(176f) Computer Aided Ionic Liquid Design (CAILD) Method for Development of Green Ionic Liquids

Solvents comprise two thirds of all industrial emissions. Traditional organic solvents easily evaporate into the atmosphere as they have high vapor pressure and are linked to a host of negative environmental effects including climate change, urban air-quality and human illness. Room temperature ionic liquids (RTIL), on the other hand, have low vapor pressure and are rarely flammable or explosive, thereby presenting fewer environmental risks and health hazards. For this reason they are considered as ‘green’ solvents. RTILs are molten salts that exist as liquids at relatively low temperatures and have unique properties. Ionic liquids consist of a large organic cation and charge-delocalized inorganic or organic anion of smaller size and asymmetric shape. The organic cation can undergo unlimited structural variations through modification of the alkyl groups attached to the side chain of the base cation skeleton (Fig. 1) and most of these structural variations are feasible, from chemical synthesis point of view, due to the easy nature of preparation of their components. Functionally, ionic liquids can be tuned to impart specific desired properties by switching anions /cations or by incorporating functionalities into the cations/anions. It is estimated that theoretically more than a trillion ionic liquid structures can be formed. Due to their tunable nature, these molten salts have the potential to be used as green solvents for a variety of applications.

This paper presents a computer aided molecular (IL) design (CAILD) methodology with an aim to design optimal task specific ionic liquid structures for different applications. We utilize group-contribution based ionic liquid property prediction models (forward problem) within a mathematical programming framework to reverse engineer functional ionic liquid structures. The CAILD model is formulated as a mixed integer non-linear programming (MINLP) optimization problem and the solution results in optimal ionic liquid cation-anion combination for a given application. The formulation of the optimization model including representation of IL structural constraints will be discussed in detail. Specific case study results on applications will be presented.