% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{ColliardGranero:905288,
      author       = {Colliard-Granero, André and Batool, Mariah and Jankovic,
                      Jasna and Jitsev, Jenia and Eikerling, Michael H. and Malek,
                      Kourosh and Eslamibidgoli, Mohammad Javad},
      title        = {{D}eep learning for the automation of particle analysis in
                      catalyst layers for polymer electrolyte fuel cells},
      journal      = {Nanoscale},
      volume       = {14},
      number       = {1},
      issn         = {2040-3364},
      address      = {Cambridge},
      publisher    = {RSC Publ.},
      reportid     = {FZJ-2022-00559},
      pages        = {10 - 18},
      year         = {2022},
      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.},
      cin          = {IEK-13 / JSC},
      ddc          = {600},
      cid          = {I:(DE-Juel1)IEK-13-20190226 / I:(DE-Juel1)JSC-20090406},
      pnm          = {1231 - Electrochemistry for Hydrogen (POF4-123) / 5112 -
                      Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and
                      Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-1231 / G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:34846412},
      UT           = {WOS:000723812900001},
      doi          = {10.1039/D1NR06435E},
      url          = {https://juser.fz-juelich.de/record/905288},
}