436 Zirong Peng et al.


Figure 4. The atomic percent of carbon in carbon and tungsten [C/(C+W) at%] (solid black lines) and the fraction of ions detected as multiple events (blue dotted lines) obtained from various APT test series: (a) 3000PE, (b) 5000PRR, and (c) 3000BT. The lines with point-up triangles represent the results from the ascending (A) test series and that with point-down triangles represent the results from the descending (D) test series.


discussion. Across all experimental conditions, the NSR resulting from the ascending and descending test series are different from each other. Whenever higher standing voltage was applied, as listed in Table 2, NSR was higher, implying that larger specimen radii will lead to higher background levels. In the 3000PE and the 3000BT series, the changes inNSR


correlate with the changes in the standing voltage, indicating that here, the specimen radius is the primary factor. Laser pulse energy, specimen base temperature, and specimen shank angle only have minor effects. As the 5000PRRD series shows, there is an apparent increase in NSR when the pulse repetition rate decreases to 250, 200, and 100 kHz. For the remaining nine tests, NSR values are almost steady and amount to 0.056±0.007. The background levels (ppm/ns) directly obtained from the IVAS software are also provided (see Sup- plementary Data). It can be seen thatwhen the pulse repetition rate is 100 kHz, the background level is around 300ppm/ns, which is nearly six times higher than the cases using relatively high pulse repetition rates (333, 500, 1,000 kHz). Therefore, the effect of the pulse repetition rate strongly depends on the specimen radius. Lowrepetition rateswill be detrimentalwhen the specimen radius is relatively large. From themass spectra shown in Figure 1, itmay look like


the measurements performed in the 3000X HR have lower background level compared with that in the 5000 XS, which seems to contradict the results of the NSR displayed in Figure 3. The reason is that the mass spectra in Figure 1 were normalized separately with respect to the highest peak, i.e. 12C2+. As the laser systems applied in the 3000X HR and the 5000 XS are different, the laser-specimen interactions are also different, giving rise to different proportions of the detected carbon ion species (Houard et al., 2010; Santhanagopalanetal.,2015).For instance,in the 5000 XS a certain part of carbon was detected asWC2+, which was never found in the data acquired using the 3000X HR.


Chemical Composition


The prerequisite to obtain chemical information from APT data sets is to identify the mass peaks and define a mass range for each peak. Figure 1 shows the result of peak assignment. To maintain consistency and exclude the resulting chemical compositions from being biased by differences in mass ranging (Hudson et al., 2011), the same evaluation procedure was applied to all the peaks. As mentioned previously, the peak range was defined by the full width down to the back- ground and the local mass-based background subtraction model, provided by the IVAS software, was used to remove the background counts. The atomic ratios between carbon and tungsten of each measurement are summarized in Supplementary Table S1 and the atomic fraction of carbon within carbon and tungsten [C/(C +W) at%] plotted against the various test variables (solid black line) are shown in Figure 4. For most acquisition conditions used in this work, the resulting compositions are close to that determined by other chemical analysis techniques. Only the measurements conducted with 1MHz pulse repetition rate show distinct deviations. Considering that the volume examined at each condition is small and there may be some chemical fluctua- tions of the specimens at the nanometer scale, it is difficult to reveal the impact of different experimental parameters on the resulting chemical composition.


Supplementary Table S1


Supplementary Table S1 can be found online. Please visit journals.cambridge.org/jid_MAM.


Proportion of Multiple Events


The fraction of multiple events is also important for asses- sing the quality of an APT data set. Figure 4 shows the


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