Thermal Hazard of Ionic Liquids: Modeling Thermal Decomposition Temperatures of Imidazolium Ionic Liquids via QSPR Method

Abstract

Thermal hazard, which is closely related to the potential fire risk under high temperature, has become one of the most important characteristics of various ionic liquids (ILs). This study proposed a quantitative structure–property relationship (QSPR) model to predict the thermal decomposition temperature (T d) of imidazolium ILs from their molecular structures. Not only the descriptors for single cation and anion but also those for describing their interactions were considered to numerically represent the structure characteristics of ILs. Genetic algorithm-based multiple linear regression was used to select the most statistically effective descriptors on the T d of imidazolium ILs. The resulted model is a multilinear equation with seven variables, including two descriptors for cations, four descriptors for anions, and one interaction descriptor. The developed model was rigorously validated using multiple strategies and further extensively compared to other previously published models. The results demonstrated the robustness, validity, and satisfactory predictivity of the proposed model. The predominant structure characteristics responsible for T d were also identified through model interpretation. The proposed model could be reasonably expected to reliably predict the thermal hazard of novel imidazolium ILs and provide guidance for prioritizing design and manufacture of safer ILs with desired properties.

Prediction of the Auto-Ignition Temperatures of Binary Miscible Liquid Mixtures from Molecular Structures

“…The key step in QSPR modeling is to find the optimal descriptors that make a significant contribution to the AITs of binary miscible liquid mixtures. The well-known genetic algorithm (GA) is a powerful optimization method to solve this problem and has been successfully applied to feature selection in previous QSPR studies [22,23,24]. In this study, genetic algorithm along with multiple linear regression (GA-MLR) was used to find the optimal subset that accurately represented the relationships between molecular structures and AITs of binary liquid mixtures.…”

Effect of ionic composition on thermal properties of energetic ionic liquids

“…External validation was performed by dividing the 316 data set randomly into a training set and an external test set, similar to the methods in previous report. 24 The training set (250 ILs, 80% of the data set) was used for descriptor selection and model development and the test set (67 ILs, 20% of the data set) was used for model validation.…”

“…The multiple linear regressions have been widely used to establish QSPR-based expressions such as models that can predict the decomposition temperature or viscosity of ionic liquids. 24,29 Among the collected data for 316 materials, 250 were randomly selected for use as the training set, and the remaining 67 were used as the test set. The established QML and QMG models for the calculation of the detonation velocity (Dv) of EILs, through the least-mean square algorithm (LS) and genetic algorithm (GA), respectively, are represented as Eq.…”

“…Since the quantitative structure-property relationship (QSPR) method is based on the conjecture that differences in the properties of chemicals is determined by differences in their chemical structures, 22 it has been generated a simulation model with which to predict the toxicity 23 and decomposition temperature (T d ) of some ILs 24 without requiring their synthesis.…”

“…Therefore, we explored a QSPR model that predicts the thermal characteristics of EILs using simple structural information on cation and anion pair without using ΔH f and ρ. Although there is a report on the QSPR model to predict the thermal decomposition temperature (T d ) of imidazolium ILs, 24 there has been no report on the energetic properties for a wide range of energetic ionic liquids including azoles and acyclic energetic salts. Herein, we report a QSPR model for the prediction of detonation characteristics for ionic liquids, through multiple linear regression derived from over 300 energetic ionic liquids.…”