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

ABSTRACT: 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.

SUBMITTER: Toots K

PROVIDER: S-EPMC9323540 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Electrochemical amperometric sensors require a constant or varying potential at the working electrode that drives redox reactions of the analyte for detection. The interfacial redox reaction(s) can result in the formation of new chemical products that could change the initial condition of the electrode/electrolyte interface. If the products are not inert and/or cannot be removed from the system such that the initial condition of the electrode/electrolyte interface cannot be restored, the sensor signal baseline would consequently drift, which is problematic for the continuous and real-time sensors. By setting the electrode potential with the periodical ON-OFF mode, electrolysis can be forestalled during the off mode which can minimize the sensor signal baseline drift and reduce the power consumption of the sensor. However, it is known that the relaxation of the structure in the electrical double layer at the ionic liquid/electrode interface to the steps of the electrode potential is slow. This work characterized the electrode/electrolyte interfacial relaxation process of an ionic liquid based electrochemical gas (IL-EG) sensor by performing multiple potential step experiments in which the potential is stepped from an open circuit potential (OCP) to the amperometric sensing potential at various frequencies with different time periods. Our results showed that by shortening the sensing period as well as extending the idle period (i.e., enlarge the ratio of idle period versus sensing period) of the potential step experiments, the electrode/electrolyte interface is prone to relax to its original state, and thus reduces the baseline drift. Additionally, the high viscosity of the ionic liquids is beneficial for electrochemical regeneration via the implementation of a conditioning step at zero volts at the electrode/electrolyte. By setting the working electrode at zero volts instead of OCP, our results showed that it could further minimize the baseline drift, enhance the sensing signal stability, and extend the functioning lifetime of a continuous IL-EG oxygen sensor.

Dataset Information

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

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