Year: 2017
Type: dissertation
Abstract: The hydrophobic effect is well-known, ubiquitous, and of critical importance to difficult problems such as protein folding and molecular self-assembly. The effect, as understood today, arises not from any direct force, but rather emerges from the statistics of many such forces acting on a variety of molecules. We have in our possession advanced theories aiming to predict the statistical outcomes of such systems without exhaustive sampling or extensive parameterization. However, certain key predictions of these theories lack validation, and their generalization to systems of nonideal hydrophobes or to mixed solutions remains an open question. This thesis aims to describe these theories, analyze their predictions in the context of available experimental evidence, and add to that evidence. To this end, traditional tools such as contact angle measurements and vibrational spectroscopy will be employed in conjunction with single polymer pulling experiments and modern statistical analysis techniques. Machine learning methods will be examined as an avenue for increasing single molecule force spectroscopy throughput, with the specific goal of trackingrare events which might contain a large amount of information. Sum frequency generation data will be examined alongside solute-correlated Raman and infrared spectra, specifically for the investigation of aromatic hydrocarbons in the presence of protein-folding agents.
Source: TSpace (University of Toronto)
Author: Duncan Halverson
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2948545640