Exploring low-cost high energy NASICON cathodes for sodium-ion batteries via a combined machine-learning, ab initio, and experimental approach

JOURNAL OF MATERIALS CHEMISTRY A(2023)

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摘要
Sodium-ion batteries (SIBs) display the essential properties required of a reliable energy-storage device, such as vast availability, good voltage output, and cost-effectiveness. Although initial SIB cathodes delivered a significantly lower capacity than their lithium-ion battery counterparts, new high-capacity cathode materials for SIBs continue to be developed today. This study employed a combined machine-learning (ML), ab initio density functional theory (DFT), and experimental approach to develop low-cost and high-energy cathode materials, i.e. Na3.5MnV0.5Ti0.5(PO4)(3) (NMVTP), Na3.5MnV0.5Fe0.5(PO4)(3) (NMVFP), and Na3.5MnV0.5Al0.5(PO4)(3) (NMVAP). Among these materials, the carbon-coated Na3.5MnV0.5Ti0.5(PO4)(3) (NMVTP/C) with the most stable formation energy (-1.99 eV) registered an exceedingly high specific capacity of 133.14 mA h g(-1), a satisfactory Na+ (de)insertion voltage of 3.42 V, and a superior energy output of 455 W h kg(-1) in the half-cell configuration. NMVTP/C also exhibits a rapid sodium storage capability for 8000 cycles with a capacity retention of 75% at a considerably high current rate of 14C and an impressive rate proficiency of 59.2 mA h g(-1) at 17.5C.
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high energy nasicon cathodes,low-cost,sodium-ion,machine-learning
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