Predicting the thermal decomposition temperature of energetic materials from a simple model.

Autor: Zhang X; Bond and Band Engineering Group, School of Physical Science and Technology, Southwest Jiaotong University, Chengdu, 610031, People's Republic of China. xuan@my.swjtu.edu.cn., Liu QJ; Bond and Band Engineering Group, School of Physical Science and Technology, Southwest Jiaotong University, Chengdu, 610031, People's Republic of China., Liu FS; Bond and Band Engineering Group, School of Physical Science and Technology, Southwest Jiaotong University, Chengdu, 610031, People's Republic of China., Liu ZT; State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China.
Jazyk: angličtina
Zdroj: Journal of molecular modeling [J Mol Model] 2024 Jul 20; Vol. 30 (8), pp. 277. Date of Electronic Publication: 2024 Jul 20.
DOI: 10.1007/s00894-024-06075-z
Abstrakt: Context: The key factor in designing heat-resistant energetic materials is their thermal sensitivity. Further research and prediction of thermal sensitivity remains a great challenge for us. This study is based on first-principles calculations and establishes a theoretical model, which comprehensively considers band gap, density of states, and Young's modulus to obtain a empirical parameter Ψ. A quantitative relationship was established between the new parameter and the thermal decomposition temperature. The value of Ψ is calculated for 10 energetic materials and is found to have a strong correlation with the experimental thermal decomposition temperature. This further proves the reliability of our model. Specifically, the larger the value of Ψ, the higher the thermal decomposition temperature, and the more stable the energetic material will be. Therefore, to some extent, we can use the new parameter Ψ calculated by the model to predict thermal sensitivity.
Methods: Based on first-principles, this paper used the Cambridge Serial Total Energy Package (CASTEP) module of Materials Studio (MS) for calculations. The Perdew-Burke-Ernzerhof (PBE) functionals in Generalized Gradient Approximation (GGA) method as well as the Grimme dispersion correction was used in this paper.
(© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Databáze: MEDLINE
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