Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 Central European Journal of Energetic Materials

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: energetic compound

Sortuj według:

Ogranicz wyniki do:

Synthesis, Structure and Energetic Properties of a Catenated N6, Polynitro Compound: 1,1’-Azobis(3,5-dinitropyrazole)

Li Y.-N., Shu Y.-J., Wang Y.-L., Wang B.-Z., Zhang S.-Y., Bi F.-Q.

Central European Journal of Energetic Materials

2017

Vol. 14, no. 2

321--335

A nitrogen-rich, polynitro energetic compound with an N,N-azo linkage, 1,1’-azobis(3,5-dinitropyrazole) (ABDNP), has been synthesized by an oxidative coupling reaction of 1-amino-3,5-dinitropyrazole with different oxidizing agents. The target compound was characterized by IR spectroscopy, 1H and 13C nuclear magnetic resonance spectroscopy, elemental analysis, mass spectra, X-ray diffraction and differential scanning calorimetry (DSC). The DSC results show that 1,1’-azobis(3,5-dinitropyrazole) decomposes at a relatively high onset temperature (202.9 °C), which indicates that 1,1’-azobis(3,5-dinitropyrazole) has acceptable thermal stability. The energetic properties were obtained, with a measured density and heat of formation matched by theoretically computed values based on the B3LYP method.

Prediction of the Density of Energetic Materials on the Basis of their Molecular Structures

Rahimi R., Keshavarz M. H., Akbarzadeh A. R.

Central European Journal of Energetic Materials

2016

Vol. 13, no. 1

73--101

The density of an energetic compound is an essential parameter for the assessment of its performance. A simple method based on quantitative structure-property relationship (QSPR) has been developed to give an accurate prediction of the crystal density of more than 170 polynitroarenes, polynitroheteroarenes, nitroaliphatics, nitrate esters and nitramines as important classes of energetic compounds, by suitable molecular descriptors. The evaluation techniques included cross-validation, validation through an external test set, and Y-randomization for multiple linear regression (MLR) and training state analysis for artificial neural network (ANN), and were used to illustrate the accuracy of the proposed models. The predicted MLR results are close to the experimental data for both the training and the test molecular sets, and for all of the molecular sets, but not as close as the ANN results. The ANN model was also used with 20 hidden neurons that gave good result. The results showed high quality for nonlinear modelling according to the squared regression coefficients for all of the training, validation and the test sets (R2 = 0.999, 0.914 and 0.931, respectively). The calculated results have also been compared with those from several of the best available predictive methods, and were found to give more reliable estimates.