Interfacethermopy

Interfacethermopy is a Python package that calculates Vapor-liquid interfcial tension (IFT) of pure fluids using emperical correlations [1,2] and Vapor-liquid IFT of mixtures using Parachor model [3]. It supports loading JSON data sources containing fluid-specific coefficients which are required to calculate IFTs.

Features

• Read and retrive fluid-specific empercial coefficients (, , and optionally ) from JSON files.
• Compute IFTs of pure fluids using the correlation,

• Compute IFTs of mixtures using Parachor model,

• is the cross Parachor term calculated using the component-specific Parachor number and the fit parameter (similar to binary interaction parameter).
• The component-specific Parachor number is obtained at the given temperature of the mixture, using the following expression,

• is the exponential constant of the Parachor model equal to 3.87 [4].
• If > 0.9* of the component in the mixture then is computed at 0.9* for numerical stability. The same approach is also applied in REFPROP V10.
• The Parachor model is used in addition with other Equation of State (EoS) models (Peng–Robinson, SRK, GERG-2008, EoS-CG, etc.) to compute , , , , , , and .
• Interfacethermopy can be used as a plugin with other thermodynamic packages like Clapeyron, FeOs and REFPROP to compute IFTs of pure fluids and mixtures.
• Compute IFTs of mixtures using the Winterfeld–Scriven–Davis (WSD) model. For a general multicomponent system,

• Here and are the IFTs of the pure components and , respectively. and are the equilibrium liquid and vapor molar densities of the pure component. and are the liquid and vapor molar densities of component in the mixture. for nonaqueous, weakly polar fluids. The Kronecker delta equals one when and zero otherwise.
• For a binary system where the mixture temperature is below the critical temperature of both components, the WSD model simplifies to,

• When the mixture temperature exceeds the critical temperature of component 2, the standard WSD formulation reduces to a form in which the supercritical component has no effect on IFT, which is physically unrealistic. To correct for this, an empirical correction is applied,

• The correction factor accounts for the IFT-reducing effect of the supercritical component dissolved in the liquid phase, analogous to a surfactant effect. Here is the liquid mole fraction of the supercritical component [5].

• Estimate metastable phase limits using Classical Nucleation Theory (CNT).
  The nucleation rate is given by

• The free energy barrier for the formation of a critical nucleus is

• Here is the nucleation rate, is the kinetic prefactor, is the interfacial tension, is the critical nucleus radius, is the Boltzmann constant, and is the temperature.
• Using the computed interfacial tension together with thermodynamic properties from an Equation of State, Interfacethermopy can estimate metastable limits associated with nucleation phenomena.

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Installation

Git clone the package and install the package,

git clone https://github.com/Darz2/Interfacethermopy.git
pip install .

External thermodynamic packages are needed to do EoS calcualtions in addition to Python's standard libraries.

For example, if one is using REFPROP to do EoS calculations, python wrapper of REFPROP and the corresponding REFPROP library files will be required.

pip install ctREFPROP

If one is using Clapeyron.jl, then using PythonCall in julia can be used to calculate IFTs using compute_gamma_pure, compute_gamma_mixture and parachor_number functions in InterfacialTension class.

JSON Input Format

Each JSON file must contain a list of dictionaries, each describing one fluid. Example:

[
  {
    "Fluid": "methane",
    "Tc": 190.56,
    "s": [0.2358, 0.0121],
    "n": [1.23, 2.34]
  },
  {
    "Fluid": "argon",
    "Tc": 150.86,
    "s": [0.152],
    "n": [1.26]
  }
]

Example Usage

from Interfacethermopy import parachor as IFT
import numpy as np

if __name__ == '__main__':
    
    json_files  = ["Mulero_2012.json"]
    model       = IFT.InterfacialTension(json_files)
    model.ctREFPROP_init('~/Software/REFPROP_BETA/REFPROP-cmake/build', gerg_enable=1)

    MIXTURE   = "CO2;Methane"
    z         = [0.95, 0.05]
    T         = 250.0
    PRESSURES = np.arange(20.0, 31.0, 5.0)
    kij       = 0.0

    Pbar, gamma = model.REFPROP_MIXTURE(MIXTURE, z, T, PRESSURES, kij)

    for P, s in zip(Pbar, gamma):
        print(f"P = {P:.1f} bar | gamma = {s:.4f} mN/m")

References

1. Cachadina, I., Vega, L. F., & de Miguel, E. (2015)
   Empirical correlations for the surface tension of pure fluids
   Journal of Chemical Thermodynamics, 87, 162–170.
   https://doi.org/10.1063/1.4921749

2. Mulero, A., Cachadina, I., & Parra, M. I. (2012)
   Recommended correlations for the surface tension of common fluids
   Journal of Physical and Chemical Reference Data, 41(4), 043105.
   https://doi.org/10.1063/5.0277723

3. Sugden, S. (1924)
   A relation between surface tension, density, and chemical composition
   Journal of the Chemical Society, Transactions, 125, 32–41.
   https://doi.org/10.1039/CT9242500032

4. Log, A. M.; Diky, V.; Huber, M. L. (2023)
   Assessment of a parachor model for the surface tension of binary mixtures
   International Journal of Thermophysics, 44, Article 110.
   https://doi.org/10.1007/s10765-023-03230-0

5. Raju, D.; Skartlien, R.; Ramdin, M.; Vlugt, T. J. H. (2025)
   Vapor–Liquid Interfacial Properties of CO2 Mixtures for Sequestration Applications: Molecular Simulations, Classical Density Functional Theory, and Equations of State
   Industrial & Engineering Chemistry Research.
   https://doi.org/10.1021/acs.iecr.5c04932

Note: More correlations can be added for n-alkanes, ethers, and esters.

TO DO:

• Python TEST need to be written
• Julia Integration using Python Call need to be added (in v0.3.0)