Molecular clusters as a bridge to understanding the fundamental interactions between radicals and water surfaces

Stephen David Belair, Purdue University

Abstract

While previously, atmospheric chemists focused their attention on gas-phase processes, they are now realizing that some chemical processes may additionally (or exclusively) occur on the surfaces or within the bulks of cloud droplets. Interactions with cloud droplets are particularly important to the chemistry of radicals because they are highly reactive species and some are known to form particularly strong complexes with single molecules. Computational studies of these systems can aid predictions of whether a particular gas-phase species, upon impact with a liquid surface, will adhere to a surface or even dissolve into the bulk, and how being incorporated with the liquid may effect aspects of its chemistry. Although it is not possible to perform quantum chemistry calculations on systems as large as cloud droplets (diameter ∼1μm), it is possible to gain insight into radical-surface interactions by using a small water cluster as a model to represent the portion of the droplet that the radical interacts with. In this thesis, cubic-shaped (H2O) 8 and dodecahedral (H2O)20 clusters are used for this purpose. The work is carried out by comparing the results of optimizations performed on two different types of systems: pure water clusters and water clusters containing an impurity. The chemical species used as impurities are HO2 and OH radicals and H2 molecules. The results suggest that some radicals have an affinity for a water surface; and furthermore, that under atmospheric conditions, a given sample of radicals interacting with a cloud droplet may be partitioned between being surface bound and dissolved. Additionally, we have performed an analysis on the different configurations of the cubic-shaped (H2O)8 clusters and found correlations between some aspects of their hydrogen bonding topologies and their relative stabilities. A study of the cubic HOx·(H2O) 7 clusters is used to apply and validate these findings.

Degree

Ph.D.

Advisors

Kais, Purdue University.

Subject Area

Chemistry

Off-Campus Purdue Users:
To access this dissertation, please log in to our
proxy server
.

Share

COinS