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The heat of detonation of energetic materials (EMs) is determined by the release of chemical energy, bond energies, and chemical structures and can be reflected by the variation of emission intensities in laser-induced breakdown spectroscopy (LIBS). Herein, we propose a new method based on laser-induced breakdown spectroscopy, combined with small-sample machine learning, to accurately determine the heat of detonation by consuming small-dose samples. A statistical correction strategy is applied to improve the spectral quality and extract spectral features including the emission peak intensity and emission shape correlation intensity. Thereby, a high-accuracy quantitative model based on the plasma spectra is developed to predict the heat of detonation with RMSEC = 0.0314 kJ g1 and Rc2 = 0.99. Excellent model robustness is verified through three independent tests at different dates, which exhibit a strong predictive power with RMSET = 0.1776, 0.1217, and 0.1207 kJ g1 and RT2 = 0.98, 0.98, and 0.98, respectively. The elements of importance for analysis in the model further clarify that the quantitative diagnosis of the heat of detonation for EMs makes sense by LIBS. Therefore, this work can significantly facilitate the safe and fast determination of the heat of detonation of explosives in small-dosage samples.
Journal of Analytical Atomic Spectroscopy – Royal Society of Chemistry
Published: Mar 7, 2023
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