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%.

Multiple stable isotope characterization as a forensic tool to distinguish acid scavenger samples.

Acid scavengers are frequently used as stabilizer compounds in a variety of applications. When used to stabilize volatile compounds such as nerve agents, the lower volatility and higher stability of acid scavengers make them more persistent in a post-event forensic setting. Compound-specific isotope analysis of carbon, nitrogen, and hydrogen in three acid-scavenging compounds (N,N-diethylaniline, tributylamine, and triethylamine) were used as a tool for distinguishing between different samples. Combined analysis of multiple isotopes improved sample resolution, for instance differentiation between triethylamine samples improved from 80% based on carbon alone to 96% when combining with additional isotope data. The compound-specific methods developed here can be applied to instances where these compounds are not pure, such as when mixed with an agent or when found as a residue. Effective sample matching can be crucial for linking compounds at multiple event sites or linking a supply inventory to an event.

Anionic forensic signatures for sample matching of potassium cyanide using high performance ion chromatography and chemometrics.

Potassium cyanide was used as a model toxicant to determine the feasibility of using anionic impurities as a forensic signature for matching cyanide salts back to their source. In this study, portions of eight KCN stocks originating from four countries were separately dissolved in water and analyzed by high performance ion chromatography (HPIC) using an anion exchange column and conductivity detection. Sixty KCN aqueous samples were produced from the eight stocks and analyzed for 11 anionic impurities. Hierarchal cluster analysis and principal component analysis were used to demonstrate that KCN samples cluster according to source based on the concentrations of their anionic impurities. The Fisher-ratio method and degree-of-class separation (DCS) were used for feature selection on a training set of KCN samples in order to optimize sample clustering. The optimal subset of anions needed for sample classification was determined to be sulfate, oxalate, phosphate, and an unknown anion named unk5. Using K-nearest neighbors (KNN) and the optimal subset of anions, KCN test samples from different KCN stocks were correctly determined to be manufactured in the United States. In addition, KCN samples from stocks manufactured in Belgium, Germany, and the Czech Republic were all correctly matched back to their original stocks because each stock had a unique anionic impurity profile. The application of the Fisher-ratio method and DCS for feature selection improved the accuracy and confidence of sample classification by KNN.