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.

Vapor Pressure and Toxicity Prediction for Novichok Agent Candidates Using Machine Learning Model: Preparation for Unascertained Nerve Agents after Chemical Weapons Convention Schedule 1 Update.

The recent terrorist attacks using Novichok agents and subsequent operations have necessitated an understanding of its physicochemical properties, such as vapor pressure and toxicity, as well as unascertained nerve agent structures. To prevent continued threats from new types of nerve agents, the organization for the prohibition of chemical weapons (OPCW) updated the chemical weapons convention (CWC) schedule 1 list. However, this information is vague and may encompass more than 10 000 possible chemical structures, which makes it almost impossible to synthesize and measure their properties and toxicity. To assist this effort, we successfully developed machine learning (ML) models to predict the vapor pressure to help with escape and removal operations. The model shows robust and high-accuracy performance with promising features for predicting vapor pressure when applied to Novichok materials and accurate predictions with reasonable errors. The ML classification model was successfully built for the swallow globally harmonized system class of organophosphorus compounds (OP) for toxicity predictions. The tuned ML model was used to predict the toxicity of Novichok agents, as described in the CWC list. Although its accuracy and linearity can be improved, this ML model is expected to be a firm basis for developing more accurate models for predicting the vapor pressure and toxicity of nerve agents in the future to help handle future terror attacks with unknown nerve agents.

Precisely predicting the 1H and 13C NMR chemical shifts in new types of nerve agents and building spectra database.

Following the recent terrorist attacks using Novichok agents and the subsequent decomposition operations, understanding the chemical structures of nerve agents has become important. To mitigate the ever-evolving threat of new variants, the Organization for the Prohibition of Chemical Weapons has updated the list of Schedule 1 substances defined by the Chemical Weapons Convention. However, owing to the several possible structures for each listed substance, obtaining an exhaustive dataset is almost impossible. Therefore, we propose a nuclear magnetic resonance-based prediction method for 1H and 13C NMR chemical shifts of Novichok agents based on conformational and density functional study calculations. Four organophosphorus compounds and five G- and V-type nerve agents were used to evaluate the accuracy of the proposed procedure. Moreover, 1H and 13C NMR prediction results for an additional 83 Novichok candidates were compiled as a database to aid future research and identification. Further, this is the first study to successfully predict the NMR chemical shifts of Novichok agents, with an exceptional agreement between predicted and experimental data. The conclusions enable the prediction of all possible structures of Novichok agents and can serve as a firm foundation for preparation against future terrorist attacks using new variants of nerve agents.

Thermochemical study for remediation of highly concentrated acid spill: Computational modeling and experimental validation.

The release of concentrated acid solutions by chemical accidents is disastrous to our environmental integrity. Alkaline agents applied to remedy the acid spill catastrophe may lead to secondary damages such as vaporization or spread out of the fumes unless substantial amount of neutralization heat is properly controlled. Using a rigorous thermodynamic formalism proposed by Pitzer to account short-range ion interactions and various subsidiary reactions, we develop a systematic computational model enabling quantitative prediction of reaction heat and the temperature change over neutralization of strongly concentrated acid solutions. We apply this model to four acid solutions (HCl, HNO3, H2SO4, and HF) of each 3 M-equivalent concentration with two neutralizing agents of calcium hydroxide (Ca(OH)2) and sodium bicarbonate (NaHCO3). Predicted reaction heat and temperature are remarkably consistent with the outcomes measured by our own experiments, showing a linear correlation factor R2 greater than 0.98. We apply the model to extremely concentrated acid solutions as high as 50 wt% where an experimental approach is practically restricted. In contrast to the extremely exothermic Ca(OH)2 agent, NaHCO3 even lowers solution temperatures after neutralization reactions. Our model enables us to identify a promising neutralizer NaHCO3 for effectively controlling concentrated acid spills and may be useful for establishment of proper strategy for other chemical accidents.

Rapid characterization of metabolites in soybean using ultra high performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q-TOF-MS/MS) and screening for α-glucosidase inhibitory and antioxidant properties through different solvent systems.

This work was the first to investigate on the simultaneous characterization of metabolite profiles in soybean using UPLC-ESI-Q-TOF-MS/MS. Twenty two compositions were observed within 14 min from the methanol extract and confirmed as twelve isoflavones of three types and ten soyasaponins (Ab, Af, I-III, αg, ßg, ßa, γg, and γa). Moreover, the patterns of two chemicals showed considerable differences in seven solvent systems by HPLC analysis and their optimal extraction was achieved by 70% methanol (isoflavone: 4102.69 µg/g; soyasaponin: ten peaks). The second abundant isoflavones were detected in 50% methanol (4054.39 µg/g), followed by 30% methanol, 100% methanol, 10% methanol, CH2Cl2, and acetone extracts with 3134.03, 2979.49, 1681.33, 366.19, and 119.00 µg/g, respectively. Soyasaponins exhibited similar tendencies as those of isoflavones. The highest total phenolic was found as 2.10 ± 0.05 mg GAE/g in 70% methanol with remarkable differences by comparing other extracts. Specifically, this extract showed potent α-glucosidase inhibitory (81%) and antioxidant capacities (DPPH: 93% and ABTS: 95%) at a concentration of 1.0 mg/mL. Our results may be contributed to enhancing the value to functional foods and evaluating the secondary metabolites concern to antioxidant properties using solvent system in soybean.

Characterization of metabolite profiles from the leaves of green perilla (Perilla frutescens) by ultra high performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry and screening for their antioxidant properties.

The objective of this research was to access the determination of metabolite profiles and antioxidant properties in the leaves of green perilla (Perilla frutescens), where these are considered functional and nutraceutical substances in Korea. A total of 25 compositions were confirmed as six phenolic acids, two triterpenoids, eight flavonoids, seven fatty acids, and two glucosides using an ultra high performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q-TOF-MS/MS) technique from the methanol extract of this species. The individual and total compositions exhibited significant differences, especially rosmarinic acid (10), and linolenic acids (22 and 23) were detected as the predominant metabolites. Interestingly, rosmarinic acid (10) was observed to have considerable differences with various concentrations in three samples (Doryong, 6.38 µg/g; Sinseong, 317.60 µg/g; Bongmyeong, 903.53 µg/g) by UPLC analysis at 330 nm. The scavenging properties against 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis(3-ethylbenzthiazoline-6-sulphonic acid) (ABTS) radicals also showed potent effects with remarkable differences at a concentration of 100 µg/mL, and their abilities were as follows: Sinseong (DPPH, 86%; ABTS, 90%) > Bongmyeong (71% and 84%, respectively) > Doryong (63% and 73%, respectively). Our results suggest that the antioxidant activities of green perilla leaves are correlated with metabolite contents, especially the five major compositions 10 and 22-25. Moreover, this study may be useful in evaluating the relationship between metabolite composition and antioxidant activity.

Changes occurring in compositions and antioxidant properties of healthy soybean seeds [Glycine max (L.) Merr.] and soybean seeds diseased by Phomopsis longicolla and Cercospora kikuchii fungal pathogens.

Changes in the compositions (isoflavone, protein, oil, and fatty acid) and antioxidant properties were evaluated in healthy soybeans and soybeans diseased by Phomopsis longicolla and Cercospora kikuchii. The total isoflavone content (1491.3 µg/g) of healthy seeds was observed to be considerably different than that of diseased seeds (P. longicolla: 292.6, C. kikuchii: 727.2 µg/g), with malonlygenistin exhibiting the greatest decrease (726.1 → 57.1, 351.9 µg/g). Significantly, three isoflavones exhibited a slight increase, and their structures were confirmed as daidzein, glycitein, and genistein, based on their molecular ions at m/z 253.1, 283.0, and 269.1 using the negative mode of HPLC-DAD-ESI/MS. The remaining compositions showed slight variations. The effects against 2,2-diphenyl-1-picrylhydrazyl and 2,2'-azino-bis(3-ethylbenzthiazoline-6-sulphonic acid) radicals in healthy seeds were stronger than the diseased soybeans, depending upon the isoflavone level. Our results may be useful in evaluating the relationship between composition and antioxidant activity as a result of changes caused by soybean fungal pathogens.

Occurrence and distribution of pharmaceuticals in wastewater from households, livestock farms, hospitals and pharmaceutical manufactures.

Twenty-four pharmaceuticals were measured in wastewater from 12 municipal wastewater treatment plants (M-WWTPs), four livestock WWTPs (L-WWTPs), four hospital WWTPs (H-WWTPs) and four pharmaceutical manufacture WWTPs (P-WWTPs). The total concentration of pharmaceuticals in the influent samples was highest in the L-WWTPs followed by the P-WWTPs, H-WWTPs and M-WWTPs. The effluents had different patterns of pharmaceuticals than their corresponding influents because of the different fate of each compound in the WWTPs. Non-steroidal anti-inflammatory drugs (NSAIDs) were the most dominant in the influents from the M-WWTPs and P-WWTPs, while antibiotics were dominantly detected in the L-WWTP. In the H-WWTP influents, NSAIDs, caffeine and carbamazepine were dominant. In the P-WWTPs, the distribution of pharmaceuticals in the effluents varied with sampling sites and periods. The M-WWTP influents had the highest daily loads, while the effluents showed somewhat similar levels in all source types.