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Chain by chain Monte Carlo simulations for polymerization processes.

机译：逐链进行聚合过程的蒙特卡洛模拟。

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摘要

Predicting chain microstructures is an important task for polymer scientists, that is made more interesting and challenging by the polydisperse nature of polymer molecules. In this work, a new method, called "Chain-by-Chain Monte Carlo Method", is presented for simulating chain microstructures one-by-one or chain-by-chain. CBC-MC is a new hybrid method that uses the mean-field background information as concentrations of polymer populations and small molecules from the deterministic solver to provide an environment in which we stochastically simulate chains one-by-one with kinetic Monte Carlo. The deterministic solver in this work uses method of moments. The main advantage of CBC-MC is that the use of the deterministic solver allows the elimination of the computational load associated with simulation of the whole ensemble. Method is suited for chemistries, or situations in which chain architecture develops slowly with respect to the background environment, such as controlled reversible-deactivation radical polymerizations. In this thesis, CBC-MC is applied to two case studies for synthesis of gradient copolymers. Gradient distribution of chain properties is analyzed in all cases since it is relatively more challenging and interesting. Chain properties are compared to results from method of moments and kinetic Monte Carlo method for different sample sizes and are found to be in perfect agreement. Results confirm that if applicable, full information regarding the microstructure of chains can be obtained using this method with reduced simulation times and smaller sample sizes. This method is also applied to a non-linear copolymerization leading to gelation. Effect of a gradient distribution of pendant double bonds along the primary chains on the simulated portion of gel molecules is investigated. Primary chain results are compared with MOM and found to be in perfect agreement. Further investigations are done on primary chain microstructures to better understand multiple phenomena going on in these systems. It has been found that a gradient in PDB distribution along the primary chains can introduce heterogeneities in gel molecules in surface-bound type polymerizations where primary chains within gels are aligned in the same direction but these heterogeneities seem to be disappearing in bulk polymerizations where the chain alignments are random.
机译：对于聚合物科学家来说，预测链的微观结构是一项重要任务，聚合物分子的多分散性使其变得更加有趣和具有挑战性。在这项工作中，提出了一种新的方法，称为“逐链蒙特卡洛方法”，用于一对一或逐链模拟链微结构。 CBC-MC是一种新的混合方法，该方法使用平均场背景信息作为确定性求解器中聚合物种群和小分子的浓度，从而提供了一种环境，在该环境中，我们使用动力学蒙特卡洛随机地逐一模拟链。这项工作中的确定性求解器使用矩量法。 CBC-MC的主要优点是确定性求解器的使用可以消除与整个集成仿真相关的计算负荷。该方法适用于化学或链结构相对于背景环境缓慢发展的情况，例如受控的可逆失活自由基聚合。本文将CBC-MC应用于合成梯度共聚物的两个案例研究中。在所有情况下都将分析链属性的梯度分布，因为它相对更具挑战性和趣味性。将链性能与矩量法和动力学蒙特卡罗方法对不同样本量的结果进行比较，发现它们完全吻合。结果证实，如果适用，可以使用这种方法以减少的模拟时间和较小的样本量获得有关链微结构的完整信息。该方法也适用于导致胶凝的非线性共聚。研究了沿着主链的侧链双键的梯度分布对凝胶分子模拟部分的影响。将主链结果与MOM进行比较，发现它们完全吻合。对主链微结构进行了进一步的研究，以更好地了解这些系统中正在发生的多种现象。已经发现，沿主链的PDB分布的梯度会在表面结合型聚合反应中在凝胶分子中引入不均一性，其中凝胶内的主链沿相同方向排列，但是这些异质性似乎在本体聚合中消失，在该聚合反应中，链比对是随机的。

著录项

  • 作者

    Demirel, Derya.;

  • 作者单位

    Illinois Institute of Technology.;

  • 授予单位 Illinois Institute of Technology.;
  • 学科 Chemical engineering.;Polymer chemistry.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 244 p.
  • 总页数 244
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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