Prediction of the thermal decomposition temperatures of imidazolium ILs based on norm indexes

Duan, Weijia; Pan, Yong; He, Hongpeng; Zhao, Shuai; Zhao, Xinyan; Jiang, Juncheng; Shu, Chi‐Min

doi:10.1016/j.molliq.2020.113780

Cited by 4 publications

(5 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering that the thermal stability is related to the various interactions of the binary IL system, the descriptors for multiple cation–anion interactions are further proposed based on the original calculation for single ILs, , which are defined as follows: …”

Section: Materials and Methodsmentioning

confidence: 99%

“…Recently, Duan et al developed the QSPR model for 158 different imidazolium ILs via norm index descriptors. 29 The descriptors they employed not only characterize the structures of anions and cations but also describe the interaction between anions and cations.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, the norm index descriptor was utilized to describe the interaction between cations and anions. 29,32,33 The QSPR modeling was carried out in this study for a total of 31 different imidazolium IL mixtures, and various rigorous model validation strategies were then performed to ensure the model performance. Through our work, it was expected to provide an efficient approach to evaluate the thermal stability of IL mixtures and provide some guidance for experimental design and mixing of ILs.…”

Section: Introductionmentioning

confidence: 99%

“…The results demonstrated satisfactory prediction of T d values of IL/MOF composites, and they found that ILs with the [NTf 2 ] − anion possess higher thermal stability in the composites. Recently, Duan et al developed the QSPR model for 158 different imidazolium ILs via norm index descriptors . The descriptors they employed not only characterize the structures of anions and cations but also describe the interaction between anions and cations.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, in silico design and data analysis (ISIDA), which represents the molecular structure based on a finite number of topological fragments, , was employed to explore the relationship between target properties and ionic structures. Besides, the norm index descriptor was utilized to describe the interaction between cations and anions. ,, The QSPR modeling was carried out in this study for a total of 31 different imidazolium IL mixtures, and various rigorous model validation strategies were then performed to ensure the model performance. Through our work, it was expected to provide an efficient approach to evaluate the thermal stability of IL mixtures and provide some guidance for experimental design and mixing of ILs.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Pan

Meng

et al. 2021

ACS Omega

Self Cite

View full text Add to dashboard Cite

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 fourvariable 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.

show abstract