Predictive methods for the estimation of thermophysical properties of ionic liquids
authors Coutinho, JAP; Carvalho, PJ; Oliveira, NMC
nationality International
journal RSC ADVANCES
keywords EQUATION-OF-STATE; ARTIFICIAL NEURAL-NETWORKS; AIDED MOLECULAR DESIGN; NORMAL BOILING TEMPERATURES; RESIDUAL VOLUME APPROACH; IN-SILICO PREDICTION; SURFACE-TENSION; MELTING-POINTS; HEAT-CAPACITIES; PHYSICAL-PROPERTIES
abstract While the design of products and processes involving ionic liquids (ILs) requires knowledge of the thermophysical properties for these compounds, the massive number of possible distinct ILs precludes their detailed experimental characterization. To overcome this limitation, chemists and engineers must rely on predictive models that are able to generate reliable values for these properties, from the knowledge of the structure of the IL. A large body of literature was developed in the last decade for this purpose, aiming at developing predictive models for thermophysical and transport properties of ILs. A critical review of those models is reported here. The modelling approaches are discussed and suggestions relative to the current best methodologies for the prediction of each property are presented. Since most of the these works date from the last 5 years, this field can still be considered to be in its infancy. Consequently, this work also aims at highlighting major gaps in both existing data and modelling approaches, identifying unbeaten tracks and promising paths for further development in this area.
publisher ROYAL SOC CHEMISTRY
issn 2046-2069
year published 2012
volume 2
issue 19
beginning page 7322
ending page 7346
digital object identifier (doi) 10.1039/c2ra20141k
web of science category Chemistry, Multidisciplinary
subject category Chemistry
unique article identifier WOS:000307185300001
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  impact metrics
journal analysis (jcr 2019):
journal impact factor 3.119
5 year journal impact factor 3.098
category normalized journal impact factor percentile 59.04
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