物理化学学报 >> 2013, Vol. 29 >> Issue (11): 2332-2338.doi: 10.3866/PKU.WHXB201307311

理论与计算化学 上一篇    下一篇

方阱链状分子临界性质的Monte Carlo模拟

李丽妍, 孙方方, 陈志同, 蔡钧   

  1. 华东理工大学化学系, 上海 200237
  • 收稿日期:2013-05-20 修回日期:2013-07-30 发布日期:2013-10-30
  • 通讯作者: 蔡钧 E-mail:caijun@ecust.edu.cn
  • 基金资助:

    国家自然科学基金(20973062)资助项目

Monte Carlo Simulations of Critical Properties for Square Well Chain Molecules

LI Li-Yan, SUN Fang-Fang, CHEN Zhi-Tong, CAI Jun   

  1. Department of Chemistry, East China University of Science and Technology, Shanghai 200237, P. R. China
  • Received:2013-05-20 Revised:2013-07-30 Published:2013-10-30
  • Contact: CAI Jun E-mail:caijun@ecust.edu.cn
  • Supported by:

    The project was supported by the National Natural Science Foundation of China (20973062).

摘要:

在巨正则系综下对阱宽为λ=1.5, 链长分别为4、8、16的方阱链状流体实施Monte Carlo模拟, 采用建立在完整标度基础上的无偏的Q-参数方法, 通过histogram reweighting技术以及有限尺寸标度理论得到了热力学极限下该系列流体的临界温度和临界密度. 模拟结果表明, 方阱链流体的临界温度随着链长的增加而升高.并且不同链长方阱流体的临界温度均低于已报道的结果. 由于本文所采用的完整标度的无偏性, 我们估计的临界点更加准确. 并且流体的临界温度与链长之间的关系与Flory-Huggins理论相一致. 我们还预测了无限链长方阱流体的临界温度, 比已有结果略高.

关键词: 临界点, 巨正则系综, Histogram reweighting, 完整标度, 连续构型偏倚

Abstract:

Square well chains of 4, 8, and 16 segments with well width λ=1.5 were investigated by grand ensemble Monte Carlo simulations. We used an unbiased, complete scaling, Q-parameter method, to estimate critical temperatures and densities in the thermodynamic limit, with the help of histogram reweighting technique and finite size scaling theory. We showed that a square well chain with more segments has a higher critical temperature than that with fewer segments. The critical temperatures for different chain lengths are all lower than those reported previously. Critical points obtained in this work are more precise because the complete scaling is totally unbiased. The relationship between critical temperature and chain length is in good agreement with the Flory-Huggins theory. We also estimated that the critical temperature for an infinitely long square well chain is a little higher than previous results.

Key words: Critical point, Grand ensemble, Histogramreweighting, Complete scaling, Continumm configurational bias

MSC2000: 

  • O642