TY - JOUR
T1 - Thermal Hazard of Ionic Liquids
T2 - Modeling Thermal Decomposition Temperatures of Imidazolium Ionic Liquids via QSPR Method
AU - Zhao, Xinyue
AU - Pan, Yong
AU - Jiang, Juncheng
AU - Xu, Shuangyan
AU - Jiang, Jiajia
AU - Ding, Li
N1 - Publisher Copyright:
© 2017 American Chemical Society.
PY - 2017/4/12
Y1 - 2017/4/12
N2 - 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 (Td) 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 Td 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 Td 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.
AB - 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 (Td) 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 Td 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 Td 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.
UR - http://www.scopus.com/inward/record.url?scp=85019988942&partnerID=8YFLogxK
U2 - 10.1021/acs.iecr.6b04762
DO - 10.1021/acs.iecr.6b04762
M3 - 文章
AN - SCOPUS:85019988942
SN - 0888-5885
VL - 56
SP - 4185
EP - 4195
JO - Industrial and Engineering Chemistry Research
JF - Industrial and Engineering Chemistry Research
IS - 14
ER -