Electrochemical Studies on Uranyl(VI) Chloride Complexes in 1-Butyl-3-methyl- imidazolium Based Ionic Liquids and Their Application to Pyro-Reprocessing and Treatment of Wastes Contaminated with Uranium

Ikeda, Yasuhisa; Asanuma, Noriko; Ohashi, Yusuke

doi:10.5772/14852

Cited by 2 publications

(1 citation statement)

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“…Ionic liquids (ILs) are a special class of chemical compounds that consist of ions and commonly refer to organic salts with a low melting point [ 1 ]. The combination of exclusive properties of ionic liquids, such as extremely low vapor pressure, high polarity and thermal stability, has been a major incentive behind their study as solvents and electrolytes in synthesis [ 2 , 3 , 4 ], catalysis [ 2 , 5 , 6 ], electrochemistry [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ], extraction and separation chemistry [ 4 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ] and in many other applications [ 22 , 23 , 24 , 25 , 26 ]. Applications of ionic liquids often involve complex molecular systems where IL is in contact with other organic compounds.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Quantitative Structure–Property Relationships as a Function of Ionic Liquid Cations for the Gas-Ionic Liquid Partition Coefficient of Hydrocarbons

Toots

Sild

Leis

et al. 2022

IJMS

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Ionic liquids (ILs) are known for their unique characteristics as solvents and electrolytes. Therefore, new ILs are being developed and adapted as innovative chemical environments for different applications in which their properties need to be understood on a molecular level. Computational data-driven methods provide means for understanding of properties at molecular level, and quantitative structure–property relationships (QSPRs) provide the framework for this. This framework is commonly used to study the properties of molecules in ILs as an environment. The opposite situation where the property is considered as a function of the ionic liquid does not exist. The aim of the present study was to supplement this perspective with new knowledge and to develop QSPRs that would allow the understanding of molecular interactions in ionic liquids based on the structure of the cationic moiety. A wide range of applications in electrochemistry, separation and extraction chemistry depends on the partitioning of solutes between the ionic liquid and the surrounding environment that is characterized by the gas-ionic liquid partition coefficient. To model this property as a function of the structure of a cationic counterpart, a series of ionic liquids was selected with a common bis-(trifluoromethylsulfonyl)-imide anion, [Tf2N]−, for benzene, hexane and cyclohexane. MLR, SVR and GPR machine learning approaches were used to derive data-driven models and their performance was compared. The cross-validation coefficients of determination in the range 0.71–0.93 along with other performance statistics indicated a strong accuracy of models for all data series and machine learning methods. The analysis and interpretation of descriptors revealed that generally higher lipophilicity and dispersion interaction capability, and lower polarity in the cations induces a higher partition coefficient for benzene, hexane, cyclohexane and hydrocarbons in general. The applicability domain analysis of models concluded that there were no highly influential outliers and the models are applicable to a wide selection of cation families with variable size, polarity and aliphatic or aromatic nature.

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