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Title: The prediction of nonlinear effects in asymmetric catalysis
Author: El-Fayyoumy, Shaimaa
ISNI:       0000 0004 2745 2007
Awarding Body: Queen Mary, University of London
Current Institution: Queen Mary, University of London
Date of Award: 2008
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Asymmetric catalysis is an ideal method for synthesising optically active compounds. A small amount of a chiral catalyst produces chiral materials. To achieve maximum chiral induction, efficient catalytic systems must be created. Enantioselective addition of organometallic reagents to aldehydes affords optically active secondary alcohols. This is one of the most important and fundamental asymmetric reactions. The optically active alcohols are components of naturally occurring compounds. The secondary alcohols are obtained by stereoselective alkylation of aldehydes using organometallic reagents. The most common ligands that are used in the alkylation of benzaldehyde in the presence of dialkylzinc are amino alcohols. The term "nonlinear effects" was first used in 1986. There are two types of NLE, a positive nonlinear effect (positive NLE) and a negative nonlinear effect (negative NLE). A positive NLE operates if eeprod is higher than eeaux whereas a negative NLE operates if eeprod is lower than eeaux• To gain a better understanding of the behaviour of catalysts during the course of a reaction that leads to NLE, mathematical models were developed. Only the ML2 and the reservoir effect are discussed in this project. One of the most notable examples of NLEs is, Noyori's example using DAIB in the ethylation of benzaldehyde. MIB was used as a starting point to verify the reported NLE and prove the reliability of our results. Following this, a set of oxazolines were tested for NLE. These ligands have proven to be successful in the I alkylation of aromatic aldehydes. We applied them to the ethylation of benzaldehyde I and assessed their catalytic activity; whether they exhibit NLE or not. Computational studies were carried out on the set of oxazolines that express NLE in hope to aid us in constructing a model to predict NLE.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available