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Published on: March 21, 2016
Thermodynamic interaction parameters for the system water/NMMO hydrate
1Institut für Physikalische Chemie der Johannes Gutenberg-Universität Mainz and Materialwissenschaftliches Forschungszentrum der Universität Mainz, Welder-Weg 13, D-55099 Mainz, Germany.
Vapor pressures of N-methyl-morpholine N-oxide (NMMO) aqueous solutions reveal negative Flory-Huggins interaction parameters. These parameters indicate an endothermal heat of mixing at low NMMO concentrations, becoming near-athermal at high concentrations.
Area of Science:
- Physical Chemistry
- Thermodynamics
- Solution Chemistry
Background:
- N-methyl-morpholine N-oxide (NMMO) is a key solvent in cellulose processing.
- Understanding the thermodynamic properties of NMMO aqueous solutions is crucial for optimizing industrial applications.
Purpose of the Study:
- To measure vapor pressures of water in NMMO aqueous solutions.
- To calculate Flory-Huggins interaction parameters (chi) and analyze their dependence on temperature and concentration.
- To investigate the heat of mixing and its composition dependence.
Main Methods:
- Vapor pressure measurements of aqueous NMMO solutions at 80, 90, and 100°C.
- Calculation of Flory-Huggins interaction parameters (chi) as a function of NMMO volume fraction (phi).
- Analysis of the heat of mixing based on the calculated chi parameters.
Main Results:
- Negative Flory-Huggins interaction parameters (chi) were observed across all concentrations.
- At low NMMO volume fractions (phi), chi decreased significantly with increasing temperature.
- At high NMMO volume fractions (approaching unity), chi remained relatively constant with temperature changes.
- The heat of mixing was found to be endothermal at low NMMO concentrations and near-athermal at low water content.
Conclusions:
- The thermodynamic behavior of NMMO aqueous solutions is well-described by the Flory-Huggins theory.
- The composition dependence of chi can be modeled using the Redlich-Kister equation or a two-step mixing approach.
- The two-step modeling offers potential for deeper molecular insights into the mixing process.
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