Application of Fourier transform and autocorrelation to cluster identification in the three-dimensional atom probe

Abstract : Because of the increasing number of collected atoms (up to millions) in the three-dimensional atom probe, derivation of chemical or structural information from the direct observation of three-dimensional images is becoming more and more difficult. New data analysis tools are thus required. Application of a discrete Fourier transform algorithm to three-dimensional atom probe datasets provides information that is not easily accessible in real space. Derivation of mean particle size from Fourier intensities or from three-dimensional autocorrelation is an example. These powerful methods can be used to detect and image nano-segregations. Using three-dimensional 'bright-field' imaging, single nano-segregations were isolated from the surrounding matrix of an iron-copper alloy. Measurement of the inner concentration within clusters is, therefore, straightforward. Theoretical aspects related to filtering in reciprocal space are developed.
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Contributor : Etienne Talbot <>
Submitted on : Tuesday, November 20, 2018 - 6:17:40 PM
Last modification on : Tuesday, May 21, 2019 - 1:44:08 PM

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F. Vurpillot, F. de Geuser, G. da Costa, D. Blavette. Application of Fourier transform and autocorrelation to cluster identification in the three-dimensional atom probe. Journal of Microscopy, 2004, 216 (3), pp.234--240. ⟨10.1111/j.0022-2720.2004.01413.x⟩. ⟨hal-01928905⟩

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