TOF‐SIMS Data: A Data Analysis Example - mikee9265/SIMS-Wiki GitHub Wiki

While no one example can span the varied paths that the interpretation of a TOF-SIMS data set can take, this one is a good demonstration of a basic approach beginning with the initial assessment of the results and ending with MVA.

The sample was created during the assessment of a new material for potential use in disk drive construction. The part, primarily a polyoxymethylene (POM) polymer, was placed onto a magnetic recording disk and treated at elevated temperature and humidity for a set time. The part was removed, and the disk examined optically. The disk had a light haze and was submitted for TOF-SIMS analysis. The SIMS results readily showed the presence of transferred materials.

The figure below showing the positive ion spectrum of the haze is dominated by the familiar (to a TOF-SIMS analyst in the disk drive industry) pattern of peaks consistent with a perfluoro-polyether (PFPE) generally to be found on magnetic recording disks. The familiar pattern is augmented by obvious added high mass ions. The most prominent of these includes a trio of peaks with 28 amu mass differences centered near 283 amu, and a prominent lone cluster of peaks with the largest at a nominal mass of 425 amu.

The 28 amu pattern is suggestive of two carbon chain length differences (CH₂CH₂). The 3 peaks are homologues. The 425 amu peak likely belongs to a separate compound, because if the lower mass trio of peaks were fragments of the larger 425 species, the 425 peak would also have to have neighbors with 28 amu mass differences. All of these peaks could be fragments of a much larger molecule whose molecular ion was not detected, however.

The figure above shows the images of the combined peaks associated with the disk lubricant (for this sample this material is part of the substrate, essentially forming the background to the analysis), a combined image from the homologue peaks at 283 and 311 amu, and finally the image of the peak at 425 amu. The images clearly show than the 425 amu peak is coming from a different compound than the 283 and 311 amu peaks. In essence, imaging laterally inhomogeneous samples allows some separation in the SIMS, the kind of separation that other mass spectrometric techniques are afforded with various forms of chromatography.

Given the images, it is possible to revisit the raw data and take spectra from selected regions of interest (ROIs). This is typically done by selecting the pixels in the images with intensities for a peak of interest. In this case, we can define the first ROI as the areas where the 283 and 311 amu peaks are most intense. The second ROI can be for pixels with intense 425 amu signals. The resulting spectra are shown below. Note that it is possible to get more sophisticated in one’s ROI selections, for example, picking pixels with large intensities for the 283 and 311 peaks but low intensities for the lubricant. Such methods can produce cleaner spectra for one species or another.

Note that the resulting spectra are not “pure”. There is a bit of the 425 amu component in the 283 and 311 species spectrum, and vice versa. Nonetheless, one has a fair sense of what the pure spectra would be like; enough to start attempting matches with spectral databases. Indeed, the 283 and 311 materials bear a strong resemblance to a library spectrum of ethylene glycol monostearate, which has a major peak at 311 amu, although that spectrum has a molecular ion M+1 peak at 329 amu missing in the unknown’s spectrum. The near match suggests the possibility of a related compound.

This is where the use of multiple techniques pays off. The Nuclear Magnetic Resonance (NMR) analysis of the extract from this part shows the presence of ethylene glycol di-esters. Now, a close inspection of the spectrum of the 283- and 311-rich ROI reveals very weak peaks at the masses for the ethylene glycol di-esters of stearic and palmitic acids. The material is, therefore, clearly identified. Even the mechanism for the formation of the 283 and 311 amu ions can be readily understood as shown below.

Inspection of the spectrum of the ROI with the most intense 425 amu signals (Spectrum labelled b in this above figure) shows peaks that belong in the spectrum of that unknown which include those at 155.152, 271.224, 425.341, and a weak peak at 595.546 amu. The mass accuracy at the lowest of these allows us to identify its empirical formula as C₁₀H₁₉. We can see from the degree of mass excess that the peaks at 271 and 425 amu have relatively more unsaturation or are more cyclic than the ions found at 155 and 595 amu. Note that the other way to obtain this list of peaks is to create an exhaustive peak list from the total spectrum and to then inspect all the images one gets from each of those peaks, determining the ones that map similarly.

We have enough information to recognize the material if we ever see it again, but the lack of a match in a spectral database makes it difficult to identify the compound. This 425 amu species remains an unknown. This kind of incomplete result from the analysis of a data set is unfortunately common. This is the kind of situation where MS/MS is most useful.

The steps we take to determine which species map similarly or which peaks are also to be found in the spectrum of pixels containing intense signals for a peak of interest are simple ways to determine what peaks covary with what other peaks in the data set. Given the ease with which MVA can be applied to a SIMS data set, it can be faster to start with MVA analysis in some cases. Alternatively, one can apply MVA to a set of data that has already yielded much to direct methods for completeness.

The figure below shows the results of the MCR analysis of this data. To obtain these results, first a complete list of peaks was defined for the spectrum. A routine that parses the raw data based on the list of peaks was used to create the matrix of peaks versus intensities, each row representing a pixel and each column a peak. The data had been taken at low enough secondary ion currents that dead time effects were not significant. Division by the square root of the mean (Poisson noise correction) was performed prior to performing the MCR analysis. Each image in the figure represents the scores for the component, that is, how much of that component can be found in each pixel. The intensities are shown in a thermal scale where black would be no intensity, and dark red the highest. The loadings show the component “spectra”.

Components 1 and 3 show the ethylene glycol di-esters. Interestingly, the mixture of the di-ester homologues is not homogeneous; it varies on this sample from location to location in the relative amounts of the ester chain lengths. As noted above, it is possible to apply added constraints to the analysis to look for the most spectrally pure of the solutions possible, but that was not done in this case.

Component 4 is the unknown 425 amu compound. As has been noted before, the loadings are like a spectrum, but the relative intensities accentuate the most unique peaks in this compound’s spectrum rather than giving an accurate rendition of what the pure spectrum would actually be. It is possible from this analysis to see peaks that are associated with the 425 amu component that were previously easily overlooked, such as the ion at 81 amu (C,sub>6H₉⁺).

Component 5 is the disk lubricant. The MCR analysis cleanly separates it from the other components. The ROI spectra had more lubricant in them, since they were created by looking for regions with the highest intensity for specific peak combinations rather than by looking for regions with the least lubricant signal. One of the strengths of MCR is its ability to produce components similar to pure spectra for compounds that are not isolated anywhere on the sample.

Finally, Component 2 is mapping for feature edges. The result appears to be catching an effect of the topography on the SIMS data. Ions scattered off the higher haze feature edges and subsequently sputtering the nearby disk are likely the cause of this interesting component. Note that the result, while an “artifact” of sorts, represents real data coming from the sample, containing information about sample topography and is thus not noise. It is also a finding that “ordinary” analysis of the data would not be likely to reveal.