High-speed 4D neutron computed tomography for quantifying water dynamics in polymer electrolyte fuel cells

Ralf F. Ziesche, Jennifer Hack, Lara Rasha, Maximilian Maier, Chun Tan, Thomas M. M. Heenan, Henning Markötter, Nikolay Kardjilov, Ingo Manke, Winfried Kockelmann, Dan J. L. Brett and Paul R. Shearing ()
Additional contact information
Ralf F. Ziesche: Electrochemical Innovation Lab, Department of Chemical Engineering, UCL
Jennifer Hack: Electrochemical Innovation Lab, Department of Chemical Engineering, UCL
Lara Rasha: Electrochemical Innovation Lab, Department of Chemical Engineering, UCL
Maximilian Maier: Electrochemical Innovation Lab, Department of Chemical Engineering, UCL
Chun Tan: Electrochemical Innovation Lab, Department of Chemical Engineering, UCL
Thomas M. M. Heenan: Electrochemical Innovation Lab, Department of Chemical Engineering, UCL
Henning Markötter: Helmholtz-Zentrum Berlin für Materialien und Energie (HZB)
Nikolay Kardjilov: Helmholtz-Zentrum Berlin für Materialien und Energie (HZB)
Ingo Manke: Helmholtz-Zentrum Berlin für Materialien und Energie (HZB)
Winfried Kockelmann: STFC, Rutherford Appleton Laboratory, ISIS Facility
Dan J. L. Brett: Electrochemical Innovation Lab, Department of Chemical Engineering, UCL
Paul R. Shearing: Electrochemical Innovation Lab, Department of Chemical Engineering, UCL

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract In recent years, low-temperature polymer electrolyte fuel cells have become an increasingly important pillar in a zero-carbon strategy for curbing climate change, with their potential to power multiscale stationary and mobile applications. The performance improvement is a particular focus of research and engineering roadmaps, with water management being one of the major areas of interest for development. Appropriate characterisation tools for mapping the evolution, motion and removal of water are of high importance to tackle shortcomings. This article demonstrates the development of a 4D high-speed neutron imaging technique, which enables a quantitative analysis of the local water evolution. 4D visualisation allows the time-resolved studies of droplet formation in the flow fields and water quantification in various cell parts. Performance parameters for water management are identified that offer a method of cell classification, which will, in turn, support computer modelling and the engineering of next-generation flow field designs.

Date: 2022
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DOI: 10.1038/s41467-022-29313-5

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