Postacquisition Mass Resolution Improvement in Time-of-Flight

Article pubs.acs.org/ac

Postacquisition Mass Resolution Improvement in Time-of-Flight Secondary Ion Mass Spectrometry Steven J. Pachuta* and Paul R. Vlasak 3M Corporate Research Analytical Laboratory, 201-2S-16 3M Center, St. Paul, Minnesota 55144, United States S Supporting Information *

ABSTRACT: Good mass resolution can be difficult to achieve in time-of-flight secondary ion mass spectrometry (TOFSIMS) when the analysis area is large or when the surface being analyzed is rough. In most cases, a significant improvement in mass resolution can be achieved by postacquisition processing of raw data. Methods are presented in which spectra are extracted from smaller regions within the original analysis area, recalibrated, and selectively summed to produce spectra with higher mass resolution than the original. No hardware modifications or specialized instrument tuning are required. The methods can be extended to convert the original raw file into a new raw file containing high mass resolution data. To our knowledge, this is the first report of conversion of a low mass resolution raw file into a high mass resolution raw file using only the data contained within the low mass resolution raw file. These methods are applicable to any material but are expected to be particularly useful in analysis of difficult samples such as fibers, powders, and freeze-dried biological specimens.

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degradation from large analysis areas is the difference in the flight times of primary ions striking the near edge of the rastered area vs those striking the far edge. The distribution of primary ion flight times will result in a decrease in mass resolution. This problem can be corrected through software compensation.10 Despite these corrections, in practice, on a well-used instrument, it is typical to achieve lower mass resolution from, for example, a 500 μm × 500 μm analysis area than it is from a 100 μm × 100 μm analysis area. “Stage raster” acquisitions, in which very large analysis areas are made possible by moving the sample stage beneath a stationary extraction element, present additional mass resolution issues, notably due to sample stage tilt and nonflat sample mounting. The situation is similar to that for rough surfaces, in which ions are emitted from different heights relative to the extraction optics. A further cause of mass resolution degradation can be found on electrically insulating materials, where charge compensation may be nonuniform over the analysis area and affect secondary ion flight times in unpredictable ways. It is sometimes possible to improve mass resolution by correcting for these geometric and electrical effects after data acquisition. There have been only a small number of published attempts at numeric mass resolution improvement in TOFSIMS. The most fully developed method is that of Heeren and co-workers11,12 who correct for sample topography by electrospraying a thin chemical matrix onto the analyte surface, measuring the time shift of a selected matrix ion at each analysis point, and applying a point-by-point time correction to the

ass resolution and mass accuracy are critical instrumental parameters in time-of-flight secondary ion mass spectrometry (TOF-SIMS).1−3 While mass accuracy is arguably the more important of the two, good mass resolution is often essential to achieving good mass accuracy and to correctly assigning ion compositions. Modern TOF-SIMS instruments provide mass resolution on the order of 10 000 (mass/fullwidth-at-half-maximum, abbreviated m/Δm) over most of the spectral range. The majority of these instruments employ pulsing of the primary ion source to initiate the timing sequence,4 although alternatives are being developed.5−7 A limitation of instruments which employ primary source pulsing is that mass resolution is degraded when the surface being analyzed is rough or when the analysis area is large.8 In the case of rough surfaces, mass resolution is degraded due to secondary ions originating from different vertical positions on the surface. For ions of identical mass, those with shorter flight paths will require less time to reach the detector than will those with longer flight paths. The net effect will be a range of flight times, broadening of the spectral peak, and suboptimal mass resolution. This is in addition to mass resolution degradation from other causes, such as the spread in initial kinetic energy of the extracted secondary ions or electrical charging. A similar problem occurs for large analysis areas as that which occurs for rough surfaces. Ions originating from the edges of the analysis area must travel longer paths than ions originating from the center, and if the analysis area is sufficiently large, this difference in flight paths will manifest itself as a decrease in mass resolution. This problem can be corrected by dynamic emittance matching,9 in which the secondary ion optics are rastered in synchronization with the primary ion beam. A further cause of mass resolution © 2012 American Chemical Society

Received: December 5, 2011 Accepted: January 6, 2012 Published: January 6, 2012 1744

dx.doi.org/10.1021/ac203229m | Anal. Chem. 2012, 84, 1744−1753

Analytical Chemistry

Article

Table 1. Effect of Raster Size on Mass Resolution for Three Nominally Smooth Materialsa raster type stage raster

primary ion beam raster

a

raster size

relative analysis area (% of largest analysis area for each raster type)

Si wafer Si+ m/z 28 m/Δm

Al plate C4H9+ m/z 57 m/Δm

PET film C7H4O+ m/z 104 m/Δm

× × × × × × × × ×

100 25 6.25 1 100 25 6.25 4 1

5999 8812 9850 10228 7362 8307 8468 8419 8207

4723 6979 7386 8859 5463 7825 8618 8522 8487

4084 5338 9374 10924 7295 9938 10813 10744 12326

20 mm 10 mm 5 mm 2 mm 500 μm 250 μm 125 μm 100 μm 50 μm

20 mm 10 mm 5 mm 2 mm 500 μm 250 μm 125 μm 100 μm 50 μm

Mass resolution is given as mass/full-width-at-half-maximum.

Figure 1. Spectral subdivision applied to a PET film: 20 mm × 20 mm stage raster, 128 × 128 pixels. (A) C7H4O+ ion (m/z 104) as-acquired, with corresponding secondary ion image. (B) 16 × 16 grid overlaid on secondary ion image. Each grid element has dimensions 1.25 mm × 1.25 mm or 8 × 8 pixels. (C) Three of the 256 subspectra derived from the grid, precalibration, showing C7H4O+. The top and bottom spectra are the extremes in peak position for the set of subspectra. (D) The same three subspectra after recalibration. (E) Final result for C7H4O+ after summing the 256 recalibrated subspectra.

spectral data. Spencer13 chooses an internal reference ion, rather than an external matrix ion, and performs a similar time correction. Spencer’s choice of reference ion is critical to the final result, and while resolution improvement is shown for a small number of peaks, it is not clear how the method performs when applied to a complete spectrum. Komatsu14 uses a similar approach to Spencer, applying a time offset to the spectral peaks relative to a particular internal reference ion. All three of these approaches are aimed at the specific case of topographic correction. Two alternative, universal approaches to mass resolution improvement are described here, both using the spatial information contained within TOF-SIMS raw data files to reconstruct improved spectra from both smooth and rough surfaces. Spectral reconstruction is accomplished through recalibration and summation of spectra from selected pixels and does not require application of a matrix or reference to either an internal or external matrix ion. A particular advantage is that these methods can be automated, requiring little or no user input. The first approach involves subdividing the analysis area into a regular grid of smaller regions and extracting mass spectra

from each region. The extracted spectra are individually calibrated by an automated process, and all or an optimized portion of the spectra are summed to produce a new spectrum with higher mass resolution than the original total spectrum. This approach can be used to correct for geometric and electrical effects in spectra of large analysis areas on smooth surfaces. Given sufficient signal, it is useful on rough surfaces as well. Interestingly, the spectral calibration information can be used as a diagnostic tool for instrument alignment and tuning. Perhaps most importantly, if the regions can be made sufficiently small, a new, higher-mass-resolution raw data file can be reconstructed from the original raw data file and used to obtain images or region-of-interest spectra per usual practice. The second approach is effective for improving mass resolution in spectra of rough surfaces such as fabrics, papers, or particles. Unlike the first approach, the analysis area is not subdivided into a regular pattern. Rather, spectra are obtained from regions of similar elevation, identified by multivariate statistical methods. These multivariate methods have the advantage of simultaneously optimizing the mass resolution and the spectral counts without need for the trial-and-error approach of defining regions-of-interest. 1745



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Table 2. Comparative Mass Resolution Data for PET after Improvement, C7H4O+ Peak at m/z 104, 128 × 128 Pixel Analysis Areaa raster type and size stage raster, 20 mm × 20 mm

primary ion beam raster, 500 μm × 500 μm

total subspectra

100% of subspectra summed m/Δm

best 50% of subspectra summed m/Δm



16 64 100b 256 400b 1024 16 64 100b 256 400b 1024

7198 8957 9288 9943 10127 10411 8532 8967 9254 8903 9325 8987

8416 9780 10192 10535 10835 11171 9557 9859 10176 9930 10544 10380

8937 10135 10461 10863 11168 11602 10333 10461 11047 10736 11201 11442

9235 10579 10936 11154 11350 11937 10615 11069 11334 11209 11592 12147

a

Subspectra were autocalibrated using C2H3+ (9 channels), C6H4+ (11 channels), and C7H4O+ (15 channels). bBecause 10 and 20 do not divide evenly into 128 (pixels), extra pixels at the edges of the raster square are omitted in these cases. This may produce a slight resolution enhancement owing to the smaller sampling area.

With these two approaches, mass resolution improvements of 1.2×−2× are typical for smooth surfaces, and much greater improvements (2×−10×) can be achieved for rough surfaces. A side consequence is that peak shapes are usually improved, with reduced skewing and tailing, enabling higher-quality spectral calibration and thus better mass accuracy. This improvement may be due to reduction of both geometric and electrical effects. It must be noted that mass resolution is ultimately limited by the mechanical design of the spectrometer, and these methods cannot improve the mass resolution beyond this fundamental limit.

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EXPERIMENTAL SECTION Materials and Sample Preparation. Experiments were conducted using four materials: a silicon wafer, a flat aluminum plate, a commercially available polyester film (polyethylene terephthalate, PET), and a commercially available polyester (PET) cleanroom cloth. All materials were analyzed as-received with no special preparation other than cutting to size and, in the case of the cleanroom cloth, marking with a ballpoint pen. Instrumental. All data were acquired as 128 × 128 pixel or 256 × 256 pixel raw files on an ION-TOF (Münster, Germany) model TOF.SIMS.5 instrument, using a bismuth primary ion source operated in the high current bunched mode (pulsed primary ion current 0.5 pA or less). Twenty-five keV Bi+ was used for the silicon wafer, aluminum plate, and PET analyses, while 25 keV Bi3+ was used for the cleanroom cloth analyses. The primary ion beam spot size was in the range of 1−3 μm. Static conditions were maintained during the analyses. The raw files contain time and x,y positional information for every count in the data set and enable reconstruction of a mass spectrum for every pixel or, alternatively, a secondary ion image for every mass channel. Two raster modes were employed: primary ion beam rastering for analysis areas of 500 μm × 500 μm and less and stage rastering for analysis areas greater than 500 μm × 500 μm. In a stage raster, also known as a macro raster, the sample stage is moved during the analysis to access much larger areas than can be accessed using primary ion beam rastering alone. A low energy electron flood gun was used to prevent charging during analysis of the two polyester materials. Data Processing. Computations were performed on a laptop computer running 64-bit Windows 7, equipped with a

Figure 2. Contour plots for large-area (20 mm × 20 mm) analysis of PET (A) before and (B) after mass resolution correction. The x,y position of each bar corresponds to the position of each subspectrum within the analysis area (in this example, a 10 × 10 grid). The height of each bar is the deviation in mass of the C7H4O+ peak centroid in each subspectrum, relative to the lowest-mass peak centroid, which is assigned a value of zero (in plot A, this element is obscured by the surrounding bars). Assuming electrical effects to be negligible, a greater deviation (higher mass) implies a longer flight time and therefore a lower position on the sample. Plot A indicates a bias from the back corner to the front corner and may indicate a tilted sample holder. Plot B illustrates the effect of correction.

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Figure 3. MCR correction procedure for cleanroom cloth. (A) C2H− ion (m/z 25) as-acquired, with corresponding secondary ion image. (B) MCR output for this peak, with seven factors specified. Concentration profiles are displayed as images on the left, and pure component spectra are displayed on the right. The pixel intensities in each image are related to the abundance of the corresponding pure spectral component; black indicates areas where the spectral component is absent, and white indicates areas where the spectral component is present in good abundance. Analysis area: 500 μm × 500 μm, 128 × 128 pixels.

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1.60 GHz quad core processor and 4 GB RAM. Raw instrument files were converted from native format to a more user-friendly format using ION-TOF’s ITRawExport program, version 5.0.0.4. Subsequent data processing was performed using a custom-written in-house TOF-SIMS software package and a custom-written in-house graphical user interface for MATLAB (The Mathworks, Natick, Massachusetts). Multivariate curve resolution was performed in MATLAB using public routines from the Multivariate Curve Resolution Homepage.15 Lest the array of software appear intimidating, it should be noted that the data sets, though large, are very simple, consisting of only three quantities: time channels, x-coordinates, and y-coordinates. Time channels T are easily converted into mass through the relation

Mass = [(T − A)/B]2

RESULTS AND DISCUSSION

Mass Resolution Improvement for Smooth Surfaces. The size of the analysis area is the primary factor influencing mass resolution on a smooth surface, and spectra taken from small analysis areas will generally show better mass resolution than spectra taken from large analysis areas. Examples of the improvement in mass resolution which occurs when going from larger to smaller analysis areas are given in Table 1, where the detected species include an elemental ion, a hydrocarbon contamination ion, and a polymer fragment ion. Note the amount of improvement is dependent on a number of factors, including sample composition, flatness of the sample as mounted on the sample holder, tilt of the sample stage, quality of instrument tuning, and (for insulators) quality of charge neutralization. If high mass resolution is the primary goal of an analysis, it is typical in practice to acquire data from the smallest possible area. The minimum analysis area is often dictated by the static limit, beyond which sputter-induced sample damage may occur and, for nonconductive materials, by the increased probability of electrical charging as the analysis area is reduced. An additional consideration is the degree to which the analysis area is representative of the overall surface composition. As shown

(1)

where A and B are the y-intercept and slope, respectively, in a plot of T vs Mass1/2 (T on y-axis, Mass1/2 on x-axis) and can be obtained for any spectrum by performing a linear least-squares fit on the exact masses of at least two known ions and their respective time channels.16 Calibration of the spectrum is accomplished by calculating a mass for each time channel using eq 1. 1747



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Figure 4. Cleanroom cloth mass resolution improvement by four methods. (A) Uncorrected negative ion spectrum, mass range of 61−64 u asacquired. (B) MCR method, 6 of 7 factors combined, low threshold, no overlapping pixels. (C) MCR method, factor 3 only (see Figure 3), high threshold, no overlapping pixels. (D) SSD method, 1024 subspectra (32 × 32 grid) with 100% of pixels retained. (E) SSD method, 1024 subspectra (32 × 32 grid) with best 10% of pixels retained. Note, in the original spectrum (A), it is impossible to determine how many genuine peaks are actually present at each nominal mass, and therefore, the ions cannot be identified.

in Table 1, the highest mass resolution can only be achieved by analysis of a small percentage of the surface, and therefore, numerous small-area analyses are necessary to obtain a statistically valid portrait of the surface composition. A further consideration, mainly an issue for rough surfaces, is that a suitable small analysis area may simply not exist. In the case that a large analysis area is desirable, high mass resolution can be achieved by dividing the analysis area into multiple small regions, each having mass resolution better than the total analysis area, and summing the spectra from each region. The specific procedure, which will be called “spectral subdivision” (SSD), is illustrated in Figure 1 and consists of the following steps: (1) From the raw file, generate multiple spectra from a regular grid of subregions. The spectra derived from each grid element will be called “subspectra.” (2) Individually calibrate each subspectrum. (3) Sum the subspectra into a single spectrum. Although each subspectrum will show better mass resolution than the original total spectrum, the peaks will not line up on summation unless each subspectrum is recalibrated (step 2 above). The reason is that the poor mass resolution of the original spectrum is due to variations in peak positions from pixel to pixel, as illustrated in Figure 1C. Recalibration corrects for this variation. Because calibration of a large number of spectra is an onerous task to perform manually, the following

simple automated calibration procedure was developed. (A) A minimum of two (usually three) ions of known composition are identified. Ideally, there will be no interfering ions at the nominal masses of the chosen ions, but if interferences are present, they must be of lower abundance than the calibration ions. The caveats noted by Green and co-workers1 apply as well. (B) For every subspectrum, the automated procedure calculates the center-of-mass of each calibration ion, performs a least-squares fit on the calibration ions as described in the Data Processing section above, and applies a new calibration using eq 1. The number of channels used to determine the center-ofmass and the width of the mass window used to find the channels are user-selectable parameters. If there are insufficient channels in the mass window to carry out a calibration, the subspectrum is not recalibrated. In general, more channels are needed to determine the center-of-mass of high-mass calibration ions than of low-mass calibration ions. A distinguishing feature of this work vs previous work11−14 is that recalibration is used instead of reference-based time shifts. Recalibration is expected to be more robust against nonlinear effects such as differential charging and analyzer chromatic aberration,1 and it is familiar to most TOF-SIMS users. It is also easy to automate because it is self-referencing: there is no need to determine the zero position of a matrix ion and thus no need 1748



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Figure 5. Cleanroom cloth mass resolution improvement using the SSD method, positive ions, mass range of 540−850 u. Poly(ethylene oxide) ions with PET trimer ion (m/z 577+).

into identical mass channels, and the final summed spectrum is stored as mass channels. Because mass resolution improves as the analysis area is made smaller, it is advantageous to divide the analysis area into as many subregions as possible. There is, however, a practical limit to the number of subregions into which the analysis area can be divided. In order to calibrate each subspectrum, there must be sufficient signal in each calibration peak to accurately determine its center-of-mass. The limit appears to be a few hundred counts (peak area), but this has not been thoroughly investigated and may be sample and instrument dependent. It is not always necessary to include all the subspectra in the summation, especially if each subspectrum contains a large number of counts. In a given set of subspectra, there will be variation in the mass resolution, and it may be desirable to sum only those subspectra having the highest mass resolution. The automated calibration procedure calculates a variety of statistics for each subspectrum, including mass resolution of a specific peak and its deviation from exact mass. The entire mass resolution improvement process can itself be automated, and user-chosen criteria can be applied to produce varying degrees of improvement on the basis of the calibration statistics. A simple user interface has been developed to make the process routine. Calculation times vary from a few seconds for a small number of subregions (such as 8 × 8) to several minutes for a large number of subregions (such as 32 × 32). Table 2 summarizes the mass resolution improvement for PET using various raster sizes and filtering criteria. Comparison

to reference to other subspectra; each element is calculated independently. The manner in which the subspectra are summed (step 3 above) is of great importance and is nontrivial. First, spectral data are acquired and stored as time channels and not mass channels. Upon recalibration, time channels will not necessarily line up by mass. For example, in one spectrum mass 27.022 may occur at time channel 87 743, while in another it may occur at channel 87 752. Summing spectra while in time channel form does not work well unless all time channels are precisely mass-aligned, and this rarely happens. It is thus necessary to convert time channels to mass channels before summing spectra. Second, because of the inverse square relationship in eq 1, the time channels are not linear with mass. For example, in one arbitrarily chosen spectrum, the mass range 0.5−1.5 u contains 9606 channels, the mass range 56.5− 57.5 u contains 1229 channels, and the mass range 576.5−577.5 u contains 386 channels. In order to sum the spectra properly and preserve all the time/mass resolution, it is necessary to vary the number of channels per mass across the spectrum. Unfortunately, the number of channels per mass can vary with each spectrum, making it necessary to customize the summation procedure for each data set. The procedure adopted in this work is to analyze every unit mass interval in the raw file total ion spectrum to determine the number of time channels in each interval. Conversion from time channels to mass channels is then performed using this number of channels for each unit mass interval in every subspectrum. All subspectra are summed 1749



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Figure 6. Cleanroom cloth with copper-containing blue ballpoint pen ink on some fibers, illustrating the creation of a new high mass resolution raw file from a low mass resolution raw file using the SSD method. Analysis area: 500 μm × 500 μm. As-acquired raw file: 256 × 256 pixels. Corrected raw file: 128 × 128 pixels (produced from 16384 subspectra). The high mass resolution version allows more accurate imaging of the 63Cu+ and C5H3+ ions, which are incompletely resolved in the original raw file. Note that registration between the optical micrograph and the SIMS images is close but not exact.

as those in Figure 2 offer a potential diagnostic tool for tuning and aligning the instrument. In the absence of electrical effects, for a perfectly flat sample having uniform chemistry, the contour map is an inverse representation of the sample stage alignment relative to the extractor. Such maps could, in theory, be used to align the sample stage. Alternatively, for a sample stage which is in correct alignment, the maps could be used to monitor the effectiveness of charge neutralization over the analyzed area or to tune the instrument’s dynamic emittance matching and primary ion flight time compensation settings. Mass Resolution Improvement for Rough Surfaces. Mass resolution degradation on rough surfaces is due primarily to the emission of secondary ions from regions of different elevation within the analysis area. A secondary cause of resolution degradation on rough surfaces, topography-induced electric field effects, recently studied by Gilmore and coworkers,17,18 will not be dealt with specifically, but the correction approaches offered here may produce some improvement in this situation as well. All examples in this section will use a cleanroom cloth, consisting of woven bundles of ∼25-μm diameter polyester fibers, for illustration.

of Table 2 with Table 1 shows that substantial improvements in mass resolution are possible without sacrificing signal (which is proportional to analysis area). For example, using 1024 subspectra, the 20 mm × 20 mm stage raster mass resolution is improved from 4084 to 10 411 with the full analysis area included. Using the best 50% of subspectra (that is, half the analysis area), resolution above 11 000 is attained. Table 1 shows that it was not possible to reach a resolution of 11 000 by conventional means even with a 99% reduction in analysis area. Similar improvements were obtained for the Si wafer and Al plate, and these results are tabulated in the Supporting Information, Tables S-1 and S-2. The power of the correction method is shown in the contour plots in Figure 2, where the large deviations in the C7H4O+ peak position in each subspectrum are dramatically reduced after correction (Figure 2B). The correction, in effect, simulates the case of ion emission from a smooth plane lying exactly perpendicular to the extraction optics. An interesting side benefit of the mass resolution improvement process is that it provides a spatially resolved method for studying the quality of data from different areas of a sample, and therefore, plots such 1750



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Regions of Similar Elevation (RSE) Approach. One strategy for improving mass resolution in spectra of rough surfaces is to find regions of similar height, or elevation, within the analysis area. This can be done manually by trial-and-error selection of regions-of-interest, either through polygon drawing on SIMS images after data acquisition or during data acquisition by searching for suitable small areas. For complex roughness, such as fibers, these approaches can be time-consuming and usually work poorly. Typically, only very small regions yield good spectra, and low signal and other issues listed in Mass Resolution Improvement for Smooth Surfaces become a problem. A more sophisticated approach is to use just a few adjoining spectral channels to produce a SIMS image and to then acquire a spectrum from this region-of-interest, the theory being that adjoining channels likely result from areas of similar elevation. This is not always true; the topic is discussed further at the end of this section. A preferred approach would be one which automatically identifies regions of similar elevation and which maximizes the signal from these regions. The multivariate statistical techniques of principal component analysis (PCA)19,20 and multivariate curve resolution-alternating least-squares (MCR-ALS, henceforth referred to as MCR)21−24 offer such an approach. Pixels from statistically chosen regions can be summed to give a corrected spectrum with mass resolution better than that of the original spectrum. The focus here will be on MCR rather than PCA. Reference 11 contains a discussion of the use of PCA for topographic correction, although the approach used in this reference differs somewhat from that employed here. Figure 3A shows the m/z 25− spectral peak and accompanying TOF-SIMS image of this peak from a 500 μm × 500 μm analysis of the cleanroom cloth. Individual fibers are visible in the SIMS image. The mass resolution is extremely poor, and the “peak” appears to be composed of multiple overlapping peaks. In TOF-SIMS, the peak at m/z 25− nearly always arises from a single component (C2H−), and the overlapping peaks here are due not to chemical interferences but rather to the fact that the spectral signal is originating from fibers of different height. MCR can be used to extract regions of similar elevation from the raw image data so that high mass resolution spectra can be isolated from these regions. The procedure is as follows: (1) A peak composed of only a single ionic species, and which is present throughout the analysis area, must be chosen. For negative ion spectra, the C2H− peak at m/z 25− usually meets this requirement, and for positive ion spectra, the C3H5+ peak at m/z 41+, or another hydrocarbon peak, can be used. (2) MCR is applied to the chosen peak. In the example in Figure 3A, all channels in the mass range of 24.95−25.10 are used (here, 278 channels). The data are arranged as a twodimensional matrix of counts, with each row containing the spectrum of one pixel. In this example, the data matrix has dimensions 16 384 × 278. It is best to avoid pretreating the data through normalization or other means because this changes the peak shape. A PCA eigenvalue plot (variance vs principal component number) can be used to choose the number of factors into which the input data will be separated. The MCR output (Figure 3B) consists of a set of concentrations and pure spectra. While not technically rigorous, it is convenient to describe the MCR output in terms of images and region-of-interest spectra. For each image in Figure 3B, there is a corresponding spectral peak. This is analogous to the use of MCR to resolve chromatographic peaks.23,24 Instead of resolving overlapping peaks in the time domain, as in

chromatography, the present method instead resolves overlapping images in the pixel domain. (3) Spectra are generated from one or more of the MCR images. Examination of the images shows that, for the most part, they represent unique areas. Since the input peak is chemically homogeneous, the images therefore represent areas of different height on the sample, and spectra acquired from the pixels in each image will show an improvement in mass resolution. Note that, although a single ion (for example, C2H−) is used to generate the regions, the mass resolution improvement will extend over the entire mass range. There are several options for generating spectra from MCR factors. The simplest is to choose the factor with the narrowest spectral peak and acquire a region-of-interest spectrum from the pixels in the accompanying image. Overlaps with adjoining factors can be reduced by applying a threshold to the image, including only those pixels above a certain threshold value. Quite significant improvements in mass resolution can be attained in this way (compare Figure 4A,C), but the use of only a single factor may limit the spectral signal. To increase the signal, spectra from multiple factors can be summed. Spectra from multiple factors are analogous to the subspectra described in Mass Resolution Improvement for Smooth Surfaces, except that they arise from MCR-derived regions of similar elevation rather than from a regular grid. As in Mass Resolution Improvement for Smooth Surfaces, recalibration is necessary before the MCR subspectra are summed, and the user may choose to optimize the results by summing only some of the factors. When summing MCR subspectra, it is essential that overlapping pixels from adjacent factors are not counted twice. An automated procedure which works from either the first factor to the last, or vice versa, is used to assign pixels to individual factors and avoid overlaps. The result of one such summation is illustrated in Figure 4B, where 80% of the signal is retained while the mass resolution is improved by over a factor of 3. The MCR approach to finding regions of similar elevation is likely the optimal approach. It is important to note that images equivalent to the MCR images cannot be derived from the data in univariate fashion by simply selecting groups of mass channels and displaying the corresponding images. This is discussed at length in the Supporting Information and illustrated in Figures S-1−S-3. The key point is that the multivariate approach produces much better results than the univariate approach, and it does so in automated fashion. Spectral Subdivision Approach. The SSD approach may be used on rough surfaces if the subregions are made sufficiently small and if there is sufficient signal within each subspectrum to carry out a recalibration. To our surprise, given the low expected signal within each subspectrum, quite impressive results were obtained from the cleanroom cloth data using the SSD approach. This is illustrated in Figure 4D,E, where a 32 × 32 grid of subspectra was employed. The SSD results surpass the MCR results in this case. To further illustrate the degree of improvement obtained, Figure 5 shows positive ion spectra of the cleanroom cloth at higher mass. In addition to an improvement in mass resolution, there is a marked improvement in signal/noise. The fact that the SSD approach can be successful on both smooth and rough surfaces does not mean that the RSE approach can be successful on smooth surfaces, however. On a smooth surface, there are no significant differences in elevation, and MCR typically extracts only two factors: one defining a 1751



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circular region in the center of the analysis area and another for the outer portion of the analysis area. Selection of pixels from one or both areas usually produces only a small improvement in mass resolution. Creation of a Mass-Corrected Raw File. The success of the SSD approach in correcting spectra of rough surfaces using a large number of subspectra opens up the possibility of creating a new raw file from the original raw file but containing high mass resolution data. The set of subspectra can be considered to be a set of pixels, and a raw file is simply a collection of pixels, each of which contains a complete mass spectrum. Given a sufficient number of subspectra, therefore, a raw file can be created. A 64 × 64 or 128 × 128 array of corrected spectra, for example, can be reassembled into a masscorrected raw file from which all the usual processing advantages obtain. This is demonstrated in Figure 6, where a new 128 × 128 pixel raw file has been created from a 256 × 256 pixel raw file. The most advantageous aspect of this conversion is shown. In the mass-corrected file, the peaks at nominal m/z 63 are well-resolved, allowing the analyst to easily recognize the presence of multiple components and to extract reliable SIMS images corresponding to each peak. Alternatively, high mass resolution region-of-interest spectra may be extracted from a SIMS image in the usual manner. To our knowledge, this is the first reported instance of converting a low mass resolution raw file into a high mass resolution raw file using only the data contained within the low mass resolution raw file.

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ASSOCIATED CONTENT

S Supporting Information *

Additional information as noted in text. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: 651-736-1922. Fax: 651-736-0129. E-mail: sjpachuta@ mmm.com.

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ACKNOWLEDGMENTS The authors thank 3M Corporate Research Analytical Laboratory managementColleen Nagel, Fred LaPlant, James Lundberg, and Diana Gerbifor funding and encouraging this work. We also thank Derk Rading of ION-TOF and Scott Bryan of ULVAC-PHI for informative discussions.

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REFERENCES

(1) Green, F. M.; Gilmore, I. S.; Seah, M. P. J. Am. Soc. Mass. Spectrom. 2006, 17, 514−523. (2) Gilmore, I. S.; Green, F. M.; Seah, M. P. Surf. Interface Anal. 2007, 39, 817−825. (3) Green, F. M.; Gilmore, I. S.; Seah, M. P. Anal. Chem. 2011, 83, 3239−3243. (4) On the basis of private communications and online statistics, an estimate of the number of such instruments sold commercially to date is approximately 400. (5) Eccles, A. J.; Vohralik, P.; Cliff, B.; Jones, C.; Long, N. Appl. Surf. Sci. 2006, 252, 7308−7311. (6) Fletcher, J. S.; Rabbani, S.; Henderson, A.; Blenkinsopp, P.; Thompson, S. P.; Lockyer, N. P.; Vickerman, J. C. Anal. Chem. 2008, 80, 9058−9064. (7) Carado, A.; Passarelli, M. K.; Kozole, J.; Wingate, J. E.; Winograd, N.; Loboda, A. V. Anal. Chem. 2008, 80, 7921−7929. (8) Gilmore, I. In ToF-SIMS: Surface Analysis by Mass Spectrometry; Vickerman, J. C., Briggs, D., Eds.; IM Publications: Chichester, UK and SurfaceSpectra Limited: Manchester, UK, 2001; pp 262−264. (9) Campana, J. E.; DeCorpo, J. J.; Wyatt, J. R. Rev. Sci. Instrum. 1981, 52, 1517−1520. (10) In the ION-TOF Acquisition Driver version 4.1.0.40, for example, this correction is found as a checkbox [“primary ion flight time correction (PI ftc)”] in the driver configuration window. (11) McDonnell, L. A.; Mize, T. H.; Luxembourg, S. L.; Koster, S.; Eijkel, G. B.; Verpoorte, E.; de Rooij, N. F.; Heeren, R. M. A. Anal. Chem. 2003, 75, 4373−4381. (12) McDonnell, L. A.; Piersma, S. R.; Maarten Altelaar, A. F.; Mize, T. H.; Luxembourg, S. L.; Verhaert, P. D. M.; van Minnen, J.; Heeren, R. M. A. J. Mass Spectrom. 2005, 40, 160−168. (13) Spencer, B. C. An Investigation into the Analysis of TOF-SIMS Data, B.S. Honors Thesis, The College of William and Mary: Williamsburg, VA, 2006. (14) Komatsu, M. Patent Abstract (Basic): JP 2011149755A (Japan), 2011. (15) Tauler, R.; de Juan, A.; Jaumot, J., http://www.mcrals.info/ (Accessed 2012). (16) Schueler, B. W. In ToF-SIMS: Surface Analysis by Mass Spectrometry; Vickerman, J. C., Briggs, D., Eds.; IM Publications: Chichester, UK and SurfaceSpectra Limited: Manchester, UK, 2001; pp 76−77. (17) Lee, J. L. S.; Gilmore, I. S.; Seah, M. P.; Fletcher, I. W. J. Am. Soc. Mass. Spectrom. 2011, 22, 1718−1728. (18) Lee, J. L. S.; Gilmore, I. S.; Seah, M. P.; Levick, A. P.; Shard, A. G. Surf. Interface Anal. 2012, 44, 238−245. (19) Tyler, B. J. In ToF-SIMS: Surface Analysis by Mass Spectrometry; Vickerman, J. C., Briggs, D., Eds.; IM Publications: Chichester, UK and SurfaceSpectra Limited: Manchester, UK, 2001; pp 475−493.

CONCLUSIONS

Two approaches for improving mass resolution in TOF-SIMS spectra have been presented. Of these, the SSD approach is the most generally applicable because it can produce significant improvement in spectra of both smooth and rough surfaces. It is expected that, in the case of low counts from a rough surface, the RSE approach will at some point be the more successful because the SSD approach relies on having sufficient signal in each subspectrum to achieve a good calibration. The low-count threshold for the SSD approach has not been fully explored, but the success of the simple, centroid-based autocalibration method presented here indicates that well-defined peaks are not necessarily required. The SSD approach is more robust than anticipated, even allowing creation of a new, improved high mass resolution raw file. The degree of improvement which can ultimately be attained using these methods varies by sample type and analysis area, and further study of these and other factors is warranted. Future work might involve development of alternative automatic calibration methods optimized for low counts. Refinements in multivariate analysis may enable improvements to the RSE approach. There is also a need to increase the conversion speed for large numbers of subspectra. The conversion of a 256 × 256 pixel raw file to a 128 × 128 pixel raw file (Figure 6), for example, required over 5 h of computation time. For certain difficult materials, this may be a small price to pay for the improvement in data quality. While a rather mundane ink-on-fabric example was chosen here for illustration purposes, this sort of topographic correction may prove to be highly useful in analyses of many types of realworld surfaces. 1752



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(20) Biesinger, M. C.; Paepegaey, P.-Y.; McIntyre, N. S.; Harbottle, R. R.; Peterson, N. O. Anal. Chem. 2002, 74, 5711−5716. (21) Lee, J. L. S.; Gilmore, I. S. In Surface Analysis − The Principal Techniques, 2nd ed.; Vickerman, J. C.; Gilmore, I. S., Eds.; John Wiley & Sons: Chichester, UK, 2009; pp 563−612. (22) Lee, J. L. S.; Gilmore, I. S.; Fletcher, I. W.; Seah, M. P. Surf. Interface Anal. 2009, 41, 653−665. (23) Sharaf, M. A.; Kowalski, B. R. Anal. Chem. 1982, 54, 1291− 1296. (24) Osten, D. W.; Kowalski, B. R. Anal. Chem. 1984, 56, 991−995.

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Postacquisition Mass Resolution Improvement in Time-of-Flight

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