Predicting Thermal Decomposition Temperature of Binary Imidazolium Ionic Liquid Mixtures from Molecular Structures.

Area of Science:

• Materials Science
• Chemical Engineering
• Computational Chemistry

Background:

• Ionic liquids (ILs) are recognized as "designer solvents" due to their tunable physicochemical properties.
• Thermal stability, indicated by decomposition temperature, is crucial for assessing the fire hazard of engineered ILs.
• Predicting the thermal stability of binary IL mixtures is essential for their safe application.

Purpose of the Study:

• To develop a quantitative structure-property relationship (QSPR) model for predicting the 5% onset decomposition temperature (Td,5%onset) of binary imidazolium IL mixtures.
• To establish a reliable method for assessing the fire hazard associated with IL mixtures.
• To provide insights into the relationship between the structure of IL mixtures and their thermal stability.

Main Methods:

• Utilized in silico design and data analysis descriptors, along with norm indices, to encode structural characteristics of binary IL mixtures.
• Employed a combination of the genetic algorithm and multiple linear regression (MLR) for optimal descriptor subset screening.
• Developed and validated a four-variable MLR model to predict Td,5%onset.

Main Results:

• The developed QSPR model achieved an average absolute error (AAE) of 12.673 K on the external test set.
• Rigorous model validation confirmed satisfactory robustness and predictive capability.
• The model effectively correlates structural features with the thermal decomposition behavior of binary IL mixtures.

Conclusions:

• The study presents a novel and reliable approach for predicting the thermal stability of binary imidazolium IL mixtures.
• The developed QSPR model offers a valuable tool for the in silico assessment of IL fire hazards.
• This work contributes to the safer design and application of ionic liquids in various fields.