Catalytic Terpene Polycyclization Reactions and Structural Determination of Small Molecules by MicroED
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Catalytic Terpene Polycyclization Reactions and Structural Determination of Small Molecules by MicroED

Abstract

This dissertation describes the development of Lewis acid-based methodology to access polycyclized sesquiterpenes and diterpenes through simple catalytic methods. This challenging biomimetic reaction is difficult to replicate in synthesis and remains understudied. In addition, this dissertation explores the application and development of an electron diffraction technique for the elucidation of small molecules in an efficient manner. Overall, this work seeks to push the boundaries of synthesis and analysis of complex structures through development of synthetic and analytical methods described herein. Chapter One is a brief overview of the current state of research on biomimetic terpene cyclization reactions and history of transmission electron microscopy techniques leading up to the development of microcrystal electron diffraction. This chapter serves as a prelude to the remaining chapters and will be referenced throughout this dissertation. Chapter Two describes our efforts in the development of a lithium-weakly coordinating anion-mediated polycyclization reaction to generate polycyclic terpenes from acyclic fluorides precursors. The analysis of complex mixtures and early efforts to characterize these species utilizing microcrystal electron diffraction is discussed. Chapter Three discusses our investigations into development and application of microcrystal electron diffraction to solve small molecule structural problems in chemistry. The analysis of pharmaceuticals and natural products is described, and the application of an automated data processing procedure is explored. Chapter Four highlights an ongoing effort in our research group to create automated tools to make microcrystal electron diffraction a more practical and efficient tool for the elucidation of small molecule structures. The development of Python automation scripts for data collection and processing is discussed.

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