Hostname: page-component-7c8c6479df-xxrs7 Total loading time: 0 Render date: 2024-03-27T09:17:18.963Z Has data issue: false hasContentIssue false

New Techniques for the Analysis of Fine-Scaled Clustering Phenomena within Atom Probe Tomography (APT) Data

Published online by Cambridge University Press:  14 November 2007

Leigh T. Stephenson
Affiliation:
Australian Key Centre for Microscopy and Microanalysis, University of Sydney, Madsen Building, F09, Sydney, NSW 2006, Australia
Michael P. Moody
Affiliation:
Australian Key Centre for Microscopy and Microanalysis, University of Sydney, Madsen Building, F09, Sydney, NSW 2006, Australia
Peter V. Liddicoat
Affiliation:
Australian Key Centre for Microscopy and Microanalysis, University of Sydney, Madsen Building, F09, Sydney, NSW 2006, Australia
Simon P. Ringer
Affiliation:
Australian Key Centre for Microscopy and Microanalysis, University of Sydney, Madsen Building, F09, Sydney, NSW 2006, Australia ARC Centre for Design in Light Metals, The University of Sydney, Sydney, NSW 2006, Australia
Get access

Abstract

Nanoscale atomic clusters in atom probe tomographic data are not universally defined but instead are characterized by the clustering algorithm used and the parameter values controlling the algorithmic process. A new core-linkage clustering algorithm is developed, combining fundamental elements of the conventional maximum separation method with density-based analyses. A key improvement to the algorithm is the independence of algorithmic parameters inherently unified in previous techniques, enabling a more accurate analysis to be applied across a wider range of material systems. Further, an objective procedure for the selection of parameters based on approximating the data with a model of complete spatial randomness is developed and applied. The use of higher nearest neighbor distributions is highlighted to give insight into the nature of the clustering phenomena present in a system and to generalize the clustering algorithms used to analyze it. Maximum separation, density-based scanning, and the core linkage algorithm, developed within this study, were separately applied to the investigation of fine solute clustering of solute atoms in an Al-1.9Zn-1.7Mg (at.%) at two distinct states of early phase decomposition and the results of these analyses were evaluated.

Type
Research Article
Copyright
© 2007 Microscopy Society of America

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Byers, S. & Rafferty, A.E. (1996). Nearest Neighbor Clutter Removal for Estimating Features in Spatial Point Processes. Seattle, WA: University of Washington.
Cerezo, A. & Davin, L. (2007). Aspects of the observation of clusters in the 3-dimensional atom probe. Surf Interface Anal 39, 184188.CrossRefGoogle Scholar
Clark, P.J. & Evans, F.C. (1979). Generalization of a nearest neighbor measure of dispersion for use in K dimensions. Ecology 60, 316317.CrossRefGoogle Scholar
Eke, V.R., Cole, S., Frenk, C.S. & Navarro, J.F. (1996). Cluster correlation functions in N-body simulations. Mon Not R Astron Soc 201, 703.CrossRefGoogle Scholar
Ester, M., Kreigel, H.P., Sander, J. & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Second International Conference on Knowledge, Discovery and Data Mining, Simoudis, E., Han, J. & Fayyad, U. (Eds.), pp. 1015. Menlo Park, CA: AAAI Press.
Heinrich, A., At-Kassab, T. & Kirchheim, R. (2003). Investigation of the early stages of decomposition of Cu-0.7at.% Fe with the tomographic atom probe. Mater Sci Eng 353A, 9298.Google Scholar
Hyde, J.M. & English, C.A. (2001). Microstructual processes in irradiated materials. In MRS 2000 Fall Meeting, Symposium R, Boston, MA., Lucas, G.E., Snead, L., Kirk, M.A.J. & Elliman, R.G. (Eds.), pp. 2730. Warrendale, PA: Materials Research Society.
Kelly, T.F. & Larson, D.J. (2000). Local electrode atom probes. Mater Character 44, 5985.CrossRefGoogle Scholar
Lorr, M. (1983). Cluster Analysis for Social Scientists—Techniques for Analyzing and Simplifying Complex Block of Data. San Francisco: Jossey-Bass Limited.
Miller, M.K. & Kenik, E.A. (2004). Atom probe tomography: A technique for nanoscale characterization. Microsc Microanal 10, 336341.CrossRefGoogle Scholar
Moon, T.K. (1996). The expectation-maximization algorithm. In IEEE Signal Processing Magazine, pp. 4760.CrossRef
Ringer, S.P. & Hono, K. (2000). Microstructual evolution and age hardening in aluminium alloys: Atom probe field-ion microscopy and transmission studies. Mater Character 44, 101131.CrossRefGoogle Scholar
Starink, M.J., Gao, N., Davin, L., Yan, J. & Cerezo, A. (2005). Room temperature precipitation in quenched Al-Cu-Mg alloys: A model for the reaction kinetics and yield strength development. Philos Mag 85, 13951417.CrossRefGoogle Scholar
Sudbrack, C.K. (2004). Decomposition Behavior in Model Ni-Al-Cr-X Superalloys: Temporal Evolution and Compositional Pathways on a Nanoscale. Chicago: Northwestern University.
Vaumousse, D., Cerezo, A. & Warren, P.J. (2003). A procedure for quantification of precipitate microstructures form three-dimensional atom probe data. Ultramicroscopy 95, 215221.CrossRefGoogle Scholar
Vurpillot, F., Renaud, L. & Blavette, D. (2003). A new step towards the lattice reconstruction in 3DAP. Ultramicroscopy 95, 223229.CrossRefGoogle Scholar

Stephenson et al

Figure 8. Clustering in A1-1.9Zn-1.7Mg aged for 3600 secs at 150 (degree) C data set. All Mg and Zn atoms are shown

Download Stephenson et al(Video)
Video 17.8 MB