Thesis advisor: Amir H. Hoveyda / Catalytic enantioselective reactions are of great importance in synthetic organic chemistry. Thus, development of efficient, selective and easily accessible catalyst for various bond formations is the main task in our laboratories. First, we have developed the first broadly applicable enantioselective boryl conjugate addition reactions to a variety of α,β-unsaturated carbonyls, promoted by a chiral Lewis basic N-heterocyclic carbene. The valuable β-boryl carbonyls were further used in complex molecule syntheses. The mechanism of these C–B bond formations was studied in details. We have also developed a practical method for enantioselective addition of an allene unit to aryl-, heteroaryl- and alkyl-substituted Boc-aldimines. These efficient C–C bond formations, catalyzed by an aminophenol-derived boron-based catalyst, were further utilized in succinct syntheses of anisomycin and epi-cytoxazone. Finally, chiral NHC–Cu complexes were employed for site-, diastereo- and enantioselective silyl conjugate additions to acyclic and cyclic dienones and dienoates. The precious enantiomerically enriched allylsilane obtained can be converted into a ketone-aldol type product, which is difficult to access through alternative methods. / Thesis (PhD) — Boston College, 2015. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Chemistry.

The Morita-Baylis-Hillman (MBH) reaction is a very versatile synthetic protocol to synthesize various useful compounds containing several functional groups. MBH acetates and carbonates are highly valued compounds as they have good potential to be precursors for organic synthesis reactions due to their ease of modification and synthesis. This thesis utilizes Density Functional Theory (DFT) calculations to understand the mechanism and selectivity of an unexpected tandem conjugate addition-elimination (CA-E) reaction of allylic alkylated Morita-Baylis-Hillman C- adducts. This synthetic protocol was developed by Prof. Zhi-Yong Jiang and co-workers from Henan University, China. The reaction required the use of sub-stoichiometric amounts of an organic or inorganic Brøndst base as a catalyst and was achieved with excellent yields (96%) in neat conditions. TBD gave the highest yield amongst the organocatalysts and Cs2CO3 gave the highest yield amongst all screened bases. A possible mechanistic pathway was proposed and three different energy profiles were modeled using 1,5,7-triaza-bicyclo-[4.4.0]-dec-5-ene (TBD), Cs2CO3 and CO32- as catalysts. All three models were able to explain the experimental observations, revealing both kinetic and thermodynamic factors influencing the selectivity of the CA-E reaction. CO32- model gave the most promising result, revealing a significant energy difference of 17.9 kcal/mol between the transition states of the two differing pathways and an energy difference of 20.9 kcal/mol between the two possible products. Although TBD modeling did not show significant difference in the transition states of the differing pathways, it revealed an unexpected secondary non-covalent electrostatic interaction, involving the electron deficient C atom of the triaza CN3 moiety of the TBD catalyst and the O atom of a neighboring NO2- group in the intermediate. Subsequent modeling using a similar substrate proved the possibility of this non-covalent electrostatic interaction, as there was significant overlap of the orbital cloud present in both the Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) of the molecule between the C atom of the triaza moiety belonging to the TBD catalyst and the O atom of the nitro group of the substrate. The Mayer bond order was of the C-O interaction was determined to be 0.138.

No description available.

A new neural network approach for performing matrix computations is presented. The idea of this approach is to construct a feed-forward neural network (FNN) and then train it by matching a desired set of patterns. The solution of the problem is the converged weight of the FNN. Accordingly, unlike the conventional FNN research that concentrates on external properties (mappings) of the networks, this study concentrates on the internal properties (weights) of the network. The present network is linear and its weights are usually strongly constrained; hence, complicated overlapped network needs to be construct. It should be noticed, however, that the present approach depends highly on the training algorithm of the FNN. Unfortunately, the available training methods; such as, the original Back-propagation (BP) algorithm, encounter many deficiencies when applied to matrix algebra problems; e. g., slow convergence due to improper choice of learning rates (LR). Thus, this study will focus on the development of new efficient and accurate FNN training methods. One improvement suggested to alleviate the problem of LR choice is the use of a line search with steepest descent method; namely, bracketing with golden section method. This provides an optimal LR as training progresses. Another improvement proposed in this study is the use of conjugate gradient (CG) methods to speed up the training process of the neural network. The computational feasibility of these methods is assessed on two matrix problems; namely, the LU-decomposition of both band and square ill-conditioned unsymmetric matrices and the inversion of square ill-conditioned unsymmetric matrices. In this study, two performance indexes have been considered; namely, learning speed and convergence accuracy. Extensive computer simulations have been carried out using the following training methods: steepest descent with line search (SDLS) method, conventional back propagation (BP) algorithm, and conjugate gradient (CG) methods; specifically, Fletcher Reeves conjugate gradient (CGFR) method and Polak Ribiere conjugate gradient (CGPR) method. The performance comparisons between these minimization methods have demonstrated that the CG training methods give better convergence accuracy and are by far the superior with respect to learning time; they offer speed-ups of anything between 3 and 4 over SDLS depending on the severity of the error goal chosen and the size of the problem. Furthermore, when using Powell's restart criteria with the CG methods, the problem of wrong convergence directions usually encountered in pure CG learning methods is alleviated. In general, CG methods with restarts have shown the best performance among all other methods in training the FNN for LU-decomposition and matrix inversion. Consequently, it is concluded that CG methods are good candidates for training FNN of matrix computations, in particular, Polak-Ribidre conjugate gradient method with Powell's restart criteria.

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published_or_final_version / Mathematics / Doctoral / Doctor of Philosophy

This report details the discovery of a method for rhodium catalyzed asymmetric conjugate additions of heteroaryl nucleophiles to α,β-unsaturated carbonyl compounds. This work has successfully employed heteraryl titanates in enantioselective conjugate addition to α,β-unsaturated carbonyl compounds for the first time. Moreover, the utility of benzofuranyl, benzothiophenyl, and pyrrolo zinc reagents has been exemplified in enantioselective conjugate additions. We have found that the precatalyst [Rh(COD)acac]/OMeBIPHEP is broadly effective for enantioselective conjugate additions. Each heterocycle tested exhibited unique reactivity with respect to both conversion and enantioselectivity; reactivities of heteroaryl zinc reagents and heteroaryl titanates in enantioselective conjugate additions to α,β-unsaturated carbonyl compounds are sometimes complementary. Efforts directed towards the development of a method for rhodium-free enantioselective conjugate addition of furan to α,β-unsaturated carbonyl compounds are also described. / text

Calcined coke is the best material for making carbon anodes for smelting of alumina to aluminum. Calcining is an energy intensive industry and a significant amount of heat is wasted in the calcining process. Efficiently managing this energy resource is tied to the profit margin and survivability of a calcining plant. 3-D computational models are developed using FLUENT to simulate the calcining process inside the long slender kiln. Simplified models are employed to simulate the moving petocke bed with a uniform distribution of moisture evaporation, devolatilization, and coke fines entrainment rate with a conjugate radiation-convection-conduction calculation. The results show the 3-D behavior of the flow, the reaction inside the kiln, heat transfer and the effect of the tertiary air on coke bed heat transfer. The ultimate goals are to reduce energy consumption, recover waste-heat, increase thermal efficiency, and increase the product yield.

This paper discusses characteristics of standard conjugate priors and their induced posteriors in Bayesian inference for von Mises-Fisher distributions, using either the canonical natural exponential family or the more commonly employed polar coordinate parameterizations. We analyze when standard conjugate priors as well as posteriors are proper, and investigate the Jeffreys prior for the von Mises-Fisher family. Finally, we characterize the proper distributions in the standard conjugate family of the (matrixvalued) von Mises-Fisher distributions on Stiefel manifolds.

Numerical simulation is progressively taking importance in the design of an aero- nautical engine. However, concerning the particular case of cooling devices, the high number of sub-millimetric cooling holes is an obstacle for computational sim- ulations. A classical approach goes through the modelling of the effusion cooling by homogenisation. It allows to simulate a full combustor but failsin representing the jet penetration and mixing. A new approach named thickened-hole model was developed during this thesis to overcome this issue. A work on improving the mesh resolution onkey areas thanks to an automatic adaptive method is also presented, leading to a clear breakthrough. In parallel, as the flame tube temperature is a cornerstone for the combustor durability,a low-cost approach is proposed to predict it. To meet the time-constraints of design, it is based on thermal modelling instead of a direct thermal resolution.