Assessment of Detonation Performance and Characteristics of 2,4,6-Trinitrotoluene Based Melt Cast Explosives Containing Aluminum by Laser Induced Breakdown Spectroscopy

Abstract

Aluminized melt cast formulations based on 2,4,6-trinitrotoluene (TNT) deliver an enhanced blast effect because the secondary combustion process of aluminum (Al) occurs beyond the detonation zone. A new method is introduced to assess the detonation performance and characteristics of aluminized TNT explosives on the basis of the laser-induced breakdown spectroscopy (LIBS) technique, in both air and argon (Ar) atmospheres. The plasma emissions of the prepared samples were recorded, where the atomic lines of Al, C, O and H as well as the molecular bands of AlO, CN and C2 were identified. A good discrimination and separation between the samples was possible using LIBS and principle component analysis (PCA), although they had similar atomic compositions. The quantitative calibration curve obtained using the relative intensity of Al/O was used to determine the detonation velocity/ pressure and aluminum content of the TNT/Al samples. Comparisons between experimental and theoretical spectra were made using a Nelder-Mead temperature program for CN bands, which provided good agreement with the fitted spectra. Finally, CN vibrational temperatures were calculated from these spectral fittings. These temperatures have higher values in an Ar atmosphere than in an air atmosphere. Thus, increasing the oxygen concentration can decrease these temperatures in TNT/Al standard samples.

Laser-Induced Breakdown Spectroscopy for the Discrimination of Explosives Based on the ReliefF Algorithm and Support Vector Machines

“…Among the multivariate techniques demonstrated to be viable to classify an unknown sample as an explosive or a harmless product, the most widely used is elemental peaks ratios [35], principal component analysis (PCA) [36][37][38][39][40][41]. Several other chemometric methods, including soft independent modeling of class analogy [42], partial least squares Discriminant Analysis (PLS-DA) [43,44], support vector machines (SVMs) [45], and artificial neural network, have been applied to LIBS spectra for classification and identification [46].…”