(337cp) Data-Driven Design and Characterization of AHA Ionic Liquids for CO2 Capture

Research Interests: Chemical Separations, Gas Separations, Experimental Thermodynamics, Phase Equilibria, Ionic Liquids, Absorption, Machine Learning, Molecular Design

Abstract:

Anthropogenic carbon dioxide emissions and resulting climate change represent an existential threat to society. However, deployment of low carbon energy sources may struggle to feasibly match the growing world demand in the near-term. Furthermore, low-carbon substitutes to many high emission industrial processes have yet to reach technological maturity for deployment. For these reasons, efficient point source carbon capture technologies must be developed to decarbonize these sectors. Aqueous amine-based solvents have been proposed as one such technology,¹ but suffer from toxic emissions due to solvent volatility and degradation,² as well as energy intensive regeneration due to unfavorable reaction thermodynamics³ and solvent and water evaporation⁴. Aprotic N-heterocyclic anion-based ionic liquids (AHA ILs) are a class of ionic liquids which reversibly react with CO₂ and have been proposed as a substitute for aqueous amines due to their high thermal stability, low corrosivity, negligible vapor pressure, and tunability.

The large variety of possible AHA ILs which may be synthesized presents an opportunity to find an optimal candidate for many diverse CO₂ capture scenarios. This potentially gives us the ability to explore complex tradeoffs between key chemical and physical properties; however, the expansive design space and nonlinear relationships for these compounds are difficult for the designer to navigate. For post-combustion capture from NGCC, a sensitivity analysis of the most influential IL properties for performance has been performed by Seo et al., identifying optimal ranges for solvent CO₂ capacity, density, viscosity, and heat capacity⁵. Machine learning models have been developed by our group to predict the latter three properties of AHA ILs in previous work,⁶ but gaps remain in prediction of CO₂ capacity and meaningful implementation of these models toward new solvent discovery.

In this work, we develop a framework to apply first principles calculations toward AHA IL CO₂ capacity prediction, use this framework and that of our previous work to identify potentially high performance ILs, and finally synthesize and test the performance of these species. We have performed over 700 density functional theory calculations to estimate the enthalpy of the CO₂ reaction with 258 unique anions. We implement a linear enthalpy correction and have fit CO₂ uptake predictions to experimental data with an average error 1.5 times the experimental uncertainty. Finally, using these models we have identified and characterized several IL candidates whose properties are better than the economically competitive benchmark IL, [P₂₂₂₈][2CNPyr].

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(5) Seo, K.; Tsay, C.; Hong, B.; Edgar, T. F.; Stadtherr, M. A.; Baldea, M. Rate-Based Process Optimization and Sensitivity Analysis for Ionic-Liquid-Based Post-Combustion Carbon Capture. ACS Sustainable Chem. Eng. 2020, 8 (27), 10242–10258. https://doi.org/10.1021/acssuschemeng.0c03061.

(6) Keller, A. N.; Kelkar, P.; Baldea, M.; Stadtherr, M. A.; Brennecke, J. F. Thermophysical Property Prediction in Anion-Functionalized Ionic Liquids for CO₂ Capture. Manuscript in Preparation. 2023.