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A robust expectation‐maximization method for the interpretation of small‐angle scattering data from dense nanoparticle samples
Author(s) -
Bakry M.,
Haddar H.,
Bunău O.
Publication year - 2019
Publication title -
journal of applied crystallography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.429
H-Index - 162
ISSN - 1600-5767
DOI - 10.1107/s1600576719009373
Subject(s) - expectation–maximization algorithm , nanoparticle , scattering , maximization , dispersity , interpretation (philosophy) , inverse , materials science , inverse problem , resolution (logic) , algorithm , biological system , computer science , mathematical optimization , optics , maximum likelihood , mathematics , physics , statistics , nanotechnology , artificial intelligence , mathematical analysis , geometry , polymer chemistry , biology , programming language
The local monodisperse approximation (LMA) is a two‐parameter model commonly employed for the retrieval of size distributions from the small‐angle scattering (SAS) patterns obtained from dense nanoparticle samples ( e.g. dry powders and concentrated solutions). This work features a novel implementation of the LMA model resolution for the inverse scattering problem. The method is based on the expectation‐maximization iterative algorithm and is free of any fine‐tuning of model parameters. The application of this method to SAS data acquired under laboratory conditions from dense nanoparticle samples is shown to provide good results.