Journal Article

FZJ-2023-01719

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Automated analysis of transmission electron micrographs of metallic nanoparticles by machine learning

Gumbiowski, N.Extern* ; Loza, K. (Corresponding author)Extern* ; Heggen, M. (Corresponding author)FZJ* ; Epple, M. (Corresponding author)Extern*

2023
Royal Society of Chemistry Cambridge

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Please use a persistent id in citations: http://hdl.handle.net/2128/34325  doi:10.1039/D2NA00781A

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).

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Contributing Institute(s):

1. Physik Nanoskaliger Systeme (ER-C-1)

Research Program(s):

1. 5351 - Platform for Correlative, In Situ and Operando Characterization (POF4-535) (POF4-535)
2. DFG project 257727131 - Nanoskalige Pt Legierungselektrokatalysatoren mit definierter Morphologie: Synthese, Electrochemische Analyse, und ex-situ/in-situ Transmissionselektronenmikroskopische (TEM) Studien (257727131) (257727131)

Appears in the scientific report 2023

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