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Improving Quantitative EDS Chemical Analysis of Alloy Nanoparticles by PCA Denoising: Part I, Reducing Reconstruction Bias

Published online by Cambridge University Press:  03 January 2022

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

Scanning transmission electron microscopy is a crucial tool for nanoscience, achieving sub-nanometric spatial resolution in both image and spectroscopic studies. This generates large datasets that cannot be analyzed without computational assistance. The so-called machine learning procedures can exploit redundancies and find hidden correlations. Principal component analysis (PCA) is the most popular approach to denoise data by reducing data dimensionality and extracting meaningful information; however, there are many open questions on the accuracy of reconstructions. We have used experiments and simulations to analyze the effect of PCA on quantitative chemical analysis of binary alloy (AuAg) nanoparticles using energy-dispersive X-ray spectroscopy. Our results demonstrate that it is possible to obtain very good fidelity of chemical composition distribution when the signal-to-noise ratio exceeds a certain minimal level. Accurate denoising derives from a complex interplay between redundancy (data matrix size), counting noise, and noiseless data intensity variance (associated with sample chemical composition dispersion). We have suggested several quantitative bias estimators and noise evaluation procedures to help in the analysis and design of experiments. This work demonstrates the high potential of PCA denoising, but it also highlights the limitations and pitfalls that need to be avoided to minimize artifacts and perform reliable quantification.

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 2 , April 2022 , pp. 338 - 349
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of the Microscopy Society of America

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