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High Resolution STEM Observations of Next Generation Battery Materials and Processes

  • Full or part time
  • Application Deadline
    Monday, September 30, 2019
  • Funded PhD Project (UK Students Only)
    Funded PhD Project (UK Students Only)

Project Description

The drive to harness alternative energy sources that avoid environmentally damaging hydrocarbon fuels has led to a global research effort into the development of new energy storage technologies. Whether it is the cars we drive, the personal electronics we use, the manufacturing or service business we work in or the way we power our houses, batteries will play a large role in all future functions of our modern life. The current research challenge is to make these batteries more efficient, more powerful, at lower cost, with less weight or with more abundant materials that have environmentally friendly processes that can also lead to more simpler recycling strategies.

High resolution and operando characterisation methods, such as scanning transmission electron microscopy (STEM) have the potential to provide unique insights into the function of next generation batteries. While operando TEM methods have been demonstrated previously (Mehdi et al, Nanoletters 15, 2168 (2015)) the direct link between the starting microstructure of the electrodes, the chemistry of the electrolyte, the charge/discharge rate and the degradation of the battery has not been definitively identified. It is the degradation of the battery components that ultimately determines the overall lifetime of a battery and whether the battery can be routinely recycled.

In this Faraday Institution supported project on the recycling of Li batteries, advances in high resolution and electrochemical controlled operando STEM will be used to perform precisely calibrated sub-nm observations of the processes that take place during battery operation and during the recycling process. By using an operando stage, it will be possible to investigate directly whether changes in the electrodes and/or electrolyte can have an effect on the recyclability of batteries used in advanced applications. In addition to contributing to a correlated set of measurements within the Faraday Institution, this project will evaluate the use of compressed sensing and machine learning to optimise the use of state-of-the-art characterisation methods for battery technologies.

Funding Notes

This project is directly supported by the school of engineering at the university of Liverpool to work in collaboration with a Faraday Institution project.

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