000K utf8 1100 2023$c2023-03-23 1500 eng 2050 urn:nbn:de:hbz:465-20230821-165458-2 2051 10.1039/d2na00781a 3000 Gumbiowski, Nina 3010 Epple, Matthias 3010 Heggen, Marc 3010 Loza, Kateryna 4000 Automated analysis of transmission electron micrographs of metallic nanoparticles by machine learning [Gumbiowski, Nina] 4209 Metallic nanoparticles were analysed with respect to size and shape by a machine learning approach. This involved a separation of particles from the background (segmentation), a separation of overlapping particles, and the identification of individual particles. An algorithm to separate overlapping particles, based on ultimate erosion of convex shapes (UECS), was implemented. Finally, particle properties like size, circularity, equivalent diameter, and Feret diameter were computed for each particle of the whole particle population. Thus, particle size distributions can be easily created based on the various parameters. However, strongly overlapping particles are difficult and sometimes impossible to separate because of an a priori unknown shape of a particle that is partially lying in the shadow of another particle. The program is able to extract information from a sequence of images of the same sample, thereby increasing the number of analysed nanoparticles to several thousands. The machine learning approach is well-suited to identify particles at only limited particle-to-background contrast as is demonstrated for ultrasmall gold nanoparticles (2 nm). 4950 https://doi.org/10.1039/d2na00781a$xR$3Volltext$534 4950 https://nbn-resolving.org/urn:nbn:de:hbz:465-20230821-165458-2$xR$3Volltext$534 4961 https://duepublico2.uni-due.de/receive/duepublico_mods_00078347 5051 540