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  • Title: Predicting Thermal Decomposition Temperature of Binary Imidazolium Ionic Liquid Mixtures from Molecular Structures.
    Author: He H, Pan Y, Meng J, Li Y, Zhong J, Duan W, Jiang J.
    Journal: ACS Omega; 2021 May 25; 6(20):13116-13123. PubMed ID: 34056461.
    Abstract:
    Ionic liquids (ILs) have been regarded as "designer solvents" because of their satisfactory physicochemical properties. The 5% onset decomposition temperature (T d,5%onset) is one of the most conservative but reliable indicators for characterizing the possible fire hazard of engineered ILs. This study is devoted to develop a quantitative structure-property relationship model for predicting the T d,5%onset of binary imidazolium IL mixtures. Both in silico design and data analysis descriptors and norm index were employed to encode the structural characteristics of binary IL mixtures. The subset of optimal descriptors was screened by combining the genetic algorithm with the multiple linear regression method. The resulting optimal prediction model was a four-variable multiple linear equation, with the average absolute error (AAE) for the external test set being 12.673 K. The results of rigorous model validations also demonstrated satisfactory model robustness and predictivity. The present study would provide a new reliable approach for predicting the thermal stability of binary IL mixtures.
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