oa Measurement Of Refractive Indices Of Ternary Mixtures Using Digital Interferometry And Multi-Wavelength Abbemat Refractometer

Abstract Knowing the liquid mixtures properties are significantly important in scientific experimentation and technological development. Thermal diffusion in mixtures plays a crucial role in both nature and technology. The temperature and concentration coefficients of refractive indices so called the Contrast factors contribute to study in various fields including, crude oil experiments (SCCO) and the distribution of crude oil components. The Abbemat Refractometer and Mach-Zehnder Interferometer technique has been proven a precise, highly accurate, and non-intrusive method for measuring the refractive index of a transparent medium. Refractive indices for three ternary mixtures containing three hydrocarbon compositions and their pure components of Tetrtahydronaphtalenene (THN), Isobutylbenzen (IBB), and Dodecane (C12), used mainly in gasoline, were experimentally measured using both the Mach-Zehnder Interferometer and a multi-wavelength Abbemat refractometer. Temperature and concentration coefficients of refractive indices, or contrast factors, as well as their individual correlation to calculate refractive indices have been presented in this research. The experimental measurements were correlated with a wide range of temperatures and wavelengths over a broad range of compositions. The experimental measurements of the refractive indices were compared with those estimated by applying several mixing rules: Lorentz-Lorenz, Gladstone-Dale, Arago-Biot, Eykman, Wiener, Newton, and Oster predictive equations. The experimental values of refractive indices are in substantial agreement with the values obtained by L-L, G-D, and A-B equations, and excepting values obtained by Oster and Newton equations. The temperature, concentration and wavelength dependence of refractive index in mixtures agrees with published data. A comparison with available literature and mixing rules shows that new correlations can predict the experimental data with deviations of less than 0.001.