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Large-Area X-Ray Micro-Fluorescence Imaging OF Heterogeneous Materials

Published online by Cambridge University Press:  06 March 2019

B.J. Cross ,
R.D. Lamb ,
S. Ma  and
J.M. Paque
B.J. Cross
Affiliation:
Kevex Instruments, San Carlos, CA 94070
R.D. Lamb
Affiliation:
Kevex Instruments, San Carlos, CA 94070
S. Ma
Affiliation:
Center for Materials Research, Stanford University Stanford, CA 94305-4045
J.M. Paque
Affiliation:
Center for Materials Research, Stanford University Stanford, CA 94305-4045
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Abstract

An X-ray Micro-Fluorescence (XRMF) spectrometer, with an analysis area of about 100 by 150 microns, has been used to collect 2-dimensional X-ray intensity maps over large-area (5 to 50 mm in X and Y) samples. These intensity maps were collected by scanning the sample on an XY stage, and converting X-ray Energy-Dispersive spectra to peak intensities for the elements of interest. The maps, when displayed using false-color or pseudogray scales, show the distribution of individual chemical elements over the analysis area. These maps can be collected at speeds from about 1 minute per frame (analysing 25 elements simultaneously). Greater precision of chemical intensities, or larger area maps, may require several hours, particularly if extensive data processing is performed at each point. XRMF has advantages over more conventional SEM-EDS X-ray mapping, including sample preparation and presentation, as well as improved signal-to-noise ratios.

A technique is described which assists in analyzing the large amount of data which is collected in each map. Principal Component Analysis (PCA) is performed on all of the elemental maps simultaneously. This technique compresses the many elemental intensity maps into a few principal components, resulting in many fewer maps to evaluate. The intensity maps of these principal components display the most pertinent information. They can also be plotted as scatter plots which can help with the partitioning of the data into individual phases. This procedure can potentially be automated as a method for phase analysis. The selected pixels from the scatter plots can be averaged and converted into phase compositions, and the phase information re-displayed on the original elemental or principal component maps.

This technique has been applied to a thin section of rock, and to a synthetic multiphase alloy sample.

Type
XV. X-Ray Imaging and Tomography
Information
Advances in X-Ray Analysis , Volume 35 , Issue B: First Pacific-International Congress on X-ray Analytical Methods (PICXAM). Fortieth Annual Conference on Applications of X-ray Analysis, August 7-16, 1991 , 1991 , pp. 1255 - 1264
Copyright
Copyright © International Centre for Diffraction Data 1991

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References

1. Nichols, M. C., Boehrae, D. R., Ryon, R. W., Wherry, D. C., Cross, B. J. and Aden, G. A., Parameters Affecting X-ray Micro-Fluorescence (XRMF) Analysis, Adv. X-Ray Analysis. 30: 45 (1987).10.1154/S0376030800021133Google Scholar
2. Wherry, D. C., Cross, B. J. and Briggs, T. H., An Automated X-Ray Micro- Fluorescence Materials Analysis System, Adv. X-Ray Analysis. 31: 93 (1988).10.1154/S0376030800021881Google Scholar
3. Cross, B. J. and Augenstine, J. E., Trace Analysis using EDS: Applications to Thin-Film and Heterogeneous Samples, accepted for publication in Adv. X-Ray Analysis. 34: (1991),Google Scholar
4. Shiraiwa, T. and Fujino, N., Micro Fluorescent X-ray Analyzer, Adv. X-ray Analysis. 11: 95 (1968).10.1154/S0376030800004766Google Scholar
5. Shimura, Y. and Kozaki, S., X-Ray Scanning Microanalyser, “Proc. 6ch Intl. Conf. X-Ray Optics a«i Microanalysis,” sds. G. Shinoda, K. Kohra and T. Ichinokawa, Univ. Tokyo Press (1972).Google Scholar
6. Watanabe, H. and Kobayashi, Y., X-Ray Induced Transport of Inorganic Elements in Living Rice Leaves Observed with X-Ray Fluorescence Element Mapping Spectrometry, Agric. Biol. Chem., 50:2077 (1986).Google Scholar
7. Carpenter, D. A., Taylor, M. A. and Holcombe, C. E, Applications of a Laboratory X-ray Microprobe to Materials Analysis, Adv. X-Ray Analysis, 34: 115 (1989).10.1007/978-1-4757-9110-5_15Google Scholar
8. Rindby, A., Engstrom, P., Larsson, S. and Stocklassa, B., Microbeam Technique for Energy-Dispersive X-Ray Fluorescence, X-Ray Spectrometry. 18: 109 (1989).10.1002/xrs.1300180307Google Scholar
9. Yattaraoto, N. and Hosokawa, Y., Development of an Innovative 5 micron diameter Focused X-Ray Beam Energy-Dispersive Spectrometer and its Applications, Jap. J. Applied Physics, 27:L2203 (1988).Google Scholar
10. Wherry, D. C. and Cross, B. J., Applications of Small-Area X-ray Fluorescence Analysis Using a Microbeam X-ray Source, Spectroscopy, 3: 38 (1988)'.Google Scholar
11. Boehirie, D. R., X-ray Microfluorescence Analysis of Thin- and ThicKSectioned Geologic Materials, Sandia Report SAHD87-3214 (1987).Google Scholar
12. Pearson, K., On lines and planes of closest fit to a system of points in space, Phil. Mag., 2;557 (1901).10.1080/14786440109462720Google Scholar
13. Daultrey, S., Principal Component Analysis, in “Concepts and Techniques of Modern Geography,” 8, Geo. Abstracts, Univ. East Anglia, UK (1976).Google Scholar
14. Manly, E. F. J., “Multivariate Statistical Methods: A primer,” Chapman and Hall, London (1986) .Google Scholar
15. King, P. L., Browning, R., Pianetta, P., Lindau, I., Keenlyside, M. and Knapp, G., Image processing of multispectral x-ray photoelectron spectroscopy images, J. Vac. sci. Technol., A. 7: 3301 (1989) .10.1116/1.576140Google Scholar
16. Paque, J. M., Browning, R., King, P. L., Pianetta, P., Quantitative Analysis of X-ray Images from Geological Materials, Microbeam Analysis, San Francisco Press, CA, 195 (1990).Google Scholar
17. Browning, R., King, P. L., Paque, J. M. and Pianetta, P., EDS Image Classification by Recursive Partitioning, Microbeam Analysis. San Francisco Press, CA, 199 (1990).Google Scholar
18. Bright, D. S. and Newbury, D. E., A Scatter Diagram Technique for Viewing Two or Three Related Images, Anal. Chem., 63:243A (1991).Google Scholar