Solute–Solvent Interaction of Benzene with Acetic Acid at Different Temperatures

Kamble, S. P.; Sudake, Y. S.; Patil, S. S.; Khirade, P. W.; Mehrotra, S. C.

doi:10.1007/s10953-014-0229-5

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…The excess refractive index ( n E ) can be expressed as follows − where n D1 and n D2 are the refractive indices of liquids 1 and 2 in their mixtures, respectively, and n D12 is the refractive index of the mixtures.…”

Section: Theoretical Considerationsmentioning

confidence: 99%

Comparative Dielectric Study and Molecular Interactions of Binary Mixtures of (Cyclohexanone + 1-Alkanols) and (Cyclopentanone or Cyclohexanone + Cyclohexanol or Cyclohexane) atT= 298.15 K

Gilani

Ramezani

2018

J. Chem. Eng. Data

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Relative permittivities, refractive indices, and densities were measured for binary liquid mixtures of cyclohexanone with 1-alkanols (ethanol, 1-butanol, 1-hexanol, 1-octanol, and 1-decanol) in the mole fraction range of (0 ≤ x 2 ≤ 1) at T = 298.15 and p = 101.3 kPa. Another comparative dielectric study was performed on the (cyclohexanone or cyclopentanone + cyclohexanol or cyclohexane) mixtures at the mentioned temperature and pressure. The experimental data were analyzed in terms of the various approaches in order to obtain information about the intermolecular interactions and the mixture structure. The excess properties of different physical quantities confirm the formation of hydrogen-bond structure in these mixtures. For the studied systems, negative excess permittivities and excess Kirkwood correlation factors were obtained, indicating a reduction of the number of effective dipoles in the binary mixtures.

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Section: Theoretical Considerationsmentioning

confidence: 99%

Comparative Dielectric Study and Molecular Interactions of Binary Mixtures of (Cyclohexanone + 1-Alkanols) and (Cyclopentanone or Cyclohexanone + Cyclohexanol or Cyclohexane) atT= 298.15 K

Gilani

Ramezani

2018

J. Chem. Eng. Data

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“…So when the mixing of solvent is assumed to be ideal, the polarity of solvent can be expressed by the permittivity constant. According to the permittivity of acetic acid, ethanoic anhydride, and tetrachloromethane, the permittivity of the mixed solvent is determined by the following formula. − The ε mix is the permittivity constant of the mixed solvent, the ε is the permittivity constant of the pure solvent, and w is the mass fraction in the mixture solvent. The results of permittivity in the solvent systems were summarized in Table .…”

Section: Resultsmentioning

confidence: 99%

Solubility Determination and Thermodynamic Modeling of Glutaric Anhydride in Diverse Solvent Systems Consisting of Acetic Acid, Ethanoic Anhydride, and Tetrachloromethane from T = (278.45 to 324.45) K

et al. 2018

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The solid−liquid equilibrium (SLE) of glutaric anhydride in three pure solvents, tetrachloromethane, acetic acid and ethanoic anhydride, and three binary solvents of acetic acid + tetrachloromethane, ethanoic anhydride + (tetrachloromethane/ acetic acid) and a ternary solvent of tetrachloromethane + acetic acid + ethanoic anhydride solvent systems was measured by dynamic method at a temperature range from 278.45 K to 324.45 K under atmospheric pressure. It turns out that the solubility of glutaric anhydride increases with the temperature increasing in all selected solvents. Moreover, it is interesting that the solubility of glutaric anhydride in two binary solvents (acetic acid + tetrachloromethane, ethanoic anhydride + tetrachloromethane) and ternary solvents mixtures (tetrachloromethane + acetic acid + ethanoic anhydride) was affected more by the decrease of mass fraction of tetrachloromethane at a given temperature. The mass fraction of added ethanoic anhydride had a smaller effect on the solubility of glutaric anhydride in the binary solvent of ethanoic anhydride + acetic acid at T = (279.45 to 314.45) K. The experimental data of solubility were correlated by the λh equation, the Apelblat equation, and nonrandom two liquid equation. All equations can be well applied, and the relative average deviation of the Apelblat equation is less than 0.05. In addition, the thermodynamic properties of the mixing processes in all selected solvents were calculated and discussed based on the nonrandom two-liquid model.

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“…In this work, a combination of the DFT hybrid functional B3LYP , with the basis set 6-311++G was used. The molecules were considered to be in a cavity within a liquid solvent composed of molecules of their own, and solvation was described according to the Polarizable Continuum Model (PCM). − This model is used to simulate the solute cavity via a set of overlapping spheres and requires the knowledge of dielectric constants, refractive indices, densities, and molar masses for each chemical species, which can be seen in Table S2, − in the Supporting Information. In addition, after the geometry optimization, a population analysis of the electrons was performed using the Natural Bond Orbital (NBO) function to determine orbital electronic densities and the consequent electrical charge distribution.…”

Section: Methodsmentioning

confidence: 99%

A Geometric Approach for the Calculation of the Nonrandomness Factor Using Computational Chemistry

Velho,

Barroca,

Macedo

2023

J. Chem. Eng. Data

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The nonrandomness factor (α) is a parameter related to the polarity (and consequent randomness of spatial orientation) of the molecules and is generally fixed between 0.20 and 0.47 since no technique has been developed so far for its accurate estimation. However, the consideration of a constant nonrandomness factor for mixtures is known to harden azeotrope description and to cause convergence issues in both the Nonrandom Two-Liquid (NRTL) and Universal Quasi-chemical (UNIQUAC) models, which have been widely applied in the correlation of phase equilibria. In this work, a novel geometric methodology, named Porto's approach, was applied in the calculation of the nonrandomness factors for 50 pure components (mainly alkanes, alcohols, and ketones) at 298.15 K and 0.1 MPa. This methodology relied on computational chemistry (Density Functional Theory, DFT) to minimize the energy of molecules in a cavity within a solvent composed of molecules of their own (applying the Polarizable Continuum Model, PCM), and thereafter define the most stable chemical configuration in the pure liquid state. Then, the nonrandomness factor of each component was determined based on its molecular dipole moment, which was calculated after a population analysis of the optimized partial electrical charges with the Natural Bond Orbital (NBO) function. This innovative approach was applied in the correlation of liquid−liquid equilibria (LLE) data for 15 ternary systems comprising only neutral molecules with NRTL and UNIQUAC. The classical methods (α = 0.2 or 0.3 for NRTL and α = 0.2 for UNIQUAC) were compared against the usage of a component-specific nonrandomness factor, and similar standard deviations were obtained. Hence, this work is considered a strong advance in the application of these excess free Gibbs energy models to phase equilibria estimation and opens the door to more accurate thermodynamic modeling of nonideal mixtures (such as the ones containing electrolytes) by providing enhanced physical meaning to the nonrandomness factors.

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Solute–Solvent Interaction of Benzene with Acetic Acid at Different Temperatures

Cited by 3 publications

References 29 publications

Comparative Dielectric Study and Molecular Interactions of Binary Mixtures of (Cyclohexanone + 1-Alkanols) and (Cyclopentanone or Cyclohexanone + Cyclohexanol or Cyclohexane) atT= 298.15 K

Comparative Dielectric Study and Molecular Interactions of Binary Mixtures of (Cyclohexanone + 1-Alkanols) and (Cyclopentanone or Cyclohexanone + Cyclohexanol or Cyclohexane) atT= 298.15 K

Solubility Determination and Thermodynamic Modeling of Glutaric Anhydride in Diverse Solvent Systems Consisting of Acetic Acid, Ethanoic Anhydride, and Tetrachloromethane from T = (278.45 to 324.45) K

A Geometric Approach for the Calculation of the Nonrandomness Factor Using Computational Chemistry

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