Date Thesis Awarded
12-2024
Access Type
Honors Thesis -- Access Restricted On-Campus Only
Degree Name
Bachelors of Science (BS)
Department
Chemistry
Advisor
Tyler Meldrum
Committee Members
John C. Poutsma
Nathan Kidwell
Daniel Vasiliu
Abstract
Nuclear magnetic resonance (NMR) relaxometry is a versatile technique with applications spanning material science, biophysics, and chemical analysis. Computational approaches, including machine learning and simulations, have significantly advanced the understanding and deconvolution of relaxometry data. In this work, we present three projects aimed at improving the analysis and modeling of relaxometry systems. The first project employs a sparsity-based method to extract meaningful insights from relaxometry data. The second introduces a novel sample-grouping approach based on relaxation decay profiles that eliminates the need for computation ally expensive calculations. Finally, the third utilizes molecular dynamic simulations to enhance the modeling of relaxation behaviors and to provide a more comprehensive understanding of multi-component systems
Recommended Citation
Li, Shinjer, "Computational Methods in NMR Relaxometry" (2024). Undergraduate Honors Theses. William & Mary. Paper 2242.
https://scholarworks.wm.edu/honorstheses/2242
