Secondary Ion Mass Spectrometry: Characterizing Complex Samples

Review pubs.acs.org/ac

Secondary Ion Mass Spectrometry: Characterizing Complex Samples in Two and Three Dimensions John S. Fletcher† and John C. Vickerman*

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Manchester Institute of Biotechnology, University of Manchester, Manchester M13 9PL, U.K.

CONTENTS

Application of NanoSIMS to the Study of Complex Organic Systems Molecular SIMS in the Static Regime Molecular SIMS beyond the Static Regime 3D Reconstruction Instrument Developments Organic Depth Profile Challenges: Contribution of GCIBs MD Simulations Providing Insights into Cluster and Polyatomic Mechanisms Molecular SIMS: The Challenges Sample Preparation Matrix Effect Secondary Ion Yields Data Interpretation Whither Molecular SIMS? Author Information Corresponding Author Present Address Notes Biographies Acknowledgments References

obtained using SIMS methodology. One is a development of the dynamic SIMS instrumentation based on the magnetic sector NanoSIMS that uses the detection of stable isotopes to follow the action and fate of molecules in biological systems sometimes referred to as multi-isotope mass spectrometry or MIMS.2 A high energy highly focused atomic ion beam, frequently Cs+, bombards samples, the secondary ions detected are elemental ions or small fragments such as CN−. The compounds to be followed will have been enriched with, for example, 13C or 15N, and the variation in the ratio of fragments with the enriched element relative to those that are not enriched is used to follow biological transformations. The technique, as will be shown later, has been powerful in following a number of significant biological processes with spectacular spatial resolution down to around 35 nm. The other main area of advance has been based on the development of new ion beams for molecular analysis using SIMS. By molecular analysis we mean the detection of larger secondary ions based either on the molecular species itself, for example, a radical cation or a protonated ion or a set of larger fragment ions that can be related to the original structure of the molecule. Of course SIMS being a sputtering technique, there are always lower mass fragments resulting from the destruction of molecules at the point of impact. Up to the mid-1990s, molecular SIMS utilized atomic primary ions such as Ar+, Xe+, and Ga+. While these ions did deliver chemically specific ions, the impact of these ions was destructive so all analysis had to be carried out under so-called static conditions in which the primary ion dose was limited (1013 primary ions/cm−2) such that less than 1% of the surface was removed.3 Under such conditions, no part of the surface is expected to be impacted by a primary ion more than once, so the detected ions should be emitted from the virgin surface and the surface can be thought of as static. Clearly such conditions limit the number of secondary ions that can be emitted and detected, and furthermore any sort of depth profile analysis is impossible. A further limitation was that the atomic ion bombardment did not seem to be capable of sputtering ions much above m/z 250, so there was a limitation on the size of molecules that could be analyzed. Although the liquid metal Ga+ beam was capable of high spatial resolution, 100 000, although in this initial report the transmission of the system is still very low and imaging speed is restricted to approximately 1 pixel per second. Example data from imaging rat brain on this instrument is shown in Figure 7f with the imaging of several extremely well mass resolved features. Organic Depth Profile Challenges: Contribution of GCIBs. Mahoney has produced a thorough review of the use of cluster and polyatomic ion beams for the analysis of polymeric materials that summarizes the effects observed during depth profiling organic samples.87 An organic depth profile is described as passing through four stages. The first stage describes an initial transient region of the profile plot (often an exponential decay) before a steady state is reached. This steady state is phase 2 of the depth profile. In a failed attempt at molecular depth profiling, the signal during phase one would have dropped to close to zero and so there is no real phase 2. A flat profile during stage 2 is considered a complete success, but in some cases a gradual decrease 622

dx.doi.org/10.1021/ac303088m | Anal. Chem. 2013, 85, 610−639

Analytical Chemistry

Review

Figure 7. Schematic of the C60-QStar instrument and the Ionoptika J105 that decouple the mass spectrometry from the sputtering process by sampling the secondary ion stream using orthogonal MS and a buncher-TOF system (parts a and b, respectively). The decoupled mass spectrometry allows mass resolved imaging of extremely topographically challenging samples such as a coronary stent, a mesh tube with diameter of approximately 2 mm (c) and a Xenopus blastomer, approximately 800 μm in diameter, immediately after fertilization (d). However, as can be seen in the images, the process does not compensate for variation in signal intensity due to topography. Mass resolution is of course also maintained during high-resolution imaging as demonstrated by imaging three peaks all with nominal m/z value 86 that have quite different distributions in the image of HeLa cells on a silicon substrate (e). FTICR SIMS has now been demonstrated with extremely high resolving power as demonstrated on a rat brain tissue section85 (f). A common feature to the new approaches to SIMS is the inclusion of MS/MS capabilities, highlighted here using the MS/MS spectrum of methionine (g) labeled product ions that match those in both the standard MS spectrum and online MS/MS databases for ESI data. Reprinted from ref14. Copyright 2008 American Chemical Society. Reprinted from ref 15. Copyright 2008 American Chemical Society. Reprinted with permission from ref 86. Copyright 2012 Springer. Reprinted from ref 85. Copyright 2011 American Chemical Society.

of organic materials containing an aromatic ring, and in complex samples the presence of a peak at m/z 91 does not necessarily indicate polystyrene. There are some samples that have been shown to be particularly difficult to depth profile, even with C60+, and this has stimulated the quest for improved capabilities for molecular depth profiling, and in the last 2 years there has been a remarkable uptake of gas cluster ion beams (GCIBs) for SIMS. These ion beams are generated from the ionization of supercooled

zero. There have been several reports suggesting various methods for “successfully” depth profiling polystyrene where success is judged by the ability to maintain a stable signal from the C7H7+ ion at m/z 91. The stable signal indicates that the cross-linking has not occurred under ion beam irradiation and a constant sputter rate is maintained. Of course stopping cross-linking is important and useful, but if analysis is limited to the C7H7+ ion there may be limited application to real world samples as this is quite a generic mass fragment and is commonly observed in spectra from a wide range 623



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These observations are in line with those from molecular dynamic (MD) simulations discussed in MD Simulations Providing Insights into Cluster and Polyatomic Mechanisms, Figure 9a. Figure 8a,b highlights the effect of reducing the energy per atom in the primary particle with regards to secondary ion generation from polystyrene, while Figure 8c,d highlights the development of these beams for analysis of organic electronic materials. Figure 8c shows a depth profile of a model system comprising multilayered organic semiconducting compounds, while Figure 8d shows the result from the analysis of a “real” organic electronic device, an RGB OLED pixel from a mobile phone display. In terms of biological analysis using GCIBs, there is limited available data. Fletcher et al. have reported increased persistence of lipid signals on tumor tissue compared to analysis using C60, and as more Ar-GCIBs are installed on SIMS instruments there is no doubt that any advantage for biological analysis will be fully explored and exploited.101

gas clusters that are formed during expansion of high-pressure gas, usually argon, into a vacuum. Such beams have been used for many years in semiconductor processing to produce extremely smooth silicon surfaces.94 Matsuo and co-workers reasoned that what was clearly a very gentle cleaning process might be applicable to low damage molecular profiling in SIMS analysis.95 Organic semisemiconductor materials have proven particularly difficult to profile using C60+, which is especially frustrating considering semiconductor analysis has always been a strength of conventional dynamic SIMS. Shard et al. attempted to depth profile a multilayer sample of Irganox 1010 and tris(8-hydroxyquinolinato)aluminum (Alq3). The erosion was initially steady through the Irganox 1010 but deteriorated once the Alq3 layer was reached.96 In contrast, by using 5.5 keV Ar700+ Nimomiya and co-workers obtained steady signals when depth profiling through the material and a number of related organic semiconductors.97 The excitement surrounding the introduction of the Ar-GCIB for SIMS led to a hasty extension of the interlaboratory Irganox delta layer depth profiling study. The report contains data from three laboratories with Ar-GCIBs and one laboratory using C60+ 13. Cryogenic temperature analysis using 40 kV C60+ allowed the measurement of all 4 delta layers using the characteristic Irganox 3114 ion at m/z 564.3 without the broadening of the delta layer peak with increasing depth that had previously been seen at room temperature and to a lesser extent at −80 °C when using 10 kV C60+. However this result was surpassed by the results obtained using Ar clusters. Ar1700−2500+ with energies from 2.5 to 5 keV were used as sputter beams while analysis was performed using Bi3 clusters with energies ranging from 13 to 60 keV. The “best” result in this study came from the use of Ar1700+ at 2.5 keV with analysis using 15 keV Bi3+ and showed depth resolutions of 5 nm for the first 3 delta layers. The potential of Ar-GCIBs as analysis beams has also been investigated by a number research groups. In many cases the static SIMS spectra obtained using argon clusters at impact energies similar to those used with LMIG or C60 ion beams look very similar. However, if the energy per atom on the cluster is altered some changes can be observed as this energy becomes close to the threshold energy for sputtering. A general observation is that as the energy per atom is reduced there is a reduction in intensity of the small fragment species in the TOF-SIMS spectrum, a result first reported by Ninomiya et al. for arginine and triglycine.98 It has also been shown by Moritani et al. that the fragmentation pattern from polystyrene varies as a function of Arn+ cluster size.99,100 Three trends in secondary ion signal were observed as a function of energy per constituent atom that could be related to the origin of those signals from within the sample. Fragment ions from main chain cleavage, but without fragmentation of the phenyl ring, showed little change in intensity as the energy per Ar atom was reduced from 22 to 3 eV, but a rapid decrease below 3 eV per Ar atom was observed. Side chain fragmentation including fragmentation within the phenyl ring decreased exponentially with the energy per Ar atom. However, small aliphatic fragments, possibly from the terminal groups of the polymer, showed no change as a function of constituent energy. Rabbani and co-workers reported a similar situation regarding the oligomer region of the SIMS spectrum of polystyrene (Mw1000). 12 The 20 keV C60+, Ar60+, and Ar1000+ were compared and showed a change in the type of ions detected. C60+ produced pseudomolecular ions generated by loss of C7H7. These ions were also present using Ar60+ but with the inclusion of lower intensity [M + H]+ ions. Analysis using Ar1000+ produced mainly [M + H]+ ions with a small contribution from the C7H7 loss peaks.

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MD SIMULATIONS PROVIDING INSIGHTS INTO CLUSTER AND POLYATOMIC MECHANISMS From relatively early in the development of static SIMS, molecular dynamics simulations have been exploited to provide insights into the mechanisms of the sputtering process that underlies the delivery of secondary ions for surface chemical analysis. Much of the work focused on molecular SIMS originates from the Penn State group of Garrison and Winograd, although several other groups have made significant contributions, including their long time collaborators Postawa, Delcorte, and Webb. The graphics of the sputtering process that emerged early on in the history of static SIMS gave a vivid impression of the sputtering process involved, although at times the mechanism seemed so energetic that it was surprising that chemical information could survive. Yet the detailed simulations accorded with experiment showing that the processes that arose from small numbers of collisions with a surface did indeed generate sputtered species that related to surface structure. MD simulations of sputtering were in a sense made for the static SIMS process, both required few surface impacts to deliver the information required. A review by Garrison and Postawa entitled “Computational View of Surface based Organic Mass Spectrometry” in 2008102 provides an excellent summary of the essence of the approach and the results reached at that time. As the title of that review implies by then the simulations had moved from the study of sputtering by atomic primary particles of metals to the investigations of cluster and polyatomic particle sputtering organic films and solids. The progress between 2008 and the present has been such that MD simulations have moved from explaining experimental data to being in a position to propose experiments. By the late 90s, simulations were able to provide insights into the sputtering of organic films on metal substrates by atomic primaries. Many-body potential energy functions were required to allow for interactions among all of the constituents of the target. This was a relatively complex system and as the collision cascade evolved it was necessary to be able to follow, via the integration of Hamilton’s equations of motion, the reactions occurring among the substrate atoms, the substrate and adsorbate atoms, and between the individual adsorbate atoms. The C−C, C−H, and H−H attractive interactions were well described by a reactive many-body potential energy function developed by Brenner.103 The repulsive interactions were found to be improved by adding a Molière repulsive potential. The many-body interactions that could occur between metal atom and C in the surface complexes were modeled by combining the Brenner hydrocarbon potential and metal−C and metal−H Lennard−Jones 624



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Figure 8. Influence of the energy per atom (Eatom) on the fragment generation from poly(styrene) (a). Changes in oligomer signals from a low-molecular weight poly(styrene), PS1000, with Ar60 and Ar1000 cluster ion beams compared to C60 all at 20 keV total impact energy (b). Depth profiling of organic semiconductor materials using 5.5 keV Ar700+ (c). 3D imaging of an OLED RGB pixel masses: m/z 699 (dark gray); m/z 774 (red); m/z 506 (green); m/z 846 (blue); m/z 651 (yellow); m/z 644 (pink); m/z 818 (aqua); m/z 74 (dark gray); m/z 113 (113In (violet)). Reprinted with permission from ref 99. Copyright 2009 The Japan Society of Applied Physics. Reprinted from ref 12. Copyright 2011 American Chemical Society. Reprinted with permission from ref 97. Copyright 2009 John Wiley & Sons, Ltd. ((c) and (d) from Ewald Neihus, IonToF communication prior to publication).

pair potentials. Using this approach, “mass spectra” of the sputter yield could be produced that were a good representation of those observed experimentally.104,105 These simulations laid the groundwork for studies of the sputtering of larger molecules supported on metal substrates. In the early 2000s, work by Delcorte and Garrison demonstrated that to get intact molecules off the surface required what were termed “high action” events, where the primary particle impact deposits a considerable amount of energy in a small volume close to the surface resulting in the concerted movement of a large number of substrate metal atoms that cooperate to lift larger molecules off.43,42 The simulations suggested that such events could result in molecules of up to 2000 u being lifted off. These studies were extended to modeling a thick film of polystyrene supported on a metal substrate, a system that required new interaction potentials to take account of the strong intramolecular bonds within the polymer and the weak van der Waals forces between the polymer and the substrate.106 These simulations confirmed that high action events were also required to lift off the larger oliogomers from thick polymer substrates.107 With atomic projectiles, such events were in fact quite rare experimentally, but the emergence of these simulations coincided with the introduction of metal cluster and polyatomic ion beam systems that were shown to be particularly effective in lifting off larger molecules, although 2000 u is still quite challenging. The simulations get more challenging when cluster and polyatomic primary projectiles are involved such that cooperative mesoscale effects have to be incorporated. This was a very significant development that has enabled the role of these new ion

beams to be effectively modeled. The effect of the impact of these multiatom particles generates something more akin to the thermal spike encountered during high flux atomic bombardment. As a consequence Garrison and Postawa have found that a fluid flow model more nearly represents the phenomenon following the impact of Au3, Bi3 or Bi5 , C60, or Ar1000 species. Because each impact resulted in a much larger sputtered yield, it was necessary to construct a much larger model substrate consisting of millions of atoms to contain the energy dissipation processes.108 It would be impossible within any reasonable time to carry out the calculation of a single trajectory under these conditions. The mesoscale energy deposition footprint (MEDF) model based on fluid flow enables simulations to be carried out over a reasonable time scale.110 The excitation region following keV primary impact can be described as a track (Figure 9a) along the path of the projectile of radius Rcyl, within which the substrate material is quickly displaced and energized. Material that is less than or equal to a depth of Rcyl from the surface can be sputtered from the surface (Figure 9b). Material that is deeper than Rcyl cannot escape due to collisions with other bulk atoms and expands radially. As the excitation diffuses outward and material within the track is ejected, the boundary of both the energized track and the surface begins to blur. Material that was in the nearsurface region but became energized after some time, now has a chance to escape. This diffusion of energy leads to the formation of a second zone of radius Rs from which material can escape. The radius Rs is dependent on the relationship between the average excitation energy in the track and the cohesive energy of the 625



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off normal. An observation that has been confirmed in some elegant angle resolved experiments that monitor the angular distribution of intact emitted benzo[a]pyrene molecules following normal impact of C60 primary ions.113 Interestingly the MD studies show that a high density of free hydrogen is also generated within the emission zone. Such hydrogen can react with molecular species and potentially form M + H species, such that if the hydrogen is ionized molecular ionization may be enhanced. However, Garrison has pointed out that the formation of hydrogen and other fragments in the central direct impact region occurs on a much shorter time scale compared to the flow off of intact molecules from the periphery region.114 As a consequence, a space-time divide occurs between the two events. The simulations show that the initial fragments are retained within the cylinder track region and will take time to react with molecules in the outer region; hence, it can be expected that molecular ions formed by such reactions would be emitted by subsequent impacts, so in a sense they could be regarded as preformed ions. It is puzzling that with all the energy about in the impact zone, ionization is so low, so this slow indirect formation process may provide an explanation. A secondary ion emission model that comprises a central highenergy primary ion impact zone, with a lower energy periphery arising as energy defuses out from the center, was postulated early on in the development of static SIMS.115−117 The central zone was seen as the source of small fragment and atomic ions resulting from the destruction of the chemistry of the analyte. The useful molecular ions and chemically informative fragment ions were thought to arise from the lower energy periphery. The MD simulations have confirmed this model and provided deeper insights as to the mechanisms involved that have enabled experimental confirmation of the model. There is a significant drive to find new primary beams to make the ion formation process less energetic to reduce fragmentation, to increase the proportion of molecular ions, and to increase the size of molecule that can be desorbed and studied. MALDI can desorb and study large peptides and small proteins, but SIMS seems to run out of capability around 1500 to 2500 Da. We saw that Delcorte’s simulations seemed to suggest that very large biomolecules should be desorbable even with atomic primaries. That is rarely seen. A number of MD studies have sought to investigate the benefits of very large polyatomic molecules and clusters as primary ions to soften up the emission process. By increasing the number of atoms in the primary particle for a given kinetic energy, the energy per atom decreases and in theory the sputtering process becomes less energetic resulting in less fragmentation. In an MD study of amorphous polyethylene being sputtered by 10 keV primary particles spanning a mass range from coronene, 324 u; 4 polystyrene oligomers (PS4), 558 u; C60, 720 u; PS16, 2000 u; PS61, 7407 u; to a nanodrop of 22 998 u, Delcorte et al. demonstrated that there was a change in the sputtering mechanism at an energy per nucleon of around 1 eV.118,119 Figure 10a presents the reported data as yields of molecular species and fragments as a function of the energy per nucleon. This plot shows that in the high velocity region above 1 eV/nucleon, the sputter yield is independent of the nuclearity of the primary particle and only dependent on the total energy of the particle (in fact the yield/nucleon scales linearly with energy/ nucleon), whereas below 1 eV/nucleon for a given primary particle energy yield is dependent on its nuclearity. Also in this low energy per nucleon region, the degree of fragmentation falls off significantly and the proportion of sputtered intact molecules rises sharply. Similar observations have been obtained modeling C60 and argon clusters on a benzene substrate.120 However, unfortunately it is

Figure 9. Diagram of the ejection model developed by Jakas et al. (a) The yellow region represents the excitation track. (b) The red area is the ejection cone as described in the text. Reprinted from ref 108. Copyright 2006 American Chemical Society. (c) Snapshots of the reaction zones created by 15 keV C60 and Au3 at 0.5 ps. The red triangle outlines the conical ejection region as identified by the MEDF model and implicitly includes the blue and yellow regions. The blue region represents the energized track created by projectile bombardment, and the yellow region is indicative of the reaction region created. Reprinted from ref 109. Copyright 2007 American Chemical Society.

substrate. Combining the two regions of excitation results in a conical emission zone, Rcyl deep and Rs in radius. The initial impact of the cluster or polyatomic primary is followed using MD calculations for the first 250 fs. This enables Rcyl and the average excitation energy to be estimated. These are then fed into the MEDF model to provide sputter yield and the extent of the sputter cone, Rs. Using this formalism the difference between the sputter mechanism of Au3 and C60 on water ice can be clearly seen in Figure 9c.111,109 Both primaries break into their constituent atoms, and the impact energy is partitioned between them. The gold atoms penetrate much further and ultimately a deeper crater is formed, whereas the lower energy carbon atoms lose their energy in the surface region and much more of the ejected material is from this region, the conical emission zone is significantly larger for C60.109 Furthermore investigation of the species emitted in this zone shows that it can be separated into a high-energy central zone where a high degree of molecular reaction/fragmentation may occur. Most of the molecules emitted from the outer pink “lower energy” region will be unreacted. The model also shows that whereas under C60 bombardment reacted molecules will be at the base of the crater, where they may escape or be removed by subsequent impacts, and under Au3 impact reacted molecules may be formed deep under the crater and may take several subsequent impacts to uncover. Further careful studies of organic substrates have shown that intact molecules are predominantly emitted with low internal energy from the periphery of the impact zone corresponding to the pink areas in Figure 9c.112 MD shows that these intact molecules are emitted 626



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Figure 10. (a) Mass sputtered as intact molecules (open symbols and dashed line) and as fragments (full symbols and full line) for amorphous PE samples under 10 keV cluster bombardment (0° incidence). Circles: (PS4)n clusters. Triangles: other light element clusters. Reprinted from ref 118. Copyright 2009 American Chemical Society. (b and c) Cross-sectional view of benzene crystal at 4 ps after the impact of 10 keV at a polar angle of 45° (b) Ar101 and (c) Ar2953 projectiles. Vectors represent the original and final position of the center of mass of system particles at 4 and 4.5 ps. Intact molecules are represented by a black vector, fragments by a red/gray vector, while projectile atoms are depicted by a green/thick black vector. The crosssectional view is 1.5 nm wide and is centered along the projectile impact point. Reprinted with permission from ref 120. Copyright 2012 John Wiley & Sons, Ltd.

insufficient argon atoms available and most of the energy is deposited into the substrate. Interestingly overall the yield from the three clusters studied was about the same, which contradicts the predictions of Figure 10a that was based on normal incidence; however, it does agree with some experimental measurements of sputter yield of cholesterol as a function of argon cluster size that suggest that yield does not vary much over quite a large range of cluster sizes.12 Overall it can be seen that as Garrison has claimed, MD simulations are a very effective “partner” of the molecular SIMS project. The simulations not only provide insights into mechanism that would be difficult or impossible to obtain experimentally, they are now able to suggest how the experimental protocol might be optimized to attain desired results.

also observed that the ionization probability also falls off significantly in this regime, so the benefits in terms of molecular ion yield may be limited if one reduces the energy per nucleon too far.12 Below 0.1 eV/nucleon, the sputter yield falls to zero. It is in the area of the role of increasing polyatomicity in the sputtering process that MD simulations have shown themselves to be particularly helpful in guiding the experimental way forward. The data in Figure 10a shows that an energy/nucleon can be identified where sputter yield and fragment/intact molecular are optimally obtained. It is somewhere between 0.5 and 1 eV. Thus, if an argon cluster beam is to be used as an analytical beam, Ar1000 at 20 keV might be appropriate. As a sputter depth profiling beam, a lower energy/nucleon maybe better to decrease damage; however, the sputter rate would be lower. The simulations also provide a fascinating insight into the changes of mechanism as cluster size increases, and they suggest that the optimum angles of incidence for highest experimental yield vary as a consequence of increasing cluster size. Postawa et al. have recently shown that yields from a smaller argon cluster (Ar101) sputtering a benzene crystal only slightly increases with the impact angle, has a broad maximum around 40°, and decreases at larger angles in a similar manner to C60 on a benzene solid121 and larger argon clusters on metal targets.122,123 In contrast to the behavior in sputtering metal substrates, the yield of benzene molecules sputtered by a large cluster, Ar2953 optimizes quite strongly at 45° incidence. Ar 366 shows intermediate behavior.120 The MD simulations show that this can be nicely explained by a transition in mechanism. In the case of the smaller Ar101, most of the projectile kinetic energy is deposited in a volume that can efficiently lead to sputtering such that changes in impact angle do not change yield very much. Whereas the mechanism for Ar2953 shows that at a low impact angle an intense flow of argon atoms flows over the opposite side of the crater, “washing” substrate molecules off in great numbers, see Figure 10c.120 This does not happen with the smaller clusters because there are

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MOLECULAR SIMS: THE CHALLENGES Sample Preparation. One of the most critical parts of experimental planning for SIMS analysis, particularly TOF-SIMS measurements, is sample preparation. This arises from two key aspects of the SIMS experiment: (i) the measurement is extremely surface sensitive, and (ii) the analysis will be performed in a vacuum. The former is important for all analysis while the latter is particularly relevant for the analysis of biological samples. The manufacture of many plastics involves the use of release agents that are used because they migrate to, and spread across, surfaces. The most common, poly(dimethylsiloxane) (PDMS), has been a cause of much frustration for SIMS analysts for many years! For biological samples, the most important consideration is maintaining biological integrity of the sample in the vacuum of the instrument. The “simplest” method for preparing cells with reasonable preservation of architecture is freeze-drying, sometimes following a chemical fixation step. Removal of excessive salt contamination from buffers is also useful. Malm et al. have reported on a thorough study of different drying techniques for TOF-SIMS analysis of cells including assessment 627



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the two devices are shown in Figure 11a,b with the Mousetrap displayed in the prefracture position, and the Bugtrap shown in both the prefracture and fractured positions. The difference between cells prepared by freeze fracture using the Mousetrap device and analyzed frozen hydrated compared with room temperature analysis has been reported by Fletcher et al. 3D reconstruction of the SIMS data shows differences in the morphology of the cell (Figure 11c,d) and improved contrast in both the spectra from different regions of the cells and improved contrast for individual mass images. Depth profile analysis of the frozen hydrated cells also showed improvement with the lower lipid membrane signal clearly resolved between the nucleus and the substrate (Figure 11e,f). Chandra has compared the advantages/disadvantages of hydration during dynamic SIMS analysis using atom primary ion beams and concludes that although freeze fracture is extremely useful, it is preferable to freeze-dry the samples to remove the water and analyze them at cryogenic temperatures.132 The maintained localization of highly mobile species such as Na+ and K+ is used to validate the procedure. The reason for the preference for sample drying is due to preferential enhancement of signal from the water rich regions in the cell that can limit the potential for quantification. This “matrix effect” is a result of changes in sputter yield in the cell, an effect that is more prevalent when using atomic ion beams. Patkin et al. reported differential erosion rates of cytoplasm and nucleus in radish roots cells,83 an effect that is not observed in the AFM/SIMS comparison by Robinson.82 In TOF-SIMS analysis of molecular type ions, it has been suggested that the presence of water ice can actually ameliorate matrix effects by providing an additional source of protons for the generation of [M + H]+ ions (see below). Preparation of tissue samples for SIMS analysis has all the challenges associated with the generation of good samples for histology with added complications arising from the nature of the SIMS analysis. The samples need to be frozen rapidly, and the freezing depends on the size and type of the tissue. For histology, tissue samples are often embedded in a material to help with the slicing and care must be taken for SIMS analysis to make sure that any waxy materials do not get smeared across the surface of the sample. To avoid this, samples can be embedded in gelatin prior to sectioning, and if any material is used to stick the tissue to the holder in the cryostat this should be a very small amount on the back of the sample that is not reached by the blade during any of the sectioning.133 Rodent brain is one of the most common tissue samples imaged by SIMS (and other imaging mass spectrometric techniques). The sample can almost be considered to be a sponge, full of chemicals that can seep through the tissue to the surface, and care must be taken to ensure that chemicals do not migrate to the surface and cover over other species of interest. As with cells, frozen hydrated analysis of tissue is expected to be the best method of preventing migration of chemistry during sample preparation. Matrix Effect. The higher detection efficiency and improved imaging capabilities provided by cluster and polyatomic ion beams suggest that conditions are now suitable, not only to image the majority components of materials but also to image the distribution of compounds that may be present in markedly lower concentrations. However, the matrix effect is an important and complicating parameter that must be factored into the assessment of any spectral and imaging data. The exploitation of this effect in MALDI, in which a matrix is added to the material to be analyzed to enhance the ionization and detection of particular molecules is well-known. However, because one com-

of the stability of cells during washing with ammonium formate.124 Ammonium formate125 and sometimes ammonium acetate have been used extensively in SIMS to remove salts while maintaining osmotic pressure.126 During the freeze-drying process and insertion into the vacuum chamber prior to SIMS analysis, the volatile ammonium formate sublimes leaving a “clean” cellular surface. Although this approach has been shown to maintain some cellular architecture and produce “good looking” SIMS images, it would be foolish to imagine that the removal of all the water from the cell, even if performed very carefully, does not lead to dislocation of small biomolecules. For this reason many laboratories have explored and developed instrumental and analytical approaches that allow cells to be analyzed in a frozen hydrated state. The rate of cooling of a sample can be critical for maintaining cellular integrity. Too slow a cooling rate can result in the formation for large ice crystals that can rupture cells. Liquid nitrogen is often not suitable for cooling biological samples due to the Leidenfrost effect and so instead the liquid nitrogen is used to cool a second cryogen. Isopentane is commonly used for snap freezing of tissue samples before storing and cryo-sectioning for imaging whereas liquid propane is more ideal for freezing samples immediately prior to SIMS analysis as the higher vapor pressure of the propane means it is easier to remove any excess in the vacuum by slight warming of the sample. An additional difficulty associated with the analysis of frozen samples is the potential for frosting of the sample surface, obviously not conducive to surface analysis. The advent of polyatomic ion beams now allow surface ice to be removed by sputtering without destroying all the underlying chemistry,127 but this process can be time-consuming and is an additional complication to an already nontrivial analysis. For cellular analysis, in vacuo freeze fracture provides a route for presenting the samples in a preserved and uncontaminated state for analysis. Cells are sandwiched between two substrates, rapidly frozen, and transferred to the SIMS instrument. Prior to analysis, the sandwich is pried apart to expose the cells. During the opening of the sandwich, the upper membrane of the cells is sometimes removed (the cell is fractured). Since the late 1990s, Winograd and Ewing have published a series of papers using freeze fracture and frozen hydrated analysis conditions to preserve cellular integrity including studies often incorporating additional techniques such as fluorescent labeling of the membranes in order identify different fractured states of the cells.128 Cliff et al. also used freeze fracture for TOF-SIMS analysis of Candida glabrata treated with the antibiotic clofazimine and observed high signal levels for the antibiotic on the outside of intact cells but no signal from the inside of fractured cells indicating that the clofazimine does not penetrate the cell wall.129 In vacuum, freeze fracture for SIMS has often been extremely fiddly and not very reproducible. Several attempts have been made to simplify the process, the most recent involving the use of spring loaded sample holders for separating the sample sandwich with minimum complexity and user influence. The J105 instrument was developed with sample handling that was optimized for the analysis of biological samples. Both the sample entry stage (sample holder/manipulator) and the sample analysis stage can be cooled to below 100 K and the entry lock is fitted with a glovebox that can be purged with a suitable inert gas to avoid frosting. During the sample handling development of the J105, the Mousetrap freeze fracture device was developed and this led to the construction of a similar type of device for use on IONTOF instruments called the Bugtrap.130,131 Diagrams of 628



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Figure 11. Schematics of the two spring loaded freeze fracture devices introduced recently for the analysis of cells by TOF-SIMS. (a) The Mousetrap and (b) the Bugtrap. The results comparing room temperature analysis (c) with analysis of frozen samples that have been prepared for analysis using the Mousetrap (d) on the J105 instrument. Reconstruction of 3D SIMS signals localized to the lipid membrane and the nucleus illustrate clear morphological differences. Many of the nuclei have been exposed in the fracture process, and there is less “spreading” of the phospholipid signal. Plots showing the variation of phosphatidylcholine and adenine signal as a function of depth through the center of a single fixed, freeze-dried (e) and frozenhydrated (f) cell. The signal was summed over a 4 × 4 pixel area through the center of the nucleus. The freeze-fractured cell has a lower phosphocholineto-adenine ratio at the start of the analysis, and clear separation of the adenine and phosphocholine layers at the end of the profile as the cell is completely consumed. Reprinted and adapted with permission from ref 130. Copyright 2011 John Wiley & Sons, Ltd. Reprinted and adapted from ref 131. Copyright 2010 American Chemical Society.

imaging study of a section of the brain of a rat that had been fed the drug suggested that the drug (M + H ion at m/z 376) had localized to the white matter of the brain that is rich in cholesterol, whereas it was absent from the gray matter rich in phosphatidylcholine lipids. Without prior knowledge of the system in question, it would be easy to assume that the drug was associated with the constituents of the white matter along with the cholesterol. This however did not accord with the known locations of the dopamine-D2 receptor sites. A model study of a related drug, haloperidol spin-cast across a section of brain tissue containing both white and gray matter showed that secondary ions from the drug were suppressed in the phospholipid rich gray matter area but enhanced in the cholesterol containing white matter region, Figure 12a. Studies into the fundamental parameters that give rise to the matrix effect in organic systems have built on earlier studies of the same effects in MALDI. It has been shown that the relative proton affinities or gas phase basicities (gpb) of the compounds participating in a mixture influence the relative yields of M ± H ions.63,62 Where the gpb of one component was significantly less than that of the other, its M + H would be completely suppressed. The proton affinity of the phospholipids is known to be very high and has been shown to suppress the formation of M + H ions, whereas cholesterol is ready to give up its protons, in this case to enhance the formation of the M + H ion of haloperidol.63 This example is a stark example of the degree to which a secondary ion image could be misleading. Another example shows that the matrix effect can complicate our understanding in the course of depth profiling a rat brain tissue sample.135 Figure 12c shows a set of images obtained when profiling through a region spanning white and gray matter. Going from left to right, the first set of images show the top surface ion yield, total ions, cholesterol (m/z 369), DPPC (M+H)+, and the

pound might strongly influence the detection of another with which it is colocalized, the matrix effect is important even when analyzing samples in an unaltered state. The matrix effect was recognized as an important parameter in inorganic SIMS because in particular it could seriously complicate the measurement of dopant concentrations in electronic materials. There is a large literature devoted to understanding and overcoming the effect. In contrast there is a surprisingly small literature on the matrix effect in organic molecular SIMS. Because many natural organic samples are extremely complex discerning how the matrix effect might influence analysis and imaging is challenging and is, we suspect, frequently assigned to the “too difficult” pile! The issue should not be so difficult with synthetic materials comprised of a small defined set of components because then calibration studies can be carried out to check the influence of each chemical involved on the ion yields of the others and as we will see later at least one group has tackled this in a rigorous manner. In the case of biosystems as indicated in the introduction, the matrix effect can be so serious that it completely suppresses the emission of ions from some components so that “the absence of evidence is not evidence of absence”, a potentially serious situation for life sciences related research. In the example in Molecular SIMS in the Static Regime on the static SIMS study of human nonalcoholic fatty liver disease, it was thought possible that the matrix effect might have suppressed the detection of some of the expected lipids.48,52 A particularly clear example of the seriousness of the effect is shown by a study of the drug raclopride in brain tissue.134 Raclopride is a molecule that specifically binds to dopamine-D2 receptors within the brain, and a 11C containing variant is commonly used in positron emission tomography to map the location of these receptors. A static SIMS 629



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Figure 12. (a) Matrix effects on the apparent distribution of the drug haloperidol ([M + H]+ signal at m/z 376) spun cast onto a section of brain with respect to the chemical domains of the tissue. The signal from cholesterol (m/z 369) and phosphatidylcholine (m/z 184) reflect the white and gray matter within the tissue surface; the analyzed area is 800 μm × 800 μm with a dose of 8 × 1010 ion/cm2. Although the drug species covered the whole area visible in the image, the molecular signal is only detected from the cholesterol rich areas. (b) Imaging of rat brain using 40 keV C60+ on the J105 3D Chemical Imager. Large area (5.4 × 9.6 mm2, 576 × 1024 pixels) image of total ion signal clearly illustrates the structure of the brain with particularly high signal levels originating from the white matter-rich regions. (c) Variation of signal from characteristic peaks at the white/gray matter boundary are imaged as a function of increase in ion beam fluence (600 × 600 μm2, 256 × 256 pixels). Total ion counts (Tot.), cholesterol (Chol.), dipalmitoyl phosphatidylcholine (DPPC), and the phosphocholine headgroup (PC). Maximum counts per pixel (mc) are also displayed. Reprinted with permission from ref 134. Copyright 2007 Elsevier. Reprinted with permission from ref 135. Copyright 2011 John Wiley & Sons, Ltd.

phosphocholine headgroup (PC at m/z 184). Cholesterol shows high yield in the white matter but decreases quite strongly as the sample is sputtered. It is known that cholesterol is quite robust under sputtering so this might be regarded as indicative of a reduction in cholesterol below the surface. The DPPC signal decays over the sputtering cycle, which may reflect the greater disappearance cross section of this molecular ion. However the PC signal, originally strongest in the gray matter region, as the surface is etched away increases in the white matter. m/z 184 is normally associated with phosphocholine containing lipids, although in this case it is anticorrelated with DPPC. The data may reflect the loss of cholesterol from the surface and the appearance of other PC containing lipids below the surface; however, the decrease in cholesterol signal and increase in the PC signal may reflect the operation of a matrix effect consequent upon the changing composition. These examples underline a serious issue, if SIMS analysis and imaging is to be used in medical or other life sciences applications, robust protocols will

have to be developed to ensure that secondary ion yield variations interpreted as concentration changes or even the apparent absence of species can be checked or tested. At present, these authors are not aware that such studies are being carried out. The problem is that the issue is just as important in imaging MALDI and DESI as it is in SIMS.136 The presence of salts in a sample is also observed to influence the ion formation from organic samples and certainly suppresses (M + H)+ formation. The result is that frequently strong (M + Na) or (M + K) ions are observed, and if these samples are depth profiled the formation of inorganic residues and alkali metal adducts to many fragment ions appear and the (M + H) ion decreases further.137 Desalination of organic samples with C18 packed Ziptips or washing tissue with ammonium formate greatly reduced or eliminated the problem. This may not always be possible without changing the state of the sample. Studies have shown that the source of the (M + H) positive ion signal loss may well be associated with salt related anions.138 Interestingly, the 630



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observed changes in ion yield. It was suggested that the charge transfer occurred in the impact region soon after primary ion impact and the extent of the transfer was composition dependent. The authors argued that the observations are fully explained by a direct charge exchange between the secondary ions and the neutrals, and they are not dependent on the generation of “free” protons by the sputtering process. However it is difficult to see how such distinctions can be made when describing what occurs in the impact region. Suffice to say that this example shows that the operation of the matrix effect can be modeled in a predictable way, and quantitative compositional data can be derived. It has to be admitted however that this is a simple model and much was known about its behavior in advance, it would be far more difficult to deal with a less well define real world sample. The matrix effect that is due to competition between components for protons during ionization to form (M ± H) ions may be ameliorated by methods proposed for increasing ionization described in the next section. However, not all the ions formed from organic molecules rely on a protonation process. Some molecules form radical cations, and some characteristic fragments observed are not formed via a protonated ion. If such ions can be identified, their use may in some cases provide a means to identify where (M ± H) suppression is taking place and even provide a route that enables quantitative measurements to be explored. Of course as demonstrated in Application of NanoSIMS to the Study of Complex Organic Systems, labeling with stable isotopes and monitoring the isotope itself or small fragments containing the isotope can overcome matrix effects and enable quantification in very focused studies of particular chemicals,142 but where the chemistry is complex and difficult to predict this approach is not easy to apply. Secondary Ion Yields. Underlying most of this review has been the sense that SIMS is always looking for more sensitivity. The fact that ionization is sensitive to chemical environment makes the technique challenging in terms of quantification and also gives rise to the matrix effect. The fact that ion yields are generally very low, 10, more than 100 for angiotensin II) in protonated ions could be obtained if the silver surface was pretreated with HBr, and a subsequent study demonstrated that this was due to providing adsorbed protons on the surface to enable protonation.148 Preformed ions existing in the analyte were not affected by these treatments. Although seemingly rather successful, the approach has not been taken up since. The approach is inhibited by the fact that it involves adding to the analyte, or being able to deposit the analyte as a thin film on a specific substrate. The latter approach is possible with pure compounds but not practical with most real world samples. The study also highlights the difficulty with most ionization enhancement methods, they “work” for some compounds and not for others. This is not really surprising because it only reflects the compound dependency of the SIMS ionization mechanisms. To expect to find a universally applicable method of ionization enhancement is probably unrealistic. Some research has been carried out into the metal deposition (usually noble metals, Cu, Ag, and Au) as a means of delivering high ion yields, so-called MetA-SIMS. This approach usually involves depositing the metal onto the analyte below monolayer coverage, ∼75%. On gold coated polymers under atomic ion bombardment, the result of this treatment is an increase in monomer ion yields by something around ×10 and the appearance of strong (M + Au) ions149.150 For some materials such as dyes, (M + Met) ions are not seen in significant yield and 632



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nucleic bases are dramatically enhanced when sputtered from water ice.21,166 The isolation of the analyte molecules in the ice matrix as well as the fact that during sputtering of ice by cluster and polyatomic ions, high yields of H3O+ are generated167 results in a favorable environment for high yields of (M + H) ions. These model studies suggest that analysis of biological samples in the frozen hydrated state should result in enhanced yields compared, for example, with freeze-dried samples; however, as yet this has not been proven in systematic studies. For many samples, the frozen hydrated state is not appropriate or possible. The idea of directing a jet of water vapor at the sample surface has been suggested by a number of researchers, and Mouhib et al. have recently shown that this could be a feasible approach.20 The (M + H) ion yield from a small antioxidant Irgafos was enhanced by over an order of magnitude and monomer yields from some polymers by about a factor 2. Indeed some Ph.D. thesis experiments from this laboratory confirm the benefits of this approach. We have shown that admitting water vapor to amino acid surfaces cooled to 170 K resulted in a 2−10 times increase in ion yield. Perhaps more significant, the almost total suppression of glutamic acid ion yield in a mixture with arginine was lifted under these conditions such that the relative glutamic acid (M + H)+ yields rose from about 5% of the arginine (M + H)+ yield to more than 30%. Although to date the database is very small and the ion yield enhancements seem to vary with sample, exposure to water vapor seems to be an approach worth exploring further because it is compatible with imaging and depth profiling without contaminating the sample. Some workers have suggested that using an electrospray beam that is composed mainly of water vapor as the primary beam may have a similarly beneficial effect on ion yields.168 Basically the sample is bombarded by very large water clusters, even droplets. A typical size seems to be [100H + (H2O)90000]100+ of mass 1.6 × 106 u. A complication is that under vacuum conditions, the electrospray jet can freeze. Methods of overcoming this by irradiating the jet with a laser beam have been proposed.169 Although results to date on this approach using it for both analysis170 and depth profiling171 seem promising, the enhancements in yield compared to other primary beams are difficult to assess precisely. The mechanism of ion formation has some relationship to the DESI process, but it seems to be sensitive to the acceleration given to the beam. At lower energies, the mechanism seems to be dominated by droplet based processes, whereas at 10 keV gas phase processes influence ion yields.22 It can be seen that there are a variety of possible routes to increased ionization. However, except in a few compound and system specific cases, as yet we do not see an enhancement method that is generally applicable even for protonation. It is possible the water route offers the most hope because of its low PA and that water is relatively chemically benign. Data Interpretation. Interpretation of TOF-SIMS data can often be challenging. There are two reasons for this. First the nature of the sputtering process usually leads to the formation of many fragment ions, and second the samples that are being investigated are often extremely complex in themselves! In many cases SIMS analysts turn to multivariate analysis (MVA) methods to help rationalize the data by reducing the dimensionality of the data while retaining the key information within the data. Most common in the analysis of TOF-SIMS data is principal components analysis (PCA). PCA is an unsupervised/unbiased method (i.e., does not rely on a priori knowledge of the sample) that “looks for” differences between samples based on changes in variables. In the case of mass spectrometry, the samples are mass spectra and the variables are m/z channels within the spectra. The analysis

protonation mainly occurs in the gas phase in the emitted plume,160 whereas in the SIMS process the emission crater seems to be the more likely locus of protonation. In the case of SIMS, the density of emitted species in the gas phase above the sample will be very low so intermolecular reactions would seem to be very unlikely, and MD simulations suggest that there will be a high density of hydrogen in the crater volume.114 Indeed as mentioned earlier, it has been suggested that association of hydrogen with analyte molecules may be a slower process such that preformation of protonated species may occur to be released in subsequent primary ion impacts. Heeren has pointed out that the requirement of efficient photon absorption for MALDI matrixes means that some compounds that might be very effective proton donors cannot be used because they fragment under photon irradiation. SIMS matrices do not need to meet this requirement, and as a consequence this has enabled Heeren’s group to synthesize some derivatives of MALDI matrices that have been shown to be very effective in generating high yields of secondary ions for SIMS but are of no use in MALDI.161 The guiding hypothesis in producing these matrices was that low matrix proton affinity would deliver high proton transfer efficiency and hence increased analyte secondary ion yields. By adding cyano, halogen, or nitro groups to classic MALDI matrices their acidity was significantly increased. A total of 21 matrices were investigated, and one of the most effective for generating (M + H) ions across a variety of peptide samples was 4-nitro-cyanocinnamic acid (4-NO2-CCA) that the authors claim has proton affinity 50 kJ/mol below that of α-CHCA. The study confirms the importance of gas phase basicity to the role of the matrix in ME-SIMS. An alternative approach to ME-SIMS has been proposed by Walker et al., namely, the use of ionic liquid derivatives of classic MALDI matrices such as α-CHCA.162 They have already been explored as MALDI matrices;163 however, the authors claim that ionic liquids are “attractive matrices for use in ME-SIMS and imaging MS because they have a very low vapor pressure and so can be used in very high vacuum. Second, they are liquids and so do not alter the sample surface chemistry by crystallizing. Thus, no “hot spots” are observed across the sample surface. Finally, there is little, or no, mass interference observed at m/z < 200 due to fragmentation of the matrix.” Increased yields are observed of protonated or deprotonated molecular ions of, for example, DPPC, DPPE, cholesterol, and bradykinin, although the yield of some molecules, e.g., angiotensin II, are not as great as using the CHCA matrix itself. Nevertheless, their use does seem to be attractive for imaging where the crystallite size of classic matrices may interfere with the spatial resolution available with SIMS. All these methods of improving ion yield entail “contaminating” the sample with additional chemicals. As we have emphasized such additions may change or interfere with the analyte chemistry. In the case of biological samples, molecular location or even molecular structures may be changed. Ideally we want to enhance ionization without significantly changing the chemistry of the analyte! If the focus is on organic analytes, protonation is a major ionization mechanism. We have seen that matrix assisted SIMS highlights matrices with a high proton transfer capability. Some studies have shown that ice and water may be a good source of protons, particularly from H3O+ species.77,21,20 The proton affinity of most organic molecules is higher than that of water, 691 kJ/mol, consequently H3O+ is highly reactive to many organic analytes as is attested by the success of proton transfer reaction mass spectrometry (PTRMS)164 and selected ion flow tube MS (SIFTMS).165 Thus it is understandable that it is observed that ion yields of biomolecules such as amino acids and 633



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matched if the optimum preprocessing for PCA was used. Henderson et al. reported on a comparison of the effect of mass binning and pixel down sampling on the result of PCA and MAF analysis of a TOF-SIMS image of HeLa cells.179 The study highlighted one important consideration when using MAF for analysis of TOF-SIMS image data: Pixel down sampling improved the PCA result as the signal-to-noise in each new pixel was greater; however, the MAF result suffered as the difference between the data in adjacent pixels became much greater. Hence, pixel size and image resolution can play a role in the successful application of MAF. The positive and negative pointing peaks in the loadings obtained from PCA and MAF can sometimes be difficult to interpret, and so in some cases multivariate curve resolution (MCR) is an attractive alternative.180 MCR assumes that the sample comprises a known number of pure compounds with spectra that combine in a linear manner when mixed together. The pure component spectra generated by MCR have no negative pointing peaks and so can be interpreted more easily. Unfortunately, the assumptions made when using MCR probably do not apply to most TOF-SIMS data. Matrix effects mean that in many cases the spectra do not combine in a linear manner, and for complicated samples such as in biological analysis it is difficult to know how many pure compounds should be specified for the analysis. This does not mean that the approach cannot be useful, but care must be taken when specifying the parameters of the MCR process as demonstrated by Aoyagi and co-workers in the analysis of skin samples.181 An alternative strategy, G-SIMS, for interpreting SIMS spectra has been suggested by Gilmore and Seah.182 The so-called gentle SIMS method involves the acquisition of two (or more) spectra of the same sample under conditions that produce different amounts of fragmentation. Initial work used spectra acquired with Ar+ and Cs+ or spectra acquired using the same beam at high and low energy, and there is now a commercial liquid metal ion source, the G-TIP, available that produces Bin+ and Mn+ that can be selected to produce the starting spectra for the G-SIMS calculation.183 On the assumption that the fragmentation in SIMS spectra is a result of the creation of a “hot” surface with a specific surface plasma temperature, G-SIMS extrapolates the data to generate a theoretical spectrum where the surface plasma temperature is very low. The result is a spectrum that contains much less signal from the low mass, less characteristic fragments, and particularly less signal from polycyclic aromatic species while at the same time increasing the intensity of the more characteristic peaks in the spectrum. Hence, interpretation is aided as the most structurally characteristic peaks are most prominent. An extension of the G-SIMS approach is to look at the changes in intensity of different species in the G-SIMS spectrum as the theoretical surface plasma temperature is changed. By noting the maximum intensity of different species at different “temperatures”, it is possible to derive potential fragmentation pathways similar to MS/MS measurement which can further aid structural elucidation, an approach referred to as fragmentation pathway mapping, hence G-SIMS-FPM.184 This can be improved further by combining positive and negative ion data.185 The most recent development has been the introduction of the “g-ogram” (the parameter that is varied to produce G-SIMS spectra at different “temperatures” is known as the “g-value”) where instead of taking just the g-value at which a peak reaches a maximum in the spectrum, a plot is generated of mass against the g-value where signal intensity is displayed using a suitable color scale. The result resembles a chromatography plot where the separating param-

generates a series of principal components (PCs) each with a corresponding “loading” which contains positive and negative pointing variables (m/z values). Each sample is given a “score” for each PC, and a high score for a specific PC means that the spectrum of that sample has relatively intense signals for the positive pointing m/z values in that PC’s loading and relatively low intensity signals for the negative pointing m/z values in the loading. Wagner applied PCA to spectra of a range of proteins acquired using TOF-SIMS and managed to classify the proteins based on the relative changes in the intensity of the amino acid fragment ions highlighting that subtle changes in relative intensity of ions in very similar looking spectra can be used for identification.172 An extension of the work also allowed conformational variation of the proteins to be detected following denaturing while bound to the sample surface.173 Powerful though this is, a subsequent study has shown that the discrimination only works when the spectra are acquired with a single primary beam and the results obtained with one beam do not transfer across to data using other beams.174 Compared to the unsupervised approach of PCA, other laboratories have used supervised methods such as principal components discriminant function analysis (PC-DFA) to classify bacteria and spectra resulting from cancerous or benign cells and tissue samples.175−177 PC-DFA requires the analyst to specify which samples belong to certain groups (bacterial strains, disease state). PCA is performed, and then combinations of PCs are used to classify the samples into the correct groups. Generally, unless the data is unreliable, separation of the different sample types is easily achieved and so the key evaluation parameter is the ability to project unknown data into the model and see if this data is then correctly classified. In the case of image analysis, there is a spectrum associated with each pixel so the pixels are now the samples. By converting the score on a PC for each individual sample to a color/intensity, then a scores image can be generated. PCA is very sensitive to different data pretreatment methods, and these need to be optimized depending on the nature of the data and the desired outcome of the analysis. Some data pretreatment is implemented with the intention of overcoming known instrumental issues. In particular, Poisson scaling and dead time correction are frequently used to compensate for artifacts arising from the ion counting systems normally used for generating the TOF-SIMS spectra; however, improper use of these methods can introduce artifacts of their own! TOF-SIMS spectra are often dominated by a few very intense peaks that will also have the greatest influence on the PCA result, and there is also an overall reduction in secondary ion signal with increasing mass and it is often the low intensity or high mass peaks that are the most interesting. In order to reduce the dynamic range of the data to allow “interesting” peaks to have more influence on the PCA result, intensity scaling can be employed. Common examples include log scaling, square root scaling, and also mass scaling where the intensity is multiplied by the mass of the ion. What these processing methods all try to do is account for the influence of the noise structure and the inherent shape of the TOF-SIMS data. Tyler et al. have assessed the effect of a range of different preprocessing methods on the ability to resolve image data and to provide scores images with the highest contrast.178 The different preprocessing approaches were compared to the result obtained using maximum autocorrelation factor analysis (MAF). MAF is an image analysis method that is independent of preprocessing as the method uses a near neighbor pixel to model the “shape” of the data. In the study by Tyler and co-workers, the MAF analysis provided the “best” result but could be 634



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eter is the g-value, which it is claimed provides an approximation to the energy required to form a particular mass fragment. This approach has been employed to separate peaks from pharmaceutical additives (codeine and, separately, bupivacaine) from a biocompatible polymer (polylactic acid). The peaks from the drug are separated from the polymer peaks using a threshold g-value (gsep).186 As with the MVA methods, the technique is not perfect and care must still be taken when using the G-SIMS approach. The chromatography approach works because different g-values produce different G-SIMS spectra. The formation of monomer fragments from a polymer requires more energy, as bonds must be broken in the sample, compared to the ejection of drug molecules and so the components can be isolated using different g-values. If simple G-SIMS was performed using a single g-value, then chemical information could be lost. This is observed in ref 184 where the SIMS spectrum of folic acid contains considerable contamination with PDMS, which is absent in the G-SIMS spectrum (generated with a g-value of 13); the virtual removal of PDMS is no bad thing, but it means that the G-SIMS spectrum is no longer capturing all the chemistry of the sample surface. A final method for species identification is the use of MS/MS, but it has been a very rarely implemented on SIMS instruments. Leggett and Vickerman reported the use of MS/MS on a triple quadrupole SIMS instrument for the study of fragment formation pathways from polymers under atomic ion bombardment.187 Many of the species observed in the SIMS spectra matched those generated by low energy (0−50 eV) collisional dissociation, although there were differences such as the m/z 91 species that dominates the polystyrene (PS) spectrum was not present in the MS/MS spectrum of the monomer ion.188 Interestingly this is one of the species that are rapidly removed from the PS spectrum using the G-SIMS approach,182 and these ions are formed at very high energy in the hot region of the impact area. More recently postsource decay has been used to identify MS/MS peaks from the spectra of lipid species on a conventional TOF-SIMS instrument, an approach that is potentially expandable to many SIMS laboratories. Also, as mentioned in the earlier section, ortho-TOF mass spectrometers with MS/MS capability have been retro-fitted with ion beam systems, and the J105 SIMS instrument has been designed to include MS/MS capability. Passarelli and Winograd have used SIMS with MS/MS to identify dominant lipid signals from a single Aplysia californica (a type of sea slug) neuron.189 Fletcher et al. have performed MS/MS on a number of metabolites to assess the capability of TOF-SIMS to perform metabolomics studies.86 As expected, MS/MS spectra of metabolite ions generated by SIMS produce similar fragments to those observed in MS/MS spectra in electrospray ionization experiments; hence, the existing metabolic MS/MS databases can be applied to contribute to the interpretation of TOF-SIMS spectra. Further, generally the SIMS spectra also contain all of the MS/MS fragments already making the MS/MS databases useful even without employing a specific collisional dissociation step, although on complex mixture samples selecting which peaks to use for a database search may be the greatest hurdle.

characterization, mass measurement, and/or mass spectral pattern recognition. As a technique that aspires to the analysis of increasingly complex systems, SIMS instruments should be able to carry out tandem mass analysis, but until recently none of the commercial molecular TOF-SIMS instruments offered this possibility. Perhaps this hints at the reason why SIMS has not been widely accepted in the organic materials chemistry and biology communities. Basically, SIMS emerged from a physics stable and the instrumentation and applications have been developed over the years in a rather self-contained community with a fundamental and physics slant to it. Although FABMS emerged from the Manchester SIMS group as the first MS technique to enable nonvolatile organic samples to be easily analyzed,192,193 and this arguably led to the rapid emergence of other MS methods for analyzing complex involatile organics, it is only very recently that SIMS has begun to appear in the general chemistry and biology based mass spectrometry conferences and associated communities. In contrast, FABMS, MALDI, and DESI were immediately embraced and exploited by “chemists” even if sometimes applications were somewhat premature in terms of the technique development! The preceding account of recent advances and the state of the art in MIMS and molecular SIMS demonstrates that these techniques have very considerable capabilities over/against other desorption mass spectrometries that suggest that far from SIMS being neglected as a means of investigating molecular chemistry in complex materials, in hypothesis driven materials research SIMS can provide uniquely valuable information that can stand alone or complement that derived from related techniques to yield the fuller information set to yield the insights required. It is clear that compared to other mass spectrometries, molecular characterization by SIMS has the following strengths: • Useful spatial resolution down to 500 nm as long as analysis is not limited by static analysis conditions, in which case the useful resolution is nearer 2 μm. Other than with argon cluster beams, this spatial resolution is easily attained using the ion beams available. Other desorption MS techniques struggle to get below 10 μm, although because they remove large amounts of material yields may be higher. • The high depth resolution during molecular depth profiling is a very considerable strength as a consequence of the emergence of polyatomic ion beams. It can be as good as 5−10 nm dependent on the initial smoothness of the sample. None of the related techniques are able to carry out this type of depth profiling. MALDI requires separate analysis of successive physically obtained slices. In MIMS where preservation of the chemical structure is not required, the sputtering mode again provides excellent depth resolution. The ability to depth profile with high depth resolution provides the 3D imaging capability. While 3D molecular SIMS imaging is in its infancy, the promise is evident and MIMS has provided some spectacular images with nanometer resolution. • On the imaging front, molecular SIMS imaging is able to rapidly switch from small area to large area analysis and produce detailed images at a relatively very fast rate compared to MS imaging by MALDI. • Chemical characterization is what we are about. The great strength of SIMS is that analysis is possible without the addition of any foreign additives. We have seen that stable

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WHITHER MOLECULAR SIMS? The history of static and molecular SIMS stretches over 40 years, a long time compared to MALDI and certainly DESI or the other recent ambient desorption mass spectrometries. Yet molecular SIMS is relatively under used and applied in chemistry and biological research and R&D compared to MALDI. If DESI has not caught up, it will very soon. This observation has puzzled one or two authors in recent years.190,191 In principle SIMS has the capabilities expected of mass spectrometry for molecular 635



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that have not been featured in detail, some of which have been surveyed very well in recent reviews. Hence, if the reader is interested in particular aspects of the field we particularly recommend the following: For a detailed review of lipid imaging with mass spectrometry, see Passarelli and Winograd in ref 59. For a detailed empirical model for molecular depth profiling see Cheng, Wucher, and Winograd in ref 194. For detailed descriptions of the application of multivariate methods to SIMS images and spectra: Tyler and Henderson in the second edition of “TOFSIMS Surface Analysis by Mass Spectrometry”, in press 2012. For a detailed review of cluster SIMS for polymer analysis see Mahoney in ref 87.

isotopes can be used very effectively in focused NanoSIMS or MIMS studies on known chemicals. Where these isotopes can be incorporated into the compounds of interest, high precision and sensitivity are possible with superb spatial resolution. There is almost no limit to the chemical types that can be followed. Static/molecular SIMS is limited in the molecular types that are accessible. Even with large polyatomic primary ions, generally the mass range of molecules that can be lifted off intact seems to be limited to 1500 to 2000 u. This is generally sufficient for nonbiological organic materials analysis except for high molecular weight polymers. In the biofield lipids, small molecule metabolites and small peptides are accessible, but large peptides, proteins, enzymes, etc. are in general inaccessible. • There is really no limit to the physical shape or composition of the materials to be analyzed using the TOFSIMS based instruments. Care has to be taken to factor in the topographical effect on relative peak intensities, but samples that can be analyzed vary from stents, contact lenses, through parts of insects to medical biopsies. • Ion yields are low, but there are some possible routes emerging that could enhance yields. Matrix enhancement and metal addition deliver some benefits in specific cases where their incorporation does not interfere with the chemistry being studied. Water delivered in some form or other to the sample surface seems like it could offer significant benefits in enhancing the ionization involving the transfer of protons. This could be a fruitful area of future research. The most challenging aspect of molecular SIMS analysis, as we saw in Molecular SIMS: The Challenges, is the low ion yields and the significant variation in ion yields between different chemicals combined with the degree to which the ion yields for particular compounds can vary depending on their chemical environment, i.e. the matrix effect. However, this problem is not unique to SIMS. It is equally as serious in MALDI and DESI although mentioned far less frequently. MALDI of course exploits the matrix effect to enhance the ionization of the analytes that are the particular focus of the study. We would suggest that focused hypothesis driven investigations are a requirement for all desorption MS based research. It is sometimes suggested that molecular SIMS can be used as a “discovery” technique. In other words, a sample whose composition is unknown can be analyzed to “discover” what it is comprised of. Although some information about composition may be provided from the peaks that are present, as we have emphasized several times the absence of peaks does not mean the corresponding compounds are absent. In a study focused on the behavior of particular compounds, background work is possible into ion yields and sensitivity to matrix effects, such that a valid hypothesis driven investigation can be carried out with some confidence that the results will be meaningful. The most successful application based studies described in this review, e.g., those by Brunelle and co-workers, had clear and directed focus and built on background work that gave confidence in the outcomes. As was very clear earlier, the MIMS studies from Lechene and co-workers are only possible on the basis of this research philosophy. However it is true to say that for all our research to be successful, it should be focused and hypothesis driven! The potential area for this review is very wide. We have been very selective in our survey to try to give an idea of the overall capabilities of molecular SIMS. Inevitably there are many areas

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Present Address †

Department of Chemistry and Molecular Biology, University of Gothenburg, SE 412 96, Gothenburg, Sweden. Notes

The authors declare no competing financial interest. Biographies John Fletcher is an assistant professor at Gothenburg University in Sweden where he is developing mass spectrometric imaging methods for the study of biological processes on a cellular scale. Prior to his move to Sweden, John worked with John Vickerman at the University of Manchester pioneering the use of polyatomic ion beams for 3D analysis of biological systems and on the development of new instrumentation for 3D SIMS imaging. John obtained his Ph.D. from UMIST in 2004 using TOF-SIMS and infrared spectroscopy for the study of tropospheric aerosol mimics. John Vickerman was born in Edinburgh, Scotland, in 1943. He holds a B.Sc. from the University of Edinburgh and Ph.D. and D.Sc. from the University of Bristol. He is a Research Professor in the School of Chemical Engineering and Analytical Science at the University of Manchester where he leads a research group in the Manchester Institute of Biotechnology devoted to developing and applying imaging mass spectrometry in biological research. Building on an early career focused on basic surface chemistry and catalysis, over the last 30 years his group has made a major contribution to developing SIMS as a molecular mass spectrometry with the analytical power to probe chemical complexity at a level that defeats other techniques. In 2009, the Theophilus Redwood award from the Royal Society of Chemistry recognized “his outstanding contribution to the development and application of secondary ion mass spectrometry techniques for surface analysis and 3D chemical imaging of organic and biological systems.” This was followed in 2012 with the award of Médaille Chevenard from the French Society for Metals and Materials for his contribution to the instrumental development of SIMS.

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ACKNOWLEDGMENTS The authors acknowledge the support of the UK Engineering and Physical Sciences Research Council (Grant EP/G0456 23/1) during much of the period when this review was being developed.

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