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  4. Data-driven selection of electrolyte additives for aqueous magnesium batteries
 
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Data-driven selection of electrolyte additives for aqueous magnesium batteries

Publikationstyp
Journal Article
Date Issued
2022-09-23
Sprache
English
Author(s)
Würger, Tim  orcid-logo
Wang, Linqian  
Snihirova, Darya 
Deng, Min  
Lamaka, Sviatlana V.  
Winkler, David A.  
Höche, Daniel  
Zheludkevich, Mikhail L.  
Meißner, Robert  orcid-logo
Feiler, Christian  
Institut
Kunststoffe und Verbundwerkstoffe M-11  
Molekulardynamische Simulation weicher Materie M-EXK2  
TORE-URI
http://hdl.handle.net/11420/13968
Journal
Journal of Materials Chemistry A  
Start Page
21672
End Page
21682
Citation
Journal of Materials Chemistry A 10 (40): 21672-21682 (2022-09-23)
Publisher DOI
10.1039/d2ta04538a
Scopus ID
2-s2.0-85140780415
Aqueous primary Mg-air batteries have considerable potential as energy sources for sea applications and portable devices. However, some challenges at the anode-electrolyte interface related to self-corrosion, aging of the electrolyte and the chunk-effect have to be solved to improve the discharge potential of the battery as well as the utilization efficiency of the anode material. Aside from alloying, an effective strategy to mitigate self-corrosion and battery failure is the use of electrolyte additives. Selecting useful additives from the vast chemical space of possible compounds is not a trivial task. Fortunately, data-driven quantitative structure-property relationship (QSPR) models can facilitate efficient searches for promising battery booster candidates. Here, the robustness and predictive performance of two QSPR models are evaluated using an active design of experiments approach. We also present a multi-objective optimization method that allows to identify new electrolyte additives that can boost the battery anode performance with respect to a target application, thus accelerating the discovery of advanced battery systems.
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