Silane Mediated Amidation and Difluoroethylation Reactions Using Carboxylic Acids

Pugh, Oska (2024) Silane Mediated Amidation and Difluoroethylation Reactions Using Carboxylic Acids. PhD thesis, University of Nottingham.

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Abstract

Initial discussion centres firstly around silicon and its use within synthetic organic chemistry with a particular focus on organosilicon reagents as reductants. Secondly there is a brief discussion on the importance of metal free chemistry.

Chapter One discusses the development of an efficient silane mediated amidation reaction. To this end two methods were attempted. The first method made use of a silane immobilised on a polystyrene support, this immobilised silane was tested and optimised in an amidation reaction. The second method re-examined the amidation with phenylsilane with the intent [to] reduce the amount of silane without compromising the reaction outcome. This reaction was applied to the synthesis of active pharmaceutical ingredients and natural products. Lastly in this chapter is described the near total synthesis of the natural product rehmagluamide.

Chapter Two discusses a practical one pot reductive amination of difluoroacetic acid for the synthesis of difluoroethylamines. This process is flexible in that it can be used for the synthesis of just difluoroacetamides in the first step, or difluoroethylamines in the full one-pot process. After the development of a Boc deprotection using difluoroacetic acid, the amide reduction reaction was also applied to the synthesis of unsymmetrical piperazines in a one pot procedure starting with N-Boc-piperazine.

Chapter Three discusses the development of a machine learning model to predict the pKa values for Brønsted acids in varying organic solvents. This model was iterated upon over 4 successive generations and did produce a viable workflow for predicting pKa values for individual acids. The most effective generation of the model was able to produce predicted pKa values for Brønsted acids known to the model but in solvents previously unknown to the model. The machine learning model is currently unable to reliably differentiate between solvents and work continues in this area.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Denton, Ross
Ozcan, Ender
Figueredo, Grazziela
Keywords: silicon, organosilicon compounds, difluoroethylamines
Subjects: Q Science > QD Chemistry > QD241 Organic chemistry
Faculties/Schools: UK Campuses > Faculty of Science > School of Chemistry
Item ID: 78237
Depositing User: pugh, oska
Date Deposited: 24 Jul 2024 04:43
Last Modified: 24 Jul 2024 04:43
URI: https://eprints.nottingham.ac.uk/id/eprint/78237

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