The statistics of ToF‐SIMS data revisited and introduction of the empirical Poisson correction

Generation of time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) data involves two overarching processes: secondary ion production and secondary ion detection. The interpretation of ToF‐SIMS data is facilitated if the intensities of the as‐measured mass spectra are proportional to the abundances of the species under investigation. While secondary ion yield is normally taken to be a linear process, ion detection is not owing to detector dead‐time effects. Consequently, methods have been devised that attempt to linearize, or correct, data that are affected by the dead time. In this article, we review the statistics of ToF‐SIMS data generation and confirm a report in the literature that abundance estimates from so‐called Poisson corrections are biased. We show that these corrections are only unbiased asymptotically and that a rigorous probabilistic analysis can quantitatively account for the observed bias. Two sources of bias are identified, one having a statistical basis and one due to the form of the correction equation at high ion detection rates. Based on insights gained from this analysis, we propose a new correction equation, the empirical Poisson correction, which largely eliminates the statistical bias. The performance of the proposed correction is illustrated by reanalyzing 14 experimentally measured datasets that suffer from varying levels of dead‐time effects. Copyright © 2016 John Wiley & Sons, Ltd.     

Comparison of a new dead‐time correction method and conventional models for SIMS analysis

Recently, secondary ion mass spectrometry (SIMS) has been used in the analysis of not only impurities but also matrix elements, thus requiring a wide dynamic range for SIMS analysis. However, SIMS detectors, which are mostly used in pulse counting systems, have difficulties with detector saturation. In this paper, we investigate whether a dead‐time model that was developed for X‐ray measurement is applicable for SIMS analysis. We then compare a new correction method with conventional correction methods for detector saturation in SIMS analysis. We report that the new method can better correct the intensity in regions of higher intensity than that achieved by conventional methods. Copyright © 2012 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.     

The accuracy and precision of the advanced Poisson dead‐time correction and its importance for multivariate analysis of high mass resolution ToF‐SIMS data

Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) data collected in single ion counting mode suffers from dead‐time effects that lead to potentially confusing artefacts when common multivariate analysis (MVA) methods are applied to the data. These artefacts can be eliminated by applying an advanced Poisson dead‐time correction that accounts for the signal intensity in the dead‐time window preceding each time channel. Because this correction is nonlinear, it changes the noise distribution in the data. In this work, the accuracy of this dead‐time correction and the noise characteristics of the corrected data have been analysed for spectra with small numbers of primary ion pulses. A simple but accurate equation for estimating the standard deviation in the advanced dead‐time corrected data has been developed. Based on these results, a scaling procedure to enable successful MVA of advanced dead‐time corrected ToF‐SIMS data has been developed. The improvements made possible by using the advanced dead‐time correction and our recommended scaling are presented for principal components analysis of a ToF‐SIMS image of aerosol particles on polytetrafluoroethylene. Recommendations are made for using the advanced dead time correction and scaling ToF‐SIMS data in order optimize the outcomes of MVA. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

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.     

ToF‐SIMS characterisation of methane‐ and hydrogen‐plasma‐modified graphite using principal component analysis

Time of flight secondary ion mass spectrometry (ToF‐SIMS) has been used to determine the extent of surface modification of highly ordered pyrolytic graphite (HOPG) samples that were exposed to radio‐frequency methane and hydrogen plasmas. The ToF‐SIMS measurements were examined with the multivariate method of principal component analysis (PCA), to maximise the amount of spectral information retained in the analysis. This revealed that the plasma (methane or hydrogen plasma) modified HOPG exhibited greater hydrogen content than the pristine HOPG. The hydrogen content trends observed from the ToF‐SIMS studies were also observed in elastic recoil detection analysis measurements. The application of the ToF‐SIMS PCA method also showed that small hydrocarbon fragments were sputtered from the hydrogen‐plasma‐treated sample, characteristic of the formation of a plasma‐damaged surface, whereas the methane‐plasma‐treated surface sputtered larger hydrocarbon fragments, which implies the growth of a polymer‐like coating. Scanning tunnelling microscopy measurements of the modified surfaces showed surface features that are attributable to either etching or film growth after exposure to the hydrogen or methane plasma. Copyright © 2009 John Wiley & Sons, Ltd.     

Image fusion combining SEM and ToF‐SIMS images

Image fusion allows for the combination of an image containing chemical information but low spatial resolution with a high‐spatial resolution image having little to no chemical information. The resulting hybrid image retains all the information from the chemically relevant original image, with improved spatial resolution allowing for visual inspection of the spatial correlations. In this research, images were obtained from two sample test grids: one of a copper electron microscope grid with a letter ‘A’ in the center (referred to below as the ‘A‐grid’), and the other a Tantalum and Silicon test grid from Cameca that had an inscribed letter ‘C’ (referred to below as the ‘Cameca grid’). These were obtained using scanning electron microscopy (SEM) and time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). Image fusion was implemented with the Munechika algorithm. The edge resolution of the resulting hybrid image was calculated compared with the edge resolution obtained for both the individual ToF‐SIMS and SEM images. The challenges of combining complimentary datasets from different instrumental analytical methods are discussed as well as the advantages of having a hybrid image. The distance across the edge for hybrid images of the A‐Grid and the Cameca grid were determined to be 21 µm and 8 µm, respectively. When these values were compared to the original ToF‐SIMS, SEM and optical microscopy measurements, the fused image had a spatial resolution nearly equal to that obtained in the SEM image for both samples. Copyright © 2014 John Wiley & Sons, Ltd.     

Simple statistically based alternatives to MAF for ToF‐SIMS spectral image analysis

The maximum autocorrelation factors technique (MAF) is becoming increasingly popular for the multivariate analysis of spectral images acquired with time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) instruments. In this article, we review the conditions under which the underlying chemical information can be separated from the large‐scale, non‐uniform noise characteristic of ToF‐SIMS data. Central to this pursuit is the ability to assess the covariance structure of the noise. Given a set of replicate images, the noise covariance matrix can be estimated in a straightforward way using standard statistical tools. Acquiring replicate images, however, is not always possible, and MAF solves a subtly different problem, namely, how to approximate the noise covariance matrix from a single image when replicates are not available. This distinction is important; the MAF approximation is not an unbiased statistical estimate of the noise covariance matrix, and it differs in a highly significant way from a true estimate for ToF‐SIMS data. Here, we draw attention to the fact that replicate measurements are made during the normal course of acquiring a ToF‐SIMS spectral image, rendering the MAF procedure unnecessary. Furthermore, in the common case that detector dead‐time effects permit no more than one ion of any specific species to be detected on a single primary ion shot, the noise covariance matrix can be estimated in a particularly simple way, which will be reported. Copyright © 2011 John Wiley & Sons, Ltd.     

Quantitative analysis of styrene‐pentafluorostyrene random copolymers by ToF‐SIMS and XPS

Poly(styrene) (PS), poly(2,3,4,5,6‐pentafluorostyrene) (5FPS) and their random copolymers were prepared by bulk radical polymerization. The spin‐cast polymer films of these polymers were analyzed using X‐ray photoelectron spectroscopy (XPS) and time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). The surface and bulk compositions of these copolymers were found to be same, implying that surface segregation did not occur. The detailed analysis of ToF‐SIMS spectra indicated that the ion fragmentation mechanism is similar for both PS and 5FPS. ToF‐SIMS quantitative analysis using absolute peak intensity showed that the SIMS intensities of positive styrene fragments, particularly C₇H₇⁺, in the copolymers are higher than the intensities expected from a linear combination of PS and 5FPS, while the SIMS intensities of positive pentafluorostyrene fragments are smaller than expected. These results indicated the presence of matrix effects in ion formation process. However, the quantitative approach using relative peak intensity showed that ion intensity ratios are linearly proportional to the copolymer mole ratio when the characteristic ions of PS and 5FPS are selected. This suggests that quantitative analysis is still possible in this copolymer system. Copyright © 2005 John Wiley & Sons, Ltd.     

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.     

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.     

Molecular identification of Asian lacquers from different trees using Py‐GC/MS and ToF‐SIMS

Traditional Asian lacquers are natural products with highly valued properties, including beauty, gloss, and durability. Pyrolysis‐gas chromatography/mass spectrometry is the technique of choice to study insoluble polymeric lacquer films. In the present study, pyrolysis‐gas chromatography/mass spectrometry results showed that the pyrolysis products of lacquer films were different for all of the studied trees, with urushiol derivatives detected in Toxicodendron vernicifluum from China, Japan, and Korea; laccol in Toxicodendron succedaneum from Vietnam; and thitsiol in Gluta usitata from Myanmar. Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) was also used to characterize the Asian lacquers, avoiding the time‐consuming and destructive processes of other techniques. The ToF‐SIMS spectra provided structural characterization of a series of urushiol, laccol, and thitsiol derivatives for T vernicifluum from China, Japan, and Korea; T succedaneum from Vietnam; and G usitata from Myanmar, respectively. To differentiate the ToF‐SIMS results for the different Asian lacquer films, principal component analysis was used because it can extract differences in the spectra and indicate what peaks are responsible for these differences. The results indicate that lacquer films from different lacquer trees can be very different. Therefore, ToF‐SIMS with principal component analysis is suitable for the characterization and differentiation of Asian lacquer films in cultural heritage applications.     

Peak‐fitting of high resolution ToF‐SIMS spectra: a preliminary study

Peak‐fitting has been performed on a series of peaks obtained by time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) analysis in order to assess whether information may be obtained from this procedure on the samples' characteristics. A variety of samples were examined including a range of treatments for aluminium leading to different surface roughnesses, polymer films with a range of polydispersities, molecular weight (MW) and thicknesses as well as aluminium samples with adsorbed adhesion promoters on the surface. Variation of peak‐fitting was assessed by varying the peak intensity, full width at half maximum (FWHM) and peak asymmetry. Although further studies are needed it is possible to say that the peak width increases with roughness whereas peak asymmetry seems to be related to oxide thickness. Polymer characteristics do not seem to influence the width whereas the peak asymmetry increases either versus MW or polydispersity. A possible assumption is that the peak asymmetry relates to the ion formation processes. Additional work with varying polymer films thickness indicates that both FWHM and peak asymmetry may be related to sample charging and this could be used for assessment of film thicknesses. Finally, peak‐fitting was used to obtain a more reliable peak area when peaks are too close in mass to use current methods. Copyright © 2009 John Wiley & Sons, Ltd.     

An investigation of hydrogen depth profiling using ToF‐SIMS

Hydrogen depth distributions in silicon, zinc oxide, and glass are of great interest in material research and industry. Time‐of‐flight SIMS has been used for hydrogen depth profiling for many years. However, some critical information, such as optimal instrumental settings and detection limits, is not easily available from previous publications. In this work, optimal instrumental settings and detection limits of hydrogen in silicon, zinc oxide, and common glass were investigated. The recommended experimental settings for hydrogen depth profiling using time‐of‐flight SIMS are: (i) keeping pressure in the analysis chamber as low as possible, (ii) using a cesium beam for sputtering and monitoring the H^– signal, (iii) employing monatomic ion analysis beams with the highest currents, and (iv) using interlace mode. In addition, monatomic secondary ions from a matrix are recommended as references to normalize the H^– signal. Detection limits of hydrogen are limited by the pressure of residual gases in the analysis chamber. The base pressure of the analysis chamber (with samples) is about 7 × 10^?10 mbar in this study, and the corresponding detection limits of hydrogen in silicon, zinc oxide, and common glass are 1.3 × 10¹⁸ atoms/cm³, 1.8 × 10¹⁸ atoms/cm³, and 5.6 × 10¹⁸ atoms/cm³, respectively. Copyright © 2011 John Wiley & Sons, Ltd.     

ToF‐SIMS for the characterization of hyperbranched aliphatic polyesters: probing their molecular weight on surfaces based on principal component analysis (PCA)

A series of 2,2‐bis(hydroxymethyl)propionic acid (Bis‐MPA) hyperbranched aliphatic polyesters with different molecular weights (generations) is analysed for the first time by time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). The main negative and positive low‐mass fragments are identified in the fingerprint part of the spectra (m/z < 400) and are principally assigned to fragmentation of the Bis‐MPA repeating units. In addition, it is shown that the fragmentation pattern is highly affected by the functional end‐groups. This is illustrated for a phthalic acid end‐capped hyperbranched polymer and for an acetonide‐terminated dendrimer analog. Also, typical fragments assigned to the ethoxylated pentaerythritol core molecule are detected. These ions show decreasing intensities with increasing molecular weight. This intensity dependency on the generation is used to calibrate the molecular weight of hyperbranched polyesters on the surface. To obtain quantitative information, a principal component analysis (PCA) multivariate statistical method is applied to the ToF‐SIMS data. The influence of different normalization procedures prior to PCA calculation is tested, e.g. normalization to the total intensity, to the intensities of ions assigned to the Bis‐MPA repeating unit or to intensities of fragments due to the core molecule. It is shown that only one principal component (PC1) is needed to describe most of the variance between the samples. In addition, PC1 takes into account the generation effect. However, different relationships between the PC1 scores and the hyperbranched mass average molecular weights are observed depending on the normalization procedure used. Normalization of data set ion intensities by ion intensities from the core molecule allows linearization of the SIMS intensities versus the molecular weight and allows the hyperbranched polymers to be discriminated up to the highest generations. In addition, PCA applied to ToF‐SIMS data provides an extended interpretation of the spectra leading to further identification of the correlated mass peaks, such as those of the Bis‐MPA repeating unit (terminal, dendritic and linear) and those of the core molecule. Finally, the work presented demonstrates the extreme potential of the static ToF‐SIMS and PCA techniques in the analysis of dendritic molecules on solid surfaces. Copyright © 2003 John Wiley & Sons, Ltd.     

Cationisation of oligomeric alkylethoxylate surfactants in ToF‐SIMS analysis

Variable cationisation of the alkylethoxylate surfactant Synperonic A7^? has been observed during a ToF‐SIMS study of its interaction with a leaf surface, either by itself or as a component in a simple herbicide formulation. Depending on the conditions, cationisation predominantly by K⁺, Na⁺ and H⁺ was found. By deliberate surfactant solution doping with alkali bromides, the cationisation channel could be manipulated. Comparison of the apparent molecular weight dependence, derived from the ToF‐SIMS spectra, with that measured by HPLC‐MS, showed a further dependence on the cationising species, with Na⁺ giving the best match. An understanding of these effects is critical to the correct interpretation of the spectra from this surfactant. Copyright © 2007 John Wiley & Sons, Ltd.     

Development of an automated in situ fracture stage for a ToF‐SIMS system

The design philosophy and implementation of an ultra high vacuum (UHV), PC controlled, automated in situ fracture stage for a surface analysis system is described. ToF‐SIMS spectra are shown to illustrate the improvement in spectral quality obtained from micro‐compact tension (CT) tests of polymer matrix fracture surfaces produced using the fracture stage in UHV compared to those obtained from a sample tested at air. This system is flexible in that by changing the capacity of the load cell it is possible to reduce or increase maximum loads as the specimen type and material demands. The stage has been designed with instrumental flexibility in mind, utilising commercial SEM‐stub type sample mounts, and can thus be used for AES/SAM and XPS investigations, as well as ToF‐SIMS analysis, in the authors' laboratory. Copyright © 2008 John Wiley & Sons, Ltd.     

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.     

XPS and ToF‐SIMS characterization of a Finemet surface: effect of cooling

The surface composition of amorphous Finemet, Fe₇₃Si_15.8B_7.2Cu₁Nb₃, was studied by X‐ray photoelectron spectroscopy (XPS) and time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). The as‐received sample in the original state and after Ar⁺ sputter‐cleaning was analyzed at room temperature as well as after cooling to ? 155 °C. In the cooled state, the surface oxide layer composed of oxides of the alloy constituents was found to become enriched with elemental iron and depleted of elemental silicon, boron, oxygen and carbon as compared to the state at room temperature. Interaction of residual water vapor and hydrogen with the complex oxide layer occurring at low temperatures is believed to be responsible for the enhanced formation of surface hydroxides of the alloy constituents. The processes resulting in the observed redistribution of the elements on the surface of Finemet at low temperatures are discussed. Copyright © 2006 John Wiley & Sons, Ltd.