Tracer-diffusion coefficients of experimental aqueous methanol mixtures (doi:10.18419/darus-3118)

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Tracer-diffusion coefficients of experimental aqueous methanol mixtures

doi:10.18419/darus-3118

DaRUS

2022-10-28

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Gültig, Matthias; Range, Jan Peter; Schmitz, Benjamin; Pleiss, Jürgen, 2022, "Tracer-diffusion coefficients of experimental aqueous methanol mixtures", https://doi.org/10.18419/darus-3118, DaRUS, V1

Tracer-diffusion coefficients of experimental aqueous methanol mixtures

doi:10.18419/darus-3118

Gültig, Matthias (Universität Stuttgart)

Range, Jan Peter (Universität Stuttgart)

Schmitz, Benjamin (Universität Stuttgart)

Pleiss, Jürgen (Universität Stuttgart)

DaRUS

Gültig, Matthias

Pleiss, Jürgen

https://doi.org/10.18419/darus-3118

Chemistry, Engineering, Mathematical Sciences, Physics, FAIR, FAIR Data Principles, Excess Properties, Transferability of Force Fields, Liquid Mixtures

In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol-water and glycerol-water, by systematically studying the dependence of densities and diffusion coefficients from water content over the whole composition range and temperatures between 278.15 and 318.15 K. Experimental data was extracted manually from literature. The same parameter space was explored by comprehensive molecular dynamics simulations, whose results were directly transferred to the analysis platform. The benefit of data integration was illustrated by assessing the transferability of the force fields, which had been developed for pure compounds to different compositions and temperatures, and by analyzing the excess mixing properties as a measure of non-ideality of methanol-water and glycerol-water mixtures. The core of the data management and analysis platform is the newly developed Python library pyThermoML, which represents metadata, the parameters and the experimentally determined or simulated properties as Python data classes. <br></br> The feasibility of a seamless data flow from data acquisition to a comprehensive data analysis was demonstrated. <a href="https://github.com/FAIRChemistry/pyThermoML">PyThermoML</a> enables interoperability and reusability of the datasets. The publication of ThermoML documents on the Dataverse installation of the University of Stuttgart (DaRUS) makes thermophysical data findable and accessible, and thus FAIR. <br></br> The usage of pyThermoML is demonstrated in the following <a href="https://github.com/FAIRChemistry/pyThermoML/blob/master/pyThermoML_example_workflow/templateThermoML.ipynb">example workflow</a> and can be utilized to read the given ThermoML file.

ThermoML file

Derlacki, Z. J., Easteal, A. J., Edge, A. V. J., Woolf, L. A., & Roksandic, Z. (1985). Diffusion coefficients of methanol and water and the mutual diffusion coefficient in methanol-water solutions at 278 and 298 K. The Journal of Physical Chemistry, 89(24), 5318-5322.

10.1021/j100270a039

Derlacki, Z. J., Easteal, A. J., Edge, A. V. J., Woolf, L. A., & Roksandic, Z. (1985). Diffusion coefficients of methanol and water and the mutual diffusion coefficient in methanol-water solutions at 278 and 298 K. The Journal of Physical Chemistry, 89(24), 5318-5322.

Gültig, M., Range, J. P., Schmitz, B., & Pleiss, J. (2022). Integration of Simulated and Experimentally Determined Thermophysical Properties of Aqueous Mixtures by ThermoML. Journal of Chemical & Engineering Data,

10.1021/acs.jced.2c00391

Gültig, M., Range, J. P., Schmitz, B., & Pleiss, J. (2022). Integration of Simulated and Experimentally Determined Thermophysical Properties of Aqueous Mixtures by ThermoML. Journal of Chemical & Engineering Data,

Label:

exp_sdiff_meth.xml

Text:

text/xml