journal article·2023-06

Preparation of porous graphitic carbon and its dual-ion capacitance energy storage mechanism

Chang-zhen Zhan, Xiao-jie Zeng, Rui-tao Lv, Yang Shen, Zheng-hong Huang, Fei-yu Kang

New Carbon Materials· Volume 38· Issue 3
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Abstract

A lithium-ion capacitor, a combination of a lithium-ion battery and a supercapacitor, is expected to have the advantages of both a battery and a capacitor and has attracted worldwide attention in recent years. However, its energy storage is limited due to the electric double-layer capacitance mechanism of the positive electrode. Consequently, to fundamentally improve the performance of the positive electrode material, a novel dual-ion hybrid capacitance energy storage mechanism is proposed. Porous graphitic carbon with a partially graphitized structure and hierarchical porous structure was synthesized by a one-step heat treatment method using potassium/magnesium/iron citrate as precursors. When used as the positive electrode material, the porous graphitic carbon has a dual-ion hybrid capacitance mechanism in an electrolyte produced using a mixture of Li-TFSI (bis(trifluoromethylsulfonyl) amine lithium salt) and BMIm-TFSI (1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide), which combines electric double-layer capacitance behavior in a lithium-ion capacitor and anion intercalation/de-intercalation behavior in a dual-ion battery. Two mechanisms were observed in the electrochemical characterization process, and the performance of the porous graphitic carbon was compared to porous carbon and artificial graphite, which indicate that its energy storage performance is significantly better due to the additional plateau capacity contributed by anion intercalation at a high potential and the improved conductivity through the local graphitic regions.
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Research context

OpenAlex topics: Supercapacitor Materials and Fabrication · Advancements in Battery Materials · Advanced Battery Materials and Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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