Thesis (Ph. D.)--University of Virginia, 2008. / Title from title page. Includes bibliographical references. Also available online through Digital Dissertations.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2004. / Includes bibliographical references. Also available in electronic version.

Thesis (M.Sc.)--City University of Hong Kong, 2004. / At head of title: City University of Hong Kong, Department of Physics and Materials Science, Master of Science in materials engineering & nanotechnology dissertation. Title from title screen (viewed on Aug. 31, 2006) Includes bibliographical references.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2005. / Includes bibliographical references. Also available in electronic version.

Thesis (M.Ch.E.)--University of Delaware, 2005. / Principal faculty advisor: Abraham M. Lenhoff, Dept. of Chemical Engineering. Includes bibliographical references.

Thesis (Ph. D.)(Metallurgical Engineering)--University of Pretoria, 2005. / Includes summary. Includes bibliographical references. Available on the Internet via the World Wide Web.

Thesis (M. S.)--Earth and Atmospheric Sciences, Georgia Institute of Technology, 2008. / Newman, Andrew, Committee Member ; Lowell, Robert, Committee Chair ; Germanovich, Leonid, Committee Member.

Crystallization is an increasingly important process in a variety of applications from drug development to single crystal X-ray diffraction structure determination. However, while there is a good deal of research into prediction of molecular crystal structure, the factors that cause a molecule to be crystallizable have so far remained poorly understood. The aim of this project was to answer the seemingly straightforward question: can we predict how easily a molecule will crystallize? The Cambridge Structural Database contains almost a million examples of materials from the scientific literature that have crystallized. Models for the prediction of crystallization propensity of organic molecular materials were developed by training machine learning algorithms on carefully curated sets of molecules which are either observed or not observed to crystallize, extracted from a database of commercially available molecules. The models were validated computationally and experimentally, while feature extraction methods and high resolution powder diffraction studies were used to understand the molecular and structural features that determine the ease of crystallization. This led to the development of a new molecular descriptor which encodes information about the conformational flexibility of a molecule. The best models gave error rates of less than 5% for both cross-validation data and previously-unseen test data, demonstrating that crystallization propensity can be predicted with a high degree of accuracy. Molecular size, flexibility and nitrogen atom environments were found to be the most influential factors in determining the ease of crystallization, while microstructural features determined by powder diffraction showed almost no correlation with the model predictions. Further predictions on co-crystals show scope for extending the methodology to other relevant applications.

La nucléation est l’étape est à l’origine de la cristallisation et c’est généralement elle qui va dicter les propriétés finales d’un cristal. Il est alors intéressant de comprendre les mécanismes qui gouvernent cette étape. Deux théories se confrontent aujourd’hui avec d’une part la théorie classique de la nucléation et d’autre part un modèle de nucléation en deux étapes. La théorie classique considère l’addition des monomères un par un pour former des clusters directement cristallins. Le modèle en deux étapes considère une première étape de concentration, au cours de laquelle les monomères vont former des agrégats denses, mais désordonnés, et une seconde étape de structuration qui permettra de réarranger les molécules au sein d’un cluster, pour obtenir une structure cristalline. Il semblerait que, dans le cas des protéines, le passage du mécanisme classique à celui en deux étapes se fasse par une augmentation des fluctuations de concentration en solution, ce qui se fait en se rapprochant de la spinodale de décomposition présente dans le diagramme de phase de certaines protéines. Ainsi, dans cette étude, nous développons des systèmes microfluidiques qui permettent d’étudier ces différents mécanismes. Dans un premier temps, nous avons conçus un dispositif utilisé pour déterminer des cinétiques de nucléation à l’aide de modèles probabilistes. Ensuite, nous avons adapté des systèmes microfluidiques à des grands équipements synchrotron, ce qui donne la possibilité de réaliser de la diffusion de rayons X aux petits angles. Ces deux approches complémentaires permettent alors d’obtenir des informations à l’échelle macroscopique et à l’échelle microscopique, et ainsi de participer à l’éclaircissement des zones d’ombre qui demeurent autour des mécanismes de nucléation. / Nucleation is the first step of crystallization and is the step that will give the final properties of crystals. It is therefore interesting to understand the mechanisms of this step. Nowadays, two theories exist : the classical nucleation theory and the two step theory. In the classical nucleation theory, monomers are added one by one to directly form crystalline clusters. In the two step theory, there is a first step of densification, in which monomers form dense amorphous aggregates and a second step of structuration in which aggregates arrange themselves to form crystalline clusters. It seems that for proteins, the two step mechanism is obtained by an increase of concentration fluctuations in solution, that happens when experimental conditions get closer to spinodal decomposition. In this study, experimental microfluidic devices are developped to study these mechanisms. Firstly, we developped microfluidic devices to determine nucleation kinetics with probabilistic models. Then, we coupled microfluidic devices with small angle X rays scattering from synchrotron sources. These two complementary studies give information on nucleation mechanisms on the macroscopic and microscopic scales.

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