Deep learning for the automation of particle analysis in catalyst layers for polymer electrolyte fuel cells
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- André Colliard-Granero
- Theory and Computation of Energy Materials (IEK-13), Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
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- Mariah Batool
- Department of Materials Science and Engineering, University of Connecticut, 97 North Eagleville Road, Unit 3136, Storrs, CT 06269-3136, USA
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- Jasna Jankovic
- Department of Materials Science and Engineering, University of Connecticut, 97 North Eagleville Road, Unit 3136, Storrs, CT 06269-3136, USA
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- Jenia Jitsev
- Julich Supercomputing Center, Forschungszentrum Jülich, 52425 Jülich, Germany
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- Michael H. Eikerling
- Theory and Computation of Energy Materials (IEK-13), Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
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- Kourosh Malek
- Theory and Computation of Energy Materials (IEK-13), Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
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- Mohammad J. Eslamibidgoli
- Theory and Computation of Energy Materials (IEK-13), Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Abstract
<jats:p>This paper presents a deep learning-based approach to automate particle size analysis in the microscopy images of catalyst layers for polymer electrolyte fuel cells.</jats:p>
Journal
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- Nanoscale
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Nanoscale 14 (1), 10-18, 2022
Royal Society of Chemistry (RSC)
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Details 詳細情報について
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- CRID
- 1360861711914334336
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- ISSN
- 20403372
- 20403364
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- Data Source
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- Crossref