(152i) Property Prediction of Amine-Functionalized Ionic Liquids for Multi-Scale Carbon Capture Design and Optimization

Atmospheric carbon dioxide remediation is one of the great challenges of the current generation. However, carbon-intensive processes are likely to remain industrially relevant. For this reason, efficient point source carbon capture technologies are a vital tool to slow the increase in atmospheric CO₂ concentration. 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 heat 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 that reversibly react with CO₂ and that have been proposed as a substitute for aqueous amines due to their high thermal stability, low corrosivity, negligible vapor pressure, and tunability.

In particular, the large variety of AHA ILs that may be synthesized presents an opportunity to find optimal candidates for each of the many CO₂ capture applications, covering a wide range of CO₂ concentrations. This presents an ideal opportunity for combined materials/process optimization, however, the expansive design space and nonlinear relationships for these compounds are difficult for the designer to navigate. Many of the most influential IL properties for the performance of a CO₂ capture operation—namely CO₂ capacity, density, viscosity, and heat capacity⁵—can be predicted reasonably well with machine learning models for ILs in general, but these models tend to perform inadequately when applied to the AHA IL dataset.

In this work, we investigate the relationship between the relevant CO₂ capture properties of AHA ILs with tetra-alkylphosphonium cations using quantum chemical and group contribution-based descriptors and supervised learning methods. We have data-mined and curated the relevant thermophysical property and CO₂ uptake data as well as performed and experimentally validated quantum chemical calculations to augment the CO₂ uptake dataset. Finally, we probe the effect of various dimension reduction techniques and machine learning methods on model performance and identify an optimal set of descriptors and a model for each property of interest. This will facilitate a future study in combined process and materials optimization to simultaneously find the optimal IL absorbent and capture process design for a given application.

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