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@ARTICLE{Gumbiowski:1006571,
      author       = {Gumbiowski, Nina and Loza, Kateryna and Heggen, Marc and
                      Epple, Matthias},
      title        = {{A}utomated analysis of transmission electron micrographs
                      of metallic nanoparticles by machine learning},
      journal      = {Nanoscale advances},
      volume       = {5},
      number       = {8},
      issn         = {2516-0230},
      address      = {Cambridge},
      publisher    = {Royal Society of Chemistry},
      reportid     = {FZJ-2023-01719},
      pages        = {2318-2326},
      year         = {2023},
      abstract     = {Metallic nanoparticles were analysed with respect to size
                      and shape by a machine learning approach. This involved a
                      separation of particles from the background (segmentation),
                      a separation of overlapping particles, and the
                      identification of individual particles. An algorithm to
                      separate overlapping particles, based on ultimate erosion of
                      convex shapes (UECS), was implemented. Finally, particle
                      properties like size, circularity, equivalent diameter, and
                      Feret diameter were computed for each particle of the whole
                      particle population. Thus, particle size distributions can
                      be easily created based on the various parameters. However,
                      strongly overlapping particles are difficult and sometimes
                      impossible to separate because of an a priori unknown shape
                      of a particle that is partially lying in the shadow of
                      another particle. The program is able to extract information
                      from a sequence of images of the same sample, thereby
                      increasing the number of analysed nanoparticles to several
                      thousands. The machine learning approach is well-suited to
                      identify particles at only limited particle-to-background
                      contrast as is demonstrated for ultrasmall gold
                      nanoparticles (2 nm).},
      cin          = {ER-C-1},
      ddc          = {540},
      cid          = {I:(DE-Juel1)ER-C-1-20170209},
      pnm          = {5351 - Platform for Correlative, In Situ and Operando
                      Characterization (POF4-535) / DFG project 257727131 -
                      Nanoskalige Pt Legierungselektrokatalysatoren mit
                      definierter Morphologie: Synthese, Electrochemische Analyse,
                      und ex-situ/in-situ Transmissionselektronenmikroskopische
                      (TEM) Studien (257727131)},
      pid          = {G:(DE-HGF)POF4-5351 / G:(GEPRIS)257727131},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37056630},
      UT           = {WOS:000959962500001},
      doi          = {10.1039/D2NA00781A},
      url          = {https://juser.fz-juelich.de/record/1006571},
}