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• W2980303046 startingPage "26432" @default.
• W2980303046 abstract "An atomistic understanding of interactions between chemical warfare agents (CWAs) and filter materials is crucial for discovery and design of new materials suitable for chemical protection. The high toxicity of CWAs is a major barrier for experimental studies and significantly impedes the development of improved filter materials. Our recent studies show that density functional theory-based modeling serves as a powerful companion to experimental studies and assists in interpretation of experiments performed on surface-enhanced degradation of CWA simulant compounds. A strong synergy between theory and experiment enables predictive modeling of interactions of toxic CWAs with filter materials. Here, we present a comprehensive study of sarin adsorption and decomposition on pristine and hydroxylated ZnO(101̅0) surfaces performed by means of quantum-chemical calculations. We found that both simulated ZnO surfaces (an idealized pristine surface and hydroxylated surface, a more realistic and relevant form of ZnO at ambient conditions) strongly adsorb sarin; however, based on energetic considerations, it’s binding to the ideal surface is preferred. We discovered a new mechanism of the sarin decomposition via an elimination of a propene molecule and predicted that this reaction has considerably higher reaction rates than the reactions of breaking of P–F and P–OC3H7 bonds, proposed earlier. Decomposition of sarin on a hydroxylated surface proceeds via fission of the P–F bond and formation of isopropyl methyl phosphonic acid. Our calculations suggest that ZnO can be effectively used to decompose organophosphorus CWAs at room temperature and humid conditions, which are important standards for CWA filtration and decontamination." @default.
• W2980303046 created "2019-10-18" @default.
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• W2980303046 date "2019-10-07" @default.
• W2980303046 modified "2023-10-16" @default.
• W2980303046 title "Sarin Decomposition on Pristine and Hydroxylated ZnO: Quantum-Chemical Modeling" @default.
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• W2980303046 doi "https://doi.org/10.1021/acs.jpcc.9b07974" @default.
• W2980303046 hasPublicationYear "2019" @default.
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