Density and Viscosity Study of Interactions of Some Amino Acids in Aqueous Solutions of Sodium Benzoate

The density (ρ) and viscosity (η) of three amino acids, glycine, l-alanine, and l-valine, have been determined as a function of amino acid concentration in aqueous solutions of 0.1011, 0.3088, and 0.5245 mol·kg^–1 sodium benzoate at temperatures 303.15, 308.15, and 313.15 K. Apparent molar volumes (<i>V</i>_ϕ), limiting values of apparent molar volumes (<i>V</i>⁰_ϕ), and transfer volumes (Δ_t<i>V</i>⁰_ϕ) have been calculated from the density data. The viscosity data could be fit to the Jones–Dole equation, and <i>B</i> coefficients and variation of <i>B</i> with temperature d<i>B</i>/d<i>T</i> were obtained at different concentrations and temperatures. Free energies of activation of the solvent (Δμ₁^0≠) and solute (Δμ₂^0≠) were also calculated by application of the Eyring transition-state theory. The obtained thermophysical data have been interpreted in terms of the structure of the amino acids and their interactions with the sodium benzoate solution

Comparing Composition- and Temperature-Dependent Viscosities of Binary Systems Involving Ionic Liquids

More than 200 composition- and temperature-dependent viscosity datasets for binary systems involving ionic liquids are analyzed with the Arrhenius model to inspect the composition dependence of the Arrhenius fit parameters activation energy, <i>E</i>_a, and <i>y</i>-intercept, ln <i>A</i>. The analysis also includes a new viscosity dataset for the binary system 1-hexyl-3-methylimidazolium bis­(trifluoromethylsulfonyl)­amide–trichloromethane. The majority of the binary systems show linear dependence of <i>E</i>_a and ln <i>A</i> with mole fraction, either over the entire range of composition or over a wide range of compositions, typically between 0.2 < <i>x</i>_IL < 1.0. These findings are useful for estimating unknown viscosities for binary systems involving ILs. As a side-outcome from the Arrhenius analysis and careful comparisons between datasets, a number of datasets are identified that are suspect of experimental inaccuracies