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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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References

Alloyeau, D, Mottet, C & Ricolleau, C (2012). Nanoalloys: Synthesis, Structure and Properties. London: Springer-Verlag.CrossRefGoogle Scholar
Belter, M, Sajnóg, A & Barałkiewicz, D (2014). Over a century of detection and quantification capabilities in analytical chemistry – historical overview and trends. Talanta 129, 606–616.CrossRefGoogle Scholar
Bevington, PR & Robinson, DK (2003). Data Reduction and Error Analysis for the Physical Science, 3rd ed. New York: MacGraw-Hill.Google Scholar
Binns, C (2014). Nanomagnetism: Fundamentals and Applications, vol. 6. Amsterdam, Netherlands: Elsevier.Google Scholar
Brown, KA, Brittman, S, Maccaferri, N, Jariwala, D & Celano, U (2020). Machine learning in nanoscience: Big data at small scales. Nano Lett 20, 2–10.CrossRefGoogle ScholarPubMed
Carter, CB & Williams, DB (2016). Transmission Electron Microscopy: Diffraction, Imaging, and Spectroscopy. New York: Springer.CrossRefGoogle Scholar
Cliff, G & Lorimer, GW (1975). The quantitative analysis of thin specimens. J Microsc 103, 203–207.CrossRefGoogle Scholar
Cueva, P, Hovden, R, Mundy, JA, Xin, HL & Muller, DA (2012). Data processing for atomic resolution electron energy loss spectroscopy. Microsc Microanal 18, 667–675.CrossRefGoogle ScholarPubMed
Currie, LA (1968). Limits for qualitative detection and quantitative determination. Application to radiochemistry. Anal Chem 40, 586–593.CrossRefGoogle Scholar
Currie, LA (1999). Detection and quantification limits: Origins and historical overview. Anal Chim Acta 391, 127–134.CrossRefGoogle Scholar
de la Peña, F, Prestat, E, Fauske, VT, Burdet, P, Lähnemann, , Furnival, T, Jokubauskas, P, Nord, M, Ostasevicius, T, MacArthur, KE, Johnstone, DN, Sarahan, M, Aarholt, T, Taillon, J, pquinn-dls, , Migunov, V, Eljarrat, A, Caron, J, Poon, T, Mazzucco, S, Francis, C, Martineau, B, actions-user, , Somnath, S, Slater, T, Tappy, N, Walls, M, Cautaerts, N, Winkler, F & DENSmerijn (2021). hyperspy/hyperspy: Release v1.6.5 (v1.6.5). Zenodo. doi:10.5281/zenodo.5608741.CrossRefGoogle Scholar
de Sá, ADT, Oiko, VTA, di Domenicantonio, G & Rodrigues, V (2014). New experimental setup for metallic clusters production based on hollow cylindrical magnetron sputtering. J Vac Sci Technol B 32, 061804.CrossRefGoogle Scholar
Drosg, M (2009). Dealing with Uncertainties: A Guide to Error Analysis, 2nd ed. Berlin: Springer.CrossRefGoogle Scholar
Faber, NM, Meinders, MJ, Geladi, P, Sjiistrijm, M, Buydens, LMC & Kateman, G (1995a). Random error bias in principal component analysis. Part I. Derivation of theoretical predictions. Anal Chim Acta 304, 257–271.CrossRefGoogle Scholar
Faber, NM, Meinders, MJ, Geladi, P, Sjiistrijm, M, Buydens, LMC & Kateman, G (1995b). Random error bias in principal component analysis. Part II. Application of theoretical predictions to multivariate problems. Anal Chim Acta 304, 273–283.CrossRefGoogle Scholar
Ferrando, R (2016). Structure and Properties of Nanoalloys in Frontiers of Nanoscience, pp. 2–337. Amsterdam: Elsevier.Google Scholar
Goldstein, JI, Newbury, DE, Michael, JR, Ritchie, NWM, Henry, J, Scott, HJ & Joy, DC (2017). Scanning Electron Microscopy and X-Ray Microanalysis, 4th ed. New York: Springer.Google Scholar
Hawkes, P & Spence, JCH (2019). Springer Handbook of Microscopy. Switzerland: Springer.CrossRefGoogle Scholar
Heiz, U & Landman, UE (2007). Nanocatalysis. Heidelberg, Berlin: Springer.CrossRefGoogle Scholar
Hughes, IG & Hase, TPA (2010). Mesurements and Their Uncertainties, A Practical Guide to Modern Error Analysis. Oxford: Oxford University Press.Google Scholar
Jolliffe, IT (2002). Principal Component Analysis, 2nd ed. New York: Springer-Verlag.Google Scholar
Jolliffe, IT & Cadima, J (2016). Principal component analysis: A review and recent developments. Philos Trans A 374, 20150202.CrossRefGoogle ScholarPubMed
Jones, L, Varambhia, A, Beanland, R, Kepaptsoglou, D, Griffiths, I, Ishizuka, A, Azough, F, Freer, R, Ishizuka, K, Cherns, D, Ramasse, QM, Lozano-Perez, S & Nellist, PD (2018). Managing dose-, damage- and data-rates in multi-frame spectrum-imaging. Microscopy 67, 98–113.CrossRefGoogle ScholarPubMed
Jutten, C & Herault, J (1991). Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture. Signal Process 24, 1–10.CrossRefGoogle Scholar
Keenan, MR & Kotula, PG (2004). Accounting for poisson noise in the multivariate analysis of TOF-SIMS spectrum images. Surf Interface Anal 36, 203–212.CrossRefGoogle Scholar
Kotula, PG & Keenan, MR (2006). Application of multivariate statistical analysis to STEM xray spectral images: Interfacial analysis in microelectronics. Microsc Microanal 12, 538–544.CrossRefGoogle Scholar
Kotula, PG & Van Benthem, MH (2015). Revisiting noise scaling for multivariate statistical analysis. Microsc Microanal 21, 1423–1424.CrossRefGoogle Scholar
Lichtert, S & Verbeeck, J (2013). Statistical consequences of applying a PCA filter on EELS spectrum images. Ultramicroscopy 125, 35–42.CrossRefGoogle ScholarPubMed
Lin, C-J (2007). Projected gradient methods for nonnegative matrix factorization. Neural Comput 19, 2756–2779.CrossRefGoogle ScholarPubMed
Lyman, CE, Lakis, RE & Stenger, HG Jr. (1995). X-ray emission spectrometry of phase separation in PtRh nanoparticles for nitric oxide reduction. Ultramicroscopy 58, 25–34.CrossRefGoogle Scholar
Malinowski, ER (2002). Factor Analysis in Chemistry, 3rd ed. New York: Wiley.Google Scholar
Moreira, M, Hillenkamp, M, Divitini, G, Tizei, LHG, Ducati, C, Cotta, MA, Rodrigues, V & Ugarte, D (2022). Improving quantitative EDS chemical analysis of alloy nanoparticles by PCA denoising: Part I, reducing reconstruction bias. Microsc Microanal. doi:10.1017/S1431927621013933.Google Scholar
Nadler, B (2008). Finite sample approximation results for principal component analysis: A matrix perturbation approach. Ann Stat 36, 2791–2817.CrossRefGoogle Scholar
Nadler, B (2009). Discussion. J Am Stat Assoc 104, 694–697.CrossRefGoogle Scholar
Odom, TW & Schatz, GC (2011). Introduction to plasmonics. Chem Rev 111, 3667–3668.CrossRefGoogle ScholarPubMed
Pennycook, SJ & Nellist, PD (2011). Scanning Transmission Electron Microscopy. New York: Springer.CrossRefGoogle ScholarPubMed
Potapov, P (2016). Why principal component analysis of STEM spectrum images results in abstract, uninterpretable loadings? Ultramicroscopy 160, 197–212.CrossRefGoogle ScholarPubMed
Potapov, P (2017). On the loss of information in PCA of spectrum-images. Ultramicroscopy 182, 191–194.CrossRefGoogle ScholarPubMed
Potapov, P & Lubk, A (2019). Optimal principal component analysis of STEM XEDS spectrum images. Adv Struct Chem Imaging 5, 4.CrossRefGoogle ScholarPubMed
Ritchie, NWM (2020). Embracing uncertainty: Modeling the standard uncertainty in electron probe microanalysis—Part I. Microsc Microanal 26, 469–483.CrossRefGoogle Scholar
Spurgeon, SR, Ophus, C, Jones, L, Petford-Long, A, Kalinin, SV, Olszta, MJ, Dunin-Borkowski, RE, Salmon, N, Hattar, K, Yang, W-CD, Sharma, R, Du, Y, Chiaramonti, A, Zheng, H, Buck, EC, Kovarik, L, Penn, RL, Li, D, Zhang, X, Murayama, M & Taheri, ML (2020). Towards data-driven next-generation transmission electron microscopy. Nat Mater 20, 274–279.CrossRefGoogle Scholar
Titchmarsh, JM (1999). EDX spectrum modelling and multivariate analysis of sub-nanometer segregation. Micron 30, 159–171.CrossRefGoogle Scholar
Tizei, LHG, Chiaramonte, T, Cotta, MA & Ugarte, D (2010). Characterization of interface abruptness and material properties in catalytically Grown III-V nanowires: Exploiting plasmon chemical shift. Nanotechnology 21, 295701.CrossRefGoogle ScholarPubMed
Williams, DB & Carter, CB (2009). Transmission Electron Microscopy Part 1: Basics, 2nd ed. New York: Springer.CrossRefGoogle Scholar
Zaluzec, NJ (2019). Improving the sensitivity of X-ray microanalysis in the analytical electron microscope. Ultramicroscopy 203, 163–169.CrossRefGoogle ScholarPubMed