Neat ionic liquids versus ionic liquid mixtures: a combination of experimental data and molecular simulation

2019 ◽  
Vol 21 (42) ◽  
pp. 23305-23309 ◽  
Author(s):  
Andreia S. L. Gouveia ◽  
Carlos E. S. Bernardes ◽  
Elena I. Lozinskaya ◽  
Alexander S. Shaplov ◽  
José N. Canongia Lopes ◽  
...  
Keyword(s):  
Experimental Data ◽  
Ionic Liquid ◽  
Ionic Liquids ◽  
Molecular Simulation ◽  
Liquid Mixtures

Simple mixtures of ionic liquids (IL–IL mixtures) can become a promising approach for the substitution of task-specific ILs.

Molecular simulation of osmometry in aqueous solutions of the BMIMCl ionic liquid: a potential route to force field parameterization of liquid mixtures

2020 ◽  
Vol 22 (48) ◽  
pp. 28325-28338
Author(s):  
Debdas Dhabal ◽  
Tanmoy Patra
Keyword(s):  
Experimental Data ◽  
Aqueous Solution ◽  
Ionic Liquid ◽  
Aqueous Solutions ◽  
Force Field ◽  
Molecular Simulation ◽  
Osmotic Coefficient ◽  
Force Fields ◽  
Liquid Mixtures ◽  
Force Field Parameterization

By means of molecular simulation, the osmotic coefficient of aqueous solution of BMIMCl ionic liquid is calculated to compare with the experimental data and use that to optimize two popular force fields available in the literature for bulk ILs.

The properties of residual water molecules in ionic liquids: a comparison between direct and inverse Kirkwood–Buff approaches

2017 ◽  
Vol 19 (29) ◽  
pp. 18924-18937 ◽  
Author(s):  
Takeshi Kobayashi ◽  
Joshua E. S. J. Reid ◽  
Seishi Shimizu ◽  
Maria Fyta ◽  
Jens Smiatek
Keyword(s):  
Experimental Data ◽  
Molecular Dynamics ◽  
Ionic Liquid ◽  
Ionic Liquids ◽  
Molecular Dynamics Simulations ◽  
Liquid Mixtures ◽  
Water Molecules ◽  
Residual Water ◽  
Dynamics Simulations

Atomistic molecular dynamics simulations of aqueous ionic liquid mixtures were performed in order to compare the resulting Kirkwood–Buff integrals with experimental data and the corresponding integrals derived by an inverse Kirkwood–Buff approach.

Rapid relaxation NMR measurements to predict rate coefficients in ionic liquid mixtures. An examination of reaction outcome changes in a homologous series of ionic liquids

2021 ◽  
Author(s):  
Daniel C Morris ◽  
Stuart W Prescott ◽  
Jason B Harper
Keyword(s):  
Ionic Liquid ◽  
Ionic Liquids ◽  
Homologous Series ◽  
Rate Coefficient ◽  
Liquid Mixtures ◽  
Solvent Mixture ◽  
Rate Coefficients ◽  
Substitution Process

A series of ionic liquids based on the 1-alkyl-3-methylimidazolium cations were examined as components of the solvent mixture for a bimolecular substitution process. The effects on both the rate coefficient...

Eutectic ionic liquid mixtures and their effect on CO2 solubility and conductivity

RSC Advances ◽  
2015 ◽  
Vol 5 (63) ◽  
pp. 51407-51412 ◽  
Author(s):  
Anna S. Ivanova ◽  
Thomas Brinzer ◽  
Elliot A. Roth ◽  
Victor A. Kusuma ◽  
John D. Watkins ◽  
...  
Keyword(s):  
Ionic Liquid ◽  
Ionic Liquids ◽  
Binary System ◽  
Liquid Mixtures ◽  
Short Chain ◽  
Co2 Solubility

A simple binary system of compounds resembling short-chain versions of popular ionic liquids has been shown to have alloying properties.

Protein packaging in ionic liquid mixtures: an ecofriendly approach towards the improved stability of β-lactoglobulin in cholinium-based mixed ionic liquids

2020 ◽  
Vol 22 (26) ◽  
pp. 14811-14821 ◽  
Author(s):  
Anamika Sindhu ◽  
Sumit Kumar ◽  
Dibyendu Mondal ◽  
Indra Bahadur ◽  
Pannuru Venkatesu
Keyword(s):  
Ionic Liquid ◽  
Ionic Liquids ◽  
Aqueous Solutions ◽  
Liquid Mixtures ◽  
Improved Stability ◽  
Β Lactoglobulin

The present work demonstrates a pioneering approach for the packaging of β-LG with improved stability in the presence of aqueous solutions containing cholinium-based ionic liquid mixtures.

scholarly journals Lyotropic liquid crystal phase behavior of a cationic amphiphile in aqueous and non-stoichiometric protic ionic liquid mixtures

Soft Matter ◽  
2020 ◽  
Vol 16 (41) ◽  
pp. 9456-9470
Author(s):  
Dilek Yalcin ◽  
Calum J. Drummond ◽  
Tamar L. Greaves
Keyword(s):  
Ionic Liquid ◽  
Ionic Liquids ◽  
Liquid Crystal ◽  
Self Assembly ◽  
Liquid Mixtures ◽  
Lyotropic Liquid Crystal ◽  
Liquid Crystal Phase ◽  
Protic Ionic Liquids ◽  
Protic Ionic Liquid ◽  
Cationic Amphiphile

Protic ionic liquids (PILs) are the largest and most tailorable known class of non-aqueous solvents which possess the ability to support amphiphile self-assembly.

Dynamic wetting of imidazolium-based ionic liquids on gold and glass

2018 ◽  
Vol 20 (3) ◽  
pp. 2084-2093 ◽  
Author(s):  
I. Delcheva ◽  
D. A. Beattie ◽  
J. Ralston ◽  
M. Krasowska
Keyword(s):  
Experimental Data ◽  
Ionic Liquid ◽  
Ionic Liquids ◽  
Dynamic Wetting

Experimental data and theoretical fitting for ionic liquid wetting on application-relevant substrates.

A theoretical study on mixtures of amino acid-based ionic liquids

2018 ◽  
Vol 20 (15) ◽  
pp. 10213-10223 ◽  
Author(s):  
Cesar Herrera ◽  
Mert Atilhan ◽  
Santiago Aparicio
Keyword(s):  
Molecular Dynamics ◽  
Ionic Liquid ◽  
Ionic Liquids ◽  
Amino Acid ◽  
Molecular Dynamics Simulations ◽  
Theoretical Study ◽  
Liquid Mixtures ◽  
Microscopic Level ◽  
Dynamics Simulations

Ionic liquid mixtures containing amino acid anions are studied at the microscopic level using molecular dynamics simulations.

Interactions and structure of ionic liquids on graphene and carbon nanotubes surfaces

RSC Advances ◽  
2014 ◽  
Vol 4 (35) ◽  
pp. 18017-18024 ◽  
Author(s):  
Alfonso S. Pensado ◽  
Friedrich Malberg ◽  
M. F. Costa Gomes ◽  
Agílio A. H. Pádua ◽  
Josefa Fernández ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Ionic Liquid ◽  
Ionic Liquids ◽  
Molecular Simulation ◽  
Graphene Surface

Molecular simulation is used to explore the structure of an ionic liquid at a graphene surface and inside carbon nanotubes.

scholarly journals Developing Machine Learning Models for Ionic Conductivity of Imidazolium-Based Ionic Liquids

2021 ◽  
Author(s):  
Pratik Dhakal ◽  
Jindal Shah
Keyword(s):  
Machine Learning ◽  
Ionic Liquid ◽  
Ionic Liquids ◽  
Ionic Conductivity ◽  
Liquid Mixtures ◽  
Support Vector ◽  
Learning Models ◽  
Ideal Behavior ◽  
Original Dataset ◽  
Machine Learning Models

In this work, we have developed machine learning models based on support vector machine (SVM) and artificial neural network (ANN) to correlate ionic conductivity of imidazolium-based ionic liquids. The data, collected from the NIST ILThermo Database, spans six orders of magnitude and ranges from 275-475 K. Both models were found to exhibit very good performance. The ANN-model was then used to predict ionic conductivity for all the possible combinations of cations and anions contained in the original dataset, which led to the identification of an ionic liquid with 30% higher ionic conductivity than the highest conductivity reported in the database at 298 K. The model was further employed to predict ionic conductivity of binary ionic liquid mixtures. A large number of ionic liquid mixtures were found to possess non-ideal behavior in that an intermediate mole fraction for such ionic liquid mixtures resulted in either a maximum or minimum in the ionic conductivity.

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