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A robust expectation-maximization method for the interpretation of small-angle scattering data from dense nanoparticle samples

Marc Bakry 1 Oana Bunău 2 Houssem Haddar 1
1 DeFI - Shape reconstruction and identification
Inria Saclay - Ile de France, CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique
Abstract : 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 on 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. Our method is based on the Expectation Maximiza-tion iterative algorithm and is free from any fine tuning of model parameters. The application of our method on SAS data acquired in laboratory conditions on dense nanoparticle samples is shown to provide good results.
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Submitted on : Tuesday, December 17, 2019 - 5:04:18 PM
Last modification on : Saturday, November 21, 2020 - 10:00:07 AM
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Marc Bakry, Oana Bunău, Houssem Haddar. A robust expectation-maximization method for the interpretation of small-angle scattering data from dense nanoparticle samples. Journal of Applied Crystallography, International Union of Crystallography, 2019, 52 (5), pp.926-936. ⟨10.1107/S1600576719009373⟩. ⟨hal-02416529⟩

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