Understanding how particles nucleate in a multi-component gas
mixture has important implications not only for climate and
weather, but also wide-ranging technological applications including gas separations,
pollution control, and nanotechnology. The goals
of this project are (i) to develop computational algorithms and
analysis tools for efficient investigations of multi-component gas-
to-particle nucleation processes, (ii) to elucidate atmospherically
relevant nucleation processes and to validate the rate predictions
through strategically selected laboratory experiments measuring
cluster size and mass distributions at the sub-3 nm scale, and
(iii) to deploy a freely-available cyber-tool that transforms data to
knowledge by enabling large-scale modelers and experimental
researchers to harvest predicted atmospheric nucleation rates
and learn about mechanisms, by providing a general framework to
visualize and analyze the abundance of digital data generated by
particle-based simulations for any type of gas-to-particles
nucleation process, and by being an aid for teaching about
nucleation.