Details of Grant 

EPSRC Reference:	EP/W036517/1
Title:	An automated high-throughput robotic platform for accelerated battery and fuels discovery - DIGIBAT
Principal Investigator:	Titirici, Professor M
Other Investigators:	Offer, Professor GJ Ryan, Professor M Greenaway, Dr R Walsh, Professor A Cooper, Dr SJ Stephens, Dr I E L
Researcher Co-Investigators:
Project Partners:	BP Deregallera Ltd DiGiFab Enserv Group Faradion Limited Imperial College London Johnson Matthey Ruhr University Bochum Shell Toyota University of Toronto
Department:	Chemical Engineering
Organisation:	Imperial College London
Scheme:	Standard Research
Starts:	01 January 2023	Ends:	31 December 2025	Value (£):	1,656,452
EPSRC Research Topic Classifications:	Energy Storage
EPSRC Industrial Sector Classifications:	Energy
Related Grants:
Panel History:	Panel Date Panel Name Outcome 28 Mar 2022 EPSRC Strategic Equipment Interview Panel March 2022 - Panel 1 Announced
Summary on Grant Application Form
Batteries and electrocatalytic devices (i.e electrolysers, fuel cells) have multiple components spanning different length scales. The materials design space in these research fields is too large to be explored empirically. While experimental work can be directed by computational modelling to make this challenge more tenable, this is time consuming, and the number of tests/syntheses is still be too large on the experimental scale. DIGIBAT will combine computational tools (e.g. atomistic and molecular modelling, process modelling, computer-aided design, machine learning algorithms, data science) and automated HT synthesis, characterisation and testing from atoms to devices to accelerate the discovery and optimisation of new batteries and electrofuels. Specifically, DIGIBAT will comprise three HT stations: Platform A dedicated to materials synthesis and characterisation, Platform B dedicated to HT electrodes manufacturing all the way to device manufacturing and Platform C dedicated to HT electrochemical testing for both batteries and electrocatalysts. DIGIBAT will be paired with materials characterisation also applied in HT, including in operando characterisation. By executing data-rich experiments, DIGIBAT will increase the pace of innovation, while enhancing reproducibility by eliminating human errors. The research enabled by ATLAS will target challenges related to: (1) the discovery and optimisation of new battery chemistries, (2) synthesising, optimising, and testing recycled battery materials; (3) Discovering precious metal free electrocatalysts for green H2 production and fuel cells; (4) Efficient N2 to ammonia and CO2 reduction to fuels and chemicals for electrocatalysts discovery
Key Findings
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Potential use in non-academic contexts
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Organisation Website:	http://www.imperial.ac.uk