Date of Thesis

Summer 2023

Description

Compounds being considered for use as active pharmaceutical agents are becoming increasingly complex and proton deficient, leading to a need to employ more complex multi-dimensional NMR techniques to analyze them. However, these experiments are often very time-consuming and impractical to employ in an industrial environment where spectrometer time is scarce and in demand. Methods of advanced data sampling, such as nonuniform sampling (NUS), can be utilized to reduce the time cost of these experiments while preserving or even improving spectral characteristics. The use of NUS is common in high dimensional NMR (3D-NMR), but has faced barriers to adoption in 2D-NMR.

This work takes up the problem of making NUS in 2D-NMR more robust, and shows that as the degree of data reduction (a.k.a. sampling coverage) falls below fifty percent in one-dimensional NUS, spectral aliasing and sampling noise begin to arise. This work focuses on improving 1D-NUS sampling schedule design to reduce spectral artefacts by introducing brief uniform regions at the beginning of sampling schedules to amend potential repeat sequences in the early parts of the sampling schedule. Metrics were developed to demonstrate that patterned sampling and associated weak aliasing cause artefacts in sparser NUS in 2D-NMR. Based on findings of beneficial scheduling parameters, a suite of high fidelity sampling schedules was created and implemented in the QSched program, with a companion website for general use. These schedules were heavily stress-tested, and are planned to be utilized in part to update the chemical shifts of natural products such as morphine. This work breaks the 50% sparsity barrier for 1D-NUS, showing robust sampling solutions for 25-33% NUS in 2D-NMR, and even showing promising routes to lower sparsity. Findings from this work are also used to design schedules with very low increment numbers.

Keywords

NMR, NUS, nonuniform sampling, aliasing, 2D-NMR

Access Type

Masters Thesis (Bucknell Access Only)

Degree Type

Master of Science

Major

Chemistry

First Advisor

David Rovnyak

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