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Data-Constrained Microstructure Characterization with Multispectrum X-Ray Micro-CT

Published online by Cambridge University Press:  03 May 2012

Sheridan C. Mayo ,
Andrew M. Tulloh ,
Adrian Trinchi  and
Sam Y.S. Yang
Sheridan C. Mayo*
Affiliation:
CSIRO Materials Science & Engineering, Private Bag 33, Clayton, Victoria 3169, Australia
Andrew M. Tulloh
Affiliation:
CSIRO Materials Science & Engineering, Private Bag 33, Clayton, Victoria 3169, Australia
Adrian Trinchi
Affiliation:
CSIRO Materials Science & Engineering, Private Bag 33, Clayton, Victoria 3169, Australia
Sam Y.S. Yang
Affiliation:
CSIRO Materials Science & Engineering, Private Bag 33, Clayton, Victoria 3169, Australia
*
Corresponding author. E-mail: [email protected]
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Abstract

Conventional X-ray microcomputed tomography (micro-CT) is not usually sufficient to determine microscopic compositional distributions as it is limited to measuring the X-ray attenuation of the sample, which for a given dataset can be similar for materials of different composition. In contrast, the present work enables three-dimensional compositional analysis with a data-constrained microstructure (DCM) modeling methodology, which uses two or more CT datasets acquired with different X-ray spectra and incorporates them as model constraints. For providing input data for DCM, we have also developed a method of micro-CT data collection that enables two datasets with different X-ray spectra to be acquired in parallel. Such data are used together with the DCM methodology to predict the distributions of corrosion inhibitor and filler in a polymer matrix. The DCM-predicted compositional microstructures have a reasonable agreement with energy dispersive X-ray images taken on the sample surface.

Keywords

microcomputed tomography (micro-CT)data-constrained modelingmultienergy X-ray
Type
Materials Applications
Information
Microscopy and Microanalysis , Volume 18 , Issue 3 , June 2012 , pp. 524 - 530
Copyright
Copyright © Microscopy Society of America 2012

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References

Alvarez, R.E. & Macovski, A. (1976). Energy-selective reconstructions in X-ray computerised tomography. Phys Med Biol 21(5), 733–744.CrossRefGoogle Scholar
Amendolia, S.R., Bisogni, M.G., Bottigli, U., Delogu, P., Dipasquale, G., Fantacci, M.E., Marchi, A., Marzulli, V.M., Oliva, P., Palmiero, R., Rosso, V., Stefanini, A., Stumbo, S. & Zucca, S. (2001). Spectroscopic and imaging capabilities of a pixellated photon counting system. Nucl Instrum Methods Phys Res A 466(1), 74–78.CrossRefGoogle Scholar
Drouin, D., Couture, A.R., Joly, D., Tastet, X., Aimez, V. & Gauvin, R. (2007). CASINO V2.42: A fast and easy-to-use modeling tool for scanning electron microscopy and microanalysis users. Scanning 29(3), 92–101.CrossRefGoogle ScholarPubMed
Firsching, M., Talla, P.T., Michel, T. & Anton, G. (2008). Material resolving X-ray imaging using spectrum reconstruction with Medipix2. Nucl Instrum Methods Phys Res A 591(1), 19–23.CrossRefGoogle Scholar
Goldstein, J., Newbury, D.E., Joy, D.C., Lyman, C.E., Echlin, P., Lifshin, E., Sawyer, L. & Michael, J.R. (2003). Scanning Electron Microscopy and X-Ray Microanalysis. New York: Springer.CrossRefGoogle Scholar
Jakubek, J., Dammer, J., Holy, T., Jakubek, A., Pospisil, S., Tichy, V., Uher, J., Vavrik, D. & IEEE (2007). Spectrometric properties of TimePix pixel detector for X-ray color and phase sensitive radiography. In 2007 IEEE Nuclear Science Symposium Conference Record, Vols 1–11, pp. 2323–2326. New York: IEEE.CrossRefGoogle Scholar
Jones, A.C., Arns, C.H., Hutmacher, D.W., Milthorpe, B.K., Sheppard, A.P. & Knackstedt, M.A. (2009). The correlation of pore morphology, interconnectivity and physical properties of 3D ceramic scaffolds with bone ingrowth. Biomaterials 30(7), 1440–1451.CrossRefGoogle ScholarPubMed
Kruger, R.A., Mistretta, C.A., Crummy, A.B., Sackett, J.F., Goodsitt, M.M., Riederer, S.J., Houk, T.L., Shaw, C.G. & Fleming, D. (1977a). Digital K-edge subtraction radiography. Radiology 125(1), 243–245.CrossRefGoogle ScholarPubMed
Kruger, R.A., Riederer, S.J. & Mistretta, C.A. (1977b). Relative properties of tomography, K-edge imaging, and K-edge tomography. Med Phys 4(3), 244–249.CrossRefGoogle ScholarPubMed
MacPherson, R.D. & Srolovitz, D.J. (2007). The von Neumann relation generalized to coarsening of three-dimensional microstructures. Nature 466, 1053–1055.CrossRefGoogle Scholar
Mayo, S., Miller, P., Gao, D. & Sheffield-Parker, J. (2007). Software image alignment for X-ray microtomography with submicrometre resolution using a SEM-based X-ray microscope. J Microsc-Oxford 228, 257–263.CrossRefGoogle ScholarPubMed
Mayo, S.C., Davis, T.J., Gureyev, T.E., Miller, P.R., Paganin, D., Pogany, A., Stevenson, A.W. & Wilkins, S.W. (2003). X-ray phase-contrast microscopy and microtomography. Optics Exp 11(19), 2289–2302.CrossRefGoogle ScholarPubMed
Mookhoek, S.D., Mayo, S.C., Hughes, A.E., Furman, S.A., Fischer, H.R. & van der Zwaag, S. (2010). Applying SEM-based X-ray microtomography to observe self-healing in solvent encapsulated thermoplastic materials. Adv Eng Mater 12(3), 228–234.CrossRefGoogle Scholar
Mourachov, S. (1997). Automata simulation of the phenomenon of multiple crystallization. Comp Mater Sci 7, 384–388.CrossRefGoogle Scholar
Muller, B.R., Lange, A., Harwardt, M. & Hentschel, M.P. (2009). Synchrotron-based micro-CT and refraction-enhanced micro-CT for non-destructive materials characterisation. Adv Eng Mater 11(6), 435–440.CrossRefGoogle Scholar
Muster, T.H., Hughes, A.E., Furman, S.A., Harvey, T., Sherman, N., Hardin, S., Corrigan, P., Lau, D., Scholes, F.H., White, P.A., Glenn, M., Mardel, J. & Garcia, S.J. (2009). A rapid screening multi-electrode method for the evaluation of corrosion inhibitors. Electrochim Acta 54(12), 3402–3411.CrossRefGoogle Scholar
Paganin, D., Mayo, S.C., Gureyev, T.E., Miller, P.R. & Wilkins, S.W. (2002). Simultaneous phase and amplitude extraction from a single defocused image of a homogeneous object. J Microsc-Oxford 206, 33–40.CrossRefGoogle ScholarPubMed
Press, W.H., Vetterling, W.T., Teukolsky, S.A. & Flannery, B.P. (2007). Numerical Recipes—The Art of Scientific Computing. Cambridge, UK: Cambridge University Press.Google Scholar
Raabe, D. (2002). Cellular automata in materials science with particular reference to recrystallization simulation. Ann Rev Mater Res 32, 53–76.CrossRefGoogle Scholar
Rohl, A.L. (2003). Computer prediction of crystal morphology. Current Opinion Solid State Mater Sci 7, 21–26.CrossRefGoogle Scholar
Scholes, F.H., Furman, S.A., Hughes, A.E., Nikpour, T., Wright, N., Curtis, P.R., Macrae, C.M., Intem, S. & Hill, A.J. (2006). Chromate leaching from inhibited primers—part I. Characterisation of leaching. Prog Org Coat 56(1), 23–32.CrossRefGoogle Scholar
Spanos, G. (2006). Viewpoint set no. 41: 3D characterization and analysis of materials. Scripta Mater 55, 1–114CrossRefGoogle Scholar
Suzudo, T. (2004). Spatial pattern formation in asynchronous cellular automata with mass conservation. Physica A 343, 185–200.CrossRefGoogle Scholar
Toda, H., Shimizu, K., Uesugi, K., Suzuki, Y. & Kobayashi, M. (2010). Application of dual-energy K-edge subtraction imaging to assessment of heat treatments in Al-Cu alloys. Mater Trans 51(11), 2045–2048.CrossRefGoogle Scholar
Uher, J., Jakubek, J., Mayo, S., Stevenson, A. & Tickner, J. (2011). X-ray beam hardening based material recognition in micro-imaging. J Instrum 6(8), 08015.CrossRefGoogle Scholar
Yang, S., Furman, S. & Tulloh, A. (2008). A data-constrained 3D model for material compositional microstructures. Adv Mater Res 32, 267–270.CrossRefGoogle Scholar
Yang, S., Gao, D., Muster, T., Tulloh, A., Furman, S., Mayo, S. & Trinchi, A. (2010a). Microstructure of a paint primer—A data-constrained modeling analysis. In 7th Pacific Rim International Conference on Advanced Materials and Processing, Nie, J.F. & Morton, A. (Eds.), pp. 1686–1689. Zurich, Switzerland: Trans Tech Publications Ltd.Google Scholar
Yang, Y.S., Blake, N., Abbott, T.B. & McCarthey, J.F. (1993). A lattice model of solidification. Scripta Metal Mater 29, 1285–1290.CrossRefGoogle Scholar
Yang, Y.S., Blake, N., Abbott, T.B. & McCarthey, J.F. (1995). A statistical mechanical model of solidification. Com Numer Methods Eng 11, 805–812.CrossRefGoogle Scholar
Yang, Y.S., Gureyev, T.E., Tulloh, A., Clennell, B. & Pervukhina, M. (2010b). Feasibility of a data-constrained prediction of hydrocarbon reservoir sandstone microstructures. Meas Sci Technol 21, 047001.CrossRefGoogle Scholar
Yang, Y.S., Tulloh, A., Cole, I., Furman, S. & Hughes, A. (2007). A data-constrained computational model for morphology structures. J Aust Ceram Soc 43, 159–164.Google Scholar
Zhang, Q., Toda, H., Takami, Y., Suzuki, Y., Uesugi, K. & Kobayashi, M. (2010). Assessment of 3D inhomogeneous microstructure of highly alloyed aluminium foam via dual energy K-edge subtraction imaging. Philos Mag 90(14), 1853–1871.CrossRefGoogle Scholar
Zschornack, G. (2007). Handbook of X-Ray Data. Berlin: Springer-Verlag.Google Scholar