DFT-Spectroscopy Integrated Identification Method on Unknown Terrorist Chemical Mixtures by Incorporating Experimental and Theoretical GC-MS, NMR, IR, and DFT-NMR/IR Data.

In the event of a chemical attack, the rapid identification of unknown chemical agents is critical for an effective emergency response and treatment of victims. However, identifying unknown compounds is difficult, particularly when relying on traditional methods such as gas and liquid chromatography-mass spectrometry (GC-MS, LC-MS). In this study, we developed a density functional theory and spectroscopy integrated identification method (D-SIIM) for the possible detection of unknown or unidentified terrorist materials, specifically chemical warfare agents (CWAs). The D-SIIM uses a combination of GC-MS, nuclear magnetic resonance (NMR) spectroscopy, infrared (IR) spectroscopy, and quantum chemical calculation-based NMR/IR predictions to identify potential CWA candidates based on their chemical signatures. Using D-SIIM, we successfully verified the presence of blister and nerve agent simulants in samples by excluding other compounds (ethyl propyl sulfide and methylphosphonic acid), which were predicted to be candidates with high probability by GC-MS. The findings of this study demonstrate that the D-SIIM can detect substances that are likely present in CWA mixtures and can be used to identify unknown terrorist chemicals.

Kinetics of benzene biodegradation by Pseudomonas aeruginosa: parameter estimation.

This study determined the model parameters describing biodegradation of benzene by conducting kinetic microcosm batch tests in both pure solution and saturated aquifer material conditions for various initial benzene (100-700 mg/L) and microbial concentrations (10(7)-10(9) colony-forming units [CFU]/ml) using Pseudomonas aeruginosa as benzene-degrading bacteria. In both tests, benzene and microbial concentrations were monitored over time in order to investigate which of two Monod kinetic equations, the Monod-with-growth or the Monod-no-growth model, was more suitable for describing benzene biodegradation and to estimate the associated model parameters. Parameter estimation was performed by fitting the numerical solution of each model obtained by the fourth-order Runge-Kutta integration to the measured data of benzene and/or microbial concentrations. For the Monod-with-growth model, the best fit of the numerical solution was significantly different than the measured benzene concentrations, especially at early times, because of the gradual increase of microbial population in the growth curve. In contrast, the solution based on the Monod-no-growth model produced reasonable agreement with the measured benzene data. The estimated parameters of maximum substrate utilization rate (kmax) and half-saturation constant (Kc) were in the range of 61 to 105 mg/L/d and about 270 mg/L, respectively, which differ significantly from values previously reported in the literature. We attribute the differences observed in our study to our experimental conditions of initial substrate and bacterial concentrations and oxygen and nutrient supply. Our results imply that an appropriate model type and reasonable values of kinetic parameters should be chosen to model the biodegradation of hydrocarbons in the subsurface environment.