Lithium-ion batteries (LIBs) are common in everyday life and the demand for their raw materials is increasing. Additionally, spent LIBs should be recycled to achieve a circular economy and supply resources for new LIBs or other products. Especially the recycling of the active material of the electrodes is the focus of current research. Existing approaches for recycling (e.g., pyro-, hydrometallurgy, or flotation) still have their drawbacks, such as the loss of materials, generation of waste, or lack of selectivity. In this study, we test the behavior of commercially available LiFePO and two types of graphite microparticles in a dielectrophoretic high-throughput filter. Dielectrophoresis is a volume-dependent electrokinetic force that is commonly used in microfluidics but recently also for applications that focus on enhanced throughput. In our study, graphite particles show significantly higher trapping than LiFePO particles. The results indicate that nearly pure fractions of LiFePO can be obtained with this technique from a mixture with graphite.
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http://dx.doi.org/10.1021/acsomega.3c04057 | DOI Listing |
Phys Chem Chem Phys
November 2024
N.N. Semenov Federal Research Center for Chemical Physics RAS, Kosygina Street 4, 119991 Moscow, Russia.
Organic carbonates and their mixtures are frequently used in electrolyte solutions in lithium-ion batteries. Rationalization and tuning of the related Li solvation processes are rooted in the proper identification of the representative low-energy spatial structures of the microsolvated Li(S) clusters. In this study, we introduce an automatically generated database of conformational energies (CEs), LICARBCONF806, comprising 806 diverse conformers of Li clusters with 7 common organic carbonates.
View Article and Find Full Text PDFIn the present work, the recovery of phosphorus and fluorine from process water generated in a water based direct physical recycling process of Li-ion batteries has been studied. The recycling process considered in this work produces significant amounts of process water, which is generated during the opening of the batteries by means of electro-hydraulic fragmentation and the subsequent sorting of the components in aqueous solution. This process produces between 21.
View Article and Find Full Text PDFSci Rep
July 2024
Chemical Process Engineering, Faculty of Production Engineering, University of Bremen, Leobener Straße 6, 28359, Bremen, Germany.
Separation and classification are important operations in particle technology, but they are still limited in terms of suspended particles in the micrometer and nanometer size-range. Electrical fields can be beneficial for sorting such particles according to material properties. A mechanism based on strong and inhomogeneous fields is dielectrophoresis (DEP).
View Article and Find Full Text PDFJ Environ Manage
August 2024
School of Metallurgical Engineering, Anhui University of Technology, No. 59 Hudong Road, Ma'anshan, Anhui Province, 243032, China. Electronic address:
The low-carbon recycling of spent lithium-ion batteries has become crucial due to the increasing need to address resource shortages and environmental concerns. Herein, a low-carbon, facile, and efficient method was developed to separate and recover Li, Al, and transition metals from spent ternary cathodes. Initially, the cathode materials post-discharge and disassembly do not require pre-sorting.
View Article and Find Full Text PDFNat Commun
December 2023
Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Unsorted retired batteries with varied cathode materials hinder the adoption of direct recycling due to their cathode-specific nature. The surge in retired batteries necessitates precise sorting for effective direct recycling, but challenges arise from varying operational histories, diverse manufacturers, and data privacy concerns of recycling collaborators (data owners). Here we show, from a unique dataset of 130 lithium-ion batteries spanning 5 cathode materials and 7 manufacturers, a federated machine learning approach can classify these retired batteries without relying on past operational data, safeguarding the data privacy of recycling collaborators.
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