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Structure‐based model for prediction of electrical conductivity of pure ionic liquids
Author(s) -
Wu KeJun,
Luo Haitang,
Yang Lingjian
Publication year - 2016
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.15429
Subject(s) - electrical resistivity and conductivity , conductivity , ionic liquid , data point , ionic bonding , training set , ion , set (abstract data type) , ionic conductivity , experimental data , materials science , approximation error , thermodynamics , biological system , chemistry , computer science , algorithm , mathematics , statistics , physics , engineering , electrical engineering , artificial intelligence , organic chemistry , electrode , electrolyte , biology , programming language , catalysis
A structure‐based method was proposed to estimate the electrical conductivity of ionic liquids covering wide ranges of temperature (238.15–484.1 K) and electrical conductivity (0.0001524–19.3 S/m) based on experimental data collect from literature from 1998 to 2015. The influences of temperature and ion structure on electrical conductivity were also discussed. The mean absolute percentage error between the calculated and literature data was 6.02%, with 6.12% for the training set (1978 data points, 177 ILs) and 5.10% for the test set (217 data points, 11 ILs). © 2016 American Institute of Chemical Engineers AIChE J , 62: 3751–3762, 2016