Research Interests in reaction engineering, reactor design, and applied thermodynamics. My doctoral work focuses on the targeted selection of novel solvents, catalysts, and reaction conditions for the upgrading of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA), a platform chemical useful for the production of bioplastics. This work utilizes thermodynamic modeling, particularly Hansen Solubility Parameters, as well as fundamental knowledge pertaining to reaction kinetics and intermolecular interactions to create an efficient pathway for FDCA synthesis.
My current research fits with my passions for advancing sustainability and minimizing the environmental impact of chemical processes at the industrial scale. Leveraging biomass feedstocks, optimizing reactor conditions to reduce waste, or substituting green solvents and catalysts are all techniques for achieving this goal. I intend to pursue a career focused on understanding the hurdles present in the implementation of such processes and overcoming them in ways that maintain efficiency and economic viability.
Related Oral Presentation: Hansen Solubility Parameters for the Prediction of 2,5-Furandicarboxylic Acid Solubility in Aqueous/Organic Solvent Mixtures at 293 K
Present Work
Hansen solubility parameters (HSPs) are an adaptable and applicable thermodynamic model for predicting the solubility of a solute in varying solvents. In this work, HSPs are used to assess the solubility of 2,5-furandicarboxylic acid (FDCA) in nine pure, eight aqueous/organic binary, and three ternary solvent blends containing H2O, acetonitrile, γ-valerolactone, γ-butyrolactone, ethanol, methanol, sulfolane, dimethyl sulfoxide (DMSO), and tetrahydrofuran (THF) at 293 K. FDCA is derived from biomass and can be upgraded to make useful biopolymers, but has low solubility in traditionally used solvents such as water. Thus, identifying compounds that can solubilize significant amounts of FDCA could allow for lower production costs and greater accessibility to sustainable bioplastics. Use of the derived Radius of Interaction (Ri,j">Ri,j) parameter allows for accurate prediction of solvent ratios for the highest FDCA solubility within a given combination of solvents. Such a predictive model addresses one of the significant challenges present in FDCA production, its low solubility, by allowing for targeted selection of high-solubility solvents.
The Ri,j">Ri,j parameter, which compares individual HSP values of the solvent(s) and solute, was found to accurately predict the solvent compositions yielding the highest FDCA solubility in over 80% of binary and ternary blends studied. Notably, mixtures containing DMSO were found to have the lowest Ri,j">Ri,j values and the highest FDCA solubility values (30.7 wt% maximum), followed by mixtures containing THF (7.2 wt% maximum). All other binary solvent mixtures investigated had less than 2.5 wt% of FDCA solubilized. Even at these lower solubilities, maxima still correlated with the minimum Ri,j">Ri,j and reported higher solubilization than either pure water or pure organic. This same correlation was observed for ternary solvent blends, which saw solubilities exceeding 20 wt% at low values of Ri,j">Ri,j. Such solubility values are amongst the highest ever presented in the literature. Building on these trends observed in the experimental data, a MATLAB-based optimization code was developed and found successful in minimizing the Ri,j">Ri,j of a solvent blend to maximize FDCA solubility in binary and ternary aqueous solvents.
