Quantum Chemical Mass Spectral Predictions of Novichok Agents after Experimental Validation and Analysis
Sungsoo Kim1, Moon Sik Shin2, Seonghoon Hong2
1School of Energy Systems Engineering, Chung-Ang University, Heukseok-Ro, Dongjak-Gu, Seoul 06974, Republic of Korea.
Abstract:
The identification of chemical warfare agents, particularly Novichok variants, presents significant challenges due to the inherent dangers and practical limitations of experimental analysis. This study advances a computational approach using quantum chemistry electron ionization mass spectrometry (QCxMS, x = EI) to predict the electron ionization mass spectra (EIMS) of these compounds. We obtained experimental mass spectral data from three synthesized Novichok compounds, providing a crucial benchmark for validating computational predictions. Through systematic comparison of the experimental and predicted spectra, we evaluated how the incorporation of additional polarization functions and expanded valence space in basis sets influences prediction accuracy. Our investigation demonstrated that more complete basis sets yielded significantly improved matching scores across seven compounds while maintaining consistent functional parameters for ionization potential (IP) calculations. Comprehensive analysis of mass spectral patterns revealed distinct correlations between the molecular structure and fragmentation behavior. We identified characteristic patterns in both high and low m/z regions that correspond to specific structural features, enabling the development of a systematic framework for spectral interpretation. This understanding of the fragmentation mechanisms allowed for the prediction of mass spectra for four additional compounds with varying structural complexity. The strong correlation between the predicted and experimental results for the synthesized compounds validates this computational approach as a promising tool for the rapid identification of new chemical agents without requiring extensive experimental analysis. This methodology represents a significant advancement in our ability to identify and characterize emerging chemical threats while minimizing exposure risks to research personnel.
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