Models to Predict Solubility in Ternary Solvents Based on Sub-binary Experimental Data

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Article on models to predict solubility in ternary solvents based on sub-binary experimental data.

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6 p.

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Jouyban, Abolghasem; Clark, Brian J. & Acree, William E. (William Eugene) December 2000.

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Article on models to predict solubility in ternary solvents based on sub-binary experimental data.

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6 p.

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Abstract: The capability of the extended forms, of two well established cosolvency models, i.e. the combined nearly ideal binary solvent/Redlich-Kister equation and the modified Wilson model, used to predict the solute solubility in non-aqueous ternary solvent mixtures is presented. These predictions are based on the measured solubilities of anthracene in binary solvent mixtures. As a result the values of average percent deviations were less than 2% for the anthracene solubility in ternary mixtures.This work was also extended to other cosolvency models, i.e. the extended Hildebrand solubility approach and the mixture response surface method, which are also commonly used for correlating solubility data in ternary solvents. The accuracy of the models is compared with each other and also with a published solubility model for ternary mixtures. The results illustrate that all models produced comparable accuracy.

Copyright 2000 Pharmaceutical Society of Japan.

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  • Chemical and Pharmaceutical Bulletin, 48(12), Pharmaceutical Society of Japan, December 1, 2000, pp. 1-6

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  • Publication Title: Chemical and Pharmaceutical Bulletin
  • Volume: 48
  • Issue: 12
  • Page Start: 1866
  • Page End: 1871
  • Peer Reviewed: Yes

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  • December 2000

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  • Aug. 29, 2014, 2:16 p.m.

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  • Nov. 21, 2023, 12:33 p.m.

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Jouyban, Abolghasem; Clark, Brian J. & Acree, William E. (William Eugene). Models to Predict Solubility in Ternary Solvents Based on Sub-binary Experimental Data, article, December 2000; [Tokyo, Japan]. (https://digital.library.unt.edu/ark:/67531/metadc333042/: accessed July 18, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT College of Arts and Sciences.

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