Journal Article

FZJ-2022-00559

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Deep learning for the automation of particle analysis in catalyst layers for polymer electrolyte fuel cells

Colliard-Granero, A.FZJ* ; Batool, M. ; Jankovic, J. ; Jitsev, J.FZJ* ; Eikerling, M. H.FZJ* ; Malek, K. (Corresponding author)FZJ* ; Eslamibidgoli, M. J. (Corresponding author)FZJ*

2022
RSC Publ. Cambridge

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Please use a persistent id in citations: http://hdl.handle.net/2128/30300  doi:10.1039/D1NR06435E

Abstract: The rapidly growing use of imaging infrastructure in the energy materials domain drives significant data accumulation in terms of their amount and complexity. The applications of routine techniques for image processing in materials research are often ad hoc, indiscriminate, and empirical, which renders the crucial task of obtaining reliable metrics for quantifications obscure. Moreover, these techniques are expensive, slow, and often involve several preprocessing steps. This paper presents a novel deep learning-based approach for the high-throughput analysis of the particle size distributions from transmission electron microscopy (TEM) images of carbon-supported catalysts for polymer electrolyte fuel cells. A dataset of 40 high-resolution TEM images at different magnification levels, from 10 to 100 nm scales, was annotated manually. This dataset was used to train the U-Net model, with the StarDist formulation for the loss function, for the nanoparticle segmentation task. StarDist reached a precision of 86%, recall of 85%, and an F1-score of 85% by training on datasets as small as thirty images. The segmentation maps outperform models reported in the literature for a similar problem, and the results on particle size analyses agree well with manual particle size measurements, albeit at a significantly lower cost.

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Contributing Institute(s):

1. IEK-13 (IEK-13)
2. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 1231 - Electrochemistry for Hydrogen (POF4-123) (POF4-123)
2. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2022

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Database coverage:
; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Essential Science Indicators ; IF >= 5 ; JCR ; National-Konsortium ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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