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Title: | Spontaneous Electrochemical Reconstruction of NiNb2O6@C for High-Rate Lithium-Ion Batteries | Authors: | Tan, Lidan Zhou, Sheng Jin, Yi Zhu , Hui Zhang, Qin Guo, Jianguang Li, Xuanke Dong, Zhijun YANG, Nianjun Cong, Ye |
Issue Date: | 2024 | Publisher: | ELSEVIER SCIENCE SA | Source: | Chemical engineering journal (1996. Print), 495 (Art N° 153397) | Abstract: | 16 Niobium oxides are widely active in the arena of lithium-ion batteries due to their 17 distinctive structure and rapid charging capability, nevertheless facing the serious 18 challenge from their inherently low electrical conductivity. Herein, a molten salt 19 method is proposed to prepare nickel niobate (NiNb2O6) anode materials for lithium-20 ion batteries, and a carbon coating derived from mesophase pitch is fabricated to 21 construct NiNb2O6@C composites. Importantly, NiNb2O6@C undergoes spontaneous 22 electrochemical reconstruction to generate metallic Ni and amorphous Nb2O5 during 23 the initial charge/discharge cycle. The presence of the outer carbon layer not only serves 24 as a uniform conductive coating to improve the conductivity and thereby deliver 25 superior rate performance, but also effectively inhibits the stripping of Ni 0 to guarantee 26 a great cycling stability. The synergistic effects of the dual conductive interfaces, 27 consisting of the carbon layer and the Mott-Schottky heterojunction between 28 amorphous Nb2O5 and metallic Ni, culminate in exceptional electrochemical 29 performance. Specifically, NiNb2O6@C-10% delivers impressive specific capacity of 30 436.3 mAh g-1 at 0.5 C and conspicuous cycling stability, maintaining 86.3% capacity 31 retention after 800 cycles at 10 C. This work underscores the substantial potential of 32 nickel niobate anode materials in advancing the development of next-generation 33 lithium-ion batteries. 34 35 | Keywords: | Nickel niobate;Carbon coating;Electrochemical reconstruction;Anode;Lithium -ion battery | Document URI: | http://hdl.handle.net/1942/43405 | ISSN: | 1385-8947 | e-ISSN: | 1873-3212 | DOI: | 10.1016/j.cej.2024.153397 | ISI #: | 001259728900001 | Rights: | 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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