高能材料的化学反应机制和模型:透视

IF 3.3 Q2 CHEMISTRY, MULTIDISCIPLINARY
Li Meng , Qing-guan Song , Chuang Yao , Lei Zhang , Si-ping Pang
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引用次数: 0

摘要

含能材料是一种具有一定势垒的亚稳态功能材料,克服势垒可以使储存在势垒中的能量迅速释放。全面了解反应机制和准确量化反应速率是优化能量输出、确保减轻危害和评估EMs安全水平的基本问题。这一观点概述了化学反应机理和模型的研究进展,特别强调了有机EMs和活性金属。有机电磁主要由碳、氢、氮和氧元素组成,使超音速和自持爆炸反应能够产生大量的能量输出。最近发现,铝、镁和硼等活性金属的掺入可以提高EM配方的燃烧热量和爆炸温度,从而引起了人们对EM配方的高度研究兴趣。这一观点首先通过多尺度模拟和实验方法揭示了EMs的反应机制,包括热分解、激波起始和燃烧后。然后,回顾了从揭示的机制中导出的物理模型的定量表征表达式,包括基本和现象学反应动力学模型等数学表达式,以及新兴的数据驱动机器学习模型。最后,对该技术的应用、存在的问题及进一步发展方向进行了展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Chemical reaction mechanisms and models of energetic materials: A perspective

Chemical reaction mechanisms and models of energetic materials: A perspective
Energetic materials (EMs) are a kind of metastable functional materials with certain potential barriers, overcoming which can quickly release the energy stored in EMs. A thorough understanding of reaction mechanisms and accurate quantification of reaction rates are fundamental issues for optimizing energy output, ensuring hazard mitigation, and assessing the safety levels of EMs. This perspective provides an overview of research progress in chemical reaction mechanisms and models, with a particular emphasis on organic EMs and reactive metals. Organic EMs are mainly composed of carbon, hydrogen, nitrogen, and oxygen elements, enabling supersonic and self-sustaining detonation reactions capable of significant energy output. The incorporation of reactive metals like aluminum, magnesium, and boron has been recently found to augment the combustion heat and explosion temperature of EM formulations, sparking heightened research interest. This perspective first presents both EMs’ reaction mechanisms revealed via multiscale simulations and experimental methods, including thermal decomposition, shock initiation, and post combustion. Then, quantitatively characterized expressions of the physical models derived from the revealed mechanisms, including mathematical expressions like elementary and phenomenological reaction kinetic models, and emerging data-driven machine learning models, are reviewed. Finally, the view of the application, existing problems, and further development directions are outlined.
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来源期刊
Energetic Materials Frontiers
Energetic Materials Frontiers Materials Science-Materials Science (miscellaneous)
CiteScore
6.90
自引率
0.00%
发文量
42
审稿时长
12 weeks
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