Database and new models based on a group contribution method to predict the refractive index of ionic liquids

Area of Science:

• Physical Chemistry
• Materials Science

Background:

• Refractive index is a critical physical property for separation and purification processes.
• Ionic liquids (ILs) are versatile materials with applications in various chemical industries.

Purpose of the Study:

• To establish a comprehensive database of ionic liquid refractive index data.
• To develop and evaluate predictive models for ionic liquid refractive indices.

Main Methods:

• Collected approximately 2138 refractive index data points for ILs from 1996 to 2014.
• Employed the Group Contribution-Artificial Neural Network (GC-ANN) model.
• Utilized the Group Contribution (GC) method for prediction.

Main Results:

• The GC-ANN model achieved an Average Absolute Relative Deviation (AARD) of 0.179%.
• The GC method yielded an AARD of 0.628%.
• Both models accurately predicted refractive indices across temperatures ranging from 283.15 K to 368.15 K.

Conclusions:

• The GC-ANN model offers an effective approach for estimating IL refractive indices.
• The GC method provides a simple and broadly applicable alternative.
• Both models are accurate and efficient for predicting IL refractive indices.