Thesis advisor: Marc L. Snapper / Chapter 1. Review of concept and methodology development for asymmetric allylation of carbonyls and imines. Chapter 2. Description of the catalytic asymmetric addition of allyltrichlorosilane to aldehydes catalyzed by a proline-based N-oxide catalyst. Chapter 3. Introduction of the first catalytic asymmetric silylation of alcohols for desymmetrization of meso-diols. Chapter 4. Presentation of asymmetric silylation for synthesis of chiral syn-1,2-diols by kinetic resolution or divergent reaction on a racemic mixture. / Thesis (PhD) — Boston College, 2008. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Chemistry.

Determining the minimum distance between two objects is a problem that has been solved using many different approaches. Most methods proposed so far are, in essence, limited to solve the problem amongst convex polyhedra. Thus, to deal with concave objects, these methods partition concave objects into convex sub-objects and solve the convex problem between all possible sub-object combinations. This adds a large computational expense, especially when the concave objects in the scene are complicated, or when concave quadratically bound objects are to be linearized. In this work, two optimization-based formulations are proposed to solve the minimum distance problem without the need for partitioning concave objects into convex sub-objects. The first one, referred to as the continuous approach, uses concepts of computational solid geometry in order to represent objects with concavities. On the other hand, in the second formulation, referred to as the combinatorial approach, the geometries of the objects are replaced by large sets of points arranged in surface meshes. Since the optimization problem is not unimodal (i.e., has more than one local minimum point), global optimization techniques are used. Simulated Annealing and Genetic Algorithms, with constraint handling techniques such as penalty and repair strategies are used in the continuous approach. In order to eliminate the computational expense of determining the feasibility of every trial point, the combinatorial approach replaces the objects' geometry by a set of points on the surface of each object. This reduces the minimum distance problem to an unconstrained combinatorial optimization problem where the combination of points (one on each object) that minimizes the distance between objects is the solution. Additionally, Genetic Algorithms with niche formation techniques were developed in order to allow the distance algorithm to track multiple minima. In a series of numerical examples, a preliminary implementation of the proposed algorithms has proven to be robust and equivalent, in terms of computational efficiency, to some conventional approaches. / Graduate

A combinatorial study was carried out for hydrogen storage alloys which involve processes similar to those normally used in their fabrication. The study utilized a single sample of combined elemental (or compound) powders which were milled and consolidated into a bulk form and subsequently deformed to heavy strains. Material library was obtained in a post annealing treatment carried out at elevated temperatures which brings about solid state reactions between the powders yielding equilibrium phases in the respective alloy system. A sample comprising the material library was then pulverized and screened for hydrogen storage composition. X-ray diffraction was used as a screening tool, the sample having been examined both in as-processed and hydrogenated state. The method was successfully applied to Mg-Ni, and Mg-Ni-Ti yielding the well known Mg2Ni as the storage composition. It is concluded that partitioning of the alloy system into regions of similar solidus temperature would be required to enrich the material library.

Ce travail de thèse porte sur l’étude de l’influence des paramètres nano et microstructuraux pour l’optimisation des propriétés thermoélectriques des siliciures de magnésium Mg2(Si,Sn) de type -n. Ces matériaux thermoélectriques ont été choisis pour leurs compatibilités avec une utilisation en génération de puissance dans le domaine de l’automobile.Par une approche combinatoire utilisant les couples de diffusion, il est premièrement ré investigué le diagramme de phase pseudo-binaire Mg2Si-Mg2Sn dans le but d’interpréter les microstructures observées. Il est ensuite présenté les effets expérimentaux des paramètres de densification par frittage flash (SPS) sur les microstructures et les propriétés thermoélectriques résultantes. Finalement, il est explicité à l’aide de la modélisation l’influence des paramètres microstructuraux sur les propriétés thermoélectriques de l’alliage optimisé et nanostructuré Mg2Si0,3875Sn0,6Sb0,0125. / This study presents the influence of nano and microstructural parameters to optimize thermoelectric properties of n-type magnesium silicides Mg2(Si,Sn). These thermoelectric materials are chosen for their compatibilities with power generation in automotive.From a combinatorial approach using diffusion couples, it is first reinvestigated the pseudo-binary phase diagram Mg2Si-Mg2Sn to rationalize the observed microstructures. Then the experimental effects of sintering parameters (SPS) on resulting microstructures and thermoelectric properties are presented. Finally, the influence of microstructural parameters on the thermoelectric properties of optimized and nanostructured Mg2Si0,3875Sn0,6Sb0,0125 alloys are explained through modelling.

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Les modèles de Markov d’ordre 2 (HMM2) sont des modèles stochastiques qui ont démontré leur efficacité dans l’exploration de séquences génomiques. Cette thèse explore l’intérêt de modèles de différents types (M1M2, M2M2, M2M0) ainsi que leur couplage à des méthodes combinatoires pour segmenter les génomes bactériens sans connaissances a priori du contenu génétique. Ces approches ont été appliquées à deux modèles bactériens afin d’en valider la robustesse : Streptomyces coelicolor et Streptococcus thermophilus. Ces espèces bactériennes présentent des caractéristiques génomiques très distinctes (composition, taille du génome) en lien avec leur écosystème spécifique : le sol pour les S. coelicolor et le milieu lait pour S. thermophilus / Second-order Hidden Markov Models (HMM2) are stochastic processes with a high efficiency in exploring bacterial genome sequences. Different types of HMM2 (M1M2, M2M2, M2M0) combined to combinatorial methods were developed in a new approach to discriminate genomic regions without a priori knowledge on their genetic content. This approach was applied on two bacterial models in order to validate its achievements: Streptomyces coelicolor and Streptococcus thermophilus. These bacterial species exhibit distinct genomic traits (base composition, global genome size) in relation with their ecological niche: soil for S. coelicolor and dairy products for S. thermophilus. In S. coelicolor, a first HMM2 architecture allowed the detection of short discrete DNA heterogeneities (5-16 nucleotides in size), mostly localized in intergenic regions. The application of the method on a biologically known gene set, the SigR regulon (involved in oxidative stress response), proved the efficiency in identifying bacterial promoters. S. coelicolor shows a complex regulatory network (up to 12% of the genes may be involved in gene regulation) with more than 60 sigma factors, involved in initiation of transcription. A classification method coupled to a searching algorithm (i.e. R’MES) was developed to automatically extract the box1-spacer-box2 composite DNA motifs, structure corresponding to the typical bacterial promoter -35/-10 boxes. Among the 814 DNA motifs described for the whole S. coelicolor genome, those of sigma factors (B, WhiG) could be retrieved from the crude data. We could show that this method could be generalized by applying it successfully in a preliminary attempt to the genome of Bacillus subtilis