22
1 Introduction
Fig. 1.15 Correlation of
experimental sensitivity
against k pr . Test set includes
a diverse range of molecules
with –NO 2 based
explosophores. Grey circles
include carbonyl moieties.
Figure from Ref. [116],
https://doi.org/10.1021/jp5
07057r. Copyright 2014
American Chemical Society
performed to obtain values of c and Z i . Despite the mathematical similarity to QSPR
methods, the physical basis used in developing this model has allowed a reduction
in the number of required parameters (from hundreds to only three), and better
correlations to large datasets [118].
Excellent correlations have been obtained using this approach, with R
2
> 0.8
based on diverse datasets of 93 nitroaliphatic compounds [118]. This could be
extended to a larger dataset (156 compounds) including nitroaromatic compounds
with the addition of one extra fitted parameter to reflect an additional bond type,
Fig. 1.15 [116].
Based on a physical model of impact-induced reactions, these semi-empirical
methods have proved very powerful for the rationalisation and prediction of impact
sensitivities.
1.3.4 Vibrational Up-Pumping: A Tool for Prediction
While many of the models discussed above describe decomposition processes,
they are not based on the relative rates of energy localisation and subsequent formation of hot-spots in EMs. To this end, models based on vibrational up-pumping
(Sect. 1.2.2) have been considered.
Since its conceptual development in the 1980/90s [36, 39, 40], and experimental
validation [44] of the up-pumping phenomenon by Dlott and colleagues, there have
been a number of attempts at employing up-pumping models to predicting impact
sensitivity. The initial numerical analyses by Dlott and co-workers [36] were based
1 Introduction
Fig. 1.15 Correlation of
experimental sensitivity
against k pr . Test set includes
a diverse range of molecules
with –NO 2 based
explosophores. Grey circles
include carbonyl moieties.
Figure from Ref. [116],
https://doi.org/10.1021/jp5
07057r. Copyright 2014
American Chemical Society
performed to obtain values of c and Z i . Despite the mathematical similarity to QSPR
methods, the physical basis used in developing this model has allowed a reduction
in the number of required parameters (from hundreds to only three), and better
correlations to large datasets [118].
Excellent correlations have been obtained using this approach, with R
2
> 0.8
based on diverse datasets of 93 nitroaliphatic compounds [118]. This could be
extended to a larger dataset (156 compounds) including nitroaromatic compounds
with the addition of one extra fitted parameter to reflect an additional bond type,
Fig. 1.15 [116].
Based on a physical model of impact-induced reactions, these semi-empirical
methods have proved very powerful for the rationalisation and prediction of impact
sensitivities.
1.3.4 Vibrational Up-Pumping: A Tool for Prediction
While many of the models discussed above describe decomposition processes,
they are not based on the relative rates of energy localisation and subsequent formation of hot-spots in EMs. To this end, models based on vibrational up-pumping
(Sect. 1.2.2) have been considered.
Since its conceptual development in the 1980/90s [36, 39, 40], and experimental
validation [44] of the up-pumping phenomenon by Dlott and colleagues, there have
been a number of attempts at employing up-pumping models to predicting impact
sensitivity. The initial numerical analyses by Dlott and co-workers [36] were based