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.

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.

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.

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.

Visible light absorption and photoelectrochemical activity of colorless molecular 1,3-bis(dicyanomethylidene)indane (BDMI) by surface complexation on TiO2.

Adsorption of the colorless 1,3-bis(dicyanomethylidene)indane (BDMI) onto a nanocrystalline TiO2 surface unusually turned the BDMI a deep blue color. Upon contact of the BDMI-adsorbed TiO2 (BDMI-TiO2) with an iodide-based redox electrolyte, a photocurrent density as high as 14.9 mA cm(-2) was generated with a photovoltage of 0.42 V, leading to a power conversion efficiency of 3.63%. This unprecedented photovoltaic performance was simultaneously investigated by spectroscopic studies of BDMI-TiO2 films and density functional theory (DFT)/time-dependent DFT (TD-DFT) computational approaches for [BDMI](-)[Ti(OH)3·H2O](+) (1) as a simple model compound to inspect the light to current conversion abilities. All these results established that the color change from colorless to deep blue and the highly efficient photocurrent generation through binding on the TiO2 surface originates from interfacial charge transfer transitions from anionic BDMI to TiO2.