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@ARTICLE{Rahmanian:1019518,
      author       = {Rahmanian, Fuzhan and Vogler, Monika and Wölke, Christian
                      and Yan, Peng and Fuchs, Stefan and Winter, Martin and
                      Cekic-Laskovic, Isidora and Stein, Helge Sören},
      title        = {{C}onductivity experiments for electrolyte formulations and
                      their automated analysis},
      journal      = {Scientific data},
      volume       = {10},
      number       = {1},
      issn         = {2052-4436},
      address      = {London},
      publisher    = {Nature Publ. Group},
      reportid     = {FZJ-2023-05465},
      pages        = {43},
      year         = {2023},
      abstract     = {Electrolytes are considered crucial for the performance of
                      batteries, and therefore indispensable forfuture energy
                      storage research. This paper presents data that describes
                      the effect of the electrolytecomposition on the ionic
                      conductivity. In particular, the data focuses on
                      electrolytes composed ofethylene carbonate (EC), propylene
                      carbonate (PC), ethyl methyl carbonate (EMC), and
                      lithiumhexafluorophosphate (LiPF6). The mass ratio of EC to
                      PC was varied, while keeping the mass ratio of(EC + PC) and
                      EMC at fixed values of 3:7 and 1:1. The conducting salt
                      concentration was also variedduring the study. Conductivity
                      data was obtained from electrochemical impedance
                      spectroscopy (EIS)measurements at various temperatures.
                      Based on the thus obtained temperature series, the
                      activationenergy for ionic conduction was determined during
                      the analysis. The data is presented here in
                      amachine-readable format and includes a Python package for
                      analyzing temperature series of electrolyteconductivity
                      according to the Arrhenius equation and EIS data. The data
                      may be useful e.g. for thetraining of machine learning
                      models or for reference prior to experiments.},
      cin          = {IEK-12},
      ddc          = {500},
      cid          = {I:(DE-Juel1)IEK-12-20141217},
      pnm          = {1222 - Components and Cells (POF4-122) / BIG-MAP - Battery
                      Interface Genome - Materials Acceleration Platform (957189)},
      pid          = {G:(DE-HGF)POF4-1222 / G:(EU-Grant)957189},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36658233},
      UT           = {WOS:000943201800006},
      doi          = {10.1038/s41597-023-01936-3},
      url          = {https://juser.fz-juelich.de/record/1019518},
}