230 Tomas L. Martin et al.
(~1.405) should give the same distribution as observed in the LEAP 5000 data, providing sufficient statistics are obtained for both.
RESULTS
Figure 1 shows example atom maps for the four types of materials used in this study. Three were steels with different types of cluster—metallic, oxide, and carbide, while the final material is an implanted semiconductor with low levels of impurity at a small region near the surface. In each case, small features were chosen to see if the change in detector efficiency resulted in the expected increase in atom count in the features, as well as comparing the effect on other aspects of the data such as cluster composition. Cluster size was defined as the number of solute ions in the cluster, except in cases where the cluster radius was used, as defined in nanometers.
Copper Clusters in an SG Steel (Voltage Mode)
The simplest comparison is between two metals with small- scale clustering in voltage mode, where the effects of laser wavelength cannot affect the measurements. In the reactor pressure vessels of nuclear reactors, even small cluster for- mation during operation can be detrimental to the lifetime of the reactor, particularly if the clusters are of an element with a larger atomic radius than the matrix such as Cu (Styman et al., 2012). This study selected one such material, where nanometer-scale Cu precipitates were known to occur after long-term aging. These clusters can often be very small, and identifying them from the matrix at the early stages of for- mation can be challenging; it is hoped that the higher effi- ciency detector will potentially aid in the observation of early-stage clustering. Figure 2a shows the size distribution of the Cu clusters
across four data sets obtained on the LEAP 3000 (in black) to five data sets obtained on the LEAP 5000 (in blue), while
Figure 2b shows the empirical distribution function for the same data. The latter more obviously shows the difference between the two distributions. There are a similar number of large-scale clusters (above ~5000 atoms), but more clusters are observed at low atom counts in the LEAP 5000, parti- cularly for clusters <1000 atoms. The median count of solute atoms was 218 atoms in the LEAP 3000, but only 129 atoms in the LEAP 5000, despite the median cluster radius remaining essentially unchanged, at 1.17 nm for the LEAP 3000 data and 1.13nm for the LEAP 5000 data. A possible interpretation of this result is that the higher efficiency of the LEAP 5000 is enabling more of the clusters, which contain fewer atoms, present in the material to be observed, as expected. As some of the clusters were not being detected previously in the LEAP 3000 data sets, it is hard to estimate the detector efficiency increase based on comparing the two distributions. Figure 3 compares the chemistry of all 119 clusters in the LEAP 3000 data and 85 clusters in the LEAP 5000 data.Over
Figure 1. Atom maps for the materials used during this study. a: Cu clusters in a thermally aged reactor pressure vessel (RPV) steel; (b) yttrium oxide clusters in an oxide-dispersion- strengthened (ODS) steel; (c) carbides in a bearing steel, and (d) phosphorus at the surface of implanted silicon. All atom maps shown were analyzed using the LEAP 3000.
four LEAP 3000 data sets, 45 million atoms of data was obtained, compared with five LEAP 5000 data sets totaling just over 30 million atoms, so although the total number of clusters should not be compared directly, sufficient data was obtained to compare the statistics between the two instru- ments. Although the scatter in the composition plots in Figures 3a and 3b is high, there is a trend for the average Fe content to be higher in the LEAP 3000 data than in the LEAP 5000 data, by ~3 at%, with a corresponding drop in Cu and Mnsolute. When the Fe content is normalized to the reduced cluster radius, as in Figure 3c, this trend, although slight, remains. The measured Fe concentration in the clusters is likely incorporating a significant contribution from the surrounding matrix as a result of magnification effects due to the difference in evaporation field between the Cu and Fe (Gault et al., 2012; Larson et al., 2013). One possible expla- nation is that the difference highlighted in Figure 3 results from the change in hit detection algorithm with respect to multiple hit events. Fe–Fe pairs account for 99% of the multiple ions detected, therefore any improvement in the recognition of multiple hits will predominantly increase the detection of Fe. Thus, if the algorithm improves the discrimination of individual ions within multiple hit events, it could explain the increased Fe content measured in these clusters.
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