Physicochemical Gas-Solid Sorption Properties of Geologic Materials Using Inverse Gas Chromatography.

The goal of this study was to determine the physicochemical properties of a variety of geologic materials using inverse gas chromatography (IGC) by varying probe gas selection, temperature, carrier gas flow rate, and humidity. This is accomplished by measuring the level of interaction between the materials of interest and known probe gases. Identifying a material's physicochemical characteristics can help provide a better understanding of the transport of gaseous compounds in different geologic materials or between different geological layers under various conditions. Our research focused on measuring the enthalpy (heat) of adsorption, Henry's constant, and diffusion coefficients of a suite of geologic materials, including two soil types (sandy clay-loam and loam), quartz sand, salt, and bentonite clay, with various particle sizes. The reproducibility of IGC measurements for geologic materials, which are inherently heterogeneous, was also assessed in comparison to the reproducibility for more homogeneous synthetic materials. This involved determining the variability of physicochemical measurements obtained from different IGC approaches, instruments, and researchers. For the investigated IGC-determined parameters, the need for standardization became apparent, including the need for application-relevant reference materials. The inherent physical and chemical heterogeneities of soil and many geologic materials can make the prediction of sorption properties difficult. Characterizing the properties of individual organic and inorganic components can help elucidate the primary factors influencing sorption interactions in more complex mixtures. This research examined the capabilities and potential challenges of characterizing the gas sorption properties of geologic materials using IGC.

Source Attribution of Cyanides Using Anionic Impurity Profiling, Stable Isotope Ratios, Trace Elemental Analysis and Chemometrics.

Chemical attribution signatures (CAS) for chemical threat agents (CTAs), such as cyanides, are being investigated to provide an evidentiary link between CTAs and specific sources to support criminal investigations and prosecutions. Herein, stocks of KCN and NaCN were analyzed for trace anions by high performance ion chromatography (HPIC), carbon stable isotope ratio (δ(13)C) by isotope ratio mass spectrometry (IRMS), and trace elements by inductively coupled plasma optical emission spectroscopy (ICP-OES). The collected analytical data were evaluated using hierarchical cluster analysis (HCA), Fisher-ratio (F-ratio), interval partial least-squares (iPLS), genetic algorithm-based partial least-squares (GAPLS), partial least-squares discriminant analysis (PLSDA), K nearest neighbors (KNN), and support vector machines discriminant analysis (SVMDA). HCA of anion impurity profiles from multiple cyanide stocks from six reported countries of origin resulted in cyanide samples clustering into three groups, independent of the associated alkali metal (K or Na). The three groups were independently corroborated by HCA of cyanide elemental profiles and corresponded to countries each having one known solid cyanide factory: Czech Republic, Germany, and United States. Carbon stable isotope measurements resulted in two clusters: Germany and United States (the single Czech stock grouped with United States stocks). Classification errors for two validation studies using anion impurity profiles collected over five years on different instruments were as low as zero for KNN and SVMDA, demonstrating the excellent reliability associated with using anion impurities for matching a cyanide sample to its factory using our current cyanide stocks. Variable selection methods reduced errors for those classification methods having errors greater than zero; iPLS-forward selection and F-ratio typically provided the lowest errors. Finally, using anion profiles to classify cyanides to a specific stock or stock group for a subset of United States stocks resulted in cross-validation errors ranging from 0 to 5.3%.

Organic Chemical Attribution Signatures for the Sourcing of a Mustard Agent and Its Starting Materials.

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(2-chloroethyl)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.

Impurity profiling to match a nerve agent to its precursor source for chemical forensics applications.

Chemical forensics is a developing field that aims to attribute a chemical (or mixture) of interest to its source by the analysis of the chemical itself or associated material constituents. Herein, for the first time, trace impurities detected by gas chromatography/mass spectrometry and originating from a chemical precursor were used to match a synthesized nerve agent to its precursor source. Specifically, six batches of sarin (GB, isopropyl methylphosphonofluoridate) and its intermediate methylphosphonic difluoride (DF) were synthesized from two commercial stocks of 97% pure methylphosphonic dichloride (DC); the GB and DF were then matched by impurity profiling to their DC stocks from a collection of five possible stocks. Source matching was objectively demonstrated through the grouping by hierarchal cluster analysis of the GB and DF synthetic batches with their respective DC precursor stocks based solely upon the impurities previously detected in five DC stocks. This was possible because each tested DC stock had a unique impurity profile that had 57% to 88% of its impurities persisting through product synthesis, decontamination, and sample preparation. This work forms a basis for the use of impurity profiling to help find and prosecute perpetrators of chemical attacks.

Hydrolysis of methylphosphonic anhydride solid to methylphosphonic acid probed by Raman and infrared reflectance spectroscopies.

Much is still unknown about the mechanisms and rates of environmental degradation of organophosphorous pesticides and agents. In this study we focus on the degradation of one organophosphorous compound, namely solid methylphosphonic anhydride [CH3P(O)OHOP(O)OHCH3, MPAN] and its rate of conversion to methylphosphonic acid (MPA) via heterogeneous hydrolysis. Pure MPAN was synthesized and loaded in open sample cups placed inside exposure chambers containing saturated salt solutions to control the relative humidity (RH). The reaction was monitored in the sample cup at various times using both infrared hemispherical reflectance (HRF) spectroscopy and Raman spectroscopy. Calibrated HRF and Raman spectra of both pure reagents as well as gravimetrically prepared mixtures were used to quantify the concentrations of MPAN and MPA throughout the reaction. Results show both HRF and Raman spectroscopies are convenient non-invasive methods for detection of solid chemicals as long as a large area is sampled to average out any spatial inhomogeneities that occur on the sample surface and minimal phase changes occur during the course of the reaction. The samples for the 54 and 75% RH studies showed significant deliquescence, and the liquid water had to be removed prior to measurement; this effect led to differences in the sample form, such that the calibration spectra were no longer valid for quantitative analysis using HRF spectroscopy. Raman spectroscopy, on the other hand, proved to be less sensitive to these effects and provided better estimation of the MPAN and MPA concentrations. The MPAN degradation rate displayed a very strong dependence on relative humidity: at room temperature the reaction showed 50% conversion of the MPAN in 761 ± 54 h at 33% RH, 33 ± 4 h at 43% RH, 17 ± 2 h at 54% RH and just 7 ± 1 h at 75% RH.

Signature-discovery approach for sample matching of a nerve-agent precursor using liquid chromatography-mass spectrometry, XCMS, and chemometrics.

This report demonstrates the use of bioinformatic and chemometric tools on liquid chromatography-mass spectrometry (LC-MS) data for the discovery of trace forensic signatures for sample matching of ten stocks of the nerve-agent precursor known as methylphosphonic dichloride (dichlor). XCMS, a software tool primarily used in bioinformatics, was used to comprehensively search and find candidate LC-MS peaks in a known set of dichlor samples. These candidate peaks were down selected to a group of 34 impurity peaks. Hierarchal cluster analysis and factor analysis demonstrated the potential of these 34 impurities peaks for matching samples based on their stock source. Only one pair of dichlor stocks was not differentiated from one another. An acceptable chemometric approach for sample matching was determined to be variance scaling and signal averaging of normalized duplicate impurity profiles prior to classification by K-nearest neighbors. Using this approach, a test set of seven dichlor samples were all correctly matched to their source stock. The sample preparation and LC-MS method permitted the detection of dichlor impurities quantitatively estimated to be in the parts-per-trillion (w/w). The detection of a common impurity in all dichlor stocks that were synthesized over a 14-year period and by different manufacturers was an unexpected discovery. Our described signature-discovery approach should be useful in the development of a forensic capability to assist investigations following chemical attacks.

Impurity profiling of a chemical weapon precursor for possible forensic signatures by comprehensive two-dimensional gas chromatography/mass spectrometry and chemometrics.

In this report we present the feasibility of using analytical and chemometric methodologies to reveal and exploit the chemical impurity profiles from commercial dimethyl methylphosphonate (DMMP) samples to illustrate the type of forensic information that may be obtained from chemical-attack evidence. Using DMMP as a model compound of a toxicant that may be used in a chemical attack, we used comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry (GC x GC/TOF-MS) to detect and identify trace organic impurities in six samples of commercially acquired DMMP. The GC x GC/TOF-MS data was analyzed to produce impurity profiles for all six DMMP samples using 29 analyte impurities. The use of PARAFAC for the mathematical resolution of overlapped GC x GC peaks ensured clean spectra for the identification of many of the detected analytes by spectral library matching. The use of statistical pairwise comparison revealed that there were trace impurities that were quantitatively similar and different among five of the six DMMP samples. Two of the DMMP samples were revealed to have identical impurity profiles by this approach. The use of nonnegative matrix factorization indicated that there were five distinct DMMP sample types as illustrated by the clustering of the multiple DMMP analyses into five distinct clusters in the scores plots. The two indistinguishable DMMP samples were confirmed by their chemical supplier to be from the same bulk source. Sample information from the other chemical suppliers supported the idea that the other four DMMP samples were likely from different bulk sources. These results demonstrate that the matching of synthesized products from the same source is possible using impurity profiling. In addition, the identified impurities common to all six DMMP samples provide strong evidence that basic route information can be obtained from impurity profiles. Finally, impurities that may be unique to the sole bulk manufacturer of DMMP were found in some of the DMMP samples.

Improved quantitative analysis of ion mobility spectrometry by chemometric multivariate calibration.

Traditional peak-area calibration and the multivariate calibration methods of principal component regression (PCR) and partial least squares (PLS), including unfolded PLS (U-PLS) and multi-way PLS (N-PLS), were evaluated for the quantification of 2,4,6-trinitrotoluene (TNT) and cyclo-1,3,5-trimethylene-2,4,6-trinitramine (RDX) in Composition B explosive mixtures analyzed by temperature step desorption ion mobility spectrometry (TSD-IMS). TSD is a technique used to partially resolve mixture components before ion mobility analysis by exploiting differences in thermal desorption profiles. While TSD was used here, the results and conclusions presented are universally applicable to IMS. Although IMS is used extensively for trace explosive detection, it has not been sufficiently demonstrated in the past for the detailed analysis of specific compositions of explosive mixtures. This manuscript combines IMS with multivariate chemometric data methods to enhance the quantitative performance of IMS needed for detailed explosive analyses. This is demonstrated using data from the replicate TSD-IMS analyses of eight different Composition B samples. The true TNT and RDX concentrations were determined by analyzing the Composition B samples by high performance liquid chromatography with UV absorbance detection. Most of the Composition B samples were found to have distinct TNT and RDX concentrations. The data from each TSD-IMS analysis were a 2-D array that was reduced by averaging into a vector or mean IMS spectrum. Although the mean IMS peaks for TNT and RDX were sufficiently resolved to use peak area to generate linear calibration curves, the peak-area variability was too large to differentiate Composition B samples based on their predicted RDX and TNT concentrations. Applying PCR and PLS on the exact same IMS spectra used for the peak-area study improved quantitative accuracy and precision approximately 3-to 5-fold and 2- to 4-fold, respectively. This in turn improved the probability of correctly identifying Composition B samples based upon the estimated RDX and TNT concentrations from 11% with peak area to 44% and 89% with PLS. The success of PLS over peak area is attributed to multivariate signal averaging and the simultaneous maximization of correlation between the entire span of the IMS mean spectra and the known TNT and RDX concentrations. In this study, PLS also outperformed PCR and had similar quantitative results to U-PLS. In terms of N-PLS, its mean bias values were up to 2.8 times larger and the mean RSD values were at least 40% larger than those obtained by PLS.

Stable-carbon isotope ratios for sourcing the nerve-agent precursor methylphosphonic dichloride and its products.

The ability to connect a chemical threat agent to a specific batch of a synthetic precursor can provide a fingerprint to contribute to effective forensic investigations. Stable isotope analysis can leverage intrinsic, natural isotopic variability within the molecules of a threat agent to unlock embedded chemical fingerprints in the material. Methylphosphonic dichloride (DC) is a chemical precursor to the nerve agent sarin. DC is converted to methylphosphonic difluoride (DF) as part of the sarin synthesis process. We used a suite of commercially available DC stocks to both evaluate the potential for δ13C analysis to be used as a fingerprinting tool in sarin-related investigations and to develop sample preparation techniques (using chemical hydrolysis) that can simplify isotopic analysis of DC and its synthetic products. We demonstrate that natural isotopic variability in DC results in at least three distinct, isotope-resolved clusters within the thirteen stocks we analyzed. Isotopic variability in the carbon feedstock (i.e., methanol) used for DC synthesis is likely inherited by the DC samples we measured. We demonstrate that the hydrolysis of DC and DF to methylphosphonic acid (MPA) can be used as a preparative step for isotopic analysis because the reaction does not impart a significant isotopic fractionation. MPA is more chemically stable, less toxic, and easier to handle than DC or DF. Further, the hydrolysis method we demonstrated can be applied to a suite of other precursors or to sarin itself, thereby providing a potentially valuable forensic tool.

Evaluating and modeling the effects of surface sampling factors on the recovery of organic chemical attribution signatures using the accelerated diffusion sampler and solvent extraction.

In this study, an experimental design matrix was created and executed to test the effects of various real-world factors on the ability of (1) the accelerated diffusion sampler with solid phase micro-extraction (ADS-SPME) and (2) solvent extraction to capture organic chemical attribution signatures (CAS) from dimethyl methylphosphonate (DMMP) spiked onto painted wall board (PWB) surfaces. The DMMP CAS organic impurities sampled by ADS-SPME and solvent extraction were analyzed by gas chromatography/mass spectrometry (GC/MS). The number of detected DMMP CAS impurities and their respective GC/MS peak areas were determined as a function of DMMP stock, DMMP spiked volume, exposure time, SPME sampling time, and ADS headspace pressure. Based on the statistical analysis of experimental results, several general conclusions are made: (1) the amount of CAS impurity detected using ADS-SPME and GC/MS was most influenced by spiked volume, stock, and ADS headspace pressure, (2) reduced ADS headspace pressure increased the amount of detected CAS impurity, as measured by GC/MS peak area, by up to a factor of 1.7-1.9 compared to ADS at ambient headspace pressure, (3) the ADS had no measurable effect on the number of detected DMMP impurities, that is, ADS (with and without reduced pressure) had no practical effect on the DMMP impurity profile collected from spiked PWB, and (4) solvent extraction out performed ADS-SPME in terms of consistently capturing all or most of the targeted DMMP impurities from spiked PWB.

Elemental source attribution signatures for calcium ammonium nitrate (CAN) fertilizers used in homemade explosives.

Calcium ammonium nitrate (CAN) is a widely available fertilizer composed of ammonium nitrate (AN) mixed with some form of calcium carbonate such as limestone or dolomite. CAN is also frequently used to make homemade explosives. The potential of using elemental profiling and chemometrics to match both pristine and reprocessed CAN fertilizers to their factories of origin for use in future forensic investigations was examined. Inductively coupled plasma-mass spectrometry (ICP-MS) was used to determine the concentrations of 64 elements in 125 samples from 11 CAN stocks from 6 different CAN factories. Using Fisher ratio and degree-of-class-separation, the elements Na, V, Mn, Cu, Ga, Sr, Ba and U were selected for classification of the CAN samples into 5 factory groups; one group was two factories from the same fertilizer company. Partial least squares discriminant analysis (PLSDA) was used to develop a classification model which was tested on a separate set of samples. The test set included samples that were analyzed at a different time period and samples from factory stocks that were not part of the training set. For pristine CAN samples, i.e., unadulterated prills, 73% of the test samples were matched to their correct factory group with the remaining 27% undetermined using strict classification. The same PLSDA model was used to correctly match all CAN samples that were reprocessed by mixing with powdered sugar. For CAN samples that were reprocessed by mixing with aluminum or by extraction of AN with tap or bottled water, correct classification was observed for one factory group, but source matching was confounded with adulterant interference for two other factories. The elemental signatures of the water-insoluble (calcium carbonate) portions of CAN provided a greater degree of discrimination between factories than the water-soluble portions of CAN. In summary, this work illustrates the strong potential for matching unadulterated CAN fertilizer samples to their manufacturing facility using elemental profiling and chemometrics. The effectiveness of this method for source determination of reprocessed CAN is dependent on how much an adulterant alters the recovered elemental profile of CAN.

The chemometric resolution and quantification of overlapped peaks form comprehensive two-dimensional liquid chromatography.

The chemometric resolution and quantification of overlapped peaks from comprehensive two-dimensional (2D) liquid chromatography (LCxLC) data are demonstrated. The LCxLC data is produced from an in-house LCxLC analyzer that couples an anion-exchange column via a multi-port valve with a reversed-phase column connected to a UV absorbance detector. Three test mixtures, each containing a target analyte, are subjected to partial LCxLC separations to simulate likely cases of signal overlap. The resulting unresolved target-analyte signals are then analyzed by the standard-addition method and two chemometric methods. The LCxLC analyses of a test mixture and its corresponding standard-addition mixture results in two data matrices, one for each mixture. The stacking of these two data matrices produces a data structure that can then be analyzed by trilinear chemometric methods. One method, the generalized rank annihilation method (GRAM), uses a non-iterative eigenvalue-based approach to mathematically resolve overlapped trilinear signals. The other method, parallel factor analysis (PARAFAC), uses an iterative approach to resolve trilinear signals by the optimization of initial estimates using alternating least squares and signal constraints. In this paper, GRAM followed by PARAFAC analysis is shown to produce better qualitative and quantitative results than using each method separately. For instance, for all three test mixtures, the GRAM-PARAFAC approach improved quantitative accuracy by at least a factor of 4 and quantitative precision by more than 2 when compared to GRAM alone. This paper also introduces a new means of correcting run-to-run retention time shifts in comprehensive 2D chromatographic data.

Chemometric approach for the resolution and quantification of unresolved peaks in gas chromatography--selected-ion mass spectrometry data.

A semiautomated and integrated chemometric approach is presented for the resolution and quantification of unresolved target-analyte signals in gas chromatography-selected-ion monitoring (GC-SIM) data collected using scanning mass spectrometers. The chemometric approach utilizes an unskewing algorithm and two multivariate chemometric methods known as rank alignment and the generalized rank annihilation method (GRAM). The unskewing algorithm corrects the retention-time differences within a single GC-SIM data matrix caused by using a scanning mass spectrometer. Rank alignment objectively corrects the run-to-run retention-time difference between a sample GC-SIM data matrix and a standard addition GC-SIM data matrix. GRAM analysis uses the sample and standard addition data matrices to mathematically resolve and quantify the target-analyte signal(s). The resolution and quantification of severely unresolved target-analyte signals are demonstrated using GC-SIM data obtained from conventional heart-cut two-dimensional gas chromatography with mass spectrometric detection. In addition, the GC-SIM data is used to demonstrate the result of chemometric analysis when the absence of a target-analyte signal is obscured by interference. Chemometric analysis is shown to unambiguously detect an analyte based on its resolved mass chromatograms in situations where the traditional approach of measuring peak height fails to positively detect it. The predicted analyte concentrations are within 8% of the reference concentrations.

Trilinear chemometric analysis of two-dimensional comprehensive gas chromatography-time-of-flight mass spectrometry data.

Two-dimensional comprehensive gas chromatography (GC x GC) is a powerful instrumental tool in its own right that can be used to analyze complex mixtures, generating selective data that is applicable to multivariate quantitative analysis and pattern recognition. It has been recently demonstrated that by coupling GC x GC to time-of-flight mass spectrometry (TOFMS), a highly selective technique is produced. One separation on a GC x GC/TOFMS provides retention times on two chromatographic columns and a complete mass spectrum for each component within the mixture. In this manuscript, we demonstrate how the selectivity of GC x GC/TOFMS combined with trilinear chemometric techniques such as trilinear decomposition (TLD) and parallel factor analysis (PARAFAC) results in a powerful analytical methodology. Using TLD and PARAFAC, partially resolved components in complex mixtures can be deconvoluted and identified using only one data set without requiring either signal shape assumptions or fully selective mass signals. Specifically, a region of overlapped peaks in a complex environmental sample was mathematically resolved with TLD and PARAFAC to demonstrate the utility of these techniques as applied to GC x GC/TOFMS data of a complex mixture. For this data, it was determined that PARAFAC initiated by TLD performed a better deconvolution than TLD alone. After deconvolution, mass spectral profiles were then matched to library spectra for identification. A standard addition analysis was performed on one of the deconvoluted analytes to demonstrate the utility of TLD-initiated PARAFAC for quantification without the need for accurate retention time alignment between sample and standard data sets.

Valve-based comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometric detection: instrumentation and figures-of-merit.

A valve-based comprehensive two-dimensional gas chromatograph coupled to a time-of-flight mass spectrometer (GC x GC/TOFMS) is demonstrated. The performance characteristics of the instrument were evaluated using a complex sample containing a mixture of fuel components, natural products, and organo-phosphorous compounds. The valve-based GC x GC, designed to function with an extended temperature of operation range, is shown to have high chromatographic resolution, high separation efficiency and low detection limits. Typical peak widths at base are nominally from 100 to 300 ms on column 2 and nominally 10 s on column 1. The injected mass and injected concentration limit of detection (LOD), defined as 3 standard deviations above the mean baseline noise, for three organo-phosphorous compounds (triethylphosphorothioate (TEPT), dimethyl methyl phosphonate (DMMP) and dimethyl phosphite (DMP)) in a complex environmental sample were from 6 to 38 pg, and 3 to 17 ng/ml, respectively. The temperature program for the environmental sample ranged from 40 to 230 degrees C, a temperature range capable of analyzing semi-volatile compounds. A new compact, stand-alone, valve-pulse generator device has been implemented and is also reported. The valve-based GC x GC instrument, therefore, offers a simple, rugged and less expensive alternative to thermally modulated instruments.

Multivariate selectivity as a metric for evaluating comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry subjected to chemometric peak deconvolution.

Two-dimensional gas chromatography (GC x GC) coupled to time-of-flight mass spectrometry (TOFMS) [GC x GC-TOFMS)] is a highly selective technique well suited to analyzing complex mixtures. The data generated is information-rich, making it applicable to multivariate quantitative analysis and pattern recognition. One separation on a GC x GC-TOFMS provides retention times on two chromatographic columns and a complete mass spectrum for each component within the mixture. In this report, we demonstrate how GC x GC-TOFMS combined with trilinear chemometric techniques, specifically parallel factor analysis (PARAFAC) initiated by trilinear decomposition (TLD), results in a powerful analytical methodology for multivariate deconvolution. Using PARAFAC, partially resolved components in complex mixtures can be deconvoluted and identified without requiring a standard data set, signal shape assumptions or any fully selective mass signals. A set of four isomers (iso-butyl, sec-butyl, tert-butyl, and n-butyl benzenes) is used to investigate the practical limitations of PARAFAC for the deconvolution of isomers at varying degrees of chromatographic resolution and mass spectral selectivity. In this report, multivariate selectivity was tested as a metric for evaluating GC x GC-TOFMS data that is subjected to PARAFAC peak deconvolution. It was found that deconvolution results were best with multivariate selectivities over 0.18. Furthermore, the application of GC x GC-TOFMS followed by TLD/PARAFAC is demonstrated for a plant metabolite sample. A region of GC x GC-TOFMS data from a complex natural sample of a derivatized metabolic plant extract from Huilmo (Sisyrinchium striatum) was analyzed using TLD/PARAFAC, demonstrating the utility of this analytical technique on a natural sample containing overlapped analytes without selective ions or peak shape assumptions.

Comprehensive two-dimensional gas chromatography of volatile and semi-volatile components using a diaphragm valve-based instrument.

A high-temperature configuration for a diaphragm valve-based gas chromatography (GCXGC) instrument is demonstrated. GCxGC is a powerful instrumental tool often used to analyze complex mixtures. Previously, the temperature limitations of valve-based GCxGC instruments were set by the maximum operating temperature of the valve, typically 175 degrees C. Thus, valve-based GCxGC was constrained to the analysis of mainly volatile components; however, many complex mixtures contain semi-volatile components as well. A new configuration is described that extends the working temperature range of diaphragm valve-based GCxGC instruments to significantly higher temperatures, so both volatile and semi-volatile compounds can be readily separated. In the current investigation, separations at temperatures up to 250 degrees C are demonstrated. This new design features both chromatographic columns in the same oven with the valve interfacing the two columns mounted in the side of the oven wall so the valve is both partially inside as well as outside the oven. The diaphragm and the sample ports in the valve are located inside the oven while the temperature-restrictive portion of the valve (containing the O-rings) is outside the oven. Temperature measurements on the surface of the valve indicate that even after a sustained oven temperature of 240 degrees C, the portions of the valve directly involved with the sampling from the first column to the second column track the oven temperature to within 1.2% while the portions of the valve that are temperature-restrictive remain well below the maximum temperature of 175 degrees C. A 26-component mixture of alkanes, ketones, and alcohols whose boiling points range from 65 degrees C (n-hexane) to 270 degrees C (n-pentadecane) is used to test the new design. Peak shapes along the first column axis suggest that sample condensation or carry-over in the valve is not a problem. Chemometric data analysis is performed to demonstrate that the resulting data have a bilinear structure. After over 6 months of use and temperature conditions up to 265 degrees C, no deterioration of the valve or its performance has been observed.

Comparative evaluation of preprocessing freeware on chromatography/mass spectrometry data for signature discovery.

Preprocessing software, which converts large instrumental data sets into a manageable format for data analysis, is crucial for the discovery of chemical signatures in metabolomics, chemical forensics, and other signature-focused disciplines. Here, four freely available and published preprocessing tools known as MetAlign, MZmine, SpectConnect, and XCMS were evaluated for impurity profiling using nominal mass GC/MS data and accurate mass LC/MS data. Both data sets were previously collected from the analysis of replicate samples from multiple stocks of a nerve-agent precursor and method blanks. Parameters were optimized for each of the four tools for the untargeted detection, matching, and cataloging of chromatographic peaks from impurities present in the stock samples. The peak table generated by each preprocessing tool was analyzed to determine the number of impurity components detected in all replicate samples per stock and absent in the method blanks. A cumulative set of impurity components was then generated using all available peak tables and used as a reference to calculate the percent of component detections for each tool, in which 100% indicated the detection of every known component present in a stock. For the nominal mass GC/MS data, MetAlign had the most component detections followed by MZmine, SpectConnect, and XCMS with detection percentages of 83, 60, 47, and 41%, respectively. For the accurate mass LC/MS data, the order was MetAlign, XCMS, and MZmine with detection percentages of 80, 45, and 35%, respectively. SpectConnect did not function for the accurate mass LC/MS data. Larger detection percentages were obtained by combining the top performer with at least one of the other tools such as 96% by combining MetAlign with MZmine for the GC/MS data and 93% by combining MetAlign with XCMS for the LC/MS data. In terms of quantitative performance, the reported peak intensities from each tool had averaged absolute biases (relative to peak intensities obtained using instrument software) of 41, 4.4, 1.3 and 1.3% for SpectConnect, MetAlign, XCMS, and MZmine, respectively, for the GC/MS data. For the LC/MS data, the averaged absolute biases were 22, 4.5, and 3.1% for MetAlign, MZmine, and XCMS, respectively. In summary, MetAlign performed the best in terms of the number of component detections; however, more than one preprocessing tool should be considered to avoid missing impurities or other trace components as potential chemical signatures.

Correlation of rocket propulsion fuel properties with chemical composition using comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry followed by partial least squares regression analysis.

There is an increased need to more fully assess and control the composition of kerosene-based rocket propulsion fuels such as RP-1. In particular, it is critical to make better quantitative connections among the following three attributes: fuel performance (thermal stability, sooting propensity, engine specific impulse, etc.), fuel properties (such as flash point, density, kinematic viscosity, net heat of combustion, and hydrogen content), and the chemical composition of a given fuel, i.e., amounts of specific chemical compounds and compound classes present in a fuel as a result of feedstock blending and/or processing. Recent efforts in predicting fuel chemical and physical behavior through modeling put greater emphasis on attaining detailed and accurate fuel properties and fuel composition information. Often, one-dimensional gas chromatography (GC) combined with mass spectrometry (MS) is employed to provide chemical composition information. Building on approaches that used GC-MS, but to glean substantially more chemical information from these complex fuels, we recently studied the use of comprehensive two dimensional (2D) gas chromatography combined with time-of-flight mass spectrometry (GC×GC-TOFMS) using a "reversed column" format: RTX-wax column for the first dimension, and a RTX-1 column for the second dimension. In this report, by applying chemometric data analysis, specifically partial least-squares (PLS) regression analysis, we are able to readily model (and correlate) the chemical compositional information provided by use of GC×GC-TOFMS to RP-1 fuel property information such as density, kinematic viscosity, net heat of combustion, and so on. Furthermore, we readily identified compounds that contribute significantly to measured differences in fuel properties based on results from the PLS models. We anticipate this new chemical analysis strategy will have broad implications for the development of high fidelity composition-property models, leading to an improved approach to fuel formulation and specification for advanced engine cycles.