Universität zu Köln

Colliard-Granero, Andre ORCID: 0000-0002-4615-3710, Batool, Mariah, Jankovic, Jasna, Jitsev, Jenia ORCID: 0000-0002-1221-7851, Eikerling, Michael H., Malek, Kourosh and Eslamibidgoli, Mohammad J. (2021). Deep learning for the automation of particle analysis in catalyst layers for polymer electrolyte fuel cells. Nanoscale, 14 (1). CAMBRIDGE: ROYAL SOC CHEMISTRY. ISSN 2040-3372

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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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Colliard-Granero, Andre UNSPECIFIED orcid.org/0000-0002-4615-3710 UNSPECIFIED Batool, Mariah UNSPECIFIED UNSPECIFIED UNSPECIFIED Jankovic, Jasna UNSPECIFIED UNSPECIFIED UNSPECIFIED Jitsev, Jenia UNSPECIFIED orcid.org/0000-0002-1221-7851 UNSPECIFIED Eikerling, Michael H. UNSPECIFIED UNSPECIFIED UNSPECIFIED Malek, Kourosh UNSPECIFIED UNSPECIFIED UNSPECIFIED Eslamibidgoli, Mohammad J. UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-587468
DOI:	10.1039/d1nr06435e
Journal or Publication Title:	Nanoscale
Volume:	14
Number:	1
Date:	2021
Publisher:	ROYAL SOC CHEMISTRY
Place of Publication:	CAMBRIDGE
ISSN:	2040-3372
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language OXYGEN REDUCTION; MICROSCOPY; IMAGE; QUANTIFICATION; NANOPARTICLES; DISTRIBUTIONS; CHALLENGES; MEMBRANE; ACCURATE; IMPACT Multiple languages Chemistry, Multidisciplinary; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/58746