Rapid multivariate analysis of 3D ToF‐SIMS data: graphical processor units (GPUs) and low‐discrepancy subsampling for large‐scale principal component analysis

Principal component analysis (PCA) and other multivariate analysis methods have been used increasingly to analyse and understand depth‐profiles in XPS, AES and SIMS. For large images or three‐dimensional (3D) imaging depth‐profiles, PCA has been difficult to apply until now simply because of the size of the matrices of data involved. In a recent paper, we described two algorithms, random vector 1 (RV1) and random vector 2 (RV2), that improve the speed of PCA and allow datasets of unlimited size, respectively. In this paper, we now apply the RV2 algorithm to perform PCA on full 3D time‐of‐flight SIMS data for the first time without subsampling. The dataset we process in this way is a 128 × 128 pixel depth‐profile of 120 layers, each voxel having a 70 439 value mass spectrum associated with it. This forms over a terabyte of data when uncompressed and took 27 h to process using the RV2 algorithm using a conventional windows desktop personal computer (PC). While full PCA (e.g. using RV2) is to be preferred for final reports or publications, a much more rapid method is needed during analysis sessions to inform decisions on the next analytical step. We have therefore implemented the RV1 algorithm on a PC having a graphical processor unit (GPU) card containing 2880 individual processor cores. This increases the speed of calculation by a factor of around 4.1 compared with what is possible using a fast commercially available desktop PC having central processing units alone, and full PCA is performed in less than 7 s. The size of the dataset that can be processed in this way is limited by the size of the memory on the GPU card. This is typically sufficient for two‐dimensional images but not 3D depth‐profiles without sampling. We have therefore examined efficient sampling schemes that allow a good approximate solution to the PCA problem for large 3D datasets. We find that low‐discrepancy series such as Sobol series sampling gives more rapid convergence than random sampling, and we recommend such methods for routine use. Using the GPU and low‐discrepancy series together, we anticipate that any time‐of‐flight SIMS dataset, of whatever size, can be efficiently and accurately processed into PCA components in a maximum of around 10 s using a commercial PC with a widely available GPU card, although the longer RV2 approach is still to be preferred for the presentation of final results, such as in published papers. Copyright © 2016 The Authors Surface and Interface Analysis 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.     

Identification of a lipid‐related peak set to enhance the interpretation of TOF‐SIMS data from model and cellular membranes

Principal component analysis (PCA) of time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS) data enables differentiating structurally similar molecules according to linear combinations of multiple peaks in their spectra. However, in order to use PCA to correctly identify variations in lipid composition between samples, the discrimination achieved must be based on chemical differences that are related to the lipid species, and not sample‐associated contamination. Here, we identify the positive‐ion TOF‐SIMS peaks that are related to phosphatidylcholine lipid headgroups and tail groups by PCA of spectra acquired from lipid isotopologs. We demonstrate that restricting PCA to a contaminant‐free lipid‐related peak set reduces the variability in the spectra acquired from lipid samples that is due to contaminants, which enhanced differentiating different lipid standards, but adversely affected the contrast in PC scores images of phase‐separated lipid membranes. We also show that PCA of a restricted data set consisting of the peaks related to lipids and amino acids increases the likelihood that the discrimination of TOF‐SIMS data acquired from intact cells is based on differences in the lipids and proteins on the cell surface, and not sample‐specific contamination without compromising sample discrimination. We expect that the lipid‐related peak database established herein will facilitate interpreting the TOF‐SIMS data and PCA results from studies of both model and cellular membranes, and enhance identifying the origins of the peaks that contribute to discriminating different types of cells. 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.     

Efficient encoding and rapid decoding for interactive visualization of large three‐dimensional hyperspectral chemical images

Interactive visualization of data from a new generation of chemical imaging systems requires coding that is efficient and accessible. New technologies for secondary ion mass spectrometry (SIMS) generate large three‐dimensional, hyperspectral datasets with high spatial and spectral resolution. Interactive visualization is important for chemical analysis, but the raw dataset size exceeds the memory capacities of typical current computer systems and is a significant obstacle. This paper reports the development of a lossless coding method that is memory efficient, enabling large SIMS datasets to be held in fast memory, and supports quick access for interactive visualization. The approach provides pixel indexing, as required for chemical imaging applications, and is based on the statistical characteristics of the data. The method uses differential time‐of‐flight to effect mass‐spectral run‐length‐encoding and uses a scheme for variable‐length, byte‐unit representations for both mass‐spectral time‐of‐flight and intensity values. Experiments demonstrate high compression rates and fast access. Copyright © 2009 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.     

Experimental shift allowance in the deconvolution of SIMS depth profiles

We study the deconvolution of the secondary ion mass spectrometry (SIMS) depth profiles of silicon and gallium arsenide structures with doped thin layers. Special attention is paid to allowance for the instrumental shift of experimental SIMS depth profiles. This effect is taken into account by using Hofmann's mixing‐roughness‐information depth model to determine the depth resolution function. The ill‐posed inverse problem is solved in the Fourier space using the Tikhonov regularization method. The proposed deconvolution algorithm has been tested on various simulated and real structures. It is shown that the algorithm can improve the SIMS depth profiling relevancy and depth resolution. The implemented shift allowance method avoids significant systematic errors of determination of the near‐surface delta‐doped layer position. Copyright © 2013 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.     

Characterization of size‐segregated aerosols using ToF‐SIMS imaging and depth profiling

Size‐segregated particles were collected with a ten‐stage micro‐orifice uniform deposit impactor from a busy walkway in a downtown area of Hong Kong. The surface chemical compositions of aerosol samples from each stage were analyzed using time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) operated in the static mode. The ToF‐SIMS spectra of particles from stage 2 (5.6–10 µm), stage 6 (0.56–1 µm), and stage 10 (0.056–0.1 µm) were compared, and the positive ion spectra from stage 2 to stage 10 were analyzed with principal component analysis (PCA). Both spectral analysis and PCA results show that the coarse‐mode particles were associated with inorganic ions, while the fine particles were associated with organic ions. PCA results further show that the particle surface compositions were size dependent. Particles from the same mode exhibited more similar surface features. Particles from stage 2 (5.6–10 µm), stage 6 (0.56–1 µm), and stage 10 (0.056–0.1 µm) were further selected as representatives of the three modes, and the chemical compositions of these modes of particles were examined using ToF‐SIMS imaging and depth profiling. The results reveal a non‐uniform chemical distribution from the outer to the inner layer of the particles. The coarse‐mode particles were shown to contain inorganic salts beneath the organics surface. The accumulation‐mode particles contained sulfate, nitrate, ammonium salts, and silicate in the regions below a thick surface layer of organic species. The nucleation‐mode particles consisted mainly of soot particles with a surface coated with sulfate, hydrocarbons, and, possibly, fullerenic carbon. The study demonstrated the capability of ToF‐SIMS depth profiling and imaging in characterizing both the surface and the region beneath the surface of aerosol particles. It also revealed the complex heterogeneity of chemical composition in size and depth distributions of atmospheric particles. Copyright © 2014 John Wiley & Sons, Ltd.     

Depth profiling in secondary ion mass spectrometry for ultra‐thin layer with nanometer order thickness by mesa‐structure fabrication

We attempted to make an accurate depth profiling in secondary ion mass spectrometry (SIMS) including backside SIMS for ultra‐thin nanometer order layer. The depth profiles for HfO₂ layers that were 3 and 5 nm thick in a‐Si/HfO₂/Si were measured using quadrupole and magnetic sector type SIMS instruments. The depth profiling for an ultra‐thin layer with a high depth resolution strongly depends on how the crater‐edge and knock‐on effects can be properly reduced. Therefore, it is important to control the analyzing conditions, such as the primary ion energy, the beam focusing size, the incidence angle, the rastered area, and detected area to reduce these effects. The crater‐edge effect was significantly reduced by fabricating the sample into a mesa‐shaped structure using a photolithography technique. The knock‐on effect will be serious when the depth of the layer of interest from the surface is located within the depth of the ion mixing region due to the penetration of the primary ions. Finally, we were able to separately assign the origin of the distortion to the crater‐edge effect and knock‐on effect. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

ToF‐SIMS multivariate characterization of surface modification of polymers by N2?H2 atmospheric pressure dielectric barrier discharge

Time of flight secondary ion mass spectrometry (ToF‐SIMS) is a powerful tool for the surface characterization of plasma‐modified surface. However, the SIMS fragmentation patterns of the resulting surface are quite complex and a full interpretation may be prohibitive. As a result, many studies are turning to multivariate statistical methods to simplify the interpretation. In this study, a principal component analysis (PCA) was used to obtain a more detailed interpretation of the surface modification of polymers by an atmospheric pressure plasma. The dataset was obtained from three polymers with different chemical compositions [namely, polyethylene, polyvinylidene fluoride, and poly(tetrafluoroethylene)], where each material was treated with an atmospheric pressure dielectric barrier discharge (DBD) in an atmosphere composed of different N₂/H₂ ratios. The results are discussed in terms of the suitability of ToF‐SIMS analysis combined with PCA for the discrimination between the three polymers and the possibility to create a predictive model that would describe the plasma surface modification, independent of the polymer substrate chemical composition. Copyright © 2010 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.     

Quantification and methodology issues in multivariate analysis of ToF‐SIMS data for mixed organic systems

Multivariate methods, such as principal component analysis (PCA) and multivariate curve resolution (MCR), are often employed to aid the analysis of large complex data sets such as time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) images. There is, however, much confusion over the most appropriate choice of method for any given application and the effects of data preprocessing, which is exacerbated by the confusing terminologies and the use of jargon in this field. In the present study, a simple model system consisting of a ToF‐SIMS image of an immiscible polymer blend is used to evaluate PCA and MCR in the accurate identification, localisation and quantification of the phase‐separated polymer domains, using four data preprocessing methods (no scaling, normalisation, variance scaling and Poisson scaling). This highlights significant issues and challenges in the quantitative multivariate analysis of mixed organic systems, including the discrimination of chemically significant features from experimental noise, the resolution of weak chemical contributions and potential bias introduced by data preprocessing. Multivariate analysis using Poisson scaling, identified as the most suitable data preprocessing method for both PCA and MCR, demonstrates a marked improvement upon traditional (manual) analysis and provides valuable additional information that is difficult to detect using traditional analysis. Using these results, we present recommendations for the optimum use of multivariate analysis by analysts and provide guidance on selecting the most appropriate methods. Confusing terminology is also clarified. © Crown copyright 2008. Reproduced with the permission of Her Majesty's Stationery Office. Published by John Wiley & Sons, Ltd.     

ToF‐SIMS depth profiling of a complex polymeric coating employing a C60 sputter source

A complex poly(vinylidene difluoride) (PVdF)/poly(methyl methacrylate) (PMMA)‐based coil coating formulation has been investigated using time‐of‐flight SIMS (ToF‐SIMS). Employing a Bi₃⁺ analysis source and a Buckminsterfullerene (C₆₀) sputter source, depth profiles were obtained through the polymeric materials in the outer few nanometres of the PVdF topcoat. These investigations demonstrate that the PVdF coating's air/coating interface is composed principally of the flow agent included in the formulation. Elemental depth profiles obtained in the negative ion mode demonstrate variations in the carbon, oxygen and fluorine concentrations within the coating with respect to depth. All three elemental depth profiles suggest that the PVdF coating bulk possesses a constant material composition. The oxygen depth profile reveals the presence of a very thin oxygen‐rich sub‐surface layer in the PVdF coating, observed within the first second of the sputter/etch profile. Retrospectively, extracted mass spectra (from the elemental depth profile raw data set) of the PVdF coating sub‐surface and bulk layers indicates this oxygen‐rich sub‐surface layer results from segregation of the acrylic co‐polymers in the formulation towards the PVdF coating air/coating interface. Molecular depth profiles obtained in both the positive and negative secondary ion modes provide supporting evidence to that of the elemental depth profiles. The molecular depth profiles confirm the presence of a sub‐surface layer rich in the acrylic co‐polymers indicating segregation of the co‐polymers towards the PVdF topcoats air‐coating surface. The molecular depth profiles also confirm that the PVdF component of the topcoat is distributed throughout the coating but is present at a lower concentration at the air‐coating interface and in the sub‐surface regions of the coating, than in the coating bulk. Copyright © 2007 John Wiley & Sons, Ltd.     

Effects of oxygen plasma treatment on the surface of bisphenol A polycarbonate: a study using SIMS,principal component analysis,ellipsometry, XPS and AFM nanoindentation

The effects of oxygen plasma treatment and the subsequent air exposure on the surface composition and properties of bisphenol A polycarbonate (BPA‐PC) were analysed by X‐ray photoelectron spectroscopy (XPS), ellipsometry, static time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) with principal component analysis (PCA) and nanoindentation using an atomic force microscope (AFM). PCA showed systematic changes in the film chemistry after short treatment times (0.1 s), with the main sites of attack being the carbonate and aromatic ring structure. On the basis of this multitechnique analysis, it was unambiguously determined that extended oxygen plasma treatment times resulted in the formation of low‐molecular‐weight material (LMWM) within the first 50 nm on the surface, and not in a cross‐linked skin as has been proposed by other researchers. The study shows that controlled surface modification of BPA‐PC polymers is possible, allowing surface oxygen incorporation without degradation of the polymer structure. This result is relevant for improved adhesion of coatings applied to BPA‐PC polymers. Copyright © 2006 John Wiley & Sons, Ltd.     

Generalized Dice's coefficient‐based multi‐block principal component analysis with Bayesian inference for plant‐wide process monitoring

Plant‐wide process monitoring is challenging because of the complex relationships among numerous variables in modern industrial processes. The multi‐block process monitoring method is an efficient approach applied to plant‐wide processes. However, dividing the original space into subspaces remains an open issue. The loading matrix generated by principal component analysis (PCA) describes the correlation between original variables and extracted components and reveals the internal relations within the plant‐wide process. Thus, a multi‐block PCA method that constructs principal component (PC) sub‐blocks according to the generalized Dice coefficient of the loading matrix is proposed. The PCs corresponding to similar loading vectors are divided within the same sub‐block. Thus, the PCs in the same sub‐block share similar variational behavior for certain faults. This behavior improves the sensitivity of process monitoring in the sub‐block. A monitoring statistic T² corresponding to each sub‐block is produced and is integrated into the final probability index based on Bayesian inference. A corresponding contribution plot is also developed to identify the root cause. The superiority of the proposed method is demonstrated by two case studies: a numerical example and the Tennessee Eastman benchmark. Comparisons with other PCA‐based methods are also provided. Copyright © 2014 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.     

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.     

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.