Published on 09 May 2024 |

Version version 1

Dataset for Selective Precipitation of REE-rich Aluminium Phosphate from Lithium Enriched Slag Leachate

View Dataset
Jankovský, Ondřej;Paušová, Šárka;Bouzek, Karel

Description

Currently, recycling of spent lithium-ion batteries is carried out using mechanical, pyrometallurgical and hydrometallurgical methods and their combination. The aim of this article is to study a part of pyro-hydrometallurgical processing of spent lithium-ion batteries which includes lithium slag hydrometallurgical treatment and refining obtained leachate. Lithium slag intended for leaching experiments contains 3,68 % of Li; 11,02 % of Al; 1,17 % of Co; 1,71 % of Cu and other metals in minority content. Leaching step was realized via dry digestion that is an effective method capable of transferring over 99% of the present metals such as Li, Al, Co, Cu and others to the leachate. The highest content in leachate reached Al (2666 µg/mL) and Li (2239 µg/mL). Extraction of metals from leachate can be conducted using various methods, with precipitation being the most used. In this work, the influence of two types of precipitation agent (NaOH, Na3PO4) on precipitation efficiency of Al and Li losses was investigated. It was found that the precipitation of aluminium with NaOH can result in the co-precipitation of lithium, causing total lithium losses up to 40 %. As suitable precipitating agent for complete Al removal from Li leachate with a minimal loss of lithium (less than 2 %), crystalline Na3PO4 was determined under following condition: pH = 3, 400 rpm, 10 minutes, room temperature. Analysis confirmed that, in addition to aluminium, the precipitate also contains REE La (3.4%), Ce (2.5%), Y (1.3%), Nd (1%) and Pr (0.3%), which selective recovery will be the subject of further study.

Citations (0)

Mentions (0)

Metrics

Dataset Index

0.6

FAIR Score

58%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Zenodo

Assigned Domain

Subfield

Fluid Flow and Transfer Processes

Field

Chemical Engineering

Domain

Physical Sciences

Confidence Score

47%

Source

Scholar Data Model

Keywords

VZ1VSCHT214 021214 023YBCOrecyclingmelt-growthsuperconductors

Normalization Factors

FT

30.77

CTw

1.00

MTw

1.00