Deep learning for the automation of particle analysis in catalyst layers for polymer electrolyte fuel cells

  • André Colliard-Granero
    Theory and Computation of Energy Materials (IEK-13), Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
  • Mariah Batool
    Department of Materials Science and Engineering, University of Connecticut, 97 North Eagleville Road, Unit 3136, Storrs, CT 06269-3136, USA
  • Jasna Jankovic
    Department of Materials Science and Engineering, University of Connecticut, 97 North Eagleville Road, Unit 3136, Storrs, CT 06269-3136, USA
  • Jenia Jitsev
    Julich Supercomputing Center, Forschungszentrum Jülich, 52425 Jülich, Germany
  • Michael H. Eikerling
    Theory and Computation of Energy Materials (IEK-13), Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
  • Kourosh Malek
    Theory and Computation of Energy Materials (IEK-13), Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
  • Mohammad J. Eslamibidgoli
    Theory and Computation of Energy Materials (IEK-13), Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

抄録

<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>

収録刊行物

  • Nanoscale

    Nanoscale 14 (1), 10-18, 2022

    Royal Society of Chemistry (RSC)

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