Experimental data on diffusion in binary liquid mixtures at 298±1 K from the literature were systematically consolidated and used to determine diffusion coefficients D∞ij of solutes i at infinite dilution in solvents j...
Methods for predicting Henry's law constants H ij are important as experimental data are scarce. We introduce a new machine learning approach for such predictions: matrix completion methods (MCMs) and demonstrate its applicability using a data base that contains experimental H ij values for 101 solutes i and 247 solvents j at 298 K. Data on H ij are only available for 2661 systems i + j. These H ij are stored in a 101 Â 247 matrix; the task of the MCM is to predict the missing entries. First, an entirely data-driven MCM is presented. Its predictive performance, evaluated using leave-one-out analysis, is similar to that of the Predictive Soave-Redlich-Kwong equation-of-state (PSRK-EoS), which, however, cannot be applied to all studied systems. Furthermore, a hybrid of MCM and PSRK-EoS is developed in a Bayesian framework, which yields an unprecedented performance for the prediction of H ij of the studied data set.
Redox flow batteries (RFBs) are considered an outstanding candidate for the integration of renewable energy sources into the existing power grids. A key property of RFBs is the open circuit voltage (OCV) corresponding to the currentless equilibrium state. In the literature, the Nernst equation describing this property is often simplified by neglecting the activity coefficients. In this work, using a thermodynamically rigorous approach, we show that activity coefficients have a significant influence on the OCV of the Iron-Cadmium and All-Vanadium RFBs. Moreover, this influence varies with the state of charge. Therefore, activity coefficients should not be neglected in the Nernst equation. We show that when doing so, the resulting offset in OCV is actually comparable to typical voltage losses occurring during operation. Hence, fitting kinetic parameters to measurement data of voltage losses can lead to ambiguous results if only the idealized OCV, obtained by neglecting the activity coefficients, is used in that evaluation. Therefore, the implementation of a thermodynamically rigorous model has the potential to significantly improve state-of-the-art models for RFBs.
Group contribution (GC) methods are widely used for predicting the thermodynamic properties of mixtures. They divide components into structural groups, which can be combined freely so that the applicability of...
Redox flow batteries (RFBs) are considered an outstanding candidate for the integration of renewable energy sources into the existing power grids. A key property of RFBs is the open circuit voltage (OCV) corresponding to the currentless equilibrium state. In the literature, the Nernst equation describing this property is often simplified by neglecting the activity coefficients. In this work, using a thermodynamically rigorous approach, we show that activity coefficients have a significant influence on the OCV of the Iron-Cadmium and All-Vanadium RFBs. Moreover, this influence varies with the state of charge. Therefore, activity coefficients should not be neglected in the Nernst equation. We show that when doing so, the resulting offset in OCV is actually comparable to typical voltage losses occurring during operation. Hence, fitting kinetic parameters to measurement data of voltage losses can lead to ambiguous results if only the idealized OCV, obtained by neglecting the activity coefficients, is used in that evaluation. Therefore, the implementation of a thermodynamically rigorous model has the potential to significantly improve state-of-the-art models for RFBs.
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