Adsorption of genetically engineered proteins studied by time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS). Part B: hierarchical cluster analysis (HCA)

In Part A, we adopted principal component analysis (PCA) for the analysis of TOF‐SIMS data to assess the binding specificity of GBP‐1 to metallic Au, Ag and Pd. Within a given set of data, PCA aids in the interpretation of the TOF‐SIMS spectra by capitalizing on the differences from one spectrum to another. In Part B, we introduce another multivariate statistical method called ‘hierarchical cluster analysis (HCA)’, where visualization of the similarity and difference in data is readily observed, from which a variety of adsorption conditions of GBP‐1 were characterized. Copyright © 2007 John Wiley & Sons, Ltd.     

Retracted: Mass analysis by Ar‐GCIB‐dynamic SIMS for organic materials

Generally, dynamic secondary ion mass spectrometry (SIMS) has been mainly used as one of the most powerful tools for inorganic mass analysis. On the other hand, an Ar gas cluster ion beam (GCIB) has been developed and spread as a processing tool for surface flattening and also a projectile for time‐of‐flight (ToF) SIMS. In this study, we newly introduced an Ar‐GCIB as a primary ion source to a commercially available dynamic SIMS apparatus, and investigated mass spectra of amino acid films (such as Arginine and Glycine) and polymer films (Polyethylene: PE and Polypropylene: PP) as organic model samples. As a result, each characteristic fragment peak indicating the original molecular organic structure was observed in the acquired mass spectra. In addition, their own molecular ions of the amino acids were also clearly observed. Mass spectra of PE/PP blended‐polymer films acquired using Ar‐GCIB‐dynamic SIMS could be identified between pure PE and PE:PP = 1:3 mixture by applying principal component analysis (PCA).     

Identifying and minimizing buffer interferences in ToF‐SIMS analyses of lignocellulose

Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) has demonstrated applicability to the analysis of lignocellulosic samples including pulp, paper, plants, and wood. One such application is to use ToF‐SIMS as a tool for detecting the activity of enzymes applied to degrade or modify plant biomass. The use of buffers for pH control of these enzymatic reactions can pose problems due to the nature of the ToF‐SIMS measurement. Specifically, inorganic species (e.g. salts) from buffer components could introduce several concerns for quantitative or semi‐quantitative ToF‐SIMS analysis. First, salts can produce additional peaks in the mass spectra, which may overlap with lignocellulose peaks of interest (mass interference). Second, salts can alter the chemical environment, or ‘matrix’, altering the ionization probability of lignocellulose‐related secondary ions during the sputtering mechanism of the ToF‐SIMS measurement (matrix effects). Third, salts may physically coat the lignocellulose surface, decreasing the signal from the lignocellulose, causing poor signal‐to‐noise in the analysis. The current work presents a simple approach for identifying interferences due to buffers, using both principal component analysis (PCA) and previously established lignocellulose‐relevant peak ratios. Furthermore, a simple acetic acid rinsing protocol is compared to distilled water rinsing and is evaluated and for its effectiveness in removing buffer‐related salts. The data shows that briefly rinsing lignocellulose samples in dilute acetic acid can be effective in restoring the validity of lignocellulose composition interpretations using ToF‐SIMS. Copyright © 2014 John Wiley & Sons, Ltd.     

ToF‐SIMS analysis of a polymer microarray composed of poly(meth)acrylates with C6 derivative pendant groups

Surface analysis plays a key role in understanding the function of materials, particularly in biological environments. Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) provides highly surface sensitive chemical information that can readily be acquired over large areas and has, thus, become an important surface analysis tool. However, the information‐rich nature of ToF‐SIMS complicates the interpretation and comparison of spectra, particularly in cases where multicomponent samples are being assessed. In this study, a method is presented to assess the chemical variance across 16 poly(meth)acrylates. Materials are selected to contain C₆ pendant groups, and ten replicates of each are printed as a polymer microarray. SIMS spectra are acquired for each material with the most intense and unique ions assessed for each material to identify the predominant and distinctive fragmentation pathways within the materials studied. Differentiating acrylate/methacrylate pairs is readily achieved using secondary ions derived from both the polymer backbone and pendant groups. Principal component analysis (PCA) is performed on the SIMS spectra of the 16 polymers, whereby the resulting principal components are able to distinguish phenyl from benzyl groups, mono‐functional from multi‐functional monomers and acrylates from methacrylates. The principal components are applied to copolymer series to assess the predictive capabilities of the PCA. Beyond being able to predict the copolymer ratio, in some cases, the SIMS analysis is able to provide insight into the molecular sequence of a copolymer. The insight gained in this study will be beneficial for developing structure–function relationships based upon ToF‐SIMS data of polymer libraries. © 2016 The Authors Surface and Interface Analysis Published by John Wiley & Sons Ltd.     

Differentiating calcium carbonate polymorphs by surface analysis techniques—an XPS and TOF‐SIMS study

Calcium carbonate has evoked interest owing to its use as a biomaterial, and for its potential in biomineralization. Three polymorphs of calcium carbonate, i.e. calcite, aragonite, and vaterite were synthesized. Three conventional bulk analysis techniques, Fourier transform infrared (FTIR), X‐ray diffraction (XRD), and SEM, were used to confirm the crystal phase of each polymorphic calcium carbonate. Two surface analysis techniques, X‐ray photoelectron spectroscopy (XPS) and time‐of‐flight secondary ion mass spectroscopy (TOF‐SIMS), were used to differentiate the surfaces of these three polymorphs of calcium carbonate. XPS results clearly demonstrate that the surfaces of these three polymorphs are different as seen in the Ca(2p) and O(1s) core‐level spectra. The different atomic arrangement in the crystal lattice, which provides for a different chemical environment, can explain this surface difference. Principal component analysis (PCA) was used to analyze the TOF‐SIMS data. Three polymorphs of calcium carbonate cluster into three different groups by PCA scores. This suggests that surface analysis techniques are as powerful as conventional bulk analysis to discriminate calcium carbonate polymorphs. Copyright © 2008 John Wiley & Sons, Ltd.     

Static TOF‐SIMS. A VAMAS interlaboratory study. Part II — accuracy of the mass scale and G‐SIMS compatibility

An interlaboratory study involving 32 time‐of‐flight static SIMS instruments from 13 countries has been conducted. In Part I of the analysis of data, we showed that 84% of instruments have excellent repeatabilities of better than 1.9% and that a relative instrument spectral response (RISR) can be used to evaluate variations between different generic types of instrument. Use of the RISR improves comparability between instruments by a factor of 33. Here, in Part II, we study the accuracy of the mass scale calibration in TOF‐SIMS and evaluate instrument compatibility with G‐SIMS. We show that the accuracy of calibration of the mass scale is much poorer than generally expected (?60 ppm for peaks <200 u and ?150 ppm for a large molecular peak at 647 u). This is a major issue for analysts. Elsewhere, we have developed a detailed study of the factors affecting the mass calibration and have developed a generic protocol that improves accuracy by a factor of 5. Here, this framework of understanding is used to interpret the results presented. Furthermore, we show that eight out of the ten participants submitting data for G‐SIMS could use operating conditions that generated G‐SIMS spectra of the PC reference material. This demonstrates that G‐SIMS may be conducted with a wide variety of instrument designs. © Crown Copyright 2007. Reproduced by permission of the Controller of HMSO. Published by John Wiley & Sons, Ltd.     

Denoising and multivariate analysis of time‐of‐flight SIMS images

Time‐of‐flight SIMS (ToF‐SIMS) imaging offers a modality for simultaneously visualizing the spatial distribution of different surface species. However, the utility of ToF‐SIMS datasets may be limited by their large size, degraded mass resolution and low ion counts per pixel. Through denoising and multivariate image analysis, regions of similar chemistries may be differentiated more readily in ToF‐SIMS image data. Three established denoising algorithms—down‐binning, boxcar and wavelet filtering—were applied to ToF‐SIMS images of different surface geometries and chemistries. The effect of these filters on the performance of principal component analysis (PCA) was evaluated in terms of the capture of important chemical image features in the principal component score images, the quality of the principal component score images and the ability of the principal components to explain the chemistries responsible for the image contrast. All filtering methods were found to improve the performance of PCA for all image datasets studied by improving capture of image features and producing principal component score images of higher quality than the unfiltered ion images. The loadings for filtered and unfiltered PCA models described the regions of chemical contrast by identifying peaks defining the regions of different surface chemistry. Down‐binning the images to increase pixel size and signal was the most effective technique to improve PCA performance. Copyright © 2003 John Wiley & Sons, Ltd.     

Evaluation and comparison of layered titanate nanosheets using TOF‐SIMS and g‐ogram analysis

The evaluation of nanostructure is important to develop the highly controlled nanomaterials. In this study, two kinds of layered titanate nanosheets, which were produced by using hexylamine and laurylamine, respectively, as surfactants were investigated by Gentle Secondary Ion Mass Spectrometry Gentle‐SIMS (G‐SIMS) and g‐ogram, which is the latest Time‐of‐Flight Secondary Ion Mass Spectrometry (TOF‐SIMS) data analysis method for detecting more intact ions and obtaining the information on original chemical structures of samples precisely from complicated TOF‐SIMS spectra. As a result, molecular related ions of the surfactants were detected from each sample, and the structural information of samples was obtained. From both samples, surfactant molecular ions connected with hydrocarbon were detected as more intact ions rather than molecular ions of themselves. It was suggested that hydrophobic domains of their lamellar mesostructure are formed robustly by more than two surfactant molecules connected with each other linearly. After all, important information on the chemical structure of the layered titanate nanosheets, which would be difficult to be found by using typical structural analysis methods such as X‐ray diffraction and transmission electron microscopy, were obtained using G‐SIMS and g‐ogram. Therefore, it was shown that g‐ogram and G‐SIMS are helpful to evaluate the nanostructured materials. And it was also shown that g‐ogram is applicable to organic–inorganic materials which contain long hydrocarbon structures. Copyright © 2015 John Wiley & Sons, Ltd.     

Time‐of‐flight secondary ion mass spectrometry and gas chromatography–mass spectrometry studies of alkanethiol self‐assembled monolayers on nanoporous gold surfaces

The dimerization of alkanethiol mixtures (hexanethiol, octanethiol, and dodecanethiol) to form self‐assembled monolayers (SAMs) from headspace on nanoporous gold surfaces was studied for the first time using gas chromatography (GC/MS) and time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS). The nanoporous gold surfaces were obtained by an acidic etching of a 585‐gold alloy. Field emission scanning electron microscopy (FE‐SEM) was utilized to study the change of the surface geometry and porosity of the gold surfaces before and after etching. Alkanethiols were deposited from the vapor phase above the thiol solutions (headspace) on nanoporous gold plates and nanoporous gold solid‐phase vmicroextraction (SPME) fibers. The nanoporous gold substrates were analyzed by TOF‐SIMS and GC/MS, respectively. The TOF‐SIMS spectra exhibited various gold–sulfur ion clusters and specific peaks related to the adsorption of thiols such as deprotonated monomers, thiolate–Au, dimers (e.g., dialkyl sulfides–Au and dialkyl disulfides–Au). The GC/MS analysis of headspace extractions of alkanethiol mixtures by nanoporous gold SPME fibers showed a high extraction efficiency of alkanethiol, dialkyl sulfide, and dialkyl disulfide when compared with the commercial SPME fibers (DVB‐CAR‐PDMS and CAR‐PDMS). Different GC/MS optimization factors were studied including the extraction time and desorption temperature.     

Correlative mass spectrometry imaging,applying time‐of‐flight secondary ion mass spectrometry and atmospheric pressure matrix‐assisted laser desorption/ionization to a single tissue section

Rationale

Mass spectrometry imaging (MSI) is a powerful tool for mapping the surface of a sample. Time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS) and atmospheric pressure matrix‐assisted laser desorption/ionization (AP‐MALDI) offer complementary capabilities. Here, we present a workflow to apply both techniques to a single tissue section and combine the resulting data for the example of human colon cancer tissue.

Methods

Following cryo‐sectioning, images were acquired using the high spatial resolution (1 μm pixel size) provided by TOF‐SIMS. The same section was then coated with a para‐nitroaniline matrix and images were acquired using AP‐MALDI coupled to an Orbitrap mass spectrometer, offering high mass resolution, high mass accuracy and tandem mass spectrometry (MS/MS) capabilities. Datasets provided by both mass spectrometers were converted into the open and vendor‐independent imzML file format and processed with the open‐source software MSiReader.

Results

The TOF‐SIMS and AP‐MALDI mass spectra show strong signals of fatty acids, cholesterol, phosphatidylcholine and sphingomyelin. We showed a high correlation between the fatty acid ions detected with TOF‐SIMS in negative ion mode and the phosphatidylcholine ions detected with AP‐MALDI in positive ion mode using a similar setting for visualization. Histological staining on the same section allowed the identification of the anatomical structures and their correlation with the ion images.

Conclusions

This multimodal approach using two MSI platforms shows an excellent complementarity for the localization and identification of lipids. The spatial resolution of both systems is at or close to cellular dimensions, and thus spatial correlation can only be obtained if the same tissue section is analyzed sequentially. Data processing based on imzML allows a real correlation of the imaging datasets provided by these two technologies and opens the way for a more complete molecular view of the anatomical structures of biological tissues.

     

Hidden information in principal component analysis of ToF‐SIMS data: On the use of correlation loadings for the identification of significant signals and structure elucidation

In this paper, an improved approach to interpret results of principal component analysis (PCA) of time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) spectra is presented. Signals are typically observed in different intensity ranges in a single ToF‐SIMS spectrum due to different sensitivity factors and surface concentrations. This can complicate the PCA interpretation, because loadings are reported to be strongly affected by these intensity changes. In contrast, it is shown here that correlation loadings are unaffected by these differences. In particular, correlation loadings were successfully used to identify signals with relatively low intensity but high significance. These signals may be overlooked when only loadings are used. This is particularly true in failure analysis, where ToF‐SIMS is used to screen for initially unknown signals that may be relevant for the characteristics/failure of a product. As a model study, the concept was applied to investigate ageing of Li‐ion batteries by ToF‐SIMS. In this data set, the significance of impurities that affect the quality of Li‐ion batteries was identified only by correlation loadings, whereas the loadings were found to overestimate the influence of other matrix signals. In addition, correlation loadings aid in the chemical identification and helped to successfully assign unknown peaks.     

High‐resolution peak analysis in TOF SIMS data

High mass resolution time‐of‐flight secondary ion mass spectrometry (TOF SIMS) can provide a wealth of chemical information about a sample, but the analysis of such data is complicated by detector dead‐time effects that lead to systematic shifts in peak shapes, positions, and intensities. We introduce a new maximum‐likelihood analysis that incorporates the detector behavior in the likelihood function, such that a parametric spectrum model can be fit directly to as‐measured data. In numerical testing, this approach is shown to be the most precise and lowest‐bias option when compared with both weighted and unweighted least‐squares fitting of data corrected for dead‐time effects. Unweighted least‐squares analysis is the next best, while weighted least‐squares suffers from significant bias when the number of pulses used is small. We also provide best‐case estimates of the achievable precision in fitting TOF SIMS peak positions and intensities and investigate the biases introduced by ignoring background intensity and by fitting to just the intense part of a peak. We apply the maximum‐likelihood method to fit two experimental data sets: a positive‐ion spectrum from a multilayer MoS₂ sample and a positive‐ion spectrum from a TiZrNi bulk metallic glass sample. The precision of extracted isotope masses and relative abundances obtained is close to the best‐case predictions from the numerical simulations despite the use of inexact peak shape functions and other approximations. Implications for instrument calibration, incorporation of prior information about the sample, and extension of this approach to the analysis of imaging data are also discussed.     

Multivariate analysis of ToF‐SIMS data using mass segmented peak lists

Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) provides detailed molecular insight into the surface chemistry of a diverse range of material types. Extracting useful and specific information from the mass spectra and reducing the dimensionality of very large datasets are a challenge that has not been fully resolved. Multivariate analysis has been widely deployed to assist in the interpretation of ToF‐SIMS data. Principal component analysis is a popular approach that requires the generation of peak lists for every spectrum. Peak list sizes and the resulting data matrices are growing, complicating manual peak selection and analysis. Here we report the generation of very large ToF‐SIMS peak lists using up‐binning, the mass segmentation of spectral data in the range 0 to 300 m/z in 0.01 m/z intervals. Time‐of‐flight secondary ion mass spectrometry data acquired from a set of 4 standard polymers (polyethylene terephthalate, polytetrafluoroethylene, poly(methyl methacrylate), and low‐density polyethylene) are used to demonstrate the efficacy of this approach. The polymer types are discriminated to a moderate extent by principal component analysis but are easily skewed with saturated species or contaminants present in ToF‐SIMS data. Artificial neural networks, in the form of self‐organising maps, are introduced and provide a non‐linear approach to classifying data and focussing on similarities between samples. The classification outcome achieved is excellent for different polymer types and for spectra from a single polymer type generated by using different primary ions. This method offers great promise for the investigation of more complex systems including polymer classes and blends and mixtures of biological materials.     

Extraction of hidden information of ToF‐SIMS data using different multivariate analyses

Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) is a powerful tool for determining surface information of complex systems such as polymers and biological materials. However, the interpretation of ToF‐SIMS raw data is often difficult. Multivariate analysis has become effective methods for the interpretation of ToF‐SIMS data. Some of multivariate analysis methods such as principal component analysis and multivariate curve resolution are useful for simplifying ToF‐SIMS data consisting of many components to that explained by a smaller number of components. In this study, the ToF‐SIMS data of four layers of three polymers was analyzed using these analysis methods. The information acquired by using each method was compared in terms of the spatial distribution of the polymers and identification. Moreover, in order to investigate the influence of surface contamination, the ToF‐SIMS data before and after Ar cluster ion beam sputtering was compared. As a result, materials in the sample of multiple components, including unknown contaminants, were distinguished. Copyright © 2014 John Wiley & Sons, Ltd.     

Differentiating diterpene resin acids using ToF‐SIMS and principal component analysis: new tools for assessing the geochemistry of amber

Amber is a polymerized plant resin having remarkable preservation potential in the geological record. Numerous analytical techniques have been applied to the study of amber organic chemistry in order to extract paleobotanical information. However, only exploratory work has been conducted using time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS), despite its immense potential due to the high mass resolution and range that can be analyzed concurrently. Detailed assessments of ion fragmentation patterns are prerequisite, given that amber is comprised of a challenging range of terpenoids, carboxylic acids, alcohols, and associated esters. In recent work, we demonstrated the applicability and efficiency of ToF‐SIMS as a tool to investigate amber chemical composition. However, only two diterpene resin acid standards were considered in this preliminary study, namely abietic acid and communic acid. We now extend this work by documenting the ToF‐SIMS spectra of ten additional diterpene resin acids and ask whether ToF‐SIMS analysis can distinguish subtle differences within a larger set of diterpenoids. Both positive and negative ToF‐SIMS spectra were produced, although negative polarity appears particularly promising for differentiating diterpene resin acids. Principal component analysis (PCA) was used to distill the data and verified that purified diterpenes have distinct ToF‐SIMS spectra that can be applied to amber chemotaxonomy as well as to the analysis of modern resins of known botanical origin. While this work is pertinent to the study of the composition and histories of ambers, the mass spectra of the 12 diterpene standards could prove valuable to any system where diterpenoid chemistry plays a role. Copyright © 2014 John Wiley & Sons, Ltd.     

Cluster TOF‐SIMS imaging of human skin remains: analysis of a South‐Andean mummy sample

A skin sample from a South‐Andean mummy dating back from the XI^th century was analyzed using time‐of‐flight secondary ion mass spectrometry imaging using cluster primary ion beams (cluster‐TOF‐SIMS). For the first time on a mummy, skin dermis and epidermis could be chemically differentiated using mass spectrometry imaging. Differences in amino‐acid composition between keratin and collagen, the two major proteins of skin tissue, could indeed be exploited. A surprising lipid composition of hypodermis was also revealed and seems to result from fatty acids damage by bacteria. Using cluster‐TOF‐SIMS imaging skills, traces of bio‐mineralization could be identified at the micrometer scale, especially formation of calcium phosphate at the skin surface. Mineral deposits at the surface were characterized using both scanning electron microscopy (SEM) in combination with energy‐dispersive X‐ray spectroscopy and mass spectrometry imaging. The stratigraphy of such a sample was revealed for the first time using this technique. More precise molecular maps were also recorded at higher spatial resolution, below 1 µm. This was achieved using a non‐bunched mode of the primary ion source, while keeping intact the mass resolution thanks to a delayed extraction of the secondary ions. Details from biological structure as can be seen on SEM images are observable on chemical maps at this sub‐micrometer scale. Thus, this work illustrates the interesting possibilities of chemical imaging by cluster‐TOF‐SIMS concerning ancient biological tissues. Copyright © 2012 John Wiley & Sons, Ltd.     

Multivariate statistical analysis of non‐mass‐selected ToF‐SIMS data

Cluster LMIGs are now regarded as the standard primary ion guns on time‐of‐flight secondary ion mass spectrometers (ToF‐SIMS). The ToF‐SIMS analyst typically selects a bombarding species (cluster size and charge) to be used for material analysis. Using standard data collection protocols where the analyst uses only a single primary bombarding species, only a fraction of the ion‐beam current generated by the LMIG is used. In this work, we demonstrate for the first time that it is possible to perform ToF‐SIMS analysis when all of the primary ion intensity (clusters) are used; we refer to this new data analysis mode as non‐mass‐selected (NMS) analysis. Since each of the bombarding species has a different mass‐to‐charge ratio, they strike the sample at different times, and as a result, each of the bombarding species generates a spectrum. The resulting NMS ToF‐SIMS spectrum contains contributions from each of the bombarding species that are shifted in time. NMS spectra are incredibly complicated and would be difficult, if not impossible, to analyze using univariate methodology. We will demonstrate that automated multivariate statistical analysis (MVSA) tools are capable of rapidly converting the complicated NMS data sets into a handful of chemical components (represented by both spectra and images) that are easier to interpret since each component spectrum represents a unique and simpler chemistry. Copyright © 2008 John Wiley & Sons, Ltd.     

Detecting changes in arabidopsis cell wall composition using time‐of‐flight secondary ion mass spectrometry

Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) was previously used to characterize lignocellulosic materials, including woody biomass. ToF‐SIMS can acquire both rapid spectral and spatial information about a sample's surface composition. In the present study, ToF‐SIMS was used to characterize the cell walls of stem tissue from the plant model organism, Arabidopsis thaliana. Using principal component analyses, ToF‐SIMS spectra from A. thaliana wild‐type (Col‐0), cellulose mutant (irx3), and lignin mutant (fah1) stem tissues were distinguished using ToF‐SIMS peaks annotated for wood‐derived lignocellulose, where spectra from the irx3 and fah1 were characterized by comparatively low polysaccharide and syringyl lignin content, respectively. Spatial analyses using ToF‐SIMS imaging furthermore differentiated interfascicular fiber and xylem vessels based on differences in the lignin content of corresponding cell walls. These new data support the applicability of ToF‐SIMS peak annotations based on woody biomass for herbaceous plants, including model plant systems like arabidopsis. Copyright © 2015 John Wiley & Sons, Ltd.     

Surface studies of halloysite nanotubes by XPS and ToF‐SIMS

This report provides detailed experimental results of thermal and surface characterization on untreated and surface‐treated halloysite nanotubes (HNTs) obtained from two geographic areas. Surface characterization techniques, including XPS and time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) were used. ToF‐SIMS surface analysis experiments were performed with both atomic and cluster ion beams. Higher ion yields and more high‐mass ions were obtained with the cluster ion beams. Static ToF‐SIMS spectra were analyzed with principal component analysis (PCA). Morphological diversities were observed in the samples although they mainly contained tubular structures. Thermogravimetric data indicated that aqueous hydrogen peroxide solution could remove inorganic salt impurities, such as alkali metal salts. The amount of grafting of benzalkonium chloride of HNT surface was determined by thermogravimetic analysis. PCA of ToF‐SIMS spectra could distinguish the samples mined from different geographical locations as well as among surface‐treated and untreated samples. Copyright © 2010 John Wiley & Sons, Ltd.     

Reduction of matrix effects in TOF‐SIMS analysis by metal‐assisted SIMS (MetA‐SIMS)

We investigated reduction of the matrix effect in time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS) analysis by the deposition of a small amount of metal on the sample surfaces (metal‐assisted SIMS or MetA‐SIMS). The metal used was silver, and the substrates used were silicon wafers as electroconductive substrates and polypropylene (PP) plates as nonelectroconductive substrates. Irganox 1010 and silicone oil on these substrates were analyzed by TOF‐SIMS before and after silver deposition. Before silver deposition, the secondary ion yields from the substances on the silicon wafer and PP plate were quite different due to the matrix effect from each substrate. After silver deposition, however, both ion yields were enhanced, particularly the sample on the PP plate, and little difference was seen between the two substrates. It was therefore found that the deposition of a small amount of metal on the sample surface is useful for reduction of the matrix effect. By reducing the matrix effect using this technique, it is possible to evaluate from the ion intensities the order of magnitude of the quantities of organic materials on different substrates. In addition, this reduction technique has clear utility for the imaging of organic materials on nonuniform substrates such as metals and polymers. MetA‐SIMS is thus a useful analysis tool for solving problems with real‐world samples. Copyright © 2005 John Wiley & Sons, Ltd.