Brunsteiner, Michael; (2003) Interactions between crystal surfaces in solution and agglomeration: A theoretical approach. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Agglomeration can have a crucial impact on the yield of crystallisation processes and the product quality. In this thesis molecular scale modelling is used to gain insights into the mechanism of crystal agglomeration and factors that determine its progress. The first study analyses the use of the attachment energy model to predict which surfaces of a crystal would be observed and hence the crystal morphology. It is shown that comparatively simple model potentials suffice to provide reasonably accurate morphology predictions within the limitations of the neglect of solvent. A classical empirical force field for potash alum (KAl(S0₄)₂.12H₂O) is developed. After having established its capability to reproduce experimentally determined properties of the crystal bulk and the solution, different potash alum crystal faces in contact with aqueous solution are modelled via Molecular Dynamics simulations. A range of different methods of modelling polar crystal surfaces, including a novel one, are investigated. The results are used to rationalise experimental results quantifying the agglomerative strength of PA crystallites as a function of super-saturation and the structure of the crystal faces. Common models for the theoretical prediction of crystal agglomeration include an efficiency parameter which is essentially a material property and a functional of the average force between two particles in solution. A set of Molecular Dynamics simulations of potassium chloride nano-crystallites in aqueous KCl solution is performed in order to establish whether it is possible to obtain reproduceable forces using an explicit water model and an extended system geometry to maintain constant chemical potential of the solution in between two crystal surfaces and a bulk phase. Although the results highlight some interesting aspects, and can give qualitative explanations of agglomeration tendencies, quantitative predictions of agglomeration will require further research.

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