A Quantum Mechanical Investigation of the Relation between Impact Sensitivity and the Charge Distribution in Energetic Molecules

Abstract

Quantum mechanically determined electrostatic potentials for isosurfaces of electron density of a variety of CHNO explosive molecules are analyzed to identify features that are indicative of sensitivity to impact. This paper describes the development of models for prediction of impact sensitivity of CHNO explosives using approximations to the electrostatic potentials at bond midpoints, statistical parameters of these surface potentials, and the generalized interaction properties function [J. S. Murray, T. Brinck, P. Lane, K. Paulsen and P. Politzer, J. Mol. Struct (THEOCHEM) 1994, 307, 55] or calculated heats of detonation. The models are parametrized using a set of 34 polynitroaromatic and benzofuroxan explosives for which impact sensitivity measurements exist. The models are then applied to a test set of 15 CHNO explosives from a variety of chemical families in order to assess the predictive capability of the models. Patterns of the surface potentials of the molecules examined in this study suggest that the level of sensitivity to impact is related to the degree of positive charge buildup over covalent bonds within the inner framework of these explosives. The highly sensitive explosives show large positive charge buildup localized over covalent bonding regions of the molecular structures, whereas the insensitive explosives do not exhibit this feature. For the nitroaromatic and benzofuroxan compounds, sensitivity appears to be related to the degree and distribution of positive charge build-up localized over the aromatic ring or over the C−NO2 bonds.

Theoretical evaluation of sensitivity and thermal stability for high explosives based on quantum chemistry methods: A brief review

“…[30][31][32][33][34] A quantitative structure-property relationship (QSPR) study was used by Wang et al [35] for prediction of impact sensitivity of nitramine, and a set of 35 molecular descriptors were calculated to represent its molecular structure. It was found that tunneling occurred from the ground state to excited states during the explosion of benzenoid molecules initiated by impact.…”

“…[42] However, it is still necessary and useful to carry out correlation studies to identify molecular properties that indicate impact sensitivity. [43] In fact, Chen and Cheng [44] discovered a relationship between the electronic properties (band gap) of cis-1,3,4,6-tetranitrooctahydroimidazo- [4,5-d]imidazole (BCHMX) and its impact sensitivity. Besides, according to Zeman, [45] Huang, [46] and Wang, [47,48] impact sensitivity is also dependent on the electrotopological state indices.…”

“…[30] All the molecular structures were optimized at the 3LYP/6-31G (d) level of theory and confirmed as minima by frequency calculations. Their new QSPR equation provided a good result (see Fig.…”

Development of a QSPR model for predicting thermal stabilities of nitroaromatic compounds taking into account their decomposition mechanisms

“…Widely used in biology [13,14], toxicology [15,16] and drug design [17,18], their applications for physico-chemical properties increased since many years [19,20], particularly for the properties of energetic materials [21][22][23][24][25][26][27][28][29][30][31][32]. Their principle consists in developing a mathematic relationship connecting a macroscopic property of a compounds series to microscopic descriptors derived from their molecular structures, using a reliable experimental data set.…”

Optimization of Neural Networks Architecture for Impact Sensitivity of Energetic Molecules

“…In addition, large errors present in some of experimental data also hamper for explosives scientists to find a good correlation between some of specific molecular descriptors and the impact sensitivity of energetic molecules. [4][5][6][7] Although some scientists report successes in finding a reasonable correlation between them, this appears to be only possible in a certain molecular type with a small number of energetic molecules. Thus, if one wants to derive a predictive methodology for impact sensitivity to cover various types of energetic molecules, neural networks (NN) may be a good choice in current circumstances.…”