Date of Award

2-16-2024

Publication Type

Thesis

Degree Name

M.Sc.

Department

Chemistry and Biochemistry

Keywords

Accessibility;Automation;Data Analytics;Machine Learning;Simulation;Small-Angle Scattering

Supervisor

Drew Marquardt

Abstract

Increasing complexity in scientific understanding leads naturally into more precise methods of experimentation, resulting in larger data-sets and mathematical models. As researchers at all levels attempt to take on fundamental questions, this evolution of process creates many barriers. Technology can help to break down these barriers, reducing the amount of manual work and time required to understand results. Creating specialized tools for analysis and automation has become a requirement to participate in modern scientific study. Here, we explore how such tools have impacted the study of lipid membranes, as the field continues to mature and more nuanced modes of understanding are developed. By eliminating challenges associated with extracting results from X-Ray and neutron scattering experiments, more work has been completed and bottlenecks have been eliminated. Additionally, this work seeks to explore the impact of machine learning techniques on existing data - exploring the potential for computational tools to extract additional results out of already processed data.

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