A step towards the a priori design of ionic liquids

Literature Information

Publication Date 2013-06-10
DOI 10.1039/C3CP50521A
Impact Factor 3.676
Authors

Claire Ashworth, Tom Welton, Patricia A. Hunt


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Abstract

A range of methods for the computational prediction of experimentally derived α and β Kamlet–Taft parameters, indicators of hydrogen bond (H-bond) acidity and basicity for ionic liquids (ILs) have been explored. Most usefully, a good correlation has been established between several simple and easily computed quantities which allow for a “quick bench-top” evaluation. More accurate, but also more sophisticated methods employing TD-DFT calculations involving the Kamlet–Taft dyes have been examined and evaluated. Importantly, these techniques open up the opportunity for pre-screening and a priori prediction of properties for ILs not yet synthesised. A key fundamental insight into IL H-bonds has been the determination of an estimate for the energy associated with replacing both neutral molecules in a H-bond with ionic molecules, thus forming the “doubly ionic” H-bond found in ILs.

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Source Journal

Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
Articles per Year: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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