Date of Completion

1-29-2019

Embargo Period

1-28-2023

Keywords

Catalysis, Partial Oxidation, Magnetic Characterization, Materials Screening

Major Advisor

Steven L. Suib

Associate Advisor

S. Pamir Alpay

Associate Advisor

Puxian Gao

Associate Advisor

William A. Hines

Associate Advisor

Jose Gascon

Field of Study

Materials Science

Degree

Doctor of Philosophy

Open Access

Open Access

Abstract

Transition metal oxide-based systems as catalysts for oxidative reactions are studied. Utilizing aerobic oxygen as a cost-efficient and benign oxidant is explored for alcohol oxidation reactions. A detailed magnetic study is performed to understand the active phases of manganese oxide catalysts. The role of a well-known homogeneous copper oxide catalyst in an oxidation reaction is explored for making alcohols from aliphatic hydrocarbons. Finally, virtual screening techniques are explored in a preliminary study for materials discovery.

In chapter I, aerobic transformation of alcohols is studied by mesoporous manganese oxide and cobalt oxide species. Catalyst optimization, characterization and substrate scope experimentation is performed for self-esterification of alcohols and aldehydes.

In chapter II, a comprehensive magnetic characterization of mesoporous manganese oxide species is provided. While conventional methods do not lead to definitive phase determination, magnetic transitions and spin eco NMR techniques prove the mixed phase of meso MnOx materials.

In chapter III, a synthetic copper complex is studied. The complex was modeled after the active site of particulate methane monooxygenase (pMMO). A detailed study of the complex structure, reaction medium and by- products showed that an important side reaction interfered with hydrocarbon oxidation. The nitrile solvent decomposes to amide by nucleophilic interactions from hydrogen peroxide.

In the final chapter, preliminary computational screening tools are introduced. Extraction of geometrical features of molecular structures and evaluation of total energy with machine learning algorithms are performed and the ground work for application of such work to crystalline materials is proposed.

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