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To confirm that surface segregation of Pd was truly occurring in these nanopaiticles, multivariate statistical analysis (MSA) was performed on the data set shown in Fig. 2B. MSA is a group of processing techniques that can be used to identify specific features within large data sets such as x-ray spectrum images and to reduce random noise components in the data sets in a statistical manner. MSA has recently been shown to be particularly useful for analysis of x-ray maps taken from nanoparticles (29). This statistical technique performs a data-smoothing calculation by portioning the XEDS data using a probability density function.
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31144447002
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note
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Sponsored by the European Union AURICAT project (contract HPRN-CT-2002-00174) and the Engineering and Physical Sciences Research Council (EPSRC) as part of the ATHENA project co-sponsored by Johnson Matthey, as well as by an EPSRC-sponsored program on Speculative Green Chemistry, and we thank them for funding this research. We also thank the World Gold Council (through the GROW scheme) and Cardiff University (AA Reed studentship) for providing support for J.K.E. D.I.E. thanks the Crystal Faraday Partnership for funding. Finally, C.J.K., M.A.W., and A.A.H. gratefully acknowledge NSF funding through the Materials Research Science and Engineering Center (NSF grants DMR-0079996, DMR-0304738, and DMR-0320906).
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