Theoretical study on the influence of different solvents on the growth morphology of 3,4-bis(3-nitrofurazan-4-yl)furoxan (DNTF)

Liang, Yan-Ting; Luo, Yan‐Ling; Gou, Ruijun; Yuan, Xiaofeng; Guo, Qianjin; Chen, Yahong; Zhang, Shuhai

doi:10.1007/s00894-023-05590-9

Cited by 4 publications

(1 citation statement)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, the application of morphology prediction models, such as the MAE models, ,− growth mechanistic model, , and dynamic Monte Carlo simulation method, has become increasing popular. Morphology prediction models have been also widely applied in energetic crystals grown under various conditions, such as vacuum, solvents, and additives, with effects of temperature, volume ratio of mixed solvents, supersaturation, and other conditions covered. − Also, this research revealed the growth mechanism of energetic crystals at the microscopic level, which presented theoretical guidance for morphology control. Meanwhile, valid force fields are important for the specified energetic compounds.…”

Section: Application Of Morphology Prediction Methods In Energetic Cr...mentioning

confidence: 99%

Morphology Prediction Methods and Their Applications in Energetic Crystals

Li,

Xue,

Wang

et al. 2023

Crystal Growth & Design

View full text Add to dashboard Cite

Crystal morphology can significantly impact rheological, machining, and loading properties of materials and additionally the energy and safety of energetic materials. With the development of computation technology and the increasing requirement of shape-tailored energetic crystals, a high-efficiency morphology prediction method is highly desired. Thus, a timely summary of existing crystal morphology prediction models at the microscopic level and their applications to energetic compounds is implemented in this article to set a basis for comparing their advantages and disadvantages and establishing more accurate ones. In general, these models take into account more structural and thermodynamic factors with fewer or even no kinetic factors, resulting in more or less deviation from experimental observation. Therefore, more accurate morphology prediction models are expected to be constructed in the future, in terms of both macroscopic crystallization conditions and microscopic structures as well as big data.

show abstract