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

Autor(es)

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]

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: Microscopy and Microanalysis; JAN 2022.
• Citações Web of Science: 0

Resumo

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)

• Processo FAPESP: 13/02300-1 - Nanofios semicondutores: mecanismos de formação e aplicação em biossensores
• Beneficiário: Mônica Alonso Cotta
• Modalidade de apoio: Auxílio à Pesquisa - Regular
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• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 16/12807-4 - Camadas de polímero com incorporação de nanopartículas metálicas: plataforma para construção de bio(sensores)
• Beneficiário: Osvaldo Novais de Oliveira Junior
• Modalidade de apoio: Auxílio à Pesquisa - Pesquisador Visitante - Internacional
• Processo FAPESP: 07/01722-9 - Desenvolvimento de uma fonte de nano-agregados metálicos selecionados em massa
• Beneficiário: Varlei Rodrigues
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 14/01045-0 - Estudo por técnicas avançadas de microscopia eletrônica de agregados metálicos e materiais nanocompósitos utilizados em aplicações fotovoltaicas
• Beneficiário: Daniel Mario Ugarte
• Modalidade de apoio: Bolsas no Exterior - Pesquisa