Fast and non-destructive determination of water content in ionic liquids at varying temperatures by Raman spectroscopy and multivariate regression analysis

Date
2021-12-15
Authors
Li, Jingzhe
Farooq, Muhammad Qamar
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Elsevier
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Research Projects
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Chemistry
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Ames Laboratory
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Abstract
Imidazolium acetate ionic liquids (ILs) have been utilized as promising solvents in many applications that involve varying water content and temperature. These experimental variables affect the anion-cation intermolecular interactions, which in turn influence the performance of the ILs in these applications. This paper shows Raman spectroscopy can be used as an operando method to measure water content in IL solvents when simultaneous temperature changes may occur. The Raman spectra of 1-alkyl-3-methylimidazolium acetate ILs (alkyl chain length n = 2, 4, 6, 8) with varying water content (from 0.028 to 0.899 water mole fraction) and temperature (from 78.1 K to 423.1 K) were measured. Increasing the water content or decreasing the temperature of the tested ILs weakens the anion-cation intermolecular interactions. The water content of these ILs can be quantified even in conditions when the temperature is changing using Raman spectroscopy combined with multivariate regression analysis, including principal component regression (PCR), partial-least-squares regression (PLSR), and artificial neural networks (ANNs). The ANN model combined with partial-least-squares (PLS) achieved the highest prediction accuracy of water content in ILs at varying temperatures (RMSECV = 0.017, R2CV = 99.1%, RMSEP = 0.019, R2P = 98.8%, RPD = 8.93). Raman spectroscopy provides a potential fast non-destructive operando method to monitor the water content of ILs even in applications when the temperature may be simultaneously altered; this information can lead to the optimized use of these ILs in many applications.
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Keywords
Imidazolium acetate ionic liquids (ILs), Raman spectroscopy, Anion-cation intermolecular interactions, Principal component regression (PCR), Partial-least-squares regression (PLSR), Artificial neural networks (ANNs)
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