Solubility Measurement, Model Correlation, and Solute–Solvent Interactions of Pidotimod in Nine Pure Solvents and Two Binary Solvents at Temperatures Ranging from 278.15 to 323.15 K

2020 ◽  
Vol 65 (11) ◽  
pp. 5483-5497
Author(s):  
Zengai Guo ◽  
Wei Sun ◽  
Weiyang Lu ◽  
Gengxiu Zheng
Keyword(s):  
Measurement Model ◽  
Binary Solvents ◽  
Solvent Interactions ◽  
Solubility Measurement ◽  
Model Correlation

Modeling Gel Swelling in Binary Solvents: A Thermodynamic Approach to Explaining Cosolvency and Cononsolvency Effects

2019 ◽  
Vol 11 (05) ◽  
pp. 1950050 ◽  
Author(s):  
Rui Xiao ◽  
Jin Qian ◽  
Shaoxing Qu
Keyword(s):  
Lattice Model ◽  
Thermodynamic Theory ◽  
Binary Solvents ◽  
Thermodynamic Approach ◽  
Solvent Mixtures ◽  
Solvent Interactions ◽  
Equilibrium Swelling ◽  
Acceptable Agreement ◽  
Swelling Behaviors ◽  
Solvent Solvent

If gel swells in binary solvents, two unusual phenomena may appear. Two solvents with relatively low swelling ability may become a good solvent for the polymer with high swelling ability when mixed, which is known as a cosolvency effect. In contrast, a cononsolvency effect indicates polymer is less soluable in solvent mixtures than it is in each of the cosolvents. In this work, we develop a thermodynamic theory to describe the equilibrium swelling behaviors of gels in binary solvents based on the Flory–Huggins lattice model. The model can reproduce both cosolvency and cononsolvency effects, showing that these effects are caused by the preferential absorption of the solvent by polymer together with solvent–solvent interactions. The model is also applied to describe experimentally observed cosolvency and cononsolvency effects in the literature, which shows an acceptable agreement.

Extensions of the Modal Assurance Criterion

1990 ◽  
Vol 112 (4) ◽  
pp. 468-472 ◽  
Author(s):  
W. Heylen ◽  
T. Janter
Keyword(s):  
Model Updating ◽  
Measurement Model ◽  
Vector Spaces ◽  
Modal Assurance Criterion ◽  
Dynamic Data ◽  
Approximate Eigenvalue ◽  
Improve Model ◽  
Element Matrix ◽  
Model Correlation ◽  
Eigenvalue Solvers

The modal assurance criterion (MAC) in general measures the degree of proportion between two (modal) vectors, in the form of a correlation coefficient of a least squares ratio estimate. The MAC principle can be extended in several ways, thus increasing its field of applications. The partial MAC (PMAC) correlates parts of (modal) vectors. The spatial MAC (SMAC) allows to compare different vector spaces. Furthermore this paper suggests a way of calculating the MAC sensitivities to model changes. All those extensions are illustrated by their possible uses in correlating measured dynamic data with (finite element) matrix models and in the area of model updating. Those applications might be helpful tools to indicate regions of poor measurement-model correlation, to complete measured vectors, to judge approximate eigenvalue solvers, or to improve model updating procedures.

Sign in / Sign up

Export Citation Format

Share Document