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Ionic liquids (ILs) are highly tailorable materials with unique physical and chemical properties that set them apart from conventional organic solvents. As the library of readily accessible ILs continues to grow, so too does their relevance in applications ranging from material processing to electrochemical energy storage as solvents capable of accessing new chemistries disallowed by traditional chemicals. While a great deal of interest has been directed towards imidazolium and quaternary ammonium based ionic liquids, there are other understudied classes of cations which have potentially favorable properties for energy related applications. One such class is that with boronium cations. These cations have a unique structure with a formally negative boron flanked by positive nitrogens. This inherently zwitterionic structure presents interesting possibilities for electrochemical applications. To date only a handful of boronium cation-based ionic liquids have been thoroughly characterized despite exhibiting impressive electrochemical stabilities (> 5.0 V). In the present study we synthesized a series of ILs with novel boronium cations coupled with the bis(trifluoro-methanesulfonyl)imide anion. We then characterized the electrochemical and physical properties of these boronium ionic liquids by techniques such as cyclic voltammetry, broadband dielectric spectroscopy, oscillatory shear rheology, and thermogravimetric analysis. We will discuss how systematic variations in boronium cation structure impacted electrochemical and physical properties.
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A generalized machine learning model for predicting ionic conductivity of ionic liquids
Ionic liquids are currently being considered as potential electrolyte candidates for next-generation batteries and energy storage devices due to their high thermal and chemical stability. However, high viscosity and low conductivity at lower temperatures have severely hampered their commercial applications. To overcome these challenges, it is necessary to develop structure–property models for ionic liquid transport properties to guide the ionic liquid design. This work expands our previous effort in developing a machine learning model on imidazolium-based ionic liquids to now include ten different cation families, representing structural and chemical diversity. The model dataset contains 2869 ionic conductivity values over a temperature range of 238–472 K collected from the NIST ILThermo database and literature values for 397 unique ionic liquids. The database covers 214 unique cations and 68 unique anions. Three machine learning models, namely multiple linear regression, random forest, and extreme gradient boosting are applied to correlate the ionic liquid conductivity data with cation and anion features. Shapely additive analysis is performed to glean insights into cation and anion features with significant impact on ionic conductivity. Finally, the extreme gradient boosting model is used to predict the ionic conductivity of ionic liquids from all the possible combinations of unique cations and anions to identify ionic liquids crossing the ionic conductivity threshold of 2.0 S m −1 .
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- Award ID(s):
- 1706978
- PAR ID:
- 10357429
- Date Published:
- Journal Name:
- Molecular Systems Design & Engineering
- ISSN:
- 2058-9689
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D2ME00046F
