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Enhancing Element Identification by Expectation–Maximization Method in Atom Probe Tomography

  • Francois Vurpillot (a1), Constantinos Hatzoglou (a1), Bertrand Radiguet (a1), Gerald Da Costa (a1), Fabien Delaroche (a1) and Frederic Danoix (a1)...

Abstract

This paper describes an alternative way to assign elemental identity to atoms collected by atom probe tomography (APT). This method is based on Bayesian assignation of label through the expectation–maximization method (well known in data analysis). Assuming the correct shape of mass over charge peaks in mass spectra, the probability of each atom to be labeled as a given element is determined, and is used to enhance data visualization and composition mapping in APT analyses. The method is particularly efficient for small count experiments with a low signal to noise ratio, and can be used on small subsets of analyzed volumes, and is complementary to single-ion decomposition methods. Based on the selected model and experimental examples, it is shown that the method enhances our ability to observe and extract information from the raw dataset. The experimental case of the superimposition of the Si peak and N peak in a steel is presented.

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Corresponding author

*Author for correspondence: Francois Vurpillot, E-mail: francois.vurpillot@univ-rouen.fr

References

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Blavette, D & Sauvage, X (2016). Early developments and basic concepts. In Atom Probe Tomography, pp. 115. Elsevier. http://linkinghub.elsevier.com/retrieve/pii/B9780128046470000012 (Accessed July 10, 2018).
Cadel, E, Fraczkiewicz, A & Blavette, D (2001). Atomic scale observation of Cottrell atmospheres in B-doped FeAl (B2) by 3D atom probe field ion microscopy. Mat Sci Eng A 309–310, 3237.
Da Costa, G, Vurpillot, F, Bostel, A, Bouet, M & Deconihout, B (2005). Design of a delay-line position-sensitive detector with improved performance. Rev Sci Instrum 76, 013304.
Dempster, AP, Laird, NM & Rubin, DB (1977). Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc. Series B (Methodological) 39(1), 138. JSTOR, http://www.jstor.org/stable/2984875
Dijkstra, M, Roelofsen, H, Vonk, RJ & Jansen, RC (2006). Peak quantification in surface-enhanced laser desorption/ionization by using mixture models. PROTEOMICS 6, 51065116.
Figueiredo, F & Gomes, I (2013). The skew normal distribution in SPC. REVSTAT – Stat J 11, 83104.
Gault, B (ed.) (2012). Atom Probe Microscopy. New York: Springer.
Goodman, SR, Brenner, SS & Low, JR (1973). An FIM-atom probe study of the precipitation of copper from iron-1.4 at. pct copper. Part II: Atom probe analyses. Metallurgical Trans 4, 23712378.
Haley, D, Choi, P & Raabe, D (2015). Guided mass spectrum labelling in atom probe tomography. Ultramicroscopy 159, 338345.
Hall, TM, Wagner, A & Seidman, DN (1977). A computer-controlled time-of-flight atom-probe field-ion microscope for the study of defects in metals. J Phys E Sci Instrum 10, 884891.
Hudson, D, Smith, GDW & Gault, B (2011). Optimisation of mass ranging for atom probe microanalysis and application to the corrosion processes in Zr alloys. Ultramicroscopy 111, 480486.
Johnson, LJS, Thuvander, M, Stiller, K, Odén, M & Hultman, L (2013). Blind deconvolution of time-of-flight mass spectra from atom probe tomography. Ultramicroscopy 132, 6064.
Larson, DJ, Prosa, TJ, Ulfig, RM, Geiser, BP & Kelly, ThF (2013). Local Electrode Atom Probe Tomography: A User's Guide. New York: Springer.
London, AJ, Haley, D & Moody, MP (2017). Single-ion deconvolution of mass peak overlaps for atom probe microscopy. Microsc Microanal 23, 300306.
Marquis, EA & Hyde, JM (2010). Applications of atom-probe tomography to the characterisation of solute behaviours. Mater Sci, Eng R, Rep 69, 3762.
McLachlan, G & Peel, D (2000) Wiley Series in Probability and Statistics. New York: John Wiley and Sons, Inc.
Müller, EW, Panitz, JA & McLane, SB (1968). The atom-probe field ion microscope. Rev Sci Instrum 39, 8386.
Pareige, C, Lefebvre-Ulrikson, W, Vurpillot, F & Sauvage, X (2016). Time-of-Flight mass spectrometry and composition measurements. In Atom Probe Tomography, pp. 123154. Elsevier http://linkinghub.elsevier.com/retrieve/pii/B978012804647000005X (Accessed July 10, 2018).
Polanski, A, Marczyk, M, Pietrowska, M, Widlak, P & Polanska, J (2015). Signal partitioning algorithm for highly efficient Gaussian mixture modeling in mass spectrometry Tang, H. (Ed.). PLoS ONE 10, e0134256.
Sarrau, J-M (1977). ‘Réalisation et Performances D'une Sonde à Atomes’. Thèse de Doctorat es Sciences Physiques, Université de Rouen.
Spainhour, JCG, Janech, MG, Schwacke, JH, Velez, JCQ & Ramakrishnan, V (2014). The application of Gaussian mixture models for signal quantification in MALDI-ToF mass spectrometry of peptides Bader, M. (Ed.). PLoS ONE 9, e111016.
Thuvander, M, Östberg, G, Ahlgren, M & Falk, LKL (2015). Atom probe tomography of a Ti–Si–Al–C–N coating grown on a cemented carbide substrate. Ultramicroscopy 159, 308313.
Zelenty, J, Dahl, A, Hyde, J, Smith, GDW & Moody, MP (2017). Detecting clusters in atom probe data with Gaussian mixture models. Microsc Microanal 23, 269278.

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