(71c) COSMO-RS Re-Parameterization for Accurately Predicting Deep Eutectic Solvent-Fluorinated Compound Systems.

Greenhouse Gases (GHG) are vaporized compounds that absorb infrared radiation (IR) and act as heat sinks in Earth’s atmosphere. Among GHG, fluorinated compounds are considered the most potent with some species like R134a having global warming potential of 1,430, meaning it is 1,430 times more potent than CO₂. The aim of this research is to analyze the potential use of “A conductor like screening model” (COSMO) to evaluate a new class of green solvents called deep eutectic solvents (DES) to selectively capture R134a. DES are considered cheap, environmentally benign, and highly tunable as they are comprised of two or more components with nearly infinite combinations and compositions. They have been proven to be highly efficient at selectively absorbing CO_2, but little research has been done regarding other GHG’s like R134a. Due to the lack of experimental research regarding DES and fluorinated compounds, and the complex nature of the solvent-solute interactions, a complete re-tuning of COSMO-RS is undertaken to make the software a viable option for screening DES on a large scale to make experimental work efficient. Through initial benchmarking for DES and R134a systems it was concluded COSMO-RS produced errors too significant for reliable computations. Thus, a deep study and reparameterization of the softwares variables including but not limited to hydrogen bond temperature dependence, vander Waals temperature dependence, gas phase transition scalar, and misfit factor are undertake to make the method suitable for such systems. The parameterization was done through literature values with similar systems and significant increase in accuracy has been achieved. The work is beneficial to all researchers who utilize COSMO as it shows the inner workings and method for training it to work with exotic systems.