Ten-Electron Count Rule of MXene-Supported Single-Atom Catalysts for Sulfur Reduction in Lithium–Sulfur Batteries

Abstract

Lithium–sulfur (Li–S) batteries are proposed as next-generation energy storage devices due to their high theoretical capacity and specific energy. However, the actual capacity utilization is greatly limited by the poor reactivity of the sulfur reduction reaction (SRR), which motivates us to develop corresponding high-efficient catalysts. Inspired by the application of MXene and single-atom catalysts (SACs) in improving SRR, a virtual screening on the MXene-supported SACs from the imp2d database is carried out. Finally, six kinds of top catalysts are identified for SRR, and most of them can be considered as variants of the previous representative SRR catalysts, which reflects the rationality of our screening. Meanwhile, the stability and reactivity metrics of the SACs are calculated by density functional theory (DFT) and show obvious trends depending on the type of adatom/MXene. For the critical intermediate binding that can tune SRR activity, further electronic structure analysis reveals the so-called 10-electron count rule, whose decisive role is also reflected by the Shapley value analysis from machine learning (ML). It is noteworthy that this count rule was used to analyze the SACs for hydrogen/carbon/nitrogen-related reactions before, and our successful attempt to optimize SRR further indicates its universality in catalysis fields. Overall, the 10-electron count rule not only rationalizes the nature of SAC–adsorbate interactions but also provides intuitive design guidance for novel SRR catalysts.

Keywords

density functional theory / lithium–sulfur battery / machine learning / MXene / single-atom catalyst

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