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Conductivity experiments for electrolyte formulations and their automated analysis
Electrolytes are considered crucial for the performance of batteries, and therefore indispensable for future energy storage research. This paper presents data that describes the effect of the electrolyte composition on the ionic conductivity. In particular, the data focuses on electrolytes composed...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9852460/ https://www.ncbi.nlm.nih.gov/pubmed/36658233 http://dx.doi.org/10.1038/s41597-023-01936-3 |
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author | Rahmanian, Fuzhan Vogler, Monika Wölke, Christian Yan, Peng Fuchs, Stefan Winter, Martin Cekic-Laskovic, Isidora Stein, Helge Sören |
author_facet | Rahmanian, Fuzhan Vogler, Monika Wölke, Christian Yan, Peng Fuchs, Stefan Winter, Martin Cekic-Laskovic, Isidora Stein, Helge Sören |
author_sort | Rahmanian, Fuzhan |
collection | PubMed |
description | Electrolytes are considered crucial for the performance of batteries, and therefore indispensable for future energy storage research. This paper presents data that describes the effect of the electrolyte composition on the ionic conductivity. In particular, the data focuses on electrolytes composed of ethylene carbonate (EC), propylene carbonate (PC), ethyl methyl carbonate (EMC), and lithium hexafluorophosphate (LiPF(6)). 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 varied during the study. Conductivity data was obtained from electrochemical impedance spectroscopy (EIS) measurements at various temperatures. Based on the thus obtained temperature series, the activation energy for ionic conduction was determined during the analysis. The data is presented here in a machine-readable format and includes a Python package for analyzing temperature series of electrolyte conductivity according to the Arrhenius equation and EIS data. The data may be useful e.g. for the training of machine learning models or for reference prior to experiments. |
format | Online Article Text |
id | pubmed-9852460 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98524602023-01-21 Conductivity experiments for electrolyte formulations and their automated analysis Rahmanian, Fuzhan Vogler, Monika Wölke, Christian Yan, Peng Fuchs, Stefan Winter, Martin Cekic-Laskovic, Isidora Stein, Helge Sören Sci Data Data Descriptor Electrolytes are considered crucial for the performance of batteries, and therefore indispensable for future energy storage research. This paper presents data that describes the effect of the electrolyte composition on the ionic conductivity. In particular, the data focuses on electrolytes composed of ethylene carbonate (EC), propylene carbonate (PC), ethyl methyl carbonate (EMC), and lithium hexafluorophosphate (LiPF(6)). 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 varied during the study. Conductivity data was obtained from electrochemical impedance spectroscopy (EIS) measurements at various temperatures. Based on the thus obtained temperature series, the activation energy for ionic conduction was determined during the analysis. The data is presented here in a machine-readable format and includes a Python package for analyzing temperature series of electrolyte conductivity according to the Arrhenius equation and EIS data. The data may be useful e.g. for the training of machine learning models or for reference prior to experiments. Nature Publishing Group UK 2023-01-19 /pmc/articles/PMC9852460/ /pubmed/36658233 http://dx.doi.org/10.1038/s41597-023-01936-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Rahmanian, Fuzhan Vogler, Monika Wölke, Christian Yan, Peng Fuchs, Stefan Winter, Martin Cekic-Laskovic, Isidora Stein, Helge Sören Conductivity experiments for electrolyte formulations and their automated analysis |
title | Conductivity experiments for electrolyte formulations and their automated analysis |
title_full | Conductivity experiments for electrolyte formulations and their automated analysis |
title_fullStr | Conductivity experiments for electrolyte formulations and their automated analysis |
title_full_unstemmed | Conductivity experiments for electrolyte formulations and their automated analysis |
title_short | Conductivity experiments for electrolyte formulations and their automated analysis |
title_sort | conductivity experiments for electrolyte formulations and their automated analysis |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9852460/ https://www.ncbi.nlm.nih.gov/pubmed/36658233 http://dx.doi.org/10.1038/s41597-023-01936-3 |
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