Project summary

Hydrometallurgy is considered as a preferred process for battery recycling over pyrometallurgy and direct recycling due to its energy efficiency and operational flexibility. Recently, deep eutectic solvents (DESs) have emerged as promising green leaching solvents due to their biodegradability, low toxicity, and tunability. This project aims to guide the optimisation of battery recycling processes using deep eutectic solvent (DES) via data-driven approaches.

Project objectives

The project will establish causal relationships between DES chemistry, physicochemical properties, processing conditions and metal leaching efficiency, distinguishing causal effects from simple correlations.

Project tasks

By combining data-driven analysis, computational screening, and machine learning, the research will identify the key molecular and process descriptors governing efficient metal recovery, while also enabling the rational design of sustainable DES systems for high-efficiency battery recycling processes.

• Organise, clean, improve (add) and analyse the datasets
• Perform molecular simulations and coding
• Develop causal relationships among different parameters to the metal leaching efficiency
• Interpret the results and validate the findings

Expected outcomes

Gaining hands-on experience in coding, data collection and analysis

Proposing battery recycling process with optimised conditions

References

https://doi.org/10.1002/adma.202312551

https://doi.org/10.1002/adma.202510376

Research environment and Supervisory team

This project will be conducted in the PartCat Laboratory at the UNSW School of Chemical Engineering, and will have access to the high-performance computing facilities. The project will be jointly supervised by Prof Rose Amal and Dr Jodie Yuwono. Students will work closely with members of the PartCat research group and collaborators with other universities.