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Development of an a priori ionic liquid design tool. 1. integration of a Novel COSMO-RS molecular descriptor on neural networks / José Palomar in Industrial & engineering chemistry research, Vol. 47 N° 13 (Juillet 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 N° 13 (Juillet 2008) . - p. 4523–4532 Titre : Development of an a priori ionic liquid design tool. 1. integration of a Novel COSMO-RS molecular descriptor on neural networks Type de document : texte imprimé Auteurs : José Palomar, Auteur ; José S. Torrecilla, Auteur ; Víctor R. Ferro, Auteur ; Francisco Rodríguez, Auteur Année de publication : 2008 Article en page(s) : p. 4523–4532 Note générale : Bibliogr. p. 4530-4532 Langues : Anglais (eng) Mots-clés : Ionic  liquids;  Charge  distribution;  COSMO-RS  methodology Résumé : An innovative computational approach is proposed to design ionic liquids (ILs) based on a new a priori molecular descriptor of ILs, derived from quantum-chemical COSMO-RS methodology. In this work, the charge distribution on the polarity scale given by COSMO-RS is used to characterize the chemical nature of both the cations and anions of the IL structures, using simple molecular models in the calculations. As a result, a novel a priori quantum-chemical parameter, Sσ-profile, is defined for 45 imidazolium-based ILs, as a quantitative numerical indicator of their electronic structures and molecular sizes. Subsequently, neural networks (NNs) are successfully applied to establish a relationship between the electronic information given by the Sσ-profile molecular descriptor and the density properties of IL solvents. As a consequence, we develop here an a priori computational tool for screening ILs with required properties, using COSMO-RS predictions to NN design and optimization. Current methodology is validated following a classical quantitative structure−property relationship scheme, which is the main aim of this work. However, a second part of the current investigation will be devoted to a more useful design strategy, which introduces the desired IL properties as input into inverse NN, resulting in selections of counterions as output, i.e., directly designing ILs on the computer. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800056q [article] Development of an a priori ionic liquid design tool. 1. integration of a Novel COSMO-RS molecular descriptor on neural networks [texte imprimé] / José Palomar, Auteur ; José S. Torrecilla, Auteur ; Víctor R. Ferro, Auteur ; Francisco Rodríguez, Auteur . - 2008 . - p. 4523–4532. Bibliogr. p. 4530-4532 Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 N° 13 (Juillet 2008) . - p. 4523–4532 Mots-clés : Ionic  liquids;  Charge  distribution;  COSMO-RS  methodology Résumé : An innovative computational approach is proposed to design ionic liquids (ILs) based on a new a priori molecular descriptor of ILs, derived from quantum-chemical COSMO-RS methodology. In this work, the charge distribution on the polarity scale given by COSMO-RS is used to characterize the chemical nature of both the cations and anions of the IL structures, using simple molecular models in the calculations. As a result, a novel a priori quantum-chemical parameter, Sσ-profile, is defined for 45 imidazolium-based ILs, as a quantitative numerical indicator of their electronic structures and molecular sizes. Subsequently, neural networks (NNs) are successfully applied to establish a relationship between the electronic information given by the Sσ-profile molecular descriptor and the density properties of IL solvents. As a consequence, we develop here an a priori computational tool for screening ILs with required properties, using COSMO-RS predictions to NN design and optimization. Current methodology is validated following a classical quantitative structure−property relationship scheme, which is the main aim of this work. However, a second part of the current investigation will be devoted to a more useful design strategy, which introduces the desired IL properties as input into inverse NN, resulting in selections of counterions as output, i.e., directly designing ILs on the computer. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800056q

Development of an a priori ionic liquid design tool. 2. Ionic liquid selection through the prediction of COSMO-RS molecular descriptor by inverse neural network / José Palomar in Industrial & engineering chemistry research, Vol. 48 N°4 (Février 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N°4 (Février 2009) . - p. 2257–2265 Titre : Development of an a priori ionic liquid design tool. 2. Ionic liquid selection through the prediction of COSMO-RS molecular descriptor by inverse neural network Type de document : texte imprimé Auteurs : José Palomar, Auteur ; José S. Torrecilla, Auteur ; Víctor R. Ferro, Auteur Année de publication : 2009 Article en page(s) : p. 2257–2265 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Ionic  liquids  Inverse  neural  networks  Molecular  descriptor Résumé : In this work, the a priori computational tool for screening ILs, developed in previous part 1, is extended to the simultaneous prediction of a set of IL properties for 45 imidazolium-based ILs. In addition, current part 2 reports the development of a more useful design strategy, which introduces the target IL properties as input, resulting in the selections of counterions as output, that is directly designing ILs on the computer. For this purpose, inverse neural networks are used to estimate the Sσ-profile molecular descriptor of a potential IL solvent by the specification of its required properties, following a reverse quantitative structure−property relationship scheme. Subsequently, a statistical tool based on Euclidean distances is developed to select an adequate set of anion+cation combinations that fulfill the estimated Sσ-profile values, to obtain, in this case, the tailor-made ILs. Finally, the proposed computational tool for designing ILs is applied in liquid−liquid extraction of a system model (toluene/n-heptane). In view of the inherent modular nature of ILs, the proposed methodology is here used in the formulation of IL mixtures to enhance the performance of extractive solvents in the aromatic/aliphatic separation. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8009507 [article] Development of an a priori ionic liquid design tool. 2. Ionic liquid selection through the prediction of COSMO-RS molecular descriptor by inverse neural network [texte imprimé] / José Palomar, Auteur ; José S. Torrecilla, Auteur ; Víctor R. Ferro, Auteur . - 2009 . - p. 2257–2265. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N°4 (Février 2009) . - p. 2257–2265 Mots-clés : Ionic  liquids  Inverse  neural  networks  Molecular  descriptor Résumé : In this work, the a priori computational tool for screening ILs, developed in previous part 1, is extended to the simultaneous prediction of a set of IL properties for 45 imidazolium-based ILs. In addition, current part 2 reports the development of a more useful design strategy, which introduces the target IL properties as input, resulting in the selections of counterions as output, that is directly designing ILs on the computer. For this purpose, inverse neural networks are used to estimate the Sσ-profile molecular descriptor of a potential IL solvent by the specification of its required properties, following a reverse quantitative structure−property relationship scheme. Subsequently, a statistical tool based on Euclidean distances is developed to select an adequate set of anion+cation combinations that fulfill the estimated Sσ-profile values, to obtain, in this case, the tailor-made ILs. Finally, the proposed computational tool for designing ILs is applied in liquid−liquid extraction of a system model (toluene/n-heptane). In view of the inherent modular nature of ILs, the proposed methodology is here used in the formulation of IL mixtures to enhance the performance of extractive solvents in the aromatic/aliphatic separation. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8009507