Aerobic Oxidation Of Phenols And Other Arenes By Transition Metal Catalysis
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Degree type
Doctor of Philosophy (PhD)
Graduate group
Chemistry
Discipline
Subject
catalysis
machine learning
organic synthesis
oxidation
transition metals
Chemistry
machine learning
organic synthesis
oxidation
transition metals
Chemistry
Funder
Grant number
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Copyright date
2022-09-09T20:20:00-07:00
Distributor
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Author
Jemison, Adriana
Contributor
Abstract
- A general and efficient method for the oxidative coupling of oxindoles with indoles and other arenes to form complex products with quaternary centers was discovered. The mild copper aerobic oxidation method was used to produce 21 unique racemic compounds bearing a variety of substituents in fair to excellent yields. Concurrently, a vanadium(V)-oxo complex was optimized in the asymmetric coupling. However, the need for a bulky substrate with a multistep synthesis and an insurmountable ceiling in selectivity at 70 %ee detracted from the utility of the method. 2. The oxidative homocoupling of para-alkenyl phenols and subsequent trapping of the resulting quinone methide with a variety of oxygen and nitrogen nucleophiles was achieved. Both β-β and the β-O coupling isomers could be achieved arising from C-C coupling and two nucleophilic additions of one water molecule (β-β isomer) vs C-O coupling followed by one nucleophilic addition of a water molecule (β-O isomer), respectively. Together 21 examples of the two scaffolds were isolated in fair to good yields. Selectivity amongst these outcomes was achieved by leveraging understanding of the mechanism. Specifically, a qualitative predictive model for the regiochemistry of the coupling was formulated based on catalyst electronics, solvent polarity, and concentration. 3. Unsupervised learning is an underutilized tool in the area of chemical synthesis. Using high throughput experimentation and accessible data science tools, hierarchical clustering was used to find relationships among a family of catalysts. Mechanistic assumptions based on this model were explored using the chemical literature, internal competition experiments, and supervised learning models.
Advisor
Marisa C. Kozlowski
Date of degree
2020-01-01