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半柔性聚合物刷响应行为的单链平均场理论研究

Single Chain Mean-Field Theory Study on Responsive Behavior of Semiflexible Polymer Brush.

作者信息

Niu Yingli, Bu Xiangyu, Zhang Xinghua

机构信息

School of Science, Beijing Jiaotong University, Beijing 100044, China.

出版信息

Materials (Basel). 2021 Feb 7;14(4):778. doi: 10.3390/ma14040778.

Abstract

The application of single chain mean-field theory (SCMFT) on semiflexible chain brushes is reviewed. The worm-like chain (WLC) model is the best mode of semiflexible chain that can continuously recover to the rigid rod model and Gaussian chain (GC) model in rigid and flexible limits, respectively. Compared with the commonly used GC model, SCMFT is more applicable to the WLC model because the algorithmic complexity of the WLC model is much higher than that of the GC model in self-consistent field theory (SCFT). On the contrary, the algorithmic complexity of both models in SCMFT are comparable. In SCMFT, the ensemble average of quantities is obtained by sampling the conformations of a single chain or multi-chains in the external auxiliary field instead of solving the modified diffuse equation (MDE) in SCFT. The precision of this calculation is controlled by the number of bonds Nm used to discretize the chain contour length and the number of conformations used in the ensemble average. The latter factor can be well controlled by metropolis Monte Carlo simulation. This approach can be easily generalized to solve problems with complex boundary conditions or in high-dimensional systems, which were once nightmares when solving MDEs in SCFT. Moreover, the calculations in SCMFT mainly relate to the assemble averages of chain conformations, for which a portion of conformations can be performed parallel on different computing cores using a message-passing interface (MPI).

摘要

综述了单链平均场理论(SCMFT)在半柔性链刷中的应用。蠕虫状链(WLC)模型是半柔性链的最佳模型,在刚性和柔性极限下,它分别可以连续恢复到刚性杆模型和高斯链(GC)模型。与常用的GC模型相比,SCMFT更适用于WLC模型,因为在自洽场理论(SCFT)中,WLC模型的算法复杂度远高于GC模型。相反,在SCMFT中这两个模型的算法复杂度相当。在SCMFT中,通过在外部辅助场中对单链或多链的构象进行采样来获得量的系综平均值,而不是求解SCFT中的修正扩散方程(MDE)。这种计算的精度由用于离散链轮廓长度的键数Nm和系综平均中使用的构象数控制。后一个因素可以通过 metropolis蒙特卡罗模拟很好地控制。这种方法可以很容易地推广到解决具有复杂边界条件或高维系统中的问题,而这些问题在求解SCFT中的MDE时曾是噩梦。此外,SCMFT中的计算主要涉及链构象的系综平均值,对于这些计算,一部分构象可以使用消息传递接口(MPI)在不同的计算核心上并行执行。

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