Design principles of heterointerfacial redox chemistry for highly reversible lithium metal anode.
Liang LinJiantao LiYinggan ZhangHongfei ZhengYouzhang HuangChengkun ZhangBaisheng SaLaisen WangJie LinDong-Liang PengJun LuKhalil AmineQingshui XiePublished in: Proceedings of the National Academy of Sciences of the United States of America (2024)
High electrochemical reversibility is required for the application of high-energy-density lithium (Li) metal batteries; however, inactive Li formation and SEI (solid electrolyte interface)-instability-induced electrolyte consumption cause low Coulombic efficiency (CE). The prior interfacial chemical designs in terms of alloying kinetics have been used to enhance the CE of Li metal anode; however, the role of its redox chemistry at heterointerfaces remains a mystery. Herein, the relationship between heterointerfacial redox chemistry and electrochemical transformation reversibility is investigated. It is demonstrated that the lower redox potential at heterointerface contributes to higher CE, and this enhancement in CE is primarily due to the regulation of redox chemistry to Li deposition behavior rather than the formation of SEI films. Low oxidation potential facilitates the formation of the surface with the highly electrochemical binding feature after Li stripping, and low reduction potential can maintain binding ability well during subsequent Li plating, both of which homogenize Li deposition and thus optimize CE. In particular, Mg hetero-metal with ultra-low redox potential enables Li metal anode with significantly improved CE (99.6%) and stable cycle life for 700 cycles at 3.0 mA cm -2 . This work provides insight into the heterointerfacial design principle of next-generation negative electrodes for highly reversible metal batteries.
Keyphrases
- ion batteries
- solid state
- electron transfer
- gold nanoparticles
- ionic liquid
- energy transfer
- human health
- drug discovery
- risk assessment
- machine learning
- molecularly imprinted
- nitric oxide
- molecular dynamics simulations
- deep learning
- climate change
- hydrogen peroxide
- diabetic rats
- binding protein
- reduced graphene oxide