Chemistry Methods
A specially designed high-throughput experimentation facility, used for the highly effective exploration of electrolyte formulations in composition space for diverse battery chemistries and targeted applications, is presented. It follows a high-throughput formulation-characterization-optimization chain based on a set of previously established electrolyte-related requirements. Here, the facility is used to acquire large dataset of ionic conductivities of non-aqueous battery electrolytes in the conducting salt-solvent/co-solvent-additive composition space. The measured temperature dependence is mapped on three generalized Arrhenius parameters, including deviations from simple activated dynamics. This reduced dataset is thereafter analyzed by a scalable data-driven workflow, based on linear and Gaussian process regression, providing detailed information about the compositional dependence of the conductivity. Complete insensitivity to the addition of electrolyte additives for otherwise constant molar composition is observed. Quantitative dependencies, for example, on the temperature-dependent conducting salt content for optimum conductivity are provided and discussed in light of physical properties such as viscosity and ion association effects.
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Data-driven analysis of high-throughput experiments on liquid battery electrolyte formulations: Unraveling the impact of composition on conductivity
Dr. Anand Narayanan Krishnamoorthy 1, Dr. Christian Wölke 1, Dr. Diddo Diddens 1, Dr. Moumita Maiti 2, Youssef Mabrouk 1, Peng Yan 1, Dr. Mariano Grünebaum 1, Prof. Dr. Martin Winter, Prof. Dr. Andreas Heuer 1,3, Dr. Isidora Cekic-Laskovic 1
1. Helmholtz-Institute Münster (IEK-12), Forschungszentrum Jülich GmbH, Corrensstraße 46, 48149 Münster, Germany
2. Institute of Physical Chemistry, University of Münster, Corrensstrasse 28/30, 48149 Münster, Germany
3. MEET Battery Research Center, University of Münster, Corrensstrasse 46, 48149 Münster, Germany
DOI: https://doi.org/10.26434/chemrxiv-2022-vbl5d
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