A machine-learning approach is used to segment 14 X-ray computed-tomography datasets acquired by lab-based scanning of laser-milled, high-temperature polymer electrolyte fuel cell samples mounted in a 3D-printed sample holder. Two modes of operation, one with constant current load and the other with current cycling, are explored and their impact on microstructural change is correlated with electrochemical performance degradation. Constant-current testing shows the overall quantity of phosphoric acid in the gas diffusion layers is effectively unchanged between 50 and 100 h of operation but that inter-electrode distribution becomes less uniform. Current-cycling tests reveal similar quantities of phosphoric acid but a different intra-electrode distribution. Membrane swelling appears more pronounced after current-cycling tests and in both cases, significant catalyst layer migration is observed. The present analysis provides a lab-based approach to monitoring microstructural degradation in high-temperature polymer electrolyte fuel cells and provides a more accessible and more statistically robust platform for assessing the impact of phosphoric acid mitigation strategies.
Bibliographical noteFunding Information:
This work has been financially supported by the UK Research Council EPSRC [ EP/009050/1 ], and JH also acknowledges the EPSRC for her Fellowship [ EP/T517793/1 ].
Copyright 2021 Elsevier B.V., All rights reserved.
- Accelerated stress testing
- High-temperature polymer electrolyte fuel cell
- Machine-learning segmentation
- Phosphoric acid leaching
- X-ray micro-computed tomography
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment
- Energy Engineering and Power Technology
- Physical and Theoretical Chemistry
- Electrical and Electronic Engineering