The adsorption of several simple methylamines on the Si(100)-(2 × 1) surface has been investigated using Auger electron spectroscopy (AES), thermal desorption spectroscopy (TDS), low-energy electron diffraction, and computational modeling. Both methylamine and dimethylamine appear to undergo mostly dissociative adsorption on this surface at room temperature, although trapping into a molecular adsorption well appears to occur to a limited extent for both molecules. Trimethylamine appears to undergo mostly molecular adsorption on this surface. These results are in agreement with recent computational, infrared, and photoemission spectroscopic studies of these systems. By comparison to the AES results from the adsorption of methyl iodide on Si(100), it was concluded that the surface saturation coverage of trimethylamine on Si(100) is 0.26 monolayers, consistent with the photoemission results, while both methylamine and dimethylamine appear to saturate at about 0.48 monolayers. TDS reveals parent desorption channels for all three of these molecules, as well as competing surface decomposition channels. The adsorption energies obtained from computational treatments of these systems are consistent with the experimentally derived values.

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Sodium and potassium cyanide are highly toxic, produced in large amounts by the chemical industry, and linked to numerous high-profile crimes. The U.S. Centers for Disease Control and Prevention has identified cyanide as one of the most probable agents to be used in a chemical terrorism event. We investigated whether stable C and N isotopic content of sodium and potassium cyanide could serve as a forensic signature for sample matching, using a collection of 65 cyanide samples. Upon analysis, a few of the cyanide samples displayed nonhomogeneous isotopic content associated with degradation to a carbonate salt and loss of hydrogen cyanide. Most samples had highly reproducible isotope content. Of the 65 cyanide samples, >95% could be properly matched based on C and N isotope ratios, with a false match rate <3%. These results suggest that stable C and N isotope ratios are a useful forensic signature for matching cyanide samples.

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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%.