利用基于磷酸的深共晶溶剂从废锂电池中一步选择性分离和高效回收有价金属

IF 9.1 Q1 ENGINEERING, CHEMICAL Green Chemical Engineering Pub Date : 2023-10-11 DOI:10.1016/j.gce.2023.10.002
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引用次数: 0

摘要

随着越来越多的锂离子电池(LIBs)投入生产和应用,锂、钴等贵金属日益稀缺,从废锂离子电池中回收各种战略金属资源迫在眉睫。与此同时,从浸出液中分离和回收金属这一复杂而困难的问题一直是亟待解决的问题。本研究开发了一种基于磷酸的深共晶溶剂(DES),用于从废锂电池中萃取金属,并一步选择性分离和高效回收过渡金属。所制备的 DES 在 100 ℃ 下对 Li(100%)和 Co(92.8%)具有优异的萃取性能。此外,该萃取系统还能通过自身成分有效分离和沉淀 Co,避免了引入新的沉淀剂和破坏 DES 的原始组成结构。这也使得萃取系统具有良好的循环稳定性,经过 5 次循环后,锂(94.3%)和钴(80.8%)的萃取性能均十分优异。这项工作提出了一种从废 LIB 中一步选择性分离和回收有价金属的绿色方法。
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One-step selective separation and efficient recovery of valuable metals from spent lithium batteries by phosphoric acid-based deep eutectic solvent

With more and more lithium-ion batteries (LIBs) being put into production and application, precious metals such as lithium and cobalt are scarce, so it is imminent to recover various strategic metal resources from spent LIBs. Meanwhile, the complex and difficult problem of separating and recovering metals from leaching solutions has been an urgent question that needs to be resolved. In this work, a phosphoric acid-based deep eutectic solvent (DES) was developed for extracting metals from spent LIBs and one-step selectively separating and efficiently recovering transition metal. The prepared DES shows excellent extraction performance for Li (100%) and Co (92.8%) at 100 °C. In addition, the extraction system can effectively separate and precipitate Co through its own components, avoiding the introduction of new precipitants and the destruction of the original composition structure of DES. This also contributes to the good cycle stability of the extraction system with excellent extraction performance for Li (94.3%) and Co (80.8%) after 5 cycles. This work proposes a green method for one-step selectively separating and recovering valuable metals from spent LIBs.

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来源期刊
Green Chemical Engineering
Green Chemical Engineering Process Chemistry and Technology, Catalysis, Filtration and Separation
CiteScore
11.60
自引率
0.00%
发文量
58
审稿时长
51 days
期刊最新文献
OFC: Outside Front Cover Outside Back Cover Outside Back Cover OFC: Outside Front Cover Integration of physical information and reaction mechanism data for surrogate prediction model and multi-objective optimization of glycolic acid production
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