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When will Low-Contrast Features be Visible in a STEM X-Ray Spectrum Image?a

Published online by Cambridge University Press:  01 April 2015

Chad M. Parish
Chad M. Parish*
Affiliation:
Oak Ridge National Laboratory, Radiation Effects and Microstructural Analysis Group, 1 Bethel Valley Road, MS6064 Oak Ridge, TN 37831, USA
*
*Corresponding author.parishcm@ornl.gov
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Abstract

When will a small or low-contrast feature, such as an embedded second-phase particle, be visible in a scanning transmission electron microscopy (STEM) X-ray map? This work illustrates a computationally inexpensive method to simulate X-ray maps and spectrum images (SIs), based upon the equations of X-ray generation and detection. To particularize the general procedure, an example of nanostructured ferritic alloy (NFA) containing nm-sized Y2Ti2O7 embedded precipitates in ferritic stainless steel matrix is chosen. The proposed model produces physically appearing simulated SI data sets, which can either be reduced to X-ray dot maps or analyzed via multivariate statistical analysis. Comparison to NFA X-ray maps acquired using three different STEM instruments match the generated simulations quite well, despite the large number of simplifying assumptions used. A figure of merit of electron dose multiplied by X-ray collection solid angle is proposed to compare feature detectability from one data set (simulated or experimental) to another. The proposed method can scope experiments that are feasible under specific analysis conditions on a given microscope. Future applications, such as spallation proton–neutron irradiations, core-shell nanoparticles, or dopants in polycrystalline photovoltaic solar cells, are proposed.

Keywords

STEMX-ray mappingspectrum imagingsimulation
Type
Techniques and Equipment Development
Information
Microscopy and Microanalysis , Volume 21 , Issue 3 , June 2015 , pp. 706 - 724
Copyright
© Microscopy Society of America 2015. This is a work of the U.S. Government and is not subject to copyright protection in the United States. 

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Footnotes

a

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy. The publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

References

Allen, T., Burlet, H., Nanstad, R.K., Samaras, M. & Ukai, S. (2009). Advanced structural materials and cladding. MRS Bull 34(1), 20–27.CrossRefGoogle Scholar
Amiri, G., Ruault, M.O., Henry, J., Bernas, H., Cadel, E. & Pareige, P. (2002). Consequences of calcium and sulphur spallation product recoils in 9Cr-1Mo steel: Simulation by ion implantation. J Phys IV 12(PR8), 85–92.Google Scholar
Bentley, J. & Hoelzer, D. (2009). Characterization of oxide nano-clusters in mechanically alloyed nickel alloys. Microsc Microanal 15(Suppl 2), 1368–1369.CrossRefGoogle Scholar
Bhattacharyya, D., Dickerson, P., Odette, G.R., Maloy, S.A., Misra, A. & Nastasi, M.A. (2012). On the structure and chemistry of complex oxide nanofeatures in nanostructured ferritic alloy U14YWT. Philos Mag 92(16), 2089–2107.CrossRefGoogle Scholar
Bonnet, N. (1998). Multivariate statistical methods for the analysis of microscope image series: Applications in materials science. J Microsc (Oxf) 190, 2–18.CrossRefGoogle Scholar
Brandes, M.C., Kovarik, L., Miller, M.K., Daehn, G.S. & Mills, M.J. (2011). Creep behavior and deformation mechanisms in a nanocluster strengthened ferritic steel. Acta Materialia 50, 1827–1839.Google Scholar
Brandes, M.C., Kovarik, L., Miller, M.K. & Mills, M.J. (2012). Morphology, structure, and chemistry of nanoclusters in a mechanically alloyed nanostructured ferritic steel. J Mater Sci 47(8), 3913–3923.CrossRefGoogle Scholar
Cadel, E., Pareige, P. & Ruault, M.O. (2002). Experimental simulation of spallation elements production in a 9Cr-1Mo martensitic steel: 3D atom probe characterisation. J Phys IV 12(PR8), 93–101.Google Scholar
Certain, A., Kuchibhatla, S., Shutthanandan, V., Hoelzer, D.T. & Allen, T.R. (2012). Radiation stability of nanoclusters in nano-structured oxide dispersion strengthened (ODS) steels. J Nucl Mater 434(1–3), 311–321.CrossRefGoogle Scholar
Certain, A.G., Field, K.G., Allen, T.R., Miller, M.K., Bentley, J. & Busby, J.T. (2010). Response of nanoclusters in a 9Cr ODS steel to 1 dpa, 525° C proton irradiation. J Nucl Mater 407(1), 2–9.CrossRefGoogle Scholar
Dai, Y., Odette, G.R. & Yamamoto, T. (2012). 1.06—The effects of helium in irradiated structural alloys. In Comprehensive Nuclear Materials, R.J.M. Konings (Eds.), pp. 141–193. Oxford: Elsevier.CrossRefGoogle Scholar
Dawson, K. & Tatlock, G.J. (2014). Characterisation of nanosized oxides in ODM401 oxide dispersion strengthened steel. J Nucl Mater 444(1–3), 252–260.CrossRefGoogle Scholar
Egerton, R.F. (2013 a). Control of radiation damage in the TEM. Ultramicroscopy 127, 100–108.CrossRefGoogle ScholarPubMed
Egerton, R.F. (2013 b). A modest proposal for the propagation of information concerning radiation damage in the TEM, and as fodder for pasturized professors. Microsc Today 2(6), 70–72.CrossRefGoogle Scholar
Egerton, R.F., Wang, F. & Crozier, P.A. (2006). Beam-induced damage to thin specimens in an intense electron probe. Microsc Microanal 12(1), 65–71.CrossRefGoogle Scholar
Field, K.G., Barnard, L.M., Parish, C.M., Busby, J.T., Morgan, D. & Allen, T.R. (2013). Dependence on grain boundary structure of radiation induced segregation in a 9 wt.% Cr model ferritic/martensitic steel. J Nucl Mater 435(1–3), 172–180.CrossRefGoogle Scholar
Genc, A., Banerjee, R., Thompson, G.B., Maher, D.M., Johnson, A.W. & Fraser, H.L. (2009). Complementary techniques for the characterization of thin film Ti/Nb multilayers. Ultramicroscopy 109, 1276–1281.CrossRefGoogle ScholarPubMed
Keenan, M.R. (2007). Multivariate analysis of spectral images composed of count data. In Techniques and Applications of Hyperspectral Image Analysis, Grahn H.F. & Geladi P. (Eds.), pp. 89–126. Chichester: John Wiley & Sons.CrossRefGoogle Scholar
Keenan, M.R. (2009). Exploiting spatial-domain simplicity in spectral image analysis. Surf Interface Anal 41, 79–87.CrossRefGoogle Scholar
Keenan, M.R. & Kotula, P.G. (2003). Apparatus and system for multivariate spectral analysis. US Patent # 6,584,413.Google Scholar
Keenan, M.R. & Kotula, P.G. (2004 a). Accounting for Poisson noise in the multivariate analysis of ToF-SIMS spectrum images. Surf Interf Anal 36(3), 203–212.CrossRefGoogle Scholar
Keenan, M.R. & Kotula, P.G. (2004 b). Method of multivariate spectral analysis. US Patent 6,675,106.Google Scholar
Keenan, M.R. & Kotula, P.G. (2004 c). Optimal scaling of ToF-SIMS spectrum-images prior to multivariate statistical analysis. Appl Surf Sci 231–232, 240–244.CrossRefGoogle Scholar
Klueh, R.L., Maziasz, P.J., Kim, I.S., Heatherly, L., Hoelzer, D.T., Hashimoto, N., Kenik, E.A. & Miyahara, K. (2002). Tensile and creep properties of an oxide dispersion-strengthened ferritic steel. J Nucl Mater 307–311, 773–777.CrossRefGoogle Scholar
Klueh, R.L., Shingledecker, J.P., Swindeman, R.W. & Hoelzer, D.T. (2005). Oxide dispersion-strengthened steels: A comparison of some commercial and experimental alloys. J Nucl Mater 341(2–3), 103–114.CrossRefGoogle Scholar
Kotula, P.G. & Keenan, M.R. (2006). Application of multivariate statistical analysis to STEM X-ray spectral images: Interfacial analysis in microelectronics. Microsc Microanal 12(6), 538–544.CrossRefGoogle Scholar
Kotula, P.G., Keenan, M.R. & Michael, J.R. (2003). Automated analysis of SEM X-ray spectral images: A powerful new microanalysis tool. Microsc Microanal 9(1), 1–17.CrossRefGoogle ScholarPubMed
Kuksenko, V., Pareige, C., Pareige, P. & Dai, Y. (2014). Production and segregation of transmutation elements Ca, Ti, Sc in the F82H steel under mixed spectrum irradiation of high energy protons and spallation neutrons. J Nucl Mater 447(1–3), 189–196.CrossRefGoogle Scholar
Lee, S.H., Zhang, X.G., Parish, C.M., Lee, H.N., Smith, D.B., He, Y.N. & Xu, J. (2011). Nanocone tip-film solar cells with efficient charge transport. Adv Mater 23(38), 4381–4385.CrossRefGoogle ScholarPubMed
McClintock, D.A., Hoelzer, D.T., Sokolov, M.A. & Nanstad, R.K. (2009 a). Mechanical properties of neutron irradiated nanostructured ferritic alloy 14YWT. J Nucl Mater 386, 307–311.CrossRefGoogle Scholar
McClintock, D.A., Sokolov, M.A., Hoelzer, D.T. & Nanstad, R.K. (2009 b). Mechanical properties of irradiated ODS-EUROFER and nanocluster strengthened 14YWT. J Nucl Mater 392(2), 353–359.CrossRefGoogle Scholar
Michael, J.R., Plimpton, S.J. & Romig, A.D. (1993). Parallel simulation of electron-solid interactions—A rapid aid for electron-microscope data interpretation. Ultramicroscopy 51(1–4), 160–167.CrossRefGoogle Scholar
Michael, J.R., Williams, D.B., Klein, C.F. & Ayer, R. (1990). The measurement and calculation of X-ray spatial resolution obtained in the analytical electron microscope. J Microsc 160(1), 41–53.CrossRefGoogle Scholar
Miller, M.K., Hoelzer, D.T., Kenik, E.A. & Russell, K.F. (2004). Nanometer scale precipitation in ferritic MA/ODS alloy MA957. J Nucl Mater 329, 338–341.CrossRefGoogle Scholar
Miller, M.K., Hoelzer, D.T., Kenik, E.A. & Russell, K.F. (2005). Stability of ferritic MA/ODS alloys at high temperatures. Intermetallics 13(3–4), 387–392.CrossRefGoogle Scholar
Miller, M.K., Hoelzer, D. & Russell, K.F. (2010 a). Radiation response of a 12YWT nanostructured ferritic steel. Mater Res Soc Symp Proc 1215, V06-02.Google Scholar
Miller, M.K., Hoelzer, D. & Russell, K.F. (2010 b). Towards radiation tolerant nanostructured ferritic alloys. Mater Sci Forum 654–656, 23–28.CrossRefGoogle Scholar
Miller, M.K., Kenik, E.A., Russell, K.F., Heatherly, L., Hoelzer, D.T. & Maziasz, P.J. (2003). Atom probe tomography of nanoscale particles in ODS ferritic alloys. Mater Sci Eng A 353(1–2), 140–145.CrossRefGoogle Scholar
Miller, M.K. & Parish, C.M. (2011). Role of alloying elements in nanostructured ferritic steels. Mater Sci Technol 27(4), 729–734.CrossRefGoogle Scholar
Miller, M.K., Parish, C.M. & Li, Q. (2013). Advanced oxide dispersion strengthened and nanostructured ferritic alloys. Mater Sci Technol 29(10), 1174–1178.CrossRefGoogle Scholar
Miller, M.K., Russell, K.F. & Hoelzer, D.T. (2006). Characterization of precipitates in MA/ODS ferritic alloys. J Nucl Mater 351(1–3), 261–268.CrossRefGoogle Scholar
Miller, M.K. & Zhang, Y. (2011). Fabrication and characterization of APT specimens from high dose heavy ion irradiated materials. Ultramicroscopy 111(6), 672–675.CrossRefGoogle ScholarPubMed
Odette, G.R., Alinger, M.J. & Wirth, B.D. (2008). Recent developments in irradiation-resistant steels. Ann Rev Mater Res 38, 471–503.CrossRefGoogle Scholar
Paatero, P. & Hopke, P.K. (2009). Rotational tools for factor analytic models. J Chemometr 23(1–2), 91–100.CrossRefGoogle Scholar
Parish, C.M. (2011). Multivariate statistics applications in scanning transmission electron microscopy x-ray spectrum imaging. In Advances in Imaging and Electron Physics, vol. 168. P.W. Hawkes (Ed.), pp. 249–295. Elsevier, Amsterdam.Google Scholar
Parish, C.M., Brennecka, G.L., Tuttle, B.A. & Brewer, L.N. (2008). Quantitative X-ray spectrum imaging of lead lanthanum zirconate titanate PLZT thin-films. J Am Ceramic Soc 91(11), 3690–3697.CrossRefGoogle Scholar
Parish, C.M. & Brewer, L.N. (2010 a). Key parameters affecting quantitative analysis of STEM-EDS spectrum images. Microsc Microanal 16(3), 259–272.CrossRefGoogle ScholarPubMed
Parish, C.M. & Brewer, L.N. (2010 b). Multivariate statistics-based segmentation methods for quantification of X-ray spectrum images. Ultramicroscopy 110(2), 134–143.CrossRefGoogle Scholar
Parish, C.M., Edmondson, P.D., Zhang, Y. & Miller, M.K. (2011). Direct observation of ion-irradiation-induced chemical mixing. J Nucl Mater 418, 106–109.CrossRefGoogle Scholar
Parish, C.M. & Miller, M.K. (2014). Aberration-corrected X-ray spectrum imaging and fresnel contrast to differentiate nanoclusters and cavities in helium-irradiated alloy 14YWT. Microsc Microanal 20(2), 613–626.CrossRefGoogle ScholarPubMed
Parish, C.M. & Miller, M.K. (in press). A review of advantages of high-efficiency X-ray spectrum imaging for analysis of nanostructured ferritic alloys. J Nucl Mater, doi:10.1016/j.jnucmat.2014.11.134.Google Scholar
Parish, C.M., White, R.M., LeBeau, J.M. & Miller, M.K. (2014). Response of nanostructured ferritic alloys to high-dose heavy ion irradiation. J Nucl Mater 445(1–3), 251–260.CrossRefGoogle Scholar
Plimpton, S.J., Michael, J.R. & Romig, A.D. (1992). Parallel simulation of electorn-solid interaction for electron-microscopy modeling. J Supercomput 6(2), 139–151.CrossRefGoogle Scholar
Reed, S.J.B. (1982). The single-scattering model and spatial resolution in X-ray analysis of thin foils. Ultramicroscopy 7, 405–410.CrossRefGoogle Scholar
Ritchie, N.W.M. (2009). Spectrum simulation in DTSA-II. Microsc Microanal 15(5), 454–468.CrossRefGoogle ScholarPubMed
Schamber, F.H. (1977). A modification of the linear least-squares fitting method which provides continuum suppression. In X-ray Fluorescence Analysis of Environmental Samples, T.G. Dzubay (Ed.), pp. 241–257. Ann Arbor, MI: Ann Arbor Scientific Publishers.Google Scholar
Smentkowski, V.S., Ostrowski, S.G. & Keenan, M.R. (2009). A comparison of multivariate statistical analysis protocols for ToF-SIMS spectral images. Surf Interf Anal 41, 88–96.CrossRefGoogle Scholar
Talukder, M.R., Bose, S. & Takamura, S. (2008). Calculated electron impact K-shell ionization cross sections for atoms. Int J Mass Spectrom 269(1–2), 118–130.CrossRefGoogle Scholar
Talukder, M.R., Bose, S. & Takamura, S. (2012). Calculated electron impact K-shell ionization cross sections for atoms (vol. 269, p. 118, 2008). Int J Mass Spectrom 309, 212–212.CrossRefGoogle Scholar
Tauler, R., Smilde, A. & Kowalski, B. (1995). Selectivity, local rank, three-way data analysis and ambiguity in multivariate curve resolution. J Chemometr 9(1), 31–58.CrossRefGoogle Scholar
Vosough, M., Mason, C., Tauler, R., Jalali-Heravi, M. & Maeder, M. (2006). On rotational ambiguity in model-free analyses of multivariate data. J Chemometr 20(6–7), 302–310.CrossRefGoogle Scholar
Was, G.S. & Averback, R.S. (2012). 1.07—Radiation damage using ion beams. In Comprehensive Nuclear Materials, R.J.M. Konings (Ed.), pp. 195–221. Oxford: Elsevier.CrossRefGoogle Scholar
Williams, D.B. & Carter, C.B. (1996). Transmission Electron Microscopy. New York, NY: Plenum.CrossRefGoogle Scholar
Williams, D.B., Michael, J.R., Goldstein, J.I. & Romig, A.D. (1992). Definition of the spatial-resolution of X-ray microanalysis in thin foils. Ultramicroscopy 47(1–3), 121–132.CrossRefGoogle Scholar
Zinkle, S.J. & Moslang, A. (2013). Evaluation of irradiation facility options for fusion materials research and development. Fusion Eng Des 88(6–8), 472–482.CrossRefGoogle Scholar