Solubility prediction of salicylic acid in water-ethanol-propylene glycol mixtures using the Jouyban-Acree model Metadata
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- Main Title Solubility prediction of salicylic acid in water-ethanol-propylene glycol mixtures using the Jouyban-Acree model
Author: Jouyban, AbolghasemCreator Type: PersonalCreator Info: Tabriz University of Medical Sciences
Author: Chew, Nora Yat KnorkCreator Type: PersonalCreator Info: Tabriz University of Medical Sciences
Author: Chan, H.Creator Type: PersonalCreator Info: Tabriz University of Medical Sciences
Author: Khoubnasabjafari, M.Creator Type: PersonalCreator Info: University of Sydney
Author: Acree, William E. (William Eugene)Creator Type: PersonalCreator Info: University of North Texas
Name: Govi-VerlagPlace of Publication: [Eschborn, Germany]
- Creation: 2006
- Content Description: Article on the solubility prediction of salicylic acid in water-ethanol-propylene glycol mixtures using the Jouyban-Acree model.
- Physical Description: 4 p.
- Keyword: salicylic acid
- Keyword: solution models
- Keyword: solvent systems
- Journal: Pharmazie, 2006, Eschborn: Govi-Verlag, pp. 318-321
- Publication Title: Pharmazie
- Volume: 61
- Page Start: 318
- Page End: 321
- Peer Reviewed: True
Name: UNT Scholarly WorksCode: UNTSW
Name: UNT College of Arts and SciencesCode: UNTCAS
- Rights Access: public
- Archival Resource Key: ark:/67531/metadc282593
- Academic Department: Chemistry
- Display Note: Abstract: To show the applicability of a solution model, i.e. the Jouyban-Acree model, for predicting the solubility of a solute in ternary solvent systems based on model constants computed using solubility data of the solute in binary solvent systems, the solubility of salicylic acid in water-ethanol, water-propylene glycol, ethanol-propylene glycol mixtures was determined. A minimum number of three data points from each binary system was used to calculate the binary interaction parameters of the model. Then the solubility in other binary solvent compositions and also in a number of ternary solvents was predicted, and the mean percentage deviation (MPD) was calculated as an accuracy criterion. The overall MPD (±SD) was 7.3 (±7.3)% and those of a similar predictive model was 15.7 (±11.5)%. The mean difference between the proposed and a previous model was statistically significant (paired t-test, p < 0.004).