Institute of Fundamental Technological Research
Polish Academy of Sciences


Z. Huang

Beijing University (CN)

Recent publications
1.  Krajewski M., Chen C.H., Huang Z.T., Lin J.Y., Li4Ti5O12 Coated by Biomass-Derived Carbon Quantum Dots as Anode Material with Enhanced Electrochemical Performance for Lithium-Ion Batteries, Energies, ISSN: 1996-1073, DOI: 10.3390/en15207715, Vol.15, No.20, pp.7715-1-13, 2022

Li4Ti5O12 (LTO) is a promising anode material for lithium-ion batteries (LIBs) due to its stable reversibility, high-rate cyclability, and high operational potential. On the other hand, it suffers from poor electronic conductivity and low capacitance. To overcome these disadvantages, modification of the LTO surface is frequently undertaken. Considering this idea, the production of a biomass-derived carbon-coated LTO material (LTO/C) and its application as an anode in LIBs is described in this work. The carbon precursor was obtained from commercial carrot juice, which was degraded using microwaves. According to the UV studies, the carbon precursor revealed similar properties to carbon quantum dots. Then, it was deposited on LTO synthetized through a sol-gel method. The LTO/C electrode exhibited a high specific capacity of 211 mAhg−1 at 0.1 C. Capacity retention equal to 53% of the initial value was found for the charge–discharge rate increase from 0.1 C to 20 C. The excellent electrochemical performance of LTO/C was caused by the carbon coating, which provided (i) short diffusion pathways for the Li+ ions into the LTO structure and (ii) enhanced electronic conductivity. The obtained results indicated that biomass-derived carbon quantum dot-coated LTO can be considered as a promising anode for LIBs.

anode material, biomass-derived carbon, carbon coating, carbon quantum dot, lithium-ion battery

Krajewski M. - IPPT PAN
Chen C.H. - other affiliation
Huang Z.T. - Beijing University (CN)
Lin J.Y. - Tunghai University (CN)

List of chapters in recent monographs
Kidziński Ł., Mohanty S.P., Ong C.F., Huang Z., Zhou S., Pechenko A., Stelmaszczyk A., Jarosik P., Pavlov M., Kolesnikov S., Plis S., Chen Z., Zhang Z., Chen J., Shi J., Zheng Z., Yuan Ch., Lin Z., Michalewski H., Milos P., Osinski B., Melnik A., Schilling M., Ritter H., Carroll S.F., Hicks J., Levine S., Salathé M., Delp S., The NIPS '17 Competition: Building Intelligent Systems, rozdział: Learning to Run Challenge Solutions: Adapting Reinforcement Learning Methods for Neuromusculoskeletal Environments, Springer, pp.121-153, 2018

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