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

Wang, Xianshuang; He, Yafeng; Cao, Weipeng; Guo, Wei; Wang, Yanna; Zhang, Jian Guo; Shu, Qinghai; Guo, Xueyong; Liu, Ruibin; Yao, Yugui

doi:10.1039/d2ta02626k

Cited by 13 publications

(7 citation statements)

References 30 publications

(37 reference statements)

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“…The plume of all samples is closer to that of the lower energy materials (images of the plume of lower energy materials can be found in the literature) 40,41 which consume more energy in luminescent radiation unrelated to the explosion, resulting in brighter radiation and lower explosive performance.…”

Section: Explosive and Propellant Performance Predictionmentioning

confidence: 68%

“…Then, a small-dosebased microexplosion test system was used to detect the detonation velocity of the samples, which consisted of a highspeed ripple shadow imaging module and a laser-induced plasma spectroscopy unit to test the detonation velocity from laserinduced shock wave images by a high-energy laser pulse combined with a machine learning algorithm; the prediction error of explosive velocity was less than 4%. 40,41 3. RESULTS AND DISCUSSION 3.1.…”

Section: Characterization Of the Mixed Crystalmentioning

confidence: 99%

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Properties of Mixed Crystal Coprecipitation Substances of Ammonium Nitrate and Potassium Perchlorate Prepared by the Evaporative Solvent Method

Zi,

Han,

et al. 2023

ACS Omega

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Ammonium perchlorate (AP) has been widely used as an oxidizer in propellants and military mixed explosives in recent years. However, its high characteristic signal, environmental pollution, and poor detonation performance have prompted the industry to seek alternatives to AP. Ammonium nitrate (AN) is a suitable substitute due to its low characteristic signal, lack of pollution, and excellent detonation performance. However, its room-temperature phase transition and hygroscopicity affect its practical use. In this work, we prepared mixed crystal coprecipitation (MCC) materials of AN and potassium perchlorate (KP) using the evaporative solvent method. The characterization of AN/KP MCCs was carried out by combining TG-DSC, XRD, FT-IR, and SEM analysis, revealing that the formation of MCCs by AN and KP is due to ion exchange between the two components. AN/KP MCCs not only solve the problem of room-temperature phase transition in AN but also reduce its hygroscopicity. Furthermore, AN/KP MCCs have mechanical sensitivity, explosive performance, and specific impulse similar to pure AN, but compared to AN, AN/KP MCCs have higher density, effective oxygen content, and thermal stability. Compared with existing oxidizers AN, AP, and KP, AN/KP MCCs with high density, low sensitivity, high oxygen content, and high safety have obvious advantages and have good prospects in the application of oxidizers in solid propellants and military mixed explosives.

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Section: Explosive and Propellant Performance Predictionmentioning

confidence: 68%

Section: Characterization Of the Mixed Crystalmentioning

confidence: 99%

Properties of Mixed Crystal Coprecipitation Substances of Ammonium Nitrate and Potassium Perchlorate Prepared by the Evaporative Solvent Method

Zi,

Han,

et al. 2023

ACS Omega

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“…More importantly, the ionization effect of peripheral air is stronger in laser-induced explosive plasma due to the more drastic exothermic chemical reactions. 37 Therefore, the radiation of oxygen and nitrogen of the laser-supported explosive plasma is stronger than that of the general organic matter. The substantial differences in radiation strength of the nanosecond LIPS spectra for each sample were noted.…”

Section: Resultsmentioning

confidence: 99%

Discrimination and classification of high explosives and other organic materials based on laser-induced plasma spectroscopy

Wang,

He,

Zhang

et al. 2023

J. Anal. At. Spectrom.

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“…In the field of energetic materials, the availability of a complete public dataset is still the bottleneck for building ML models. In most studies, researchers chose to gather data from literature [12][13][14] and extracted them from public databases [15][16][17], while a few others used results from theoretical calculations [18,19]. In this study, we extracted data from the literature.…”

Section: Data Collectionmentioning

confidence: 99%

Predicting Enthalpy of Formation of Energetic Compounds by Machine Learning: Comparison of Featurization Methods and Algorithms

Tian

Wang

et al. 2022

Propellants Explo Pyrotec

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Machine learning (ML) is an emerging approach for predicting molecular properties. The prediction of the properties of energetic molecules by ML is still in its infancy. In order to improve the accuracy of ML-based predictions, it is important to pay attention to aspects such as data preparation, model selection, and hyperparameter tuning. In this work, we focused on the influence of different featurization methods and algorithms on predicting the enthalpy of formation (EOF) of energetic compounds. We manually extracted a dataset consisting of 649 EOF values of energetic materials from the literature and compared different combinations of featurization methods and algorithms. The experimental results confirmed that ML can effectively map the relationship between molecular structure and EOF. Custom descriptor sets were found to perform best in featurization with a mean absolute error of 90.10 kJ mol À 1 , after training by kernel ridge regression algorithm.

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

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.

Cited by 13 publications

References 30 publications

Properties of Mixed Crystal Coprecipitation Substances of Ammonium Nitrate and Potassium Perchlorate Prepared by the Evaporative Solvent Method

Properties of Mixed Crystal Coprecipitation Substances of Ammonium Nitrate and Potassium Perchlorate Prepared by the Evaporative Solvent Method

Discrimination and classification of high explosives and other organic materials based on laser-induced plasma spectroscopy

Predicting Enthalpy of Formation of Energetic Compounds by Machine Learning: Comparison of Featurization Methods and Algorithms

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