QRL MODEL FOR ELECTROLYTE SOLUTIONS. Theory and computational methods. An example of software implementation.

Published: 7 October 2024| Version 1 | DOI: 10.17632/yvm7ttkfz8.1
Contributor:
Elsa Moggia

Description

The most recent publication concerned with the Quasi-Random Lattice (QRL) Model and its application to electrolyte solutions is now available at https://doi.org/10.1016/j.fluid.2024.114243 Temporary share link https://authors.elsevier.com/a/1jtWx1M2A%7ESaAC The Supplementary Information includes a software version (MATLAB code) implementing computational methods and procedures connected with QRL theory and equations. The code is cast into four modules, each of them is a stand-alone module. The examplifying case considered is aqueous HCl at 25°C. Short overview of the code (currently present in the Supporting Information, https://doi.org/10.1016/j.fluid.2024.114243) - Module "Power_Series_Density_HCl_25.m" Implementation of the procedure to calculate solution density and apparent molar volume of solute from 0 to 8 mol/kg; the calibration set (experimental densities used) refers to molalities within 1-3 mol/kg (about). - Module "cL_Parameterization_HCl_25.m" Calculation of mean activity / osmotic coefficient from 0 to 1.88 mol/kg after determining the main QRL parameterization (cL) by means of experimental osmotic data (at molalities 1, 1.5, 2, 2.5 mol/kg). - Module "dcL_dP_Parameterization_HCl_25.m" Computation of the pressure derivative (dcL_dP) of the cL parameterization and further quantities propeduetical to next module. - Module "QRL_Extended_Parameterization_HCl_25.m" Implementation of the procedure to calculate mean activity coefficient, osmotic coefficient, (Abelian) solution density and apparent molar volume of solute for molalities from 1.88 to 16 mol/kg; the calibration set (experimental density and experimental osmotic data used in addition to previous ones) refers to 16 mol/kg (about).

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Theoretical Calculation, Thermodynamics Modeling, Electrolyte Solution, Computational Chemistry, Mathematical Modeling for Materials Science

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