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Improving Quantitative EDS Chemical Analysis of Alloy Nanoparticles by PCA Denoising: Part II. Uncertainty Intervals

Published online by Cambridge University Press:  18 April 2022

Murilo Moreira Open the ORCID record for Murilo Moreira [Opens in a new window] ,
Matthias Hillenkamp ,
Giorgio Divitini ,
Luiz H. G. Tizei ,
Caterina Ducati ,
Monica A. Cotta ,
Varlei Rodrigues  and
Daniel Ugarte Open the ORCID record for Daniel Ugarte [Opens in a new window]
Murilo Moreira
Affiliation:
Instituto de Fisica “Gleb Wataghin”, Universidade Estadual de Campinas-UNICAMP, 13083-859 Campinas, SP, Brazil
Matthias Hillenkamp
Affiliation:
Instituto de Fisica “Gleb Wataghin”, Universidade Estadual de Campinas-UNICAMP, 13083-859 Campinas, SP, Brazil Institute of Light and Matter, Université Claude Bernard Lyon 1, CNRS, UMR5306, F-69622 Villeurbanne, France
Giorgio Divitini
Affiliation:
Department of Materials Science and Metallurgy, University of Cambridge, Cambridge CB3 0FS, UK Electron Spectroscopy and Nanoscopy Group, Istituto Italiano di Tecnologia, via Morego 30, Genoa, Italy
Luiz H. G. Tizei
Affiliation:
Laboratoire de Physique des Solides, Université Paris-Saclay, CNRS, 91405 Orsay, France
Caterina Ducati
Affiliation:
Department of Materials Science and Metallurgy, University of Cambridge, Cambridge CB3 0FS, UK
Monica A. Cotta
Affiliation:
Instituto de Fisica “Gleb Wataghin”, Universidade Estadual de Campinas-UNICAMP, 13083-859 Campinas, SP, Brazil
Varlei Rodrigues
Affiliation:
Instituto de Fisica “Gleb Wataghin”, Universidade Estadual de Campinas-UNICAMP, 13083-859 Campinas, SP, Brazil
Daniel Ugarte*
Affiliation:
Instituto de Fisica “Gleb Wataghin”, Universidade Estadual de Campinas-UNICAMP, 13083-859 Campinas, SP, Brazil
*
*Corresponding author: Daniel Ugarte, E-mail: [email protected]
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Abstract

Analytical studies of nanoparticles (NPs) are frequently based on huge datasets derived from hyperspectral images acquired using scanning transmission electron microscopy. These large datasets require machine learning computational tools to reduce dimensionality and extract relevant information. Principal component analysis (PCA) is a commonly used procedure to reconstruct information and generate a denoised dataset; however, several open questions remain regarding the accuracy and precision of reconstructions. Here, we use experiments and simulations to test the effect of PCA processing on data obtained from AuAg alloy NPs a few nanometers wide with different compositions. This study aims to address the reliability of chemical quantification after PCA processing. Our results show that the PCA treatment mitigates the contribution of Poisson noise and leads to better quantification, indicating that denoised results may be reliable from the point of view of both uncertainty and accuracy for properly planned experiments. However, the initial data need to be of sufficient quality: these results can only be obtained if the signal-to-noise ratio of input data exceeds a minimal value to avoid the occurrence of random noise bias in the PCA reconstructions.

Keywords

denoisingenergy-dispersive x-ray spectroscopy (EDS)nanoparticlesprincipal components analysis (PCA)quantitative chemical analysis
Type
Software and Instrumentation
Information
Microscopy and Microanalysis , Volume 28 , Issue 3 , June 2022 , pp. 723 - 731
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of the Microscopy Society of America

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