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Glucose-derived hard carbon/carbon nanotube neural network architectures for enhanced sodium ion storage

  • Carbon Letters
  • Abbr : Carbon Lett.
  • 2025, 35(2), pp.699~707
  • Publisher : Korean Carbon Society
  • Research Area : Natural Science > Natural Science General > Other Natural Sciences General
  • Received : May 31, 2024
  • Accepted : September 8, 2024
  • Published : June 5, 2025

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ABSTRACT

Developing advanced anode materials is one of the effective strategies to enhance the electrochemical performance of sodium-ion batteries (SIBs). Herein, inspired by the biological central nervous system structure, we report a facile and efficient strategy to fabricate the three-dimensional hierarchical neural network-like carbon architectures, where the glucose-derived hard carbon (HC) nanospheres are in situ assembled and embedded in carbon nanotube (CNT) network nanostructure (HC/CNT hybrid networks). The HC nanospheres with large carbon interlayer spacing help to decrease the diffusion length of sodium ions and the interconnected CNT networks enable the rapid electron transfer during charge/discharge process. Benefiting from these structure merits, the as-made HC/CNT hybrid networks can deliver a superior rate capacity of 162 mA h g−1 at the current density of 5 A g−1. Additionally, it exhibits excellent cycling performance with a capacity retention rate of 86.3% after 140 cycles. This work offers a promising candidate anode material for SIBs and a new prospect towards carbon-based composites design, simultaneously.

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