In the last two decades, new classes of designer liquids have emerged whose properties can be tuned through changes to the molecular level interactions. The first of these, ionic liquids (ILs), which have negligible emissions and/or significantly lower energy requirements than traditional methods, have received considerable attention for use as environmentally friendly solvents. However, production of ILs can be costly, hindering large-scale application. More recently, IL analogues, termed deep eutectic solvents (DESs) have emerged, that show similar physical and chemical properties to ILs but are much cheaper to produce, as they are constituted from natural and renewable non-toxic bioresources. DESs have great potential as environmentally friendly solvent alternatives that can be custom designed for specific separations and have already found application in important areas such as catalysis, polymer synthesis, gas separation and biomass treatment. A key challenge for designing these new systems lies in the fact that a near infinite number of DESs can be created. The proposed research focuses on developing a predictive methodology for designing optimal DESs for specific separations processes, that can be then leveraged to transform the chemical separations industry. Success of the proposal is likely to lead to a high throughput screening of environmentally friendly solvents for various applications.
Deep eutectic solvents are liquids of Lewis or Bronsted acids and bases that consist of a solid hydrogen bond donor (HBD; e.g., glycerol) and a solid hydrogen bond acceptor (HBA; frequently a salt with a higher melting point than an IL) that form a eutectic mixture through molecular association. While IL mixtures have fixed molar ratios dictated by their charges, DESs can be formed at different molar ratios of HBD and HBA, allowing effectively infinite combinations. DESs therefore offer a tremendous opportunity toward designing more sustainable solvents for separations; however, the time and cost of synthesis of such an impossibly large number of DESs creates a scientific challenge. As such, a predictive design methodology based upon the structure and interactions between the HBD, HBA and solutes, is crucial to optimize DESs for specific applications. The proposal seeks to address the question of DES design by developing molecular models based on group contribution statistical association fluid theory-variable range (GC-SAFT-VR). The proposed research focuses on identifying the level of theory suitable for a given eutectic solvent, determining the optimal parameterization process that leads to predictive, transferable parameters, and validating the models. To achieve these goals information from ab initio calculations and molecular simulations will be used to elucidate the underlying molecular interactions in DESs; derive and apply new developments in liquid state theory to describe the combined effects of dispersion, excluded volume, electrostatic interactions, charge delocalization, hydrogen bonding and polarity in DESs; and apply these new developments to experimental DES systems within a group-contribution framework based on the statistical associating fluid theory. Python libraries developed for the proposed research will be distributed as open-source software. A graduate student will be trained in advanced molecular-based equations of state and several undergraduate students will work on the project. New materials will also be developed for training activities and outreach to local area schools.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
