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 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.     

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.     

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.     

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.     

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.     

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.     

Efficient and sample-specific interpretation of ToF-SIMS data by additional postprocessing of principal component analysis results

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful tool for surface analysis, but fragmentation of molecular species during the SIMS process may lead to complex mass spectra. While the fragmentation pattern is typically characteristic for each compound, industrial samples are engineered materials, and, thus, may contain a mixture of many compounds, which may result in a variety of overlapping peak patterns in ToF-SIMS spectra. Consequently, the process of data evaluation is challenging and time-consuming. Principal component analysis (PCA) can be used to simplify data analysis for complex sample systems. Especially, correlation loadings were observed as an ideal tool to identify relevant signals in PCA results, which induce the separation of different sample groups. This is because correlation loadings show the relevance of signals independent from their intensity in the raw data. In correlation loadings, however, fragmentation patterns are no longer observed and the identification of peaks' sum formulas is challenging. In this study, a new approach is presented, which simplifies peak identification and assignment in ToF-SIMS spectra after PCA is performed. The approach uses a mathematical transformation that projects PCA results, in particular loadings and correlation loadings, in the direction of specific sample groups. The approach does not change PCA results but rather presents them in a new way. This method allows to visualize characteristic spectra for specific sample groups that contain only relevant signals and, additionally, visualize fragmentation patterns. Data analysis is simplified and helps the user to focus on data interpretation rather than processing.     

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.     

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.     

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.     

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.     

ToF‐SIMS characterization of glyoxal surface oxidation products by hydrogen peroxide: A comparison between dry and liquid samples

As one of the simplest volatile organic compounds, glyoxal and its oxidation products were considered to be important precursors to aqueous secondary organic aerosol formation. Herein, we analyzed products from glyoxal oxidation by hydrogen peroxide in dry and liquid samples using time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). ToF‐SIMS spectra and spectral principal component analysis (PCA) were used to investigate surface oxidation products. Dry samples were prepared on clean silicon wafers. Liquid samples consisting of glyoxal and hydrogen peroxide (H₂O₂) were introduced to a vacuum compatible microfluidic reactor prior to UV illumination or dark aging followed by in situ liquid SIMS analysis. A number of reaction products were observed in both dry and liquid samples; different oligomers and carboxylic acids could be formed depending on reaction conditions. In addition, hydrolyzed products were observed in the liquid samples, but not in the dry samples. Although dry samples reveal some products of the aqueous process, they are not fully representative as results from those of the aqueous samples. Our findings suggest that the ability to characterize the liquid surface reaction products provides more realistic information of the reaction products associated with aqueous secondary organic aerosol formation in the atmosphere. Meanwhile, the high mass resolution spectra from the dry sample SIMS measurement are helpful to identify oxidation products in the liquid samples.     

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).     

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.     

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.     

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.     

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.