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通过一个简单模型预测含能材料的热分解温度。

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

作者信息

Zhang Xuan, Liu Qi-Jun, Liu Fu-Sheng, Liu Zheng-Tang

机构信息

Bond and Band Engineering Group, School of Physical Science and Technology, Southwest Jiaotong University, Chengdu, 610031, People's Republic of China.

State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China.

出版信息

J Mol Model. 2024 Jul 20;30(8):277. doi: 10.1007/s00894-024-06075-z.

Abstract

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.

摘要

背景

设计耐热含能材料的关键因素是其热敏感性。对热敏感性的进一步研究和预测对我们来说仍然是一个巨大的挑战。本研究基于第一性原理计算,建立了一个理论模型,该模型综合考虑了带隙、态密度和杨氏模量以获得一个经验参数Ψ。在新参数与热分解温度之间建立了定量关系。计算了10种含能材料的Ψ值,发现其与实验热分解温度有很强的相关性。这进一步证明了我们模型的可靠性。具体而言,Ψ值越大,热分解温度越高,含能材料就越稳定。因此,在一定程度上,我们可以使用该模型计算出的新参数Ψ来预测热敏感性。

方法

基于第一性原理,本文使用Materials Studio(MS)的剑桥序列总能量包(CASTEP)模块进行计算。本文采用广义梯度近似(GGA)方法中的Perdew-Burke-Ernzerhof(PBE)泛函以及Grimme色散校正。

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