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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Moreira, Murilo [1] ; Hillenkamp, Matthias [1, 2] ; Divitini, Giorgio [3] ; Tizei, Luiz H. G. [4] ; Ducati, Caterina [3] ; Cotta, Monica A. [1] ; Rodrigues, Varlei [1] ; Ugarte, Daniel [1]
Total Authors: 8
Affiliation:
[1] Univ Estadual Campinas, UNICAMP, Inst Fis Gleb Wataghin, BR-13083859 Campinas, SP - Brazil
[2] Univ Claude Bernard Lyon 1, Univ Lyon, Inst Light & Matter, CNRS, UMR5306, F-69622 Villeurbanne - France
[3] Univ Cambridge, Dept Mat Sci & Met, Cambridge CB3 0FS - England
[4] Univ Paris Saclay, Lab Phys Solides, CNRS, F-91405 Orsay - France
Total Affiliations: 4
Document type: Journal article
Source: Microscopy and Microanalysis; JAN 2022.
Web of Science Citations: 0
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. (AU)

FAPESP's process: 13/02300-1 - Semiconductor nanowires: formation mechanisms and biosensing applications
Grantee:Mônica Alonso Cotta
Support Opportunities: Regular Research Grants
FAPESP's process: 13/10957-0 - Xylella fastidiosa-vector-host plant interaction and approaches for citrus variegated chlorosis and citrus canker control
Grantee:Alessandra Alves de Souza
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 16/12807-4 - Polymer layers with embedded metal nanoparticles: building blocks for (bio)sensors
Grantee:Osvaldo Novais de Oliveira Junior
Support Opportunities: Research Grants - Visiting Researcher Grant - International
FAPESP's process: 07/01722-9 - Development of a mass selected metallic nano-aggregate source
Grantee:Varlei Rodrigues
Support Opportunities: Regular Research Grants
FAPESP's process: 14/01045-0 - Advanced electron microscopy studies of metallic clusters and nanocomposites materials for photovoltaic applications
Grantee:Daniel Mario Ugarte
Support Opportunities: Scholarships abroad - Research