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Doctor of Philosophy (PHD)
Chemistry (Arts and Sciences)
Date of Defense
First Committee Member
Second Committee Member
Third Committee Member
Fourth Committee Member
Due to the negative environmental impact of damaging organic solvents and the high-cost of chemical waste disposal, the search for alternative, renewable solvents is a top priority in the chemical industry. Part of a worldwide emphasis on improving sustainability, the push has led to increasing interest in a set of environmentally-friendly solvents: ionic liquids (ILs) and deep eutectic solvents (DES). These solvents, primarily composed of ions, provide numerous advantages from both chemical and environmental perspectives. Despite the excitement surrounding these solvents, a fundamental understanding of how they operate is still lacking. The goal of this thesis is to develop and apply computer models to both elucidate how these solvents function and to design the next generation of cost-effective “green” solvents. Studies performed within include (1) development of new computer models, i.e., force fields, that accurately predict experimental IL and DES data, (2) insight into an important Diels-Alder chemical reaction enhanced by ILs, and (3) development of new artificial intelligence tools (i.e., machine learning) that could potentially shift the paradigm by reducing the cost of the simulations with increased accuracy.
Force Field; Molecular Dynamics; Machine Learning; Ionic Liquids; Deep Eutectic Solvents
Doherty, Brian, "Development of Computational Tools for the Study of Ionic Liquids and Deep Eutectic Solvents" (2019). Open Access Dissertations. 2280.
Available for download on Thursday, April 29, 2021