Imaging Dynamic Electrochemical Interfaces

Synergistic advances in operando characterisation methods to establish a robust, correlated multi-scale scientific framework for quantifying battery function.

The project aims to take the most advanced characterisation capabilities at leading research universities and national facilities in the UK and enhance them to address the pressing materials challenges associated with the development of new batteries with enhanced performance. By emphasising calibration meta-data to accompany each individual method, artificial intelligence (AI) is being used to advance the achievable correlated temporal precision, chemical sensitivity and spatial resolution across the vital length/time scales for battery performance.

The project is focusing on two critical aspects of energy storage: Researchers are identifying how the structure/ composition of the electrode/electrolyte interface controls the initial stages of ion transfer (i.e., the charge/discharge process). The second area of interest is to observe the evolution of both performance-degrading stable phases and metal dendrites during extended battery cycling.

By expanding the number of methods that provide key performance indicators, this project is increasing characterisation options for businesses working in the battery supply chain, speeding up the establishment of new IP and the development of new products.

Project presentation from the Faraday Institution Conference, November 2021

Timeline with milestone/deliverables (October 2021)

  • Optimum acquisition for correlated data measurements SECCM-FIB-STEM-Raman to quantify atomic scale kinetics during first 3 cycles.
  • AI methods for correlated multi-scale 3-D datasets over extended cycling.
  • Dissemination of new methods to battery researchers in the UK.

Project innovations

This project has developed an approach to specimen preparation which permits reproducible, artefact-free operando battery component characterisation across multiple length and time scales. It has developed and demonstrated combined use of chemical reaction and measurement technique with imaging techniques. The project delivered a high-throughput technique which should accelerate characterisation of degradation processes as well as future battery materials and coatings. A negotiation with commercial instrument supplier is taking place regarding the AI software and algorithms. Potential intellectual property from the technique is being evaluated.

Project funding
£1m
1 July 2018- 30 September 2021
Principal Investigator
Professor Nigel Browning
University of Liverpool
University Partners
University of Liverpool (Lead)
University of Bath
University of Birmingham
University of Manchester
University College London
University of Warwick
Research Organisations, Facilities and Institutes
Henry Royce Institute
+ 3 Industrial Partners

 

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