Proceedings of MATSUS Spring 2025 Conference (MATSUSSpring25)
DOI: https://doi.org/10.29363/nanoge.matsusspring.2025.411
Publication date: 16th December 2024
Lithium-sulfur (Li-S) batteries are among the most promising of the ‘beyond Li-ion’ battery technologies, due to their high theoretical gravimetric capacity (1675 mAh g-1S) and energy density (2500 Wh kg-1S), which far exceed those of Li-ion batteries (LIBs). Li-S batteries benefit from the use of sulfur as an earth-abundant, low-cost and geographically widespread positive-electrode material. These systems exploit the reversible 16-electron interconversion of S8 and Li2S at the positive electrode, coupled with the plating and stripping of Li at the negative electrode.
Despite the promise of the Li-S battery, practical cell performance is often limited due to poor interconversion of S8 and Li2S, which is exacerbated at higher rates of discharge and charge. While there are many reasons for this performance limitation, it is generally accepted the battery requires a catalyst at the positive electrode to achieve high energy efficiency and rates. Despite this, the exact role of the catalyst in the Li-S battery remains elusive and the nature of the rate limiting steps is unknown. Here we will discuss the fundamental reaction routes within the lithium sulphur battery. Using a combination of analytical electrochemical techniques we have identify those steps which are rate limiting during discharge of the cell. In addition, we will discuss the development of molecular catalysts able to drive the interconversion of S8 and Li2S. Two types of molecular catalysts will be described; 1) simple redox shuttles and 2) homogeneous catalysts able to trigger disproportionation reactions. A variety of electrochemical and analytical techniques have been utilised to assess the role of the molecular catalysts in sulfur redox chemistry. Using galvanostatic cycling, we are able to demonstrate the impact of these molecular catalysts in cells, which display enhanced cycling performance. Finally, we present an alternative statistical approach to cell data analysis.
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