Bayesian Integration and Classification of Composition C-4 Plastic Explosives. Based on Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) and

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1 Bayesian Integration and Classification of Composition C-4 Plastic Explosives Based on Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS): Supplemental Information Christine M. Mahoney*, Ryan Kelly, Liz Alexander, Matt Newburn, Sydney Bader Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA USA Robert G. Ewing, Albert J. Fahey 1 and David A. Atkinson National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA USA Nathaniel Beagley Johns Hopkins University Applied Physics Laboratory, Johns Hopkins Road, Laurel, MD *mahoneycm@corning.com 1 Current address: U.S. Naval Research Laboratory, Code 6367, Building 222, Room 257, 4555 Overlook Avenue SW, Washington, DC Abstract This supplemental material section contains additional details, that are not included in the primary manuscript. This includes a description of the C-4 deposition process used for ToF-SIMS experiments, representative positive ion and negative ion ToF-SIMS spectra, and PLS-DA of the negative ion ToF-SIMS data. A more detailed description of the Partial Least Squares-Discriminant Analysis (PLS- DA) procedure is also given here. Finally, detailed discussions of sources of error are included. S-1

2 Details of nanolithography for C-4 dry transfer process. Each C-4 sample analyzed with SIMS was dry transferred onto a clean Si wafer substrate by using an Obducat Eitre 3 Nanoimprint Lithography system (Lund, Sweeden). Si wafers were utilized as a first step because they provide a conductive substrate, which is ideal for SIMS analysis. The dry transfer process is illustrated in Figure 1S. Briefly,~8-20 mg of C-4 sample was transferred onto a clean Si wafer, and placed into the nanoimprinter. A plastic template sheet (poly(ethylene terephthalate) (PET)) was placed on top of the C-4 prior to nanoimprintation (see step 1, Figure 1S). During nanoimprintation, a constant pressure of 20 mg of force was applied at 25 o C for 1 minute, resulting in the transference of some of the C-4 to the PET template. The PET sheet with the C-4 residue was then placed on top of a clean Si wafer, and inserted back into the nanoimprinter under identical imprinting conditions as described above, and the residue was transferred from the PET template to a new Si substrate (step 2, Figure 1S). When the sample was removed from the nanoimprinter, and the template removed, C-4 residue was easily observable on the samples surface. It should be noted that the fingerprint template shown in Figure 1S, while interesting for demonstration purposes, was not utilized for actual experimentation, rather a flat, featureless template was used for more consistent depositions of material. Furthermore, it should be mentioned that previous work involved dissolving the samples in tetrahydrofuran (THF) and dry casting them onto a wafer. 1 This method does not preserve the natural structure/heterogeneity of the sample, which is in part what imaging mass spectrometry is used for. Hence, this method of nanoimprintation was developed to preserve such structure and for possible future use in developing controlled fingerprint standards. S-2

3 Figure 1S. Illustration of nanoimprinting process. A plastic template is used to dry transfer the C-4 bulk material to a Si substrate. The plastic template in this example has ridge patterns of a model fingerprint. This pattern is transferred to the substrate by following a two step procedure. In step 1, the material is transferred from the bulk C- 4 sample to the plastic template. In step 2, the material is transferred from the template to a clean Si substrate. The result is deposition of C-4 in the pattern of the template. ROI selection and image analysis with SIMS. Large area mapping was performed by acquiring individual 500 µm x 500 µm images from each region and subsequently stitching the images together to form a tiled image. Some examples of images are displayed in Figure 2S, which shows large area secondary ion mapping of the fingerprint as prepared in in Figure 1S. In Figure 2S(a) and 2S(b), secondary ion image maps from the Si substrate (m/z 28, Si + ) and nitrate explosives (m/z 62, NO - 2 ) are displayed, while the total positive secondary ion image is shown in Figure 2S(c). More detailed chemical information is shown in the image overlays in Figures 2S(d) and 2S(e). Figure 2S(d) shows the molecular ion distributions of explosive molecules, RDX and HMX, where HMX is a bi-product commonly observed during the synthesis of RDX. Figure 2S(e) shows the distribution of additive molecules, including poly(isiobutylene) (PIB) and dicapryladipate (DCA). All mass spectral data that is analyzed in this work was selected retrospectively from regions of interest (ROI s) as depicted in Figure 2S(f). The regions S-3

4 selected for retrospective analysis indicate regions where the intensity of Si signal (background) is low (or zero), and thus represent signal characteristic only of the C-4. For the current study, the ROIs were broken down further into twelve separate regions, each region representing a single data point in the final dataset. Thus, an assessment of heterogeneity within a sample can be made by looking at the distribution of these 12 datapoints (from the 12 different regions). Once again, a featureless template was used for our experiments in this work in order to achieve a more uniform deposition. The reason for this can easily be explained by taking a closer look at Figure 2S(e). As can be seen in this Figure, there is a nonuniform distribution of the components, where the fingerprint ridges tended to be comprised of more binder (PIB) components relative to the particles. This type of inhomogeneity is introduced by the structure of the template and is not inherent in the natural structure of the C-4. Although this is of interest for fingerprinting studies, it is not the scope of the current work. Figure 2S. Secondary ion imaging (13 mm x 10 mm) of fingerprint described in Figure 1S: a) Si + (m/z 28), (b) NO 2 - (m/z 46), (c) total ion image, (d) overlay of HMX (m/z 342, M+NO 2 - ) and RDX (m/z 268, M+NO 2 - ) molecules, (e) overlay of polyisobutylene (PIB) signal (m/z 97), dicapryl adipate (DCA) signal (m/z 371) and NO 2 - signal, and (f) regions of interest used for mass spectral data acquisition (see text for description). Images were acquired using large area mapping methods (described in text). S-4

5 Secondary Ion Mass Spectral Data. Both positive ion and negative ion mass spectra were acquired from each of the 18 samples described in Table 1, and some examples of these spectra are displayed in Figures 3S and 4S. Figure 3S shows representative positive ion mass spectra acquired from two different samples, Hall explosives [Figure 3S(a)] and 2001_L3 military explosives [Figure 3(b)]. In SIMS, positive ion mass spectral data is typically more representative of the additive composition of the C-4. In this particular example, it can be seen that the plasticizer, di-isooctyl sebacate (DOS) is present only in the Hall explosives. In fact, this is the only sample out of the 18 samples analyzed that contained DOS and is therefore a very strong signature associated with this particular source. All of the other samples contained a different plasticizer, di-capryl adipate (DCA). A second signature is the peak intensities associated with poly(isobutylene) (PIB), where it can be seen that in 2001_L3 samples, the peaks are significantly larger. Figure 3S. Positive SIMS spectra from C-4 residue on a Si surface. Spectra obtained from: a) Hall explosives commercial brand C-4 (class 12) and, b) 2001_L3 military C-4 (class 11). Peaks at m/z 185 and m/z 427 (green) (a) are consistent with the structure of di-isooctylsebacate (DOS), m/z 371 (red) (b) is consistent with the structure of di-capryladipate (DCA), and m/z 83 and m/z 97 (blue) (b) are consistent with poly(isobutylene) (PIB). Corresponding structures of DOS, DCA and PIB are shown in the insets. For more detailed peak assignments, see references. 1 S-5

6 Figure 4S shows the corresponding negative ion mass spectra from the same two samples. The main differences observed in this mode are attributed to varying explosive compositions, though changes in other easily ionizable additives, such as surfactants are also typically observed in this mode. The most significant difference observed in the data is in the presence/absence of HMX. HMX is a very important signature as it is often a byproduct in the RDX synthesized by the Bachmann process. 2 RDX is either synthesized by the Bachmann process, or more rarely, the Woolwich process. The Woolwich process results in very low HMX content as compared to the Bachmann process. In the case of the Hall explosives, there was no evidence of HMX in any of the samples analyzed, whereas all other samples contained HMX. This indicates that the RDX in Hall explosives may have been obtained from a lab that uses Woolwich processing. Figure 4S. Negative SIMS spectra from C-4 residue on a Si surface. Spectra obtained from: a) Hall explosives commercial brand C-4 (class 12) and, b) 2001_L3 military C-4 (class 11). Peaks at m/z 129, 248, 268 and 490 (blue) are consistent with the structure of RDX. Peaks at m/z 311 and 325 (green) are consistent with the structure of do-decyl benzene sulphonate (DBBS), a surfactant. Peaks at m/z 342, 358, 398, 564 and 638 (red) are consistent with the structure of HMX. Corresponding structures of RDX and HMX are shown in the insets. For more detailed peak assignments, see references. 1 S-6

7 Partial Least Squares Discriminant Analysis: a detailed description. PLS-DA is a method for constructing predictive models when the factors are many and can be highly collinear. As mentioned in the manuscript, it uses similar notions and projection methods as principal components analysis (PCA) to optimize a binary classification model. 3-5 In PLS, as in any linear regression model, the aim is to build a model that satisfies: Y=X B + A, where Y is the response matrix (observables), X is the matrix of predictors (in this case, data from mass spectra), B is the matrix of regression coefficients and A is often referred to as a noise term. The specific goal of PLS is to predict Y from X and to describe their common structure. PLS does this by finding components, latent (hidden) variables, describing the covariance between X and Y as much as possible. In the case of PLS-DA the operation is performed by regressing against a matrix of ones and zeros. The reference data are given a value of 1 for the specified class (known apriori), and a zero for all other classes. Unknown data put into this model should therefore have predicted Y values that fall nominally between 1 and zero depending on how well the data fit. The Venetian blind method of cross validation was used, producing the root mean square errors of cross validation and of calibration, allowing for optimal choice of the number of latent variables. Furthermore, this information is used to determine Gaussian parameters used for classification. The PLS_Toolbox implementation of PLS-DA produces extensive output from the analysis including the predicted Y-values (see Figure 7s) as well as the probabilities that a given predicted Y-value belongs to a particular class. This is determined through assigning a threshold with the Gaussian parameters (see discriminant analysis and classification section) as described in the associated references. 6-7 An example of this process is also shown in Figure 7S. PLS-DA: Negative ion ToF-SIMS training data. PLS-DA analysis of the negative ion SIMS data is shown in Figure 5S. The most significant finding in the negative data shows that Hall explosives do not contain any HMX (as was shown in the mass spectra presented earlier in Figures 2S and 3S). Hall explosives is highly correlated with the 2M + NO - 2 RDX peak at m/z 490 as they are located in the same general region of the quad S-7

8 chart in Figure 5S. Similarly, it is seen that samples from 2001 contained greater amounts of HMX peaks (m/z 342, M + NO 2 - ). Figure 5S. PLS-DA analysis of negative ion SIMS data: training dataset to be used for classification of test samples. (a) Scores plots of latent variable 1 (LV1) vs. latent variable 2 (LV2), (b) Loadings plots of LV1 vs. LV2. Together LV1 and LV2 encompass 86.2% of the total variance observed in the data. Ellipses are not statistically relevant, but are used to guide the eye to groupings in the data. Data points circled in green are correlated with RDX peaks and data points circled in red are correlated with HMX peaks. Scores for test samples A (dark blue stars) and B (aqua filled circles) are overlayed with the data in the scores plot, where test sample A scores are circled in blue. Discriminant Analysis and Determination of Predicted Probabilities: Figure 6S shows an example of outputs produced using PLS-DA. It plots the predicted Y values as calculated by discriminant analysis for class 12, Hall explosives. The algorithm finds the area of minimum overlap between the different classes (in this case it is S-8

9 grouped either into class 12, assigned a value of 1, and not class 12, which is assigned a value of zero) and defines this as the threshold. This threshold value is then used to calculate the probabilities that the particular samples belong to class 12. If the data point is located at threshold, there is a 50% chance it belongs to the class and a 50% chance that it does not belong to the class. Gaussian parameters as determined during cross validation are then used to define the probabilities associated with points located both above and below the threshold. As can be seen, the most prevalent data points that are over the threshold are associated with Hall explosives (green crosses) and test sample A (stars). Since we know a priori that the class of test sample A is class 12 (Hall explosives), we can see that this method is a good predictor for this particular type of C-4. Figure 6S(b) shows the most important signatures (VIP scores) that are associated with this class of compounds. It is shown that the classification in class 12 Hall explosives samples, is based mostly on the presence of DOS peaks at m/z 185 and 429, and the absence of PIB at m/z 97. Samples/Scores Plot of pca_c4_more recent data.xlsx,c & pca_c4_more recent a) data.xlsx, Variables/Loadings Plot for pca_c4_more recent data.xlsx b) (+) hall explosives test sample A 140 Y Predicted 12 (Hall) Sample VIP Scores for Y (-) (+) Variable Figure 6S. Discriminant analysis and classification process. The example shown in (a) shows the predicted Y values, the output values from the linear regression (see text), as calculated for all samples including both the training dataset as well as in the test sample A dataset. Predicted Y values closer to 1 are more likely to belong to class 12 (Hall explosives). The red dashed line represents the threshold of the data (see text). This value is determined based on the Gaussian distributions of scores and is located at the point at which the least amount of Gaussian overlap occurs (between data groupings), such that the probability of false classifications is minimized. (b) shows the VIP scores for class 12. These indicate the most important signatures that contribute to the classification. (+) and (-) labels indicate whether these signatures are more prominent (+), or less prominent (-) in class 12 samples as compared to other classes. S-9

10 Figure 7S. Discriminant analysis and classification process. Conversion of Predicted Y values to probability using LA-ICP-MS as an example. (a) Predicted Y values for all samples characterized with LA-ICP-MS. The red dashed line represents the threshold as described in the text, and (b) Application of Gaussian distributions surrounding threshold in (a), to determine probabilities. A similar example of the discriminant analysis process is shown in Figure 7s, for the LA-ICP-MS data. Figure 7Sa shows the predicted Y values for all the samples analyzed. Hall explosives data (from training data cross validation) are the closes to 1. Also, test sample A is above threshold as is expected. 1994, 1975 and a single Omni data point are located close to threshold. Figure 7Sb shows the resultant probabilities associated with the predicted Y values in Figure 7Sa. The values of Y predicted (in Figure 7Sa) that are close to threshold have a 50% probability (in Figure 7Sb) of being from class 12. As the data points move above threshold, the values get closer to having a probability of 1. Similarly. as the data points move below threshold, the values for probability decrease towards zero. This second plot represents the output probabilities that are used in this work. Sources of Error. Though ToF-SIMS is a promising tool for use in future forensic investigations, one has to keep in mind that it is a surface analysis tool, and therefore is susceptible to surface segregation effects and surface contamination. Often times certain components, particularly low surface energy contaminants (e.g. PDMS) will preferentially migrate to the surface and spread across the surface, masking the more important signatures S-10

11 characteristic of the C-4. This was readily observable in the current data. In addition (as was the case with class 9, Figure 9), an unknown signature/contaminant was observed that was not consistent between measurements. This signature was originally most prominent in the PNNL samples. The specific peaks associated with this unknown were observed in the negative mode at m/z 205 and m/z 225, and m/z 133 and m/z 312 in the positive ion mode. While the original Hall explosives and 1994 samples did not contain these peaks when originally analyzed in the training dataset, when they were re-run as unknowns six month subsequent to the original training dataset, these peaks became very intense. This signature threw off the results significantly, even resulting in misclassification of the 1994 C-4. It is particularly difficult to avoid surface contamination in ToF-SIMS experiments. It is recommended that future studies performed be done on samples prepped and stored in a clean environment, containing no low surface energy components such as PDMS or low molecular weight fluoropolymers. Surfactants also create a significant problem with ToF-SIMS analysis. Pre-sputtering with a cluster source may be useful for removing the surface contaminants. Finally, it is recommended that continuous current cluster beams be used for more rapid analysis. The method used in this work uses a pulsed beam, which makes it particularly surface sensistive. In addition to surface contamination issues, ToF-SIMS investigations are difficult to quantify, as the data are susceptible to matrix effects, as are most mass spectrometric based methods. For example, if a certain sample contains more salts than another sample there may be a different set of peaks displayed, such as Na cationized peaks, with different intensity ratios than observed without the salt. Similarly, if a different topography is observed, there will be changes in the intensity ratios of the peaks. Therefore care should be taken in analysis of the data, and comparison with more quantitative approaches should be attempted. Finally, peak selections may play an important role in classification and may need to be further modified. While for a first pass, selection of all peaks is ideal for signature identification, it can be seen that certain peaks constitute noise in the data, and are not relevant. For this proof of principle paper, it was necessary to observe all S-11

12 features and variables of the dataset in order to assess the importance and role of each. However, for more robust identification, careful selection of specific data features is warranted in future studies. Despite these shortcomings, ToF-SIMS is clearly a useful method which can be used to directly characterize the chemical signatures in C-4, and when integrated with multiple instrument platforms, it has proven to be particularly useful. It is the only tool that has both the spatial resolution and the chemical specificity required to differentiate chemical signatures. Furthermore, it is one of the few tools, that can be used to directly characterize and classify solid sample particulates collected directly from the field. PLS-DA with subsequent Bayesian integration is proving to be a surprisingly robust data analysis method. However, it is expected that there will be errors in using such a method, particularly when the unknown sample is not one of the samples included in the original training dataset. The peaks selected for analysis can affect the results of the PLS-DA analysis. Furthermore, the number of components selected for the model will affect the results accordingly. One has to take care as always that the results displayed in the PLS-DA are consistent with known compositional variations. Finally, in cases where the variance in the data is not significant, but still very relevant, for example, if one or two explosives particles were present in a large area fingerprint image, those small number of pixels compared to the whole will not register as unique for a given sample. Thus less significant features of the data can easily be overlooked. References 1. Mahoney, C. M.; Fahey, A. J.; Steffens, K. L.; Benner Jr, B. A.; Lareau, R. T., Anal. Chem. 2010, 82 (17), Zukas, J. A.; Walters, W.; Walters, W. P., Explosive effects and applications. Springer Verlag: Barker, M.; Rayens, W., J. Chemom. 2003, 17 (3), Liu, Y.; Rayens, W., Comput. Stat. 2007, 22 (2), S-12

13 5. Webb-Robertson, B.-J. M.; Mccue, L. A.; Beagley, N.; Mcdermott, J. E.; Wunschel, D. S.; Varnum, J. Z. H.; Isern, N. G.; Buchko, G. W.; Mcateer, K.; Pounds, J. G., Pac Symp Biocomput 2009, Pérez, N. F.; Ferré, J.; Boqué, R., Chemometrics Intellig. Lab. Syst. 2009, 95 (2), (accessed 01/12/2016). S-13