Cycling-induced structure refinement of MnO nanorods wrapped by N-doped carbon with internal void space for advanced lithium-ion anodes
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Abstract
Abstract To date, addressing the vital and ubiquitous issues of dramatical volume variation and inferior electro-conductivity is still a hindrance for promoting the practical application of metal oxide-based anode materials for lithium-ion batteries (LIBs). Here, a simple and facile tactic is explored to build an intriguing architecture of N-doped carbon-wrapped MnO nanorod with suitably internal void space (MnO@NC). The resulting N-doped carbon sheath could provide many merits towards booting electron/ion transfer, well encapsulating the refined MnO nanograins, partially buffering volume expansion of MnO during lithiation, and reducing the unwanted reaction for lithium-depletion as a separation layer. Moreover, the internal void space offers adequate space to accommodate volume change upon cycling, ensuring the structural integrity of the electrode. Therefore, the MnO@NC electrode delivers high Li+ storage capability (460 mAh g−1 at 0.1 A g−1), superior cycling durability (570 mAh g−1 at 1 A g−1 over 600 loops). Analysis of the lithiation and delithiation behavior indicates that the further oxidation of Mn2+ has an insignificant contribution for the ever-increasing capacity during cycling. The intrinsic collapse and reconstruction of conversion reaction could transform the pea-like MnO nanorod into ultrafine nanograins well dispersed within N-doped carbon sheath, significantly contributing more capacity in the continuous discharge/charge process. This work is helpful to understand the microstructure-dependent capacity-increasing and believed to have great promising in tailoring conversion-type electrode materials with outstanding lithium-ion storage.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advancements in Battery Materials · Supercapacitor Materials and Fabrication · Advanced Battery Materials and Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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