Imaging Dynamic Electrochemical Interfaces

This project aims to make synergistic advances in operando characterisation methods needed to establish a robust, correlated multi-scale scientific framework for quantifying battery function. 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. By expanding the number of methods that provide key performance indicators, this project will increase characterisation options for businesses working on 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 2020

Objectives

The underlying project hypothesis is that fundamental transient effects at electrode/electrolyte interfaces control battery performance and lifetime. While experimental methods exist to measure these transients, no one method has the required spatial, temporal and chemical sensitivity to uniquely define the process. The advanced correlated methods being developed will be employed to identify and control:

  • The structural/chemical parameters determining the kinetics of ion transport across pristine electrode/electrolyte interfaces and leading to the formation of the solid electrolyte interface (SEI); and
  • The structural/chemical parameters that suppress the evolution dendrites and other degradation mechanisms under extended cycling.

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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