Fast explosive performance prediction <i>via</i> small-dose energetic materials based on time-resolved imaging combined with machine learning-Reference-Cited by-同舟云学术

Fast explosive performance prediction via small-dose energetic materials based on time-resolved imaging combined with machine learning

Published:2022 Issue:24 Volume:10 Page:13114-13123
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Wang Xianshuang¹^ORCID,He Yage¹,Cao Wenli²,Guo Wei¹^ORCID,Zhang Tonglai²^ORCID,Zhang Jianguo²^ORCID,Shu Qinghai³^ORCID,Guo Xueyong²,Liu Ruibin¹^ORCID,Yao Yugui¹⁴

Affiliation:

1. Key Lab of Advanced Optoelectronic Quantum Architecture and Measurement (Ministry of Education), Beijing Key Lab of Nanophotonics & Ultrafine Optoelectronic Systems, School of Physics, Beijing Institute of Technology, Beijing 100081, China

2. State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China

3. School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China

4. Frontiers Science Center for High Energy Material (MOE), Beijing Institute of Technology, Beijing 100081, China

Abstract

Fast, reproducible, and quantitative performance evaluation of monomolecular energetic materials (EMs) is a significant challenge that limits the tailored applications of EMs and the development of new high-energy-density materials.

Funder

State Key Laboratory of Explosion Science and Technology

National Key Research and Development Program of China

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/TA/D2TA02626K

Reference33 articles.

1. Heat of explosion of commercial and brisant high explosives

2. Anisotropic shock sensitivity and detonation temperature of pentaerythritol tetranitrate single crystal

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Micro-scale crystallization thermodynamics study of typical energetic compounds integrating optofluidics and machine learning;Chemical Engineering Science;2024-10

2. Rapid modulation of electrostatic sensitivity and explosive performance by X‐ray radiation;Propellants, Explosives, Pyrotechnics;2024-08-07

3. Volume of detonation determination based on gaseous products of energetic materials by time resolved LIPS combined with schlieren image;Optics Express;2024-06-24

4. Microscale techniques for investigating the chemistry and energy release of reactive materials;MRS Advances;2024-03-25

5. Interpretable-machine-learning-guided discovery of dominant intrinsic factors of sensitivity of high explosives;Materials Advances;2024