Organic Chemical Attribution Signatures for the Sourcing of a Mustard

Apr 26, 2016 - The work reported here demonstrates for the first time trace impurities from the synthesis of tris(2-chloroethyl)amine (HN3) that point...
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Organic Chemical Attribution Signatures for the Sourcing of a Mustard Agent and Its Starting Materials Carlos G. Fraga,* Krys Bronk, Brian P. Dockendorff, and Alejandro Heredia-Langner Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99352, United States S Supporting Information *

ABSTRACT: Chemical attribution signatures (CAS) are being investigated for the sourcing of chemical warfare (CW) agents and their starting materials that may be implicated in chemical attacks or CW proliferation. The work reported here demonstrates for the first time trace impurities from the synthesis of tris(2chloroethyl)amine (HN3) that point to the reagent and the specific reagent stocks used in the synthesis of this CW agent. Thirty batches of HN3 were synthesized using different combinations of commercial stocks of triethanolamine (TEA), thionyl chloride, chloroform, and acetone. The HN3 batches and reagent stocks were then analyzed for impurities by gas chromatography/mass spectrometry. All the reagent stocks had impurity profiles that differentiated them from one another. This was demonstrated by building classification models with partial least-squares discriminant analysis (PLSDA) and obtaining average stock classification errors of 2.4, 2.8, 2.8, and 11% by cross-validation for chloroform (7 stocks), thionyl chloride (3 stocks), acetone (7 stocks), and TEA (3 stocks), respectively, and 0% for a validation set of chloroform samples. In addition, some reagent impurities indicative of reagent type were found in the HN3 batches that were originally present in the reagent stocks and presumably not altered during synthesis. More intriguing, impurities in HN3 batches that were apparently produced by side reactions of impurities unique to specific TEA and chloroform stocks, and thus indicative of their use, were observed.

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demonstrated as potential CAS indicative of a manufacturing facility,8 synthetic route,12 purification process,11 reaction solvent,12 reaction catalyst,13 lot,8,13,14,17,18 and precursor stock.16 Herein, we investigated the use of impurity profiling of HN3 and its synthesis reagents to determine (1) if organic impurity profiles can distinguish different stocks of the same reagent and (2) if individual organic impurities or impurity profiles can be found that would permit the matching of HN3 to the specific reagents and stocks used in its manufacture. We show that impurity profiles can distinguish different stocks of reagents but highlight the need for a comprehensive classification model. The impurity profiles of HN3 batches could not, however, be matched compound for compound to the specific reagent stocks used in the synthesis of that batch. On the other hand, hydrocarbon impurities in the CW agent that were common to all stocks of the same reagent were detected and therefore likely indicative of a specific reagent. Finally, this work demonstrates for the first time trace impurities produced during the synthesis of a CW agent that point to the specific reagent stocks used in the synthesis of the CW agent.

ustard agents, also called blister agents or vesicants, are chemical warfare (CW) agents. One such mustard agent is tris(2-chloroethyl)amine or HN3. HN3 belongs to a series of tertiary bis(2-chloroethyl)amines collectively called nitrogen mustards. Nitrogen mustards are simple-to-make irritants that on contact can damage skin, eyes, and breathing tract and whose cytotoxic properties can quickly harm the immune system and bone marrow.1 While nitrogen mustards have therapeutic value as anticancer agents,2 specific nitrogen mustards and precursors such as HN3 and triethanolamine are regulated by the Organization for the Prohibition of Chemical Weapons (OPCW) because of their potential use in CW attacks or proliferation.3 While never used in warfare, the Germans during World War II produced 2000 tons of HN3 that was filled into artillery shells and rockets.4 On the other hand, sulfur mustard or bis(2-chloroethyl) sulfide, which is similar to HN3 in structure and toxicity,5 has been used as a CW agent on military and civilian populations in several instances over the past 3 decades.6,7 Given the threat posed by mustard agents, efforts are underway to discover chemical attribution signatures (CAS) for them and their synthesis reagents to help locate and charge those responsible for their use in attacks or proliferation. In similar investigations with other highly toxic chemicals such as cyanides,8,9 ammonium metavanadate,10 ricin,11 isopropyl bicyclophosphate, 12 tetramine, 13,14 brodifacoum, 15 and sarin,16−18 several potential CAS, including trace impurities, have been identified. Specifically, trace impurities have been © XXXX American Chemical Society

Received: February 26, 2016 Accepted: April 26, 2016

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EXPERIMENTAL SECTION

Table 1. Reagent Stock Combinations for the Synthesis of 30 HN3 Batchesa

Safety and Regulations. All HN3 work was done in accordance to the Chemical Weapons Convention Regulations.19 HN3 synthesis was peformed in a fume hood by staff wearing butyl aprons, butyl gloves, and eye protection according to safety protocols and training developed by Battelle’s Hazardous Materials Research Center (West Jefferson, OH). Lab nitrogen was circulated through each reaction vessel and was scrubbed through a bleach-containing bubbler prior to exiting into the hood exahust. All HN3 batches were stored in a locked fume hood cabinet within an accessrestricted lab. HN3 Reagents. The reagent compounds used for HN3 synthesis were triethanolamine (TEA), thionyl chloride, chloroform, acetone, and sodium carbonate. Three stocks of TEA (P, Q, S), three stocks of thionyl chloride (AD, TD, TM), eight stocks of chloroform (A, B, C, D, E, X, Y, Z), seven stocks of acetone (F, H, J, T, U, V, W), and one stock of sodium carbonate were used in this study. The sodium carbonate was dissolved in 18 MΩ-cm deionized water to create one aqueous stock of sodium carbonate. Relevant information for each stock including listed purity, supplier, lot number, and country of origin are provided in Table S-1 of the Supporting Information. For clarity, the phrase “reagent type” refers to one reagent compound regardless of stock while “reagent stock” refers to one specific source for a given reagent compound. HN3 Synthesis. Thirty HN3 batches were produced using two synthesis steps as reported by Kyle Ward.20 The first step involved reacting TEA with thionyl chloride in distilled chloroform to produce the hydrochloride salt of nitrogen mustard (HN3·HCl) which was recovered by solvent evaporation and purified by recrystallization with acetone. In the second step, HN3·HCl was mixed with aqueous sodium carbonate to create a separate liquid HN3 layer that was removed with a glass pipet and bulb. An average of 170 mg of neat HN3 was recovered per batch with an average purity of 97% as determined by peak areas from gas chromatography/ mass spectrometry (GC/MS) analysis. For all batches, TEA was the limiting reagent and the same reagent amounts and synthesis conditions were used. Experimental Design and Execution. Each of the 30 HN3 batches was produced using a specific combination of reagent stocks as stipulated by a statistically designed matrix created using JMP 10.0.2 (SAS Institute Inc., Cary, NC). The experimental matrix (see Table 1) is a D-optimal design where all main effects and two-factor interactions are free of aliasing, that is, can be estimated free from the effect of any other factor in the model. As shown in Table 1, the number of different stocks for each reagent is three for TEA, three for chloroform, three for acetone, two for thionyl chloride, and one for aqueous sodium carbonate. The stock combinations for the first 26 HN3 batches were designed to study main effects and two-factor interactions for four reagents (TEA, thionyl chloride, acetone and chloroform). The last four HN3 batches (nos. 27−30) were each a replicate for one of the previous 26 HN3 batches in order to address any experimental variability associated with HN3 batches made with the same reagent stock combinations. These replicate batches were randomly selected by the synthetic chemist except for batch no. 29; it was purposely selected to replicate batch no. 4 because the HN3·HCl product from batch no. 4 was spilled and recovered resulting in a potentially poor HN3 product.

HN3 batch (n = 30)

block (n = 10)

TEA (n = 3)

chloroform (n = 3)

SOCl2 (n = 2)

acetone (n = 3)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

I I I II II II III III III IV IV IV V V V VI VI VI VII VII VII VIII VIII VIII IX IX IX X X X

S S Q P S P Q P Q P P S S P S P P S Q Q P Q Q Q Q S Q P P P

D E E A E A D A A D E D A E A D D A D D E E E A A D E D A A

AD AD TD AD TD AD AD TD TD AD AD TD AD TD TD AD TD AD AD TD TD AD AD TD AD TD AD AD AD TD

F H H J J F J H F H J J H F F F J J H F H F J J H H J H J H

a

Only one aqueous stock of sodium carbonate was used for all HN3 batches.

The 30 HN3 batches were synthesized in 10 blocks (see Table 1) of 3 batches per block or day. Synthesis of HN3·HCl (step 1) was performed in sequential order by block (I−X) and then all 30 HN-3·HCl products were stored in separate glass vials prior to the synthesis of HN3 (step 2) in the same block order. The HN3 batches were stored in glass vials at −10 °C for 12 months prior to sample preparation and analysis by GC/ MS. This 12-month span resulted from a desire to simulate the long-term storage of HN3 and to not conflict with other parallel research efforts in our lab. GC/MS Sample Preparation. In preparation for analysis by GC/MS, a 2 μL (2.48 mg) sample of each HN3 batch was taken and diluted to 2 mL with dichloromethane (Acros Organic, 99.5%, lot no. A0291307). The same dichloromethane (DCM) lot from one bottle was used for all sample preparations and method blanks. This particular lot of DCM was found to be the cleanest of four analyzed DCM lots in terms of the number and level of impurities detected by GC/ MS. The use of DCM to dilute samples of the HN3 batches and TEA stocks was found to dramatically increase the signalto-noise of impurities in HN3 and TEA when compared to the neat analysis of HN3 and TEA by GC/MS. TEA was prepared by weighing out 160 mg of TEA and diluting it to 10 mL with DCM. Because of its corrosive nature, 100 μL of thionyl chloride was first reacted with 3.2 mL of 10% aqueous sodium B

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medium DCS and minimum DCS were considered the optimal F-ratio and subset of peaks for distinguishing reagent (or HN3 samples) based on reagent stock. Each reagent peak table resulted in one group of selected chromatographic peaks and their intensities while the HN3 peak table resulted in one group for each reagent type for a total of four groups. Impurity Analysis. Automated Mass Spectral Deconvolution and Identification System (AMDIS) and the National Institute of Standards and Technology (NIST) MS library 2011 were then used to tentatively identify the impurities that corresponded to the chromatographic peaks selected by the Fratio and DCS. Only library compounds having a reverse match factor greater than 800 and a rank of one were selected as tentative identifications. Impurities that could not be identified according to the above criteria were given an arbitrary name (e.g., “unknown T-1” for a TEA impurity) corresponding to a specific retention time and two specific m/z ion fragments. The impurities for either reagent or HN3 samples were grouped and recorded according to reagent type. In the case of the reagent impurities, all of them were targeted in the 30 HN3 batches during GC/MS selected ion monitoring (SIM) analysis (see GC/MS Analysis). An in-house software tool developed using LabWindows/ CVI 2010 version 10.0.1 (National Instruments Co., Austin, TX) was used to measure peak areas and determine the presence or absence of the selected impurities in the GC/MS full scan data for the reagent samples and in the GC/MS SIM data for the HN3 samples. An impurity’s presence or absence in each sample was determined by visual inspection of the raw chromatographic data to determine if a chromatographic peak was within a given retention time window having a relative ion ratio for two selected m/z (usually top two in abundance) that matched the expected value for the given impurity. A retention time within ±0.2 min of the Metalign reported retention time and ion ratio within 30% absolute of expected values were deemed a positive detection for a given impurity.24 The expected ion ratio for each impurity was derived from a GC/ MS run having a positive hit for the impurity based on identification by AMDIS and NIST. In the case of the HN3 GC/MS full scan data, Agilent Chemstation (including AMDIS and the NIST library) was used to measure peak areas and confirm the presence or absence of those impurities selected from the Peak Selection of the HN3MetAlign peak table. For all samples, the peak area for an impurity’s most abundant m/z ion was used as the impurity’s signal intensity. Impurity Profiles. For each reagent type, an impurity profile was created for each corresponding reagent sample (note, 3 samples per reagent stock were analyzed by GC/MS) using only those impurities selected based on their potential to differentiate reagent samples according to stock. Each impurity profile was a vector of data with each data point being the signal intensity for a specific impurity; in the case of nondetects (i.e., baseline noise), a value of zero was used for signal intensity. Hierarchical cluster analysis (HCA)25from PLS Toolbox 7.9.2 (Eigenvector Research Inc., Manson, WA) was used to determine if the selected impurities for each reagent type (TEA, thionyl chloride, acetone, and chloroform) resulted in samples clustering according to stock. Impurity profiles based on the selected reagent impurities were also created for each of the 30 analyzed HN3 batches (GC/MS SIM data) such that each HN3 batch had an impurity profile for each reagent type for a total of four. The targeted impurities and their signal intensities from the GC/MS SIM data were used in generating

hydroxide and the resulting aqueous solution extracted with a 1 mL aliquot of DCM and then a 0.5 mL aliquot of DCM. The DCM extracts were combined and concentrated by roomtemperature nitrogen blow down to 100 μL. In the case of chloroform and acetone, no sample preparation or dilution was performed for GC/MS analysis. GC/MS Analysis. Samples were analyzed in two sets by GC/MS using electron impact (EI) ionization. The first set contained the reagent samples that were analyzed first in order to determine what reagent impurities to target for analysis in the HN3 samples that were part of the second set. For the first set, triplicate samples (i.e., three prepared samples in separate GC vials) for each stock of chloroform (8 stocks), acetone (7 stocks), TEA (3 stocks), and thionyl chloride (3 stocks) were analyzed once in random order by GC/MS in full scan MS mode. For the second set, one prepared sample from each of the 30 HN3 batches was analyzed by GC/MS in full scan MS mode and in selected ion monitoring (SIM) MS mode. The SIM MS mode specifically targeted 107 reagent impurities discovered in the GC/MS full scan data from the first set (see Impurity Analysis). Prepared samples from a subset of the HN3 batches were also analyzed by GC/MS full scan using both methane- and ammonia-based chemical ionization in order to determine the molecular weight of two HN3 impurities. Details on the actual GC/MS analyzer and experimental conditions are provided in the Supporting Information. Peak Table Generation. MetAlign 3.0 (www.metalign.nl) was used for peak table generation given its demonstrated performance in signature discovery.21 It was used to generate peak tables for the GC/MS full scan data in order to locate chromatographic peaks corresponding to impurities that were characteristically or uniquely present in samples according to reagent stock. Each generated peak table contained the intensities (including a baseline-noise intensity for nondetects) for chromatographic peaks characterized by a specific retention time and m/z that were detected in at least three GC/MS sample runs and absent in a majority of GC/MS blank runs. The (method) blank was the DCM solvent used to dilute the TEA, thionyl chloride, and HN3 samples. A peak table was generated for each of the four HN3 reagents: TEA, thionyl chloride, acetone, and chloroform. Similarly, one peak table was also generated using the GC/MS full scan data for the 30 HN3batch samples and nine blanks. MetAlign parameters used to generate the peak tables are addressed in the Supporting Information and Figure S-1. Peak Selection. The peak tables generated by MetAlign were analyzed using the Fisher-ratio (F-ratio) method22 and the degree-of-class separation (DCS) metric23 in Matlab R2014a (Mathworks Inc., Natick, MA) using in-house codes to select those peaks best suited for distinguishing samples according to stock. Peak selection for the HN3 peak table and each reagent peak table was performed by selecting only those chromatographic peaks having an F-ratio, calculated based on the number of given stocks (e.g., 8 for chloroform), that exceeded a threshold value determined by DCS calculations. Specifically, for each peak table, the product of the median DCS and minimum DCS was calculated for all unique two-stock combinations (e.g., 28 for chloroform) as a function of chromatographic peaks starting with the one having the largest F-ratio and then iteratively including the peak with the next highest F-ratio (and recalculating DCS values) until all peaks were included. The F-ratio and its corresponding subset of chromatographic peaks producing the maximum product of C

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Analytical Chemistry the HN3 impurity profiles. HCA was used to determine if the HN3 impurity profiles, according to reagent type, would result in HN3 batches clustering with other HN3 batches made from the same reagent stock and with samples from the reagent stock used in HN3 syntheses. Finally, the HN3 impurity profiles originally based on the Peak Selection and Impurity Analysis of the HN3 MetAlign peak table and corresponding HN3 GC/ MS full scan data were analyzed by HCA to determine if those impurity profiles resulted in HN3 batches clustering with other HN3 batches made from the same reagent stock for each reagent type. These impurity profiles were based on impurities separate from those found in the reagent stocks. Classification Analysis. The impurity profiles for four reagent types (TEA, thionyl chloride, acetone, and chloroform) were also used to investigate classification according to reagent stock using the multivariate classification methods of partial least-squares discriminant analysis (PLSDA)26 and K nearest neighbors (KNN)25 from PLS Toolbox 7.9.2. One PLSDA and one KNN model were built for each reagent type. Crossvalidation of a training sample set was performed to determine the optimal latent variables (LVs) for PLSDA and to measure model classification performance for both PLSDA and KNN. Model classification performance was measured by the average of the class classification error (E) achieved by cross-validation. For each class, E equaled 1 − NE, where NE was the nonerror fraction (0−1) calculated as the average of class sensitivity and class selectivity. A more detailed explanation of PLSDA including classification error is provided in the Supporting Information. Specifically for chloroform, the PLSDA and KNN models were also validated against a test set of stock E samples that were not part of the training sample set. Additionally, seven PLSDA models and seven KNN models were built such that each model’s training set excluded one chloroform stock whose samples were part of the test set. This was done to see if the test samples would be classified as “no class”, i.e., no match. For all models in this study, each impurity profile was preprocessed by normalizing to unit area and autoscaling.

Figure 1. Overlaid total-ion current (TIC) chromatograms for HN3 batch no. 2 (solid line) and TEA stock S (dashed line) dissolved in DCM. The HN3 and TEA concentrations were 1.2 and 16 mg/mL, respectively, which were high in order to accentuate trace impurity peaks. TEA stock S had two main impurities, DEA (diethanolamine) and MOSN (methoxysilatrane), that were also detected in TEA stocks P and Q.

useful for differentiating stocks of each reagent type, collectively, 12 in the TEA stocks, 11 in the thionyl chloride stocks, 63 in the acetone stocks, and 22 in the chloroform stocks. Tables S-2−S-5 in the Supporting Information list the targeted impurities for each reagent type including their tentative identities. Interestingly, all impurities except for one (tetrachloroethylene, reported for both chloroform and thionyl chloride), were associated with one reagent type even though no attempts were made to locate impurities to differentiate reagent types. Impurity data was used to test whether the impurity profiles based on these selected impurities could differentiate reagent samples according to stock. Figure 2 depicts the GC/MS TIC signals for the chloroform impurities in chloroform stocks C and E which obviously have

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RESULTS AND DISCUSSION Figure 1 depicts two chromatograms obtained from the GC/ MS full scan analysis of an HN3 batch and its respective reagent TEA stock illustrating the data generated in search of CAS impurities. In this study, two approaches were investigated to locate potential CAS impurities for the matching of an HN3 batch to each of its specific reagent stocks. The first approach involved the GC/MS full scan analysis of several stocks of four HN3 reagents to discover impurities that differentiated the stocks of each reagent type prior to HN3 synthesis. The discovered impurities were then targeted in GC/MS SIM analyses of 30 HN3 batches to determine if their relative levels in the HN3 batches, i.e., impurity profiles, would best match those from their corresponding reagent stocks. The second approach was based on GC/MS full scan analysis of the 30 HN3 batches to determine what impurities other than the targeted impurities differentiated HN3 batches according to their reagent stocks. Forensic Impurities in HN3 Reagents. Nontargeted analysis of impurities in several HN3 reagent stocks was achieved by GC/MS analysis in full scan MS mode as described in the Experimental Section. MetAlign analysis of the GC/MS full scan data followed by Peak Selection and Impurity Analysis (see respective subsections) identified numerous impurities

Figure 2. Overlaid TIC chromatograms for chloroform stocks C (dashed line) and E (solid line). Impurities are labeled with numbers and correspond to those listed in Table S-2. Only those targeted impurities detected in chloroform stocks C or E are labeled. D

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Analytical Chemistry Table 2. Supervised Classification Results for Chloroform Stocks Using PLSDA and KNN no. of stocks

stocks (3 training samples per stock class)

test samples

LVsa

7 6 6 6 6 6 6 6

B, C, D, E, X, Y, Z [ ], C, D, E, X, Y, Z B, [ ], D, E, X, Y, Z B, C, [ ], E, X, Y, Z B, C, D, [ ], X, Y, Z B, C, D, E, [ ], Y, Z B, C, D, E, X, [ ], Z B, C, D, E, X, Y, [ ]

E4, E5, E6, E7 B1, B2, B3 C1, C2, C3 D1, D2, D3 E1, E2, E3, E4, E5, E6, E7 X1, X2, X3 Y1, Y2, Y3 Z1, Z2, Z3

5 4 4 5 5 4 3 5

PLSDA strictb (no. of test samples)e E (4) no class no class no class no class E (3) B (3) no class

(3) (2) D (1) (3) (6) D (1)

(3)

PLSDA probablec (no. of test samples)e

KNNd (no. of test samples)e

E (4) D (3) D (3) C (3) D (7) E (3) B (3) X (3)

E (4) D (3) Z (3) C (3) D (7) E (3) B (3) X (3)

a

The number of latent variables (LVs) for each PLSDA model was automatically selected by the PLS Toolbox software. bTest samples are classified to a specific stock if the probability is greater than a specified threshold probability value (0.5) for only one class. If no class has a probability greater than the threshold or if more than one class has probability exceeding the threshold, then the sample is assigned “no class”. cTest samples are matched to a stock that has the highest probability regardless of the probability’s magnitude. dTest samples are matched to the nearest stock whose samples make up the majority of three nearest neighbors. eNumber of test samples classified to the reported stock or “no class”.

On the other hand, all or a majority of samples from the other five unmodeled stocks were correctly classified as “no class”. Interestingly, the most probable stock for C was D and vice versa. This makes sense given that stock D originated from stock C as stock D was prepared in our lab by distilling stock C. In summary, the work described in this section addressed the use of impurity profiles for distinguishing stocks of the same reagent, which was a stated goal of this paper. The following two sections address the other stated goal of the paper of determining if impurities either individually or as a profile can be used for matching HN3 to specific reagents and stocks. Impurities in HN3. Targeted analysis of the four sets of reagent impurities (Tables S-2−S-5) by GC/MS SIM analysis of the 30 HN3 batches resulted in the detection of 67% of all reagent impurities (not necessarily together) in at least three of the HN3 batches using the retention time and ion ratio criteria described in the Impurity Analysis section. 4-Hydroxy-4methyl-2-pentanone (or diacetone alcohol) is a recognized acetone impurity27 that was exclusive to acetone and detected in all acetone stocks. Diacetone alcohol is the first product from the aldol condensation of acetone with itself. It was detected in all HN3 batches (e.g., see Figure S-3) and therefore believed to be a reagent impurity unaltered by HN3 synthesis that points to acetone usage. Similarly,1,2-dichloroethane for thionyl chloride was specific and common to the analyzed thionyl chloride samples and found in all HN3 batches. In contrast, no targeted impurities specific to either TEA or chloroform were detected in the HN3 batches. Note, chloroform was detected in all HN3 batches but it was detected at similar levels in the DCM method blanks. Similarly, the DCM method blanks also contained trace levels of two acetone impurities (toluene and p-xylene) and one chloroform impurity (3-methyl 2-butanone). Given that all HN3 samples were diluted in the DCM solvent for GC/MS analysis, those three compounds were detected in all analyzed HN3 samples at levels similar to those in the DCM solvent. No other selected reagent impurities were detected in the DCM solvent. Unlike our previous work with sarin batches,16 HCA did not uniquely cluster any of the HN3 batches with any of their respective reagent stocks based on their impurity profiles. No obvious patterns where seen by comparing the HCA dendrograms (one per reagent type) and Table 1 that could explain the observed connections among HN3 batches. In fact, even the four sets of duplicate HN3 batches (i.e., no. 27/no. 23, no. 28/no. 10, no. 29/no. 4, and no. 30/no. 8) where usually far from one another in the dendrograms indicating their impurity

impurities that are different based on stock. HCA of the impurity profiles created from the GC/MS signal intensity from the most abundant ion for each impurity in a reagent-impurity set resulted in all reagent samples clustering according to stock for each reagent type. The HCA dendrogram depicted in Figure S-2 clearly depicts chloroform samples clustering according stock. Similar HCA dendrograms were obtained for TEA, thionyl chloride, and acetone and supported the assertion that impurity profiles can be used to differentiate different stocks of the same bulk chemical. Further, the unique impurity profiles detected for each reagent stock should permit the matching of any of the reagent samples to its stock. This was investigated using the supervised classification method of PLSDA (see Classification Analysis). The PLSDA models built for each reagent type resulted in an average stock classification error by cross-validation of 2.4, 2.8, 2.8, and 11% for chloroform (7 stocks; A was excluded), thionyl chloride (3 stocks), acetone (7 stocks), and TEA (3 stocks), respectively. Chloroform stock A was not modeled because it had none of the targeted chloroform impurities. The PLSDA models for thionyl chloride and TEA used a subset of impurities selected by iPLS-forward selection as it has been shown to improve classification performance.8 The mostly low classification errors achieved by cross-validation for the PLSDA models support the viability of using organic impurity profiles as CAS for sourcing reagents or other synthetic chemicals to specific stocks. Additional support for stock classification through impurity profiling is provided in Table 2, row 1, which shows the correct classification (0% misclassified) of four chloroform stock E samples (E4, E5, E6, E7) that were part of a test set used to verify the PLSDA model built to classify seven chloroform stocks. These four stock E samples were analyzed 4 months after the samples used in the training set. In addition, Table 2, rows 2−8, depict the classification results for test samples whose reagent stocks were not included in the PLSDA models. The classification results for KNN are also shown to complement PLSDA. As shown in Table 2, all samples from stocks X and Y are matched to a specific stock as opposed to “no class”, which is the preferred answer because those two stocks were not modeled and had unique impurity profiles. The uniqueness of their impurity profiles was supported by obtaining zero PLSDA cross-validation errors for the X and Y stocks when all seven stocks were modeled. This demonstrates the potential danger of false classifications when stocks expected to be encountered as unknowns are not modeled. E

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parent molecular ion and is a characteristic fragment for tertiary amines such as HN3 and HN1 (bis(2-chloroethyl)ethylamine) that contain two chloroethyl groups and a C2 or larger alkyl group. Given that component A was not detected in any of the TEA stock S samples, it was surmised that it was produced during HN3 synthesis from an impurity unique to TEA stock S having a m/z 118 ion that is attributable to the bis(2hydroxyethyl)NCH2 cation. The GC/MS signal for this impurity (3) is depicted in Figure 4 along with the signals of

profiles were quite different from each other and more similar to others. In summary, no obvious correlations were revealed between HN3 batches, HN3 batch duplicates, and specific reagent stocks using impurity profiles collectively based on 107 reagent impurities. The fact that stocks from different reagent types had positive hits for the same impurities was not a likely cause for this outcome given its extremely low occurrence; for example, in terms of the 22 chloroform impurities, one was detected in acetone stock H, another two in acetone stock J, a different one in TEA stock S, and none in TEA stocks P and Q, thionyl chloride stocks AD and TD, and acetone stock F. Three potential reasons for the HN3 impurity profiles not resembling those from their respective reagents stocks are (1) loss of some targeted reagent impurities during the purification of HN3·HCl by acetone recrystallization, (2) consumption of some targeted impurities by chemical side reactions during HN3 synthesis, and (3) production of some targeted reagent impurities by side reactions during HN3 synthesis. In fact, we detected two impurities unique to HN3 that appear to be products of side reactions with specific impurities present in specific reagent stocks. Reaction-Produced Stock-Specific Impurities in HN3. Figure 3A,B depict two sets of overlaid chromatograms showing

Figure 4. Overlaid extracted ion chromatograms (m/z 118) for TEA stocks Q (dotted line), P (dashed line), and S (solid line) depicting four TEA impurities including impurity 3 that was only detected in TEA stock S. Impurity 3 is believed to be the precursor for component A.

three other impurities (1, 2, 4) believed to bis(2-hydroxyethyl) tertiary amines and present in all three TEA stocks. The HCl salt of component A is believed to have been produced by the reaction of thionyl chloride and the presumed bis(2hydroxyethyl)hydroxy C4 alkyl amine uniquely present in TEA stock S during HN3 synthesis. The free amine version of component A was then formed during the final step of HN3 synthesis. A more precise identification of the presumed bis(2hydroxyethyl)hyrdoxy C4 alkyl amine in TEA stock S was not possible because there existed no reported EI mass spectra for any of its isomers. In addition, the impurity was partially resolved so it was not possible to get a complete mass spectrum. Further work is needed to confidently determine the full identity of this TEA impurity. Another impurity, component B, points to the use of either chloroform stocks E or A (not D) independent of the stocks used for the other three reagents as shown in Figure S-5A,B. Component B was tentatively identified as N, N-bis(2chloroethyl)-N-(2-ethoxyethyl)amine with a molecular weight (35Cl isotope only) of 213 based on its CI spectrum (Figure S6A) and EI mass spectrum (Figure S-6B). The number of chlorines in component B, in this case two, was supported by the relative signal intensities in the CI mass spectrum for m/z 214 (M + H), 216, and 218 of 100:65:11. It is believed that component B was formed after the formation of HN3·HCl by nucleophilic substitution of a chlorine atom from one of the 2chloroethyl groups in HN3 by an ethoxy group originating from ethanol present in chloroform stocks A and E. According to the certificates of analysis, chloroform stocks A and E

Figure 3. (A) Overlaid extracted ion chromatograms (m/z 154) depicting component A present in all HN3 batches (n = 8) synthesized using TEA stock S. (B) Absence of signal for component A in HN3 batches (n = 22) made not using TEA stock S. Component A had an estimated concentration of 40 ppm in HN3 based on relative TIC peak areas.

an impurity that was only detected in the HN3 batches made with TEA stock S. This impurity, component A, was discovered in the HN3 GC/MS full scan data using F-ratio and DCS analysis of the MetAlign peak table as described in the Peak Selection section and was tentatively identified as a bis(2chloroethyl)chloro C4 alkyl amine with a molecular weight (35Cl isotope only) of 231. Its molecular weight was determined by inspection of its CI mass spectrum (Figure S4A), which is provided with its EI mass spectrum (Figure S-4B) in the Supporting Information. The number of chlorines in component A, in this case three, was supported by the relative signal intensities in the CI mass spectrum for m/z 232 (M + H), 234, 236, and 238 of 100:98:32:3.5. Its most abundant ion in the EI mass spectrum was m/z 154 and was attributed to the even-electron cation fragment bis(2-chloroethyl)NCH2. This fragment was presumably produced from alpha cleavage of its F

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Analytical Chemistry contained 1% (v/v) ethanol as stabilizer while stock D contained amylene as a stabilizer. Ethanol was also detected by GC/MS full scan in chloroform stocks A and E and not in stock D. The presence of components A and B make it possible to match an HN3 batch to either TEA stock S or to chloroform stocks E and A.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected].

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Notes

The authors declare no competing financial interest.

CONCLUSION The work reported here demonstrates for the first time trace impurities produced during the synthesis of a chemical warfare (CW) agent that point back to specific reagent stocks used in the synthesis of the CW agent. These reaction-produced impurities complement those reagent impurities that remain unaltered during product synthesis. Experimental work involving chemical synthesis is needed to discover these and other CAS impurities at least until a fundamental understanding of CAS impurities is obtained to enable accurate predictive modeling. Also in this work, supervised classification using organic impurity profiles for classifying reagent samples according to stock were demonstrated including samples whose stocks were not modeled. While classification errors were as low as zero for stocks that were modeled, two of seven chloroform stocks that were not modeled had their samples matched to a stock rather than assigned a “no class” classification. This illustrates the importance of building classification models that take into account all probable classes (e.g., reagent stocks) and the need to get a fundamental understanding of where organic CAS impurities originate in order to estimate the likelihood of different chemical stocks having experimentally indistinguishable impurity profiles. In terms of applicability to real-world samples, work is needed to determine the operational usability and limits for sourcing using specific impurities and impurity profiles for actual CW-related events especially in cases were CW-related samples are potentially adulterated or altered by environmental factors either before or after collection. Recently, a study was done that investigated the effects of real-world factors on the recovery and exploitation of impurity profiles for source matching using a CW simulant that was aerosolized onto actual office-space materials.28 In that study, source matching was demonstrated after simulant dissemination, sample preparation, and analysis; also, several lessons were learned including the importance of selecting an extraction solvent with impurities that would minimize interference with a CW agent’s impurity profile. Similar studies may be done on the HN3 batches to include measuring the effect of storage time on the HN3 forensic impurities since their last analysis. In terms of environmental interference, one may expect some of the impurities in a chemical’s impurity profile to be masked by other chemicals present in the sample matrix; however, there are real-world samples, such as from the Tokyo 1995 sarin subway attack29 and CW-related samples collected from unexploded ordinances or storage containers, that will be relatively pristine, like the HN3 batches studied here, and therefore almost ideal for some degree of sourcing by impurities.

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Experimental details and additional analytical data (PDF)

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ACKNOWLEDGMENTS The authors would like to thank Sedric B. Granger, Michael D. Crenshaw, and Greg Kastner from Battelle for information and guidance regarding CW agent handling. Helen W. Kreuzer and Nikhil S. Mirjankar from PNNL are also thanked for helpful edits and suggestions in the writing of this paper. Funding for this work was provided by the Science and Technology Directorate, U.S. Department of Homeland Security under Contract HSHQPM-11-X-00040.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.6b00766. G

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