Lab-based X-ray micro-computed tomography coupled with machine-learning segmentation to investigate phosphoric acid leaching in high-temperature polymer electrolyte fuel cells

Josh J. Bailey; Jianuo Chen; Jennifer Hack; Maria Perez-Page; Stuart M. Holmes; Dan J.L. Brett; Paul R. Shearing

doi:10.1016/j.jpowsour.2021.230347

Lab-based X-ray micro-computed tomography coupled with machine-learning segmentation to investigate phosphoric acid leaching in high-temperature polymer electrolyte fuel cells

Josh J. Bailey, Jianuo Chen, Jennifer Hack, Maria Perez-Page, Stuart M. Holmes, Dan J.L. Brett, Paul R. Shearing^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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Abstract

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.

Original language	English
Article number	230347
Journal	Journal of Power Sources
Volume	509
Early online date	18 Aug 2021
DOIs	https://doi.org/10.1016/j.jpowsour.2021.230347
Publication status	Published - 15 Oct 2021

Bibliographical note

Funding 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 ].

Publisher Copyright:
© 2021

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Lab-based X-ray micro-computed tomography coupled with machine-learning segmentation to investigate phosphoric acid leaching in high-temperature polymer electrolyte fuel cells
Copyright 2021 Elsevier. This manuscript is distributed under a Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits distribution and reproduction for non-commercial purposes, provided the author and source are cited
Accepted author manuscript, 3.1 MBLicence: CC BY-NC-ND

Cite this

Bailey, J. J., Chen, J., Hack, J., Perez-Page, M., Holmes, S. M., Brett, D. J. L., & Shearing, P. R. (2021). Lab-based X-ray micro-computed tomography coupled with machine-learning segmentation to investigate phosphoric acid leaching in high-temperature polymer electrolyte fuel cells. Journal of Power Sources, 509, [230347]. https://doi.org/10.1016/j.jpowsour.2021.230347

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abstract = "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.",

keywords = "Accelerated stress testing, High-temperature polymer electrolyte fuel cell, Machine-learning segmentation, Phosphoric acid leaching, X-ray micro-computed tomography",

author = "Bailey, {Josh J.} and Jianuo Chen and Jennifer Hack and Maria Perez-Page and Holmes, {Stuart M.} and Brett, {Dan J.L.} and Shearing, {Paul R.}",

note = "Funding 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 ]. Publisher Copyright: {\textcopyright} 2021 Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

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doi = "10.1016/j.jpowsour.2021.230347",

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volume = "509",

journal = "Journal of Power Sources",

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Lab-based X-ray micro-computed tomography coupled with machine-learning segmentation to investigate phosphoric acid leaching in high-temperature polymer electrolyte fuel cells. / Bailey, Josh J.; Chen, Jianuo; Hack, Jennifer; Perez-Page, Maria; Holmes, Stuart M.; Brett, Dan J.L.; Shearing, Paul R.

In: Journal of Power Sources, Vol. 509, 230347, 15.10.2021.

Research output: Contribution to journal › Article › peer-review

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T1 - Lab-based X-ray micro-computed tomography coupled with machine-learning segmentation to investigate phosphoric acid leaching in high-temperature polymer electrolyte fuel cells

AU - Bailey, Josh J.

AU - Chen, Jianuo

AU - Hack, Jennifer

AU - Perez-Page, Maria

AU - Holmes, Stuart M.

AU - Brett, Dan J.L.

AU - Shearing, Paul R.

N1 - Funding 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 ]. Publisher Copyright: © 2021 Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/10/15

Y1 - 2021/10/15

N2 - 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.

AB - 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.

KW - Accelerated stress testing

KW - High-temperature polymer electrolyte fuel cell

KW - Machine-learning segmentation

KW - Phosphoric acid leaching

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DO - 10.1016/j.jpowsour.2021.230347

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JO - Journal of Power Sources

JF - Journal of Power Sources

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ER -

Lab-based X-ray micro-computed tomography coupled with machine-learning segmentation to investigate phosphoric acid leaching in high-temperature polymer electrolyte fuel cells

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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Cite this