Jun Jiang , Yin Yu , Zheng Mei , Zhen-Xin Yi , Xue-Hai Ju
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引用次数: 0
Abstract
A neural network deep potential (DP) is developed for the shock response and thermal decomposition mechanisms of 3,4-Bis(3-nitrofurazan-4-yl)furoxan (DNTF). This DP potential, trained on ab initio datasets, achieves the accuracy of density functional theory (DFT) and higher computational efficiency. The simulation results show that DNTF is anisotropic under shock loading. And the critical shock decomposition temperatures along the [100], [010], and [001] directions are 463.64 K, 451.85 K, and 486.69 K, respectively. For the first time, the low-temperature (<750 K) decomposition of DNTF is successfully simulated using molecular dynamics combined with DP model. The decomposition of DNTF begins with the O-N bond opening of the furoxan ring, followed by the breaking of the C-NO2 bond and opening of the furazan rings, under thermal conditions. The critical thermal decomposition is between 458.3 K and 562.5 K at a heating rate of 13.5 K/ps. The final products are CO2, N2, and CO, and the main intermediates are NO2, NO, and N2O. The activation energy of decomposition is 142.4 kJ/mol obtained by Ozawa method. This study not only provides a powerful tool for investigating the performance of DNTF but also offers a feasible approach for other energetic materials, advancing the field significantly.
期刊介绍:
Energy is a multidisciplinary, international journal that publishes research and analysis in the field of energy engineering. Our aim is to become a leading peer-reviewed platform and a trusted source of information for energy-related topics.
The journal covers a range of areas including mechanical engineering, thermal sciences, and energy analysis. We are particularly interested in research on energy modelling, prediction, integrated energy systems, planning, and management.
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