首页|Graphene effectively activating"dead"water molecules between manganese dioxide layers in potassium-ion battery

Graphene effectively activating"dead"water molecules between manganese dioxide layers in potassium-ion battery

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Aqueous potassium-ion batteries(APIBs),recognized as safe and reliable new energy devices,are consid-ered as one of the alternatives to traditional batteries.Layered MnO2,serving as the main cathode,exhi-bits a lower specific capacity in aqueous electrolytes compared to organic systems and operates through a different reaction mechanism.The application of highly conductive graphene may effectively enhance the capacity of APIBs but could complicate the potassium storage environment.In this study,a MnO2 cathode pre-intercalated with K+ions and grown on graphene(KMO@rGO)was developed using the microwave hydrothermal method for APIBs.KMO@rGO achieved a specific capacity of 90 mA h g-1 at a current den-sity of 0.1 A g-1.maintaining a capacity retention rate of＞90％after 5000 cycles at 5 A g-1.In-situ and ex-situ characterization techniques revealed the energy-storage mechanism of KMO@rGO:layered MnO2 traps a large amount of"dead"water molecules during K+ions removal.However,the introduction of gra-phene enables these water molecules to escape during K+ions insertion at the cathode.The galvanostatic intermittent titration technique and density functional theory confirmed that KMO@rGO has a higher K+ions migration rate than MnO2.Therefore,the capacity of this cathode depends on the interaction between dead water and K+ions during the energy-storage reaction.The optimal structural alignment between layered MnO2 and graphene allows electrons to easily flow into the external circuit.Rapid charge compensation forces numerous low-solvent K+ions to displace interlayer dead water,enhancing the capacity.This unique reaction mechanism is unprecedented in other aqueous battery studies.

GrapheneK-ion batteriesMn-based layered oxideWater moleculesDensity functional theory

Xinhai Wang、Wensheng Yang、Shengshang Lu、Shangshu Peng、Tong Guo、Quan Xie、Qingquan Xiao、Yunjun Ruan

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Institute of Advanced Optoelectronic Materials and Technology,College of Big Data and Information Engineering,Guizhou University,Guiyang 550025,Guizhou,China

Scientific and Technological Plan Project of Guizhou ProvinceIndustry and Education Combination Innovation Platform of Intelligent Manufacturing and the Graduate Joint Training Base at Guizhou Engineering Research Center for smart services

[2021]0602020-520000-83-01-3240612203-520102-04-04-298868

2024

10.1016/j.jechem.2024.01.078

能源化学
中国科学院大连化学物理研究所 中国科学院成都有机化学研究所

能源化学

CSTPCDEI
影响因子：0.654
ISSN：2095-4956
年,卷(期)：2024.93(6)
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