Aerobic oxidation of phenols and other arenes by transition metal catalysis / Adriana L. Jemison.

Format:

Book

Thesis/Dissertation

Author/Creator:

Jemison, Adriana L., author.

Contributor:

Kozlowski, Marisa C., 1967- degree supervisor.

University of Pennsylvania. Department of Chemistry, degree granting institution.

Language:

English

Subjects (All):

Chemistry.

Artificial intelligence.

Chemistry--Penn dissertations.

Penn dissertations--Chemistry.

Local Subjects:

Chemistry.

Artificial intelligence.

Chemistry--Penn dissertations.

Penn dissertations--Chemistry.

Genre:

Academic theses.

Physical Description:

1 online resource (313 pages)

Contained In:

Dissertations Abstracts International 82-07B.

Place of Publication:

[Philadelphia, Pennsylvania] : University of Pennsylvania ; Ann Arbor : ProQuest Dissertations & Theses, 2020.

Language Note:

English

System Details:

Mode of access: World Wide Web.

text file

Summary:

1. 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.

Notes:

Source: Dissertations Abstracts International, Volume: 82-07, Section: B.

Advisors: Kozlowski, Marisa C.; Committee members: Gary Molander; Amos Smith; Neil Tomson.

Department: Chemistry.

Ph.D. University of Pennsylvania 2020.

Local Notes:

School code: 0175

ISBN:

9798557066310

Access Restriction:

Restricted for use by site license.

This item is not available from ProQuest Dissertations & Theses.

This item must not be sold to any third party vendors.