Characterization of non-aqueous electrolytes
Title: Characterization of non-aqueous electrolytes
SNIC Project: SNIC 2021/22-135
Project Type: SNIC Small Compute
Principal Investigator: Simon Colbin <simon.colbin@kemi.uu.se>
Affiliation: Uppsala universitet
Duration: 2021-02-19 – 2022-03-01
Classification: 10403
Keywords:

Abstract

Electrolytes are central components in many different devices. Even though many compounds share the label electrolyte, the composition and properties vary greatly. For state-of-the-art sodium ion batteries, an electrolyte needs to have a high conductivity, and a high electrochemical stability or good passivating properties. The well-established electrolyte models are mostly applicable for low concentrations and water-based solvents. None of which leads to high conductivity, nor great electrochemical stability. However, characterizing—even moderately—concentrated non-aqueous electrolytes is challenging, because of the complexity that arise within these systems. Still, we believe that the modern approach of combining experimental and computational descriptions of these systems can help us overcome some of these challenges. We want to be able to understand and fairly portray realistic systems. In this regard, it is possible to use modern chemical simulations techniques, together with experiments, to obtain a better description of these systems. The aim of this project is to use DFT calculations as a complement to experimental characterization of different electrolyte systems. The focus will be on understanding the structure of non-aqueous electrolytes, electrochemical stability, possible decomposition products from reduction and oxidation of electrolytes with different compositions. Vibrational spectra will be simulated for ion-solvent model systems. The results will be compared with data from IR and Raman spectroscopy. From our previous round we learnt to use QM-methods to help qualitatively deconvolute the spectra. Now, we want to develop a methodology where we can use QM-methods to help obtain quantitative insights. We want to continue our work on simulating electrochemical stability. We now aim to predict the reduction potential of irreversible processes. This would include the use of QM-methods to find possible chemical and electrochemical decomposition pathways of electrolyte components.