Prediction of Viscosity−Temperature−Composition Surfaces in a Single Expression for Methanol−Water and Acetonitrile−Water Mixtures

Abstract

The modeling of the surface of viscosity in composition and temperature at atmospheric pressure for methanol−water and acetonitrile−water was performed by interconnecting various elementary functions and testing them in a systematic manner to minimize the least-squares error. The systematic approach involved developing expressions that were the sum or product of elementary functions in temperature and composition, then visually observing their fit and quantitating the error. To reduce the error, transformation of the data using elementary functions was necessary to create a modified surface simpler in form. For MeOH−H2O, the least-squares error was 0.6 and 0.01 for untransformed data and transformed data, respectively. Similarly, for ACN−H2O, the error was 0.3 and 0.008 for untransformed and transformed data, respectively. The expression describing the viscosity−composition−temperature relationship for both the transformed and untransformed data was a quadratic in x with exponential functions of T as coefficients where T° is a nondimensionalizing term of value one. The transformed temperature is log(T/T°) + 2, while the transformed viscosity was η1/8.

Universal Correlation Between Solvent Polarity, Fluorescence Lifetime and Macroscopic Viscosity of Alcohol Solutions

“…Addition of alcohol to water changes the physical parameters of the solutions considerably which is well documented in the literature [28][29][30][31] and Fig. 3 depicts is clearly.…”

Development of a Solvent-Compatible High-Throughput Static Filtration Test Equipment

“…Finally, for the binary water (1)−acetonitrile (2) mixtures, both the temperature and acetonitrile concentration had to be included as predictors in the regression equation for estimating the viscosity and density changes [i.e., μ(T,x 1 ) and ρ(T,x 1 ), respectively]. 19,20…”

“…For n -propanol, the functional forms of the dynamic viscosity and density were developed based on literature-reported experimental data by fitting regression models. Finally, for the binary water (1)–acetonitrile (2) mixtures, both the temperature and acetonitrile concentration had to be included as predictors in the regression equation for estimating the viscosity and density changes [i.e., μ­( T , x 1 ) and ρ­( T , x 1 ), respectively]. , …”

Dependence of cobaltocenium diffusion in ionic liquids on the alkyl chain length of 1-alkyl-3-methylimidazolium cations

“…43 This difference is a result of the relatively high viscosity of [C 2 mim][Tf 2 N] as compared with organic solvents at the same temperature (e.g., 38.2 mPa s for [C 2 mim][Tf 2 N] and 0.40 mPa s for acetonitrile at 293.15 K). 44 The diffusion coefficients for Cc + in [C 2 mim][Tf 2 N] and [C 4 mim][Tf 2 N] at 293.15 K were estimated to be almost analogous to those reported in the literatures (for example, (3.16 AE 0.03) Â 10 À7 cm 2 s À1 at (299 AE 1) K 16 and (3.27 AE 0.02) Â 10 À7 cm 2 s À1 at (291.15 AE 1) K 20 for Cc + in [C 2 mim][Tf 2 N] and (2.22 AE 0.04) Â 10 À7 cm 2 s À1 at (299 AE 1) K 16…”