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

Yingbo Kang; Yinglong Weng; Xiaotong Han; Jianping Zhang; Xu Yu; Bo Wang

doi:10.1007/s42823-024-00815-0

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
DOI : 10.1007/s42823-024-00815-0
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

Yingbo Kang ¹, Yinglong Weng ¹, Xiaotong Han ², Jianping Zhang ², Xu Yu ³, Bo Wang ⁴

¹Liaoning University of Technology
²Chongqing University
³Yangzhou University
⁴Jilin Engineering Normal University

Accredited

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.

KEYWORDS

Carbon material, Hard carbon, Carbon nanotube, Sodium-ion battery, Anode material

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This is the result of checking the information with the same ISSN, publication year, volume, and start page between the WoS and the KCI journals. (as of 2026-07-09)

Total Citation Counts(KCI+WOS) (12) This is the number of times that the duplicate count has been removed by comparing the citation list of WoS and KCI.

* References for papers published after 2025 are currently being built.

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Carbon Letters 2025 KCI Impact Factor : 0.73 KCI-WoS Impact Factor : 6.26

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

ABSTRACT

KEYWORDS

Citation status

This is the result of checking the information with the same ISSN, publication year, volume, and start page between the WoS and the KCI journals. (as of 2026-07-09)

Total Citation Counts(KCI+WOS) (12) This is the number of times that the duplicate count has been removed by comparing the citation list of WoS and KCI.

* References for papers published after 2025 are currently being built.

Carbon Letters 2025 KCI Impact Factor : 0.73 KCI-WoS Impact Factor : 6.26

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

ABSTRACT

KEYWORDS

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KCI Citation Counts (2)

WoS Citation Counts (10) This is the result of checking the information with the same ISSN, publication year, volume, and start page between the WoS and the KCI journals. (as of 2026-07-09)

Total Citation Counts(KCI+WOS) (12) This is the number of times that the duplicate count has been removed by comparing the citation list of WoS and KCI.

REFERENCES (37) * References for papers published after 2025 are currently being built.

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Citation

This is the result of checking the information with the same ISSN, publication year, volume, and start page between the WoS and the KCI journals. (as of 2026-07-09)

* References for papers published after 2025 are currently being built.