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Quantitative Analysis of Particle Distributions by Comparison with Simulations

Published online by Cambridge University Press:  19 November 2010

Sascha Vongehr
Affiliation:
National Laboratory of Solid State Microstructures, Department of Materials Science and Engineering, Nanjing University, Nanjing 210093, Jiangsu Province, P.R. China
Shaochun Tang
Affiliation:
National Laboratory of Solid State Microstructures, Department of Materials Science and Engineering, Nanjing University, Nanjing 210093, Jiangsu Province, P.R. China
Xiangkang Meng*
Affiliation:
National Laboratory of Solid State Microstructures, Department of Materials Science and Engineering, Nanjing University, Nanjing 210093, Jiangsu Province, P.R. China
*
Corresponding author. E-mail: [email protected]
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Abstract

In characterization of metal nanoparticle doped spherical composites, the two-dimensional nature of transmission electron microscopy (TEM) images leads to ambiguities about the true location of the nanoparticles. Walking-in of simulated projections in comparison with actual TEM images leads to quantitative results such as location-dependent particle sizes and particle number density. This method takes advantage of the strength of fuzzy neural network computations via the human hunter-gatherer's visual system's evolved superiority while still allowing quantitative results by use of exact numerical simulations.

Type
TEM and STEM Materials Applications
Copyright
Copyright © Microscopy Society of America 2011

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References

REFERENCES

Koster, A.J., Braunfeld, B., Fung, J.C., Abbey, C.K., Han, K.F., Liu, W., Chen, H., Sedat, J.W. & Agard, D.A. (1993). Towards automatic three dimensional imaging. MSA Bull 23, 176188.Google Scholar
Koster, A.J., Grimm, R., Typke, D., Hegerl, R., Stoschek, A., Walz, J. & Baumeister, W. (1997). Perspectives of molecular and cellular electron tomography. J Struct Biol 120, 276308.CrossRefGoogle ScholarPubMed
Müller, S.A., Aebi, U. & Engel, A. (2008). What transmission electron microscopes can visualize now and in the future. J Struct Biol 163, 235245.CrossRefGoogle ScholarPubMed
Russel, D.A., Hanson, J.D. & Ott, E. (1980). Dimensions of strange attractors. Phys Rev Lett 45, 11751178.CrossRefGoogle Scholar
Tang, S.C., Tang, Y.F., Vongehr, S., Zhao, X.N. & Meng, X.K. (2009). Nanoporous carbon spheres and their application in dispersing silver nanoparticles. Appl Surf Sci 255, 60116016.CrossRefGoogle Scholar
Tang, S.C., Vongehr, S. & Meng, X.K. (2010a). Carbon spheres with controllable silver nanoparticle doping. J Phys Chem C 114, 977982.CrossRefGoogle Scholar
Tang, S.C., Vongehr, S. & Meng, X.K. (2010b). Controllable incorporation of Ag and Ag-Au nanoparticles in carbon spheres for tunable optical and catalytic properties. J Mater Chem 20, 54365445.CrossRefGoogle Scholar
Wang, N., Wu, J. & Daniel, S. (2005). A novel vertical grid transferring technique to prepare 3D TEM sample from lift-out sample. Microsc Microanal 11, 21002101 (CD-ROM).CrossRefGoogle Scholar
Wang, S.Z. & Xin, H.W. (2000). Fractal and dendritic growth of metallic Ag. J Phys Chem B 104, 56815685.Google Scholar
Wang, Z.L. (2000). Transmission electron microscopy of shape-controlled nanocrystals and their assemblies. J Phys Chem B 104, 11531175.CrossRefGoogle Scholar
Weyland, M., Yates, T.J.V., Dunin-Borkowski, R.E., Laffonta, L. & Midgleya, P.A. (2006). Nanoscale analysis of three-dimensional structures by electron tomography. Scripta Mater 55, 2933.CrossRefGoogle Scholar