ABSTRACT
The contamination of water resources with nitrate is a growing and significant problem. Here we report the use of ultramicroporous carbon as a capacitive deionization (CDI) electrode for selectively removing nitrate from an anion mixture. Through moderate activation, we achieve a micropore-size distribution consisting almost exclusively of narrow (<1 nm) pores that are well suited for adsorbing the planar, weakly hydrated nitrate molecule. Cyclic voltammetry measurements reveal an enhanced capacitance for nitrate when compared to chloride as well as significant ion sieving effects when sulfate is the only anion present. We measure high selectivities (S) of both nitrate over sulfate (SNO3/SO4 = 17.8 ± 3.6 at 0.6 V) and nitrate over chloride (SNO3/Cl = 6.1 ± 0.4 at 0.6 V) when performing a constant voltage CDI separation on 3.33 mM/3.33 mM/1.67 mM Cl/NO3/SO4 feedwater. These results are particularly encouraging considering that a divalent interferant was present in the feed. Using molecular dynamics simulations, we examine the solvation characteristics of these ions to better understand why nitrate is preferentially electrosorbed over sulfate and chloride.
Subject(s)
Carbon , Water Purification , Adsorption , Electric Capacitance , Electrodes , NitratesABSTRACT
A multivariate model was developed to attribute samples to a synthetic method used in the production of sulfur mustard (HD). Eleven synthetic methods were used to produce 66 samples for model construction. Three chemists working in both participating laboratories took part in the production, with the aim to introduce variability while reducing the influence of laboratory or chemist specific impurities in multivariate analysis. A gas chromatographic/mass spectrometric data set of peak areas for 103 compounds was subjected to orthogonal partial least squares - discriminant analysis to extract chemical attribution signature profiles and to construct multivariate models for classification of samples. For one- and two-step routes, model quality allowed the classification of an external test set (16/16 samples) according to synthesis conditions in the reaction yielding sulfur mustard. Classification of samples according to first-step methodology was considerably more difficult, given the high purity and uniform quality of the intermediate thiodiglycol produced in the study. Model performance in classification of aged samples was also investigated.
ABSTRACT
Attribution of the origin of an illicit drug relies on identification of compounds indicative of its clandestine production and is a key component of many modern forensic investigations. The results of these studies can yield detailed information on method of manufacture, starting material source, and final product, all critical forensic evidence. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic fentanyl, N-(1-phenylethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods, all previously published fentanyl synthetic routes or hybrid versions thereof, were studied in an effort to identify and classify route-specific signatures. A total of 160 distinct compounds and inorganic species were identified using gas and liquid chromatographies combined with mass spectrometric methods (gas chromatography/mass spectrometry (GC/MS) and liquid chromatography-tandem mass spectrometry-time of-flight (LC-MS/MS-TOF)) in conjunction with inductively coupled plasma mass spectrometry (ICPMS). The complexity of the resultant data matrix urged the use of multivariate statistical analysis. Using partial least-squares-discriminant analysis (PLS-DA), 87 route-specific CAS were classified and a statistical model capable of predicting the method of fentanyl synthesis was validated and tested against CAS profiles from crude fentanyl products deposited and later extracted from two operationally relevant surfaces: stainless steel and vinyl tile. This work provides the most detailed fentanyl CAS investigation to date by using orthogonal mass spectral data to identify CAS of forensic significance for illicit drug detection, profiling, and attribution.
