Theory-based screening of ionic liquids for digestion of extra-terrestrial regolith
Creators: name: Rexhepi, Fatlum
Publisher: name: Washington State University
Year: 2025
Subjects: Hansen Solubility Sphere
Ionic Liquids
Lunar Regolith
Metal Oxides
Solubility Parameter
Machine Learning

description: Lunar regolith is primarily composed of silicates and metal oxides, which are excellent in situ resources for metals as well as oxygen. Developing a technology for extracting metals from regolith would enable additive manufacturing of components for sustainable space exploration. This thesis focuses on identifying solvents for in situ extraction of metals from metal oxides found in lunar regolith. In particular, this study focuses on room temperature ionic liquids (RTIL), a class of salts that exist in the liquid state at room temperature. RTILs are excellent solvent and show significant promise in digesting Lunar regolith in a cyclical manner where they can be recovered at the end of the process. However, experimentally identifying an IL with ideal properties, out of tens of thousands possible formulations, is an expensive and time-consuming process. In this study, machine learning tools were used to develop a model to predict the Hildebrand’s solubility parameter, which is widely used for predicting miscibility of solvents in specific solutes. This model, augmented by experiments to observe solubility of metal oxides in regolith, was used to develop a Hansen Solubility Sphere analysis that can identify ionic liquids to digest specific metal oxides.Measured solubility parameters of different ILs, augmented with theoretical calculations, were used to create a large database. Using statistical and data science methods, we investigated the functional relationship of solubility parameters of ionic liquids with their intrinsic thermophysical properties. We established correlation between solubility parameter and other properties of ILs such as molar volume, enthalpy of vaporization, and heat capacity among others. Machine Learning (ML) algorithms were used to fit predictive models which are able to forecast solubility parameter of ILs. Three different models were fit using Support Vector Machine (SVM), Decision Tree (DT), and Random Forest. Out of these three models, through careful statistical and performance analysis, SVM–regression approach was employed to the properties available in the database to perform a regression analysis. A key feature of the developed model is the ability to predict solubility parameter based on the heat capacity, which has been widely measured for ionic liquids. Using this approach, we were able to down select ILs with promising capabilities for regolith digestion.
description: The trends captured by SVM were validated by employing Hansen Solubility Sphere method and assisted by experimental results. A list of 9 ILs were selected and the solubility of TiO2, SiO2 and ZnO in these ILs was analyzed through experimentation. This list of ILs chemically represent a varied space with regards to the ability to dissolve the solvents, based on the solubility parameters. From recorded experimental results we were able to find the maximum radius ?0 of Hansen Solubility Sphere that represents a threshold value for solvating metal oxides. Positive solubility results observed for solutions of ZnO in four different ILs (C4mim Ac, C4mim HSO4, C2mim Cl, and C2mim Br) were recorded.