Static TOF‐SIMS — a VAMAS interlaboratory study. Part I. Repeatability and reproducibility of spectra

An interlaboratory study involving 32 Time‐of‐Flight Static SIMS instruments from 12 countries has been conducted. Analysts were supplied, by NPL, with a protocol for analysis together with three reference materials; a thin layer of polycarbonate (PC) on a silicon wafer, a thin layer of polystyrene (PS) oligomers on etched silver and poly(tetrafluoroethylene) (PTFE). The study involved static SIMS analysis of each reference material for both positive and negative polarity secondary ions. The option to test instrument suitability for G‐SIMS was also provided. The results of this study show that over 84% of instruments have excellent repeatabilities of better than 1.9%. Repeatabilities can be as good as 0.4%. A relative instrument spectral response (RISR) is calculated for each instrument for each reference material and ion polarity. The RISR is used to evaluate variations in spectral response between different generic types of SIMS instruments. Use of the RISR allows the identification of contamination, charge stabilisation problems and incorrectly functioning ion detectors. The high quality of the data presented here allows the RISR to reveal differences in individual operation of each instrument such as the use of apertures to remove metastables from the spectra and the use of different post‐acceleration voltages for ion detection. Spectral reproducibility can be measured, here, by the equivalence of RISRs between materials and ion polarities. It is found that reproducibilities are on average 10% but can be as good as 4% for the best instruments. This figure shows the consistency between instruments in measuring spectra from different samples. This study sets out the basic framework to develop static secondary ion mass spectrometry (SSIMS) as a reliable measurement method. © Crown Copyright 2005. Reproduced with the permission of Her Majestry's Stationery Office. Published by John Wiley & Sons, Ltd.     

A comparison of the static SIMS and G‐SIMS spectra of biodegradable homo‐polyesters

Static SIMS (SSIMS) is a surface analytical technique capable of providing molecular chemical information from solids. A major barrier to the wider take‐up of the technique is the complexity associated with the interpretation of SSIMS spectra. Quality of the interpretation depends on the expertise of analysts and making references to the limited mass spectral libraries. For many materials, there are no SSIMS library spectra. A new library‐independent method, G‐SIMS, is capable of facilitating the interpretation of SSIMS data. G‐SIMS spectra contain parent fragments, which are formed without substantial degradation or rearrangements, and highlight molecular fragments, which are directly related to the surface. In our study, G‐SIMS has been tested on medically relevant biodegradable polyester series, including poly (glycolic acid) (PGA), poly‐l‐(lactic acid) (PLA), poly‐β‐(hydroxybutyrate) (PHB) and poly‐ε‐(caprolactone) (PCL). The polyester series chosen here have closely related structures, which allow us to explore the capabilities of G‐SIMS. The G‐SIMS spectra have facilitated the identification of different polyesters by exhibiting mainly characteristic ions, representative of the polymers' molecular structures. The results also indicated that for the chosen polyester series, the larger the repeating monomer structures, the smaller the maximum number of repeat units were seen in the G‐SIMS spectra. The G‐SIMS spectra for the homologous polyester series have provided an insight into the fragmentation mechanisms as a function of repeating monomer molecular weights and structures. Copyright © 2008 John Wiley & Sons, Ltd.     

TOF-SIMS: Accurate mass scale calibration

A study is presented of the factors affecting the calibration of the mass scale in time-of-flight secondary ion mass spectrometry (TOF-SIMS). At the present time, TOF-SIMS analysts using local calibration procedures achieve a rather poor relative mass accuracy of only 150 ppm for large molecules (647 u) whereas for smaller fragments of <200 u this figure only improves to 60 ppm. The instrumental stability is 1 ppm and better than 10 ppm is necessary for unique identification of species. The above experimental uncertainty can lead to unnecessary confusion where peaks are wrongly identified or peaks are ambiguously assigned. Here we study, in detail, the instrumental parameters of a popular single stage reflection TOF-SIMS instrument with ion trajectory calculations using SIMION. The effect of the ion kinetic energy, emission angle, and other instrumental operating parameters on the measured peak position are determined. This shows clearly why molecular and atomic ions have different relative peak positions and the need for an aperture to restrict ions at large emission angles. These data provide the basis for a coherent procedure for optimizing the settings for accurate mass calibration and rules by which calibrations for inorganics and organics may be incorporated. This leads to a new generic set of ions for mass calibration that improves the mass accuracy in our interlaboratory study by a factor of 5. A calibration protocol is developed, which gives a relative mass accuracy of better than 10 ppm for masses up to 140 u. The effects of extrapolation beyond the calibration range are discussed and a recommended procedure is given to ensure that accurate mass is achieved within a selectable uncertainty for large molecules. Additionally, we can alternatively operate our instrument in a regime with good energy discrimination (i.e., poor energy compensation) to study the fragmented energies of molecules. This leads to data that support previous concepts developed in G-SIMS.     

Static SIMS–VAMAS interlaboratory study for intensity repeatability,mass scale accuracy and relative quantification

An interlaboratory study involving 19 time‐of‐flight static secondary ion mass spectrometer (TOF‐SSIMS) instruments from 12 countries has been conducted. Analysts were supplied, by the National Physical Laboratory, with a protocol for analysis together with three reference materials: poly(tetrafluoroethylene), a thin layer of polycarbonate on a silicon wafer and a patterned sample with different amounts of Irganox 1010 in each of four quadrants on a silicon wafer. The objectives of the study are (i) to determine the repeatability and constancy of the relative intensity scale achievable using the draft ISO standard (DIS 23830), (ii) to evaluate the effectiveness of mass scale calibration and optimisation procedure and (iii) to evaluate the current capability of relative quantification using SSIMS. The results of this study show that the constancy of the relative intensity scale has an approximate scatter standard deviation of only 5%. This is excellent and demonstrates that SSIMS measurements are significantly more stable than often thought by analysts. The draft ISO standard (DIS) CD 13084 for calibration of the mass scale in TOF‐SIMS was evaluated and found to be consistent with our previous study. Four laboratories optimised the instrument mass calibration accuracy using this procedure leading to improvements in mass scale calibration by factors of 1.8, 2.2, 2.3 and 8.6. Using a novel patterned Irganox sample it is shown that the precision of relative quantification may be as good as a standard deviation of only 5% for 16 instruments—this is a remarkable result. Further work is required to develop more robust reference materials. © Crown copyright 2010. Reproduced with the permission of Her Majesty's Stationery Office. Published by John Wiley & Sons, Ltd.     

Development of B‐doped Si multiple delta‐layer reference materials for SIMS profiling

B‐doped Si multiple delta‐layers (MDL) were developed as certified reference materials (CRM) for secondary ion mass spectrometry (SIMS) depth profiling analysis. Two CRMs with different delta‐layer spacing were grown by ion beam sputter deposition (IBSD). The nominal spacing of the MDL for shallow junction analysis is 10 nm and that for high energy SIMS is 50 nm. The total thickness of the film was certified by high resolution transmission electron microscopy (HR‐TEM). The B‐doped Si MDLs can be used to evaluate SIMS depth resolution and to calibrate the depth scale. A consistency check of the calibration of stylus profilometers for measurement of sputter depth is another possible application. The crater depths measured by a stylus profilometer showed a good linear relationship with the thickness measured from SIMS profiling using the calibrated film thickness for depth scale calibration. The sputtering rate of the amorphous Si thin film grown by sputter deposition was found to be the same as that of the crystalline Si substrate, which means that the sputtering rate measured with these CRMs can be applied to a real analysis of crystalline Si. Copyright © 2005 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.     

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.     

Extracting information on the surface monomer unit distribution of PLGA by ToF‐SIMS

The surface chemistry of a range of random poly l‐lactide‐co‐glycolide (PLGA) materials has been investigated using XPS, static secondary ion mass spectrometry (SSIMS) and gentle secondary ion mass spectrometry (G‐SIMS). The estimated mole fraction of lactide units provided by SSIMS was in good agreement with bulk composition and appeared not to have been affected by contamination. Conversely, XPS assessment of lactide compositions was unreliable due to hydrocarbon contamination contributions. In this study, we propose a novel model to demonstrate that by using SSIMS it is possible to infer the degree of trans‐esterification for PLGA co‐polymers synthesised from a mixture of lactide and glycolide homo‐dimers. This was determined by introducing two independent parameters, the ratio of trans‐esterified bonds to the total number of ester bonds, P_T, and the lactide composition. The model has indicated that, for this set of polymers, P_T was approximately 0.25. Furthermore, we have demonstrated that G‐SIMS successfully identified the structurally important key fragments leading to direct identification. Analysis by G‐SIMS showed that the glycolic acid units from all PLGA compositions are emitted in a lower energy‐fragmentation process than lactic acid units. Copyright © 2008 John Wiley & Sons, Ltd.     

Quantitative SIMS depth profiling of Al in AlGaN/AlN/GaN HEMT structures with nanometer‐thin layers

The possibilities of quantitative secondary ion mass spectrometry (SIMS) depth profiling of Al in AlxGa1 ? xN/AlN/GaN transistor heterostructures are shown. Using a series of test structures for a TOF.SIMS‐5 time‐of‐flight mass spectrometer, we obtained a refined linear calibration dependence of the secondary‐ion yield on the composition ×, namely, Y(CsAl+)/Y(CsGa+) = K × x/(1 ? x), with a high linear correlation coefficient, Rl = 0.9996, which permits quantitative SIMS analysis of relatively thick AlGaN barrier layers. The method of profile reconstruction with allowance for the main artifacts of ion sputtering has been first applied for the analysis of GaN/AlGaN/AlN/GaN high electron mobility transistor structure. This method permits to perform quantitative analysis of the thickness and composition of a nanometer‐thin AlN sublayer and to estimate the measurement error. For the structure being studied, the AlN sublayer is 1.2 ± 0.2 nm thick. Copyright © 2016 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.     

Study of the end‐group contribution to ToF‐SIMS and G‐SIMS spectra of poly (lactic acid) using deuterium labelling

In this study, polymeric (MW 50 000) and oligomeric (MW 2000) poly (lactic acid) (PLA), both with and without end‐group deuterium exchange, were analysed using static secondary ion mass spectrometry (SSIMS) to investigate the contribution of end‐group‐derived secondary ions to the SSIMS spectra. By monitoring the SSIMS intensities between the non‐deuterated and deuterated PLA, it is evident that the only significant end‐group‐derived secondary ions are [nM + H]⁺ (n > 1) and C₄H₉O₂⁺. The gentle‐SIMS (G‐SIMS) methodology was employed to establish that deuterated fragments were produced through low energy processes and were not the result of substantial rearrangements. It was noted that end‐group‐derived secondary ions had higher G‐SIMS intensities for oligomeric PLA than polymeric PLA, showing that these secondary ions are simple fragment products that are not the result of rearrangement or degraded product ions. Copyright © 2007 John Wiley & Sons, Ltd.     

Mitigating dead‐time effects during multivariate analysis of ToF‐SIMS spectral images

ToF‐SIMS spectra are formed by bombarding a surface with a pulse of primary ions and detecting the resultant ionized surface species using a time‐of‐flight mass spectrometer. Typically, the detector is a time‐to‐digital converter. Once an ion is detected using such detectors, the detector becomes insensitive to the arrival of additional ions for a period termed as the (detector) dead‐time. Under commonly used ToF‐SIMS data acquisition conditions, the time interval over which ions arising from a single chemical species reach the detector is on the order of the detector dead‐time. Thus, only the first ion reaching the detector at any given mass is counted. The event registered by the data acquisition system, then, is the arrival of one or more ions at the detector. This behavior causes ToF‐SIMS data to violate, in the general case, the assumption of linear additivity that underlies many multivariate statistical analysis techniques. In this article, we show that high‐mass‐resolution ToF‐SIMS spectral‐image data follow a generalized linear model, and we propose a data transformation and scaling procedure that enables such data sets to be successfully analyzed using standard methods of multivariate image analysis. Copyright © 2008 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.     

Adsorption of genetically engineered proteins studied by time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS). Part A: data acquisition and principal component analysis (PCA)

Recent progress in the adaptation of combinatorial biology selection protocols to materials science has created a new class of polypeptides with specific affinity to inorganics. Here, we use one of the genetically engineered proteins, a gold binding protein (GBP‐1), to assess quantitatively its binding specificity to Au, Ag and Pd surfaces by using time‐of‐flight secondary ion mass spectroscopy (TOF‐SIMS). The GBP‐1, originally selected using cell‐surface display techniques, consisting of 14 amino acids with a sequence of MHGKTQATSGTIQS, was used in this study. Three‐repeat and single‐repeat forms of GBP‐1 were prepared. In earlier studies, GBP‐1 was shown to bind to Au particles and self‐assemble on flat Au surfaces. Through the fingerprint analysis of these specific peptides, their role in binding can be investigated in terms of their contribution to surface interaction possibly forming the right molecular architecture for binding. To achieve this purpose, a large‐sized data matrix produced by TOF‐SIMS must be properly treated for analysis. In Part A, we use principal component analysis (PCA) to visualize the spectral variations for a variety of adsorption conditions and suggest possible contribution of the specific types of amino acids (binding site) to the interactions. Copyright © 2007 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.     

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.     

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.     

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).     

Molecular ME‐ToF‐SIMS yield as a function of DHB matrix layer thicknesses obtained from brain sections coated by sublimation/deposition techniques

Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) can be used to image biological samples with nanometer‐scale resolution, albeit with the drawback that it often cannot detect large molecular signals. One way to increase secondary ion molecular yield is to chemically modify the surface in the so‐called matrix‐enhanced SIMS (ME‐SIMS) approach, which is based on embedding analyte molecules in low‐weight organic matrices. In this study, a solvent‐free sample preparation technique was employed using sublimation/deposition for coating a mouse brain section with a thin layer of a 2,5‐dihydroxybenzoic acid (DHB) matrix. Using this preparation technique, signal enhancements of up to a factor of 18 could be detected. It was found that the matrix layer thickness plays an important role in the efficiency of yield enhancement. Also, a complex influence of the matrix layer on various signals was observed. 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.