ACS Applied Materials & Interfaces · 2022 · 25 citations · 42 references
Natural SciencesMaterials EngineeringMaterials ScienceElectrochemistryFunctional MaterialsSurface ScienceBattery Cell ChemistryBattery MaterialsElectrochemical CellsElectrode MaterialsElectrode-electrolyte InterfacesMetal OxidesCharging TimeCrystallizationCrystallization BehaviorGraphite AnodesInterfacial KineticsInterfacial ReactionsLithium-ion BatteriesLithium-ion Battery Electrodes Based On Metal Oxides
Graphite is the most popular anode material for lithium-ion batteries (LIBs) owing to its high reversibility and stable cycling performance. With the rapid growth of the global electric vehicle (EV) market, it has become necessary to improve the quick-charge performance of graphite to reduce the charging time of LIBs. Therefore, from a structural viewpoint, it is crucial to control interfacial reactions and stabilize the surface of graphite to improve the sluggish interfacial kinetics. Herein, we propose a facile approach for integrating functional metal oxides on the surface of natural graphite (NG) via a surface-coating technique in combination with a facile-crystallization process. The functionality of the metal oxides, i.e., MoO2 and Fe3O4, on the surface of NG was thoroughly investigated based on various structural and electrochemical analyses. The results demonstrate that the metal oxides play critical roles in stabilizing the surface of NG and facilitating faster Li+ migration at the interface between NG and the electrolyte during cycling. In particular, the full cell configured with the c-Fe3O4-NG anode shows remarkably improved charging behavior (3 C charging–1 C discharging) without any significant loss of reversible capacity during 300 cycles. This study has conclusively established that tailoring the surface of NG with functional metal oxides would be a utilitarian way to improve the charging capability of NG. We are confident that the study results would provide utilitarian insights into the development of advanced LIBs for successful implementation in EV applications in the future.
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Generalized Gradient Approximation Made Simple
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