAAR Kenneth T. Whitby Award. Cari received her Ph.D. from the University of California, Berkeley in Chemical Engineering and was a postdoc at the University of California, Davis in the Air Quality Research Center.
Join the ISC 1127 watch party or virtually from your device. Zoom Meeting ID: 988 8849 0191, Passcode: dutcher