A systematic ab initio optimization of monohydrates of HCl•HNO3•H2SO4 aggregates

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Abstract

Hydrates of HCl, HNO3 and H2SO4 involved in polar stratospheric clouds capture the attention of researchers due to the mixtures composed with them. The molecular aggregates generated with these strong acids show different behaviors, geometries and nucleation reactions at atmospheric temperatures. Here is presented a systematic ab initio optimization study of monohydrates of HCl•HNO3•H2SO4 using the Density Functional Theory, by means of geometry optimizations carried out with B3LYP hybrid method and aug-cc-pVTZ basis set, a high level of theory, within Gaussian 09 program. This systematic optimization procedure consists to situate systematically the H2O molecule around the cluster in study, on the favorable positions to develop higher quantity of hydrogen bonds as possible, in order to obtain major quantity of different electronic structures of these monohydrates. Applying this systematic optimization methodology over previously optimized complexes of HCl, HNO3 and H2SO4, the present theoretical approach provides thirty-two different optimized electronic structures of monohydrates that were yielded from seven initial groups of (HCl•HNO3•H2SO4)-complex, placing the H2O in eight positions around them. Moreover, their Infrared spectra have been predicted for all (HCl•HNO3•H2SO4)-monohydrates achieved. Likewise, It is shown the outcomes of the electronic energies, relative Gibbs free energies, Infrared spectra, the wavenumbers of hydrogen bonds, inter-monomeric parameters, electronic structures of (HCl•HNO3•H2SO4)-monohydrates. These monohydrates could be considered precursors of the atmospheric heterogeneous nucleation reactions. These results can be useful to experimentalists of Catalysis, Astrophysics, Corrosion of metals and ceramics, aromatic compounds reactions, even environmental pollution and industrial smog.

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Verdes, M. (2019). A systematic ab initio optimization of monohydrates of HCl•HNO3•H2SO4 aggregates. Journal of Molecular Graphics and Modelling, 86, 256–263. https://doi.org/10.1016/j.jmgm.2018.10.025

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