The binding mechanisms of molecules to cyclodextrins continues to be studied to better explain the interactions occurring. The majority of published models focus on one-to-one molecular binding thermodynamics to explain experimental results. They rely on physical concepts of energies and forces to guide the actions of molecules expressed mathematically in terms of differential and non-linear equations. These models are limited in scope due to their complexity and are not easily expanded to study many diverse analytes. Conversely, cellular automata uses simple mathematical idealizations of systems governed by deterministic and probabilistic rules that are easily adaptable to many types of molecular interactions. The primary goal of this research is to develop a model that is easy to use in the prediction of beta-cyclodextrin chromatographic separations of enantiomers. The model uses variegated square cells to simulate the physical environment of the molecules involved, evolving by a series of discrete time-steps referred to as iterations. Governing probabilistic rules define the physical and chemical interactions. Rules are randomly applied to all the cells of the system during each iteration and the system is updated accordingly. Micro and macro visual analysis is possible in addition to statistical output. Results demonstrate the model’s capability to use probabilistic rules for breaking of analyte-to-cyclodextrin complexes that were correlated to published experimentally determined equilibrium constants. The model was further expanded to predict the strength of interactions between enantiomer pairs to beta-cyclodextrin and their potential separation. The model accurately predicted the order of strength for six enantiomer pairs. To truly predict chromatographic separation of enantiomers, the model was expanded from one-to-one interactions between enantiomers and beta-cyclodextrin to a larger modeled chromatographic scale. At this scale enantiomer separation was modeled and evaluated for peak resolution and selectivity while varying column temperature, mobile phase pH and flow, and injection volumes. All results agreed well with published laboratory results. With the cost of research and development increasing, ongoing budget cuts, and the rush to get products to market first, an analytical model that can run multiple chromatographic simulations in minutes versus days could prove a valuable tool to many industries.
| Identifer | oai:union.ndltd.org:vcu.edu/oai:scholarscompass.vcu.edu:etd-3667 |
| Date | 29 March 2012 |
| Creators | Darren, DeSoi |
| Publisher | VCU Scholars Compass |
| Source Sets | Virginia Commonwealth University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | © The Author |
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