Single-Ion Deconvolution of Mass Peak Overlaps 305


data, this is specially true of peak tail overlaps. The result of not applying any formof background correction can be seen in the Al concentration of the matrix in Figure 4d, which is higher than expected, 1.0 at% in the per-ion deconvolution but 0.6 at% from the global deconvolution. The over-estimated counts of Al result from tail overlaps of the Fe and Cr peaks near the 27Da peak. To overcome this problem we suggest that the background level could be estimated using a similar local environment approach, but with a larger scale, to that detailed in Figure 2, and a probabilistic assignment made to either a signal ion or background ion in the same way that ion identity was stochastically assigned.


SUMMARY


We have developed a new method for solving peak overlaps at the atomic scale, which uses the surrounding ions to determine the ionic identity using known isotopic abundances. The use of a flexible local environment and deconvolution by MLE mitigate some of the existing challenges of working limited discrete data. We demonstrated the utility of the technique for studying clusters and interfaces where the extent of the overlapping ions varied spatially. This per-ion deconvolution technique is particularly useful when the geometry of the feature of interest is not easily isolated for “bulk” analysis or when analysis of an interface between features containing different overlaps is required. This new technique produces standard point cloud data, meaning it can be exploited further using existing techniques such as proximity histograms, composition line profiles, isosurfaces, or cluster identification without further modification. Implementations of the programs described, written in MATLAB, are available at http://Atom- ProbeLab.sourceforge.net


ACKNOWLEDGMENTS


Many thanks to T. Martin of the University of Oxford for the use of the maraging steel alloy data. A.J.L. is funded by EPSRC project EP/I012400/1 and EP/I003274/1. D.H. acknowledges EPSRC funding from EP/L014742/1.


Figure 6. Simulated point cloud of two ions (A, B) each with isotopes of equal abundance [total composition shown in (a)]. b: The sharp interface between two regions of pure A and pure B. c: The measured composition after per-ion peak deconvolution (1nm local environment radius) using either least squares (LS) or maximum likelihood estimation (MLE).


signal, which represents a relatively small fraction of the total data, and appears as spurious points in 3D with arbitrary m/z values (Gault et al., 2012). In the global spectrum this back- ground is routinely subtracted from the total signal (Larson et al., 2013), however, there are insufficient counts in each local environment to reliably correct for the background in the per- ion deconvolution method. Although it is possible to estimate the background or noise contribution globally it is not reliable to assume the noise level will be constant throughout the 3D


REFERENCES


CAIRNEY, J.M., RAJAN,K.,HALEY,D.,GAULT,B., BAGOT, P.A., CHOI,P.-P., FELFER, P.J., RINGER, S.P., MARCEAU,R.K.&MOODY, M.P. (2015). Mining information from atom probe data. Ultramicroscopy 159 (Pt 2), 324–337.


CHEN,Y., CHOU,P.H.&MARQUIS,E.A.(2014).Quantitativeatom probe tomography characterization of microstructures in a proton irradiated 304 stainless steel. JNuclMater 451(1–3), 130–136.


GAULT, B.,MOODY, M.P., CAIRNEY,J.&RINGER, S. (2012). Atom Probe Microscopy (vol. 160. New York: Springer-Verlag.


HALEY, D., CHOI,P.&RAABE, D. (2015). Guided mass spectrum labelling in atom probe tomography. Ultramicroscopy 159(Pt 2), 338–345.


HAYDOCK,R.&KINGHAM, D.R. (1980). Post-ionization of field- evaporated ions. Phys Rev Lett 44(23), 1520–1523.


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