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Prediction of solubilities of solid biomolecules in modified supercritical fluids using group contribution methods and equations of state / Thippawan Kumhom in Industrial & engineering chemistry research, Vol. 49 N° 5 (Mars 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 5 (Mars 2010) . - pp. 2433–2441 Titre : Prediction of solubilities of solid biomolecules in modified supercritical fluids using group contribution methods and equations of state Type de document : texte imprimé Auteurs : Thippawan Kumhom, Auteur ; Peter L. Douglas, Auteur ; Douglas Supaporn, Auteur Année de publication : 2010 Article en page(s) : pp. 2433–2441 Note générale : Industrial Chemistry Langues : Anglais (eng) Mots-clés : Biomolecules;  Solubilities Résumé : A method to predict the solubilities of biomolecules in supercritical fluids (SCF) with cosolvents was developed, and predictions were compared with experimental data available in the literature. The method used the Constantinou−Gani and Joback group contribution methods to estimate pure component solute properties and the Lee−Kesler−Plöcker (LKP) and Mohsen-Nia−Moddaress−Mansoori (MMM) equations of state (EOS) to determine solute solubilities in the ternary solute−SCF−cosolvent systems. The ternary systems were modeled as pseudobinary systems with the SCF−cosolvent binary pair being modeled as a single pseudocomponent using Kay’s mixing rule to estimate the pseudocomponent properties. Twenty-one (21) systems consisting of polar and nonpolar solutes and cosolvents were evaluated over a range of temperatures, pressures, and cosolvent concentrations. The results demonstrated that both the LKP and MMM EOS are useful for modeling the solubility of solids in supercritical fluids (SCF) with cosolvents. When the Aromaticity index (AI) was less than or equal to 0.3, the MMM EOS was found to be more suitable; otherwise the LKP EOS was found to provide the best fit. If this so-called AI criterion was used, the average error between predicted and experimental results ranged from 0.5 to 25.5% with an average of 10.0% for the 21 systems studied. However, when the AI criterion was not followed, the error between predicted and experimental results ranged from 9.6 to 99% with an average of 69.0 Note de contenu : Blibliogr. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900945q [article] Prediction of solubilities of solid biomolecules in modified supercritical fluids using group contribution methods and equations of state [texte imprimé] / Thippawan Kumhom, Auteur ; Peter L. Douglas, Auteur ; Douglas Supaporn, Auteur . - 2010 . - pp. 2433–2441. Industrial Chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 5 (Mars 2010) . - pp. 2433–2441 Mots-clés : Biomolecules;  Solubilities Résumé : A method to predict the solubilities of biomolecules in supercritical fluids (SCF) with cosolvents was developed, and predictions were compared with experimental data available in the literature. The method used the Constantinou−Gani and Joback group contribution methods to estimate pure component solute properties and the Lee−Kesler−Plöcker (LKP) and Mohsen-Nia−Moddaress−Mansoori (MMM) equations of state (EOS) to determine solute solubilities in the ternary solute−SCF−cosolvent systems. The ternary systems were modeled as pseudobinary systems with the SCF−cosolvent binary pair being modeled as a single pseudocomponent using Kay’s mixing rule to estimate the pseudocomponent properties. Twenty-one (21) systems consisting of polar and nonpolar solutes and cosolvents were evaluated over a range of temperatures, pressures, and cosolvent concentrations. The results demonstrated that both the LKP and MMM EOS are useful for modeling the solubility of solids in supercritical fluids (SCF) with cosolvents. When the Aromaticity index (AI) was less than or equal to 0.3, the MMM EOS was found to be more suitable; otherwise the LKP EOS was found to provide the best fit. If this so-called AI criterion was used, the average error between predicted and experimental results ranged from 0.5 to 25.5% with an average of 10.0% for the 21 systems studied. However, when the AI criterion was not followed, the error between predicted and experimental results ranged from 9.6 to 99% with an average of 69.0 Note de contenu : Blibliogr. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900945q