Understanding Homogeneous and Heterogeneous Transition Metal Oxide Catalytic Systems for Partial Oxidation Reactions PDF Download

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Understanding Homogeneous and Heterogeneous Transition Metal Oxide Catalytic Systems for Partial Oxidation Reactions

Understanding Homogeneous and Heterogeneous Transition Metal Oxide Catalytic Systems for Partial Oxidation Reactions PDF Author: Ehsan Moharreri
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages :

Book Description
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.