Yuan Quan, Li Yunlong, Wang Shijie, He Enqiu, Yang Bin, Nie Rui
School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China.
School of Chemical Equipment, Shenyang University of Technology, Liaoyang 111003, China.
Polymers (Basel). 2023 Sep 18;15(18):3799. doi: 10.3390/polym15183799.
The molecular models of nitrile-butadiene rubber (NBR) with varied contents of acrylonitrile (ACN) were developed and investigated to provide an understanding of the enhancement mechanisms of ACN. The investigation was conducted using molecular dynamics (MD) simulations to calculate and predict the mechanical and tribological properties of NBR through the constant strain method and the shearing model. The MD simulation results showed that the mechanical properties of NBR showed an increasing trend until the content of ACN reached 40%. The mechanism to enhance the strength of the rubber by ACN was investigated and analyzed by assessing the binding energy, radius of gyration, mean square displacement, and free volume. The abrasion rate (AR) of NBR was calculated using Fe-NBR-Fe models during the friction processes. The wear results of atomistic simulations indicated that the NBR with 40% ACN content had the best tribological properties due to the synergy among appropriate polarity, rigidity, and chain length of the NBR molecules. In addition, the random forest regression model of predicted AR, based on the dataset of feature parameters extracted by the MD models, was developed to obtain the variable importance for identifying the highly correlated parameters of AR. The torsion-bend-bend energy was obtained and used to successfully predict the AR trend on the new NBR models with other acrylonitrile contents.
开发并研究了具有不同丙烯腈（ACN）含量的丁腈橡胶（NBR）分子模型，以了解ACN的增强机理。通过分子动力学（MD）模拟进行研究，采用恒应变法和剪切模型计算并预测NBR的力学和摩擦学性能。MD模拟结果表明，NBR的力学性能呈上升趋势，直至ACN含量达到40%。通过评估结合能、回转半径、均方位移和自由体积，对ACN增强橡胶强度的机理进行了研究和分析。在摩擦过程中，使用Fe-NBR-Fe模型计算NBR的磨损率（AR）。原子模拟的磨损结果表明，由于NBR分子的极性、刚性和链长之间的协同作用，ACN含量为40%的NBR具有最佳的摩擦学性能。此外，基于MD模型提取的特征参数数据集，建立了预测AR的随机森林回归模型，以获得识别AR高度相关参数的变量重要性。获得了扭转-弯曲-弯曲能量，并成功用于预测其他丙烯腈含量的新型NBR模型的AR趋势。
