Molecular Interactions and IR Spectral Insights in [Emim][Triflate] and 2-Alkoxyethanol Mixtures: Refractive Index Predictions Using Classical Theories and Machine Learning

This work investigates the molecular interactions in binary mixes of the ionic liquid [Emim][triflate] with the organic solvents 2-ethoxyethanol (2-EE) and 2-propoxyethanol (2-PE) by analyzing their refractive indices across varying temperatures and mole fractions. Ionic liquids possess low vapor pressure and thermal stability; when amalgamated with organic solvents, they yield combinations with superior properties, rendering them advantageous for synthesis, catalysis, and separation operations. The refractive index, a sensitive parameter for detecting molecular interactions, was complemented by IR spectral analysis to provide insights into the specific bonding interactions between the ionic liquid and the solvents. Additionally, various refractive index mixing rules such as Arago–Biot, Gladstone–Dale, and Lorentz–Lorenz were evaluated against the experimental data, comparing the accuracy levels. Furthermore, machine learning (ML) techniques were applied to develop a predictive model for the refractive index of these mixtures. The ML models were trained using a polynomial regression and Random Forest approaches, evaluating their performance using standard metrics such as the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R² using k-fold cross-validation. This combination of experimental data and ML offers a comprehensive method for predicting the behavior of such binary mixtures, helping to further understand their dynamics and applications.