摘要
超细六硝基茋(HNS)因其优异的热稳定性和良好的高压短脉冲性能,在冲击片雷管等领域得以广泛应用。然而,在超细HNS的使役过程中因其高表面能,易发生固相熟化。尽管已有研究从不同角度探讨了温度、残余溶剂和时间等因素对超细HNS固相熟化过程的影响,但这些研究大多集中于单一或少数几个因素的分析,尚未建立能够整合多种影响因素的预测模型。为此,研究基于先前通过小角X射线散射(SAXS)在不同温度条件和残余N,N-二甲基甲酰胺(DMF)含量下获得的比表面积(SSA)和相对比表面积(RSSA)数据,采用机器学习方法以及优化的经验模型,构建了一个综合考虑时间、温度和残余DMF含量的预测模型。结果显示,在训练集上,随机森林预测的R2达到了0.9989,多项式回归模型拟合的R2为0.9091,优化后的经验模型的R2为0.9129。通过对比这三个模型的预测效果,找出了最适合预测超细HNS固相熟化进程的模型。此外,通过纯度测试、扫描电子显微镜(SEM)等手段揭示了颗粒特性的差异对超细HNS固相熟化程度具有显著影响。本研究提供了一种预测超细HNS固相熟化进程的方法,为探索其熟化机理及优化贮存稳定性奠定了基础。
Ultrafine hexanitrostilbene(HNS)is widely used in explosion foil initiators and related applications due to its outstand-ing thermal stability and excellent high-voltage short-pulse performance.However,its high surface energy during service process leads to solid-phase ripening.Previous studies have explored the effects of temperature,residual solvents,and time on the solid-phase ripening of ultrafine HNS,but these investigations primarily focused on isolated or narrowly factors.Currently,no multivariate predictive model has been established.In this study,a predictive model was developed based on previously ob-tained small angle X-ray scattering(SAXS)data,including specific surface area(SSA)and relative specific surface area(RSSA),obtained under varying temperatures and residual dimethylformamide(DMF)contents.The model was constructed using ma-chine learning algorithms and optimized empirical models.It comprehensively accounts for time,temperature,and residual DMF content in its predictions.The results show that on the training dataset,the random forest(RF)model achieved an R^(2)of 0.9989 in predictions,while the polynomial regression(PR)model and optimized empirical model attained R^(2)values of 0.9091 and 0.9129,respectively.By comparing the prediction performance of these three models,the most suitable model for predict-ing the solid-phase ripening process of ultrafine HNS was identified.Furthermore,purity tests and scanning electron microscopy(SEM)characterization revealed that particle characteristic variations exert significantly influence on the extent of solid-phase rip-ening in ultrafine HNS.A predictive method was established for the solid-phase ripening process of ultrafine HNS,laying a foun-dation for investigating its aging mechanisms and optimizing storage stability.
作者
朱金灿
王超
曹洪滔
王敦举
张浩斌
李诗纯
金波
刘渝
ZHU Jin-can;WANG Chao;CAO Hong-tao;WANG Dun-ju;ZHANG Hao-bin;LI Shi-chun;JIN Bo;LIU Yu(School of Materials and Chemistry,Southwest University of Science and Technology,Mianyang 621010,China;Institute of Chemical Materials,China Academy of Engineering Physics,Mianyang 621999,China;School of Chemistry and Chemical Engineering,Chongqing University,Chongqing 400044,China)
出处
《含能材料》
北大核心
2025年第6期625-634,共10页
Chinese Journal of Energetic Materials
基金
国家自然科学基金(22375191)
CAEP院长基金(YZJJZQ2023005)。
关键词
超细HNS
SAXS
固相熟化
机器学习
颗粒特性
ultrafine HNS
SAXS
solid-phase ripening
machine learning
particle characteristics
