Melting-Point Estimation of Ionic Liquids by a Group Contribution Method

Aguirre, Claudia L.; Cisternas, Luís A.; Valderrama, José O.

doi:10.1007/s10765-011-1133-5

Cited by 42 publications

(26 citation statements)

References 24 publications

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“…This study is not to invalidate the group contribution models used predict viscosity and melting point of ionic liquids since these methods can be very useful in trend analysis in which the effect of different structural groups on physical properties can be studied. Only it is noteworthy that models errors in this study for prediction of two of the most important physical properties of ionic liquids (melting point and viscosity) are comparable to those achieved by group contribution models [9][10][11][12][13][14][15][16] (i.e. less than 10% absolute error).…”

Section: Comparisonsupporting

confidence: 60%

“…There have been several studies focusing [9][10][11][12][13][14][15][16] on prediction of the physical properties of ionic liquids using group contribution (GC) approaches. In these methods, a value is attributed to each structural group (a cation head group, a side chain group attached to the cation and an anion).…”

Section: Comparisonmentioning

confidence: 99%

“…Several models exist for prediction of melting point and viscosity of ILs. In general, these models are either Quantitative Structure Property Relationship (QSPR) correlations [6][7][8] , group contribution (GC) type [9][10][11][12][13][14][15][16] , or molecular dynamics (MD) simulations. [17][18][19][20][21][22][23] The main drawback of the QSPR and GC models is the narrow restrictions in choice of applicable compounds while the main limitation of MD simulations is the high computational demands and the need for starting geometry.…”

Section: Introductionmentioning

confidence: 99%

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New quantum chemistry-based descriptors for better prediction of melting point and viscosity of ionic liquids

Mehrkesh

Karunanithi

2016

Fluid Phase Equilibria

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Ionic liquids (ILs) are an emerging group of chemical compounds which possess promising properties such as having negligible vapor pressure. These so called designer solvents have the potential to replace volatile organic compounds in industrial applications. A large number of ILs, through the combination of different cations and anions, can potentially be synthesized. In this context, it will be useful to intelligently design customized ILs through computer-aided methods. Practical limitations dictate that any successful attempt to design new ILs for industrial applications requires the ability to accurately predict their melting point and viscosity as experimental data will not be available for designed structures. In this paper, we present two new correlation equations towards the more precise prediction of melting point and viscosity of ILs solely based on the inputs from quantum chemistry calculations (no experimental data or simulation results are needed). To develop these correlations we utilized data related to size, shape, and electrostatic properties of cations and anions that constitutes ILs. In this work, new descriptors such as dielectric energy of cations and anions as well as the values predicted by an 'ad-hoc' model for the radii of cations and anions (instead of their van der waals radii) were used. An enormous form of correlation equations constituent of all different combinations of descriptors (as the inputs to the model) were tested. The average relative errors were measured to be 3.16% and 6.45% for the melting point, Tm, and ln(vis), respectively.

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Section: Comparisonsupporting

confidence: 60%

Section: Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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New quantum chemistry-based descriptors for better prediction of melting point and viscosity of ionic liquids

Mehrkesh

Karunanithi

2016

Fluid Phase Equilibria

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“…Por ejemplo su aplicación a fluidos modernos y actuales como son los líquidos iónicos. Ya hay intentos en la literatura (Huo et al 2009;Gharagheizi et al 2012;Aguirre et al 2012;Valderrama et al 2013 …”

Section: Ii) Extensión Al Cálculo De Tf De Líquidos Iónicosunclassified

Método de Contribución de Grupos: una Herramienta Fundamental en cursos Avanzados de Termodinámica y Física de Fluidos para la Estimación de Propiedades de Sustancias

Fierro¹,

Faúndez²,

Valderrama

2016

Form. Univ.

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ResumenSe presenta un Método de Contribución de Grupos (MCG) para la estimación de propiedades de sustancias y se plantea como un método básico que podría ser incluido en cursos avanzados de termodinámica, fisicoquímica y física de fluidos. La idea es mostrar a los alumnos que las propiedades físicas y fisicoquímicas de las sustancias están relacionadas con su estructura química y características físicas y químicas de los átomos y grupos químicos que forman una molécula. Por lo tanto el método considera que una molécula está formada por grupos definidos a los que se les asigna un determinado valor como contribución al valor de una propiedad. Luego se supone que dicha contribución es la misma en todo compuesto donde dicho grupo esté presente. Este trabajo presenta algunos ejemplos que pueden ser mostrados en un curso formal y propone temas de tareas cortas y semestrales. Las tareas involucran conceptos generales asociados a la termodinámica, conocimiento sobre planillas de cálculo y manejo de bases de datos. Se discute sobre la utilidad del MCG como una herramienta en la metodología de aprendizaje basado en problemas. Se concluye que este tipo de conceptos y métodos se ajustan bien a cursos avanzados de pregrado o en tópicos y cursos de postgrado, en disciplinas como física, química, ingeniería química y otras afines.Palabras clave: contribución de grupos; método Joback-Reid; termodinámica; temperatura de fusión; aprendizaje basado en problemas Group Contribution Method: A Fundamental Tool in Advanced Courses of Thermodynamics and Physics of Fluids for Estimating Properties of Substances AbstractThe so-called Group Contribution Method for estimating properties of substances is presented and discussion about its use as a basic method that could be included in advanced courses of thermodynamics, physicalchemistry and physics of fluids is done. The idea is to show students that physical and physical-chemical properties of substances are related to their chemical structure and to the physical and chemical nature of the atoms and groups that form a molecule. Therefore, the method considers that a molecule is formed by defined groups to which a contribution to a given property is assigned. After that, it is assumed that such a contribution is the same in any compound in which that group appears. This works presents some examples that can be shown in a formal course and proposes short and long term assignments. The assignments involve general concepts of thermodynamics, knowledge of spreadsheets calculations, and management of data bases. The usefulness of the Group Contribution Method as a tool in learning methodology based on problems is also discussed. It is concluded that that this types of methods are appropriate for advanced undergraduate and graduate courses of physics, chemistry, chemical engineering and related areas.

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“…For group contribution methods which are popular and widely used to predict thermodynamic properties of organic compounds [21][22][23][24], there are few models that have been proposed for the prediction of f H • solid in the solid state. The best known and most widely used of these are the methods developed by Domalski and Hearing [19], Cohen [10], and Salmon and Dalmazzone [7,8].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Group Contribution Method for Predicting Standard Enthalpy of Formation in the Solid State: C–H, C–H–O, C–H–N, and C–H–N–O Compounds

Guella¹,

Argoub

Benkouider

et al. 2015

Int J Thermophys

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In this work, an artificial neural network-group contribution model is developed to predict the standard enthalpy of formation in the solid (crystal) state of pure compounds. Several classes of hydrocarbon compounds CH, oxygenated compounds CHO, nitrogen compounds CHN, and energetic compounds CHNO are investigated to propose a comprehensive and predictive model. The new model is developed and tested for 1222 organic compounds containing complex molecular structures. The performance of the new model has been compared with previous work and is shown to be far more accurate. The obtained results show an average absolute deviation of 9.33 kJ·mol −1 and a coefficient of determination of 0.9972 for the experimental values.

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Melting-Point Estimation of Ionic Liquids by a Group Contribution Method

Cited by 42 publications

References 24 publications

New quantum chemistry-based descriptors for better prediction of melting point and viscosity of ionic liquids

New quantum chemistry-based descriptors for better prediction of melting point and viscosity of ionic liquids

Método de Contribución de Grupos: una Herramienta Fundamental en cursos Avanzados de Termodinámica y Física de Fluidos para la Estimación de Propiedades de Sustancias

Artificial Neural Network-Group Contribution Method for Predicting Standard Enthalpy of Formation in the Solid State: C–H, C–H–O, C–H–N, and C–H–N–O Compounds

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