Presentation Title

Enantioselective Electrophilic Aromatic Nitration

Abstract

Enantiomers are non-superimposable mirror images of each other which look almost identical but can have different properties. Often reactions produce racemic (equal) mixtures of these two enantiomers. A recently discovered enantioselective aromatic nitration method allows for the formation of one enantiomeric form of our nitrated substrate in excess of the other. Forming specific desired enantiomers has broad implications to organic synthesis techniques such as Chirality Assisted Synthesis and for the pharmaceutical industry in drug design and development. This presentation reports the development of a chiral auxiliary that made this nitration methodology possible. Chiral auxiliary development was guided, and the success of the auxiliaries explained, by use of Density Functional Theory modeling.

Primary Faculty Mentor Name

Severin T. Schneebeli

Graduate Student Mentors

Joseph Cambell

Faculty/Staff Collaborators

Joseph Cambell (Graduate Student Mentor)

Status

Undergraduate

Student College

College of Arts and Sciences

Program/Major

Biochemistry

Primary Research Category

Engineering & Physical Sciences

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Enantioselective Electrophilic Aromatic Nitration

Enantiomers are non-superimposable mirror images of each other which look almost identical but can have different properties. Often reactions produce racemic (equal) mixtures of these two enantiomers. A recently discovered enantioselective aromatic nitration method allows for the formation of one enantiomeric form of our nitrated substrate in excess of the other. Forming specific desired enantiomers has broad implications to organic synthesis techniques such as Chirality Assisted Synthesis and for the pharmaceutical industry in drug design and development. This presentation reports the development of a chiral auxiliary that made this nitration methodology possible. Chiral auxiliary development was guided, and the success of the auxiliaries explained, by use of Density Functional Theory modeling.