Highly selective metal recovery from spent lithium-ion batteries through stoichiometric hydrogen ion replacement

Abstract

Spent lithium-ion battery recycling has attracted significant attention because of its importance in regard to the environment and resource importance. Traditional hydrometallurgical methods usually leach all valuable metals and subsequently extract target meals to prepare corresponding materials. However, Li recovery in these processes requires lengthy operational procedures, and the recovery efficiency is low. In this research, we demonstrate a method to selectively recover lithium before the leaching of other elements by introducing a hydrothermal treatment. Approximately 90% of Li is leached from high-Ni layered oxide cathode powders, while consuming a nearly stoichiometric amount of hydrogen ions. With this selective recovery of Li, the transition metals remain as solid residue hydroxides or oxides. Furthermore, the extraction of Li is found to be highly dependent on the content of transition metals in the cathode materials. A high leaching selectivity of Li (>98%) and nearly 95% leaching efficiency of Li can be reached with LiNi_0.8Co_0.1Mn_0.1O₂. In this case, both the energy and material consumption during the proposed Li recovery is significantly decreased compared to traditional methods; furthermore, the proposed method makes full use of H⁺ to leach Li⁺. This research is expected to provide new understanding for selectively recovering metal from secondary resources.

Key words： recycling; spent LIBs; selective recovery; hydrothermal treatment

Acknowledgements

The authors acknowledge Dr. Chunwei Liu from Chinese Academy of Sciences for the great help on the article language. The authors acknowledge Ms. Guo from Delft University of Technology for the great help on thermodynamics calculation using HSC 6.0 software and the financial support on this research from the National Key Research and Development Program of China (Grant Nos. 2017YFB0403300/2017YFB0403305), the National Natural Science Foundation of China under Grant Nos. 51874269 and 51934006, and 1000 Talents Program of China (Z.S.). This research was also financially supported by the Innovation Academy for Green Manufacture, Chinese Academy of Sciences (IAGM-2019-A15), as well as the Key Program of Chinese Academy of Sciences (ZDRW_CN_2020-1).

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