Quantitative predictions for structured polymeric melts

结构化聚合物熔体的定量预测

• 批准号: RGPIN-2020-07091
• 负责人: Matsen, Mark
• 金额: $ 2.99万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741589
• 关键词: Quantitative; predictions; structured; polymeric; melts

Quantitative simulation methods will be developed and applied to structured polymeric melts, in particular, those involving block copolymers. This is only possible because all models and experiments reduce to a standard Gaussian-chain model (GCM) at high molecular weights. The profound implication of this universality is that even simple models can be quantitatively matched to experiments by mapping both onto the GCM. Block copolymer are much like ordinary polymers, except that they consist of chemically distinct sections (or blocks). The tendency for the distinct blocks to separate tempered by their connectivity causes a liquid (or melt) of these molecules to self-assemble into structures with nanometer-sized domains. The ability to combine components with different characteristics into well-defined nanostructures provides a powerful way of controlling material properties. Furthermore, the ordered nanostructures have many applications in the emerging field of nanotechnology. The GCM treats polymers as thin elastic threads interacting by contact forces, the strength of which is control by a Flory-Huggins chi parameter. The key to mapping other models or experiments onto the GCM is knowing how to calibrate chi, which is something that we have made great progress on over the past few years. Although the GCM underpins most theoretical calculations on block copolymers, it is impractical to simulate directly and thus one needs other approaches. One strategy is to use particle-based lattice models, where the simulation is made efficient by restricting the polymers to a lattice. Although the lattice is artificial, quantitative predictions are still possible due to universality. In the past, these simulations have been limited to low molecular-weight polymers, but we have now developed a lattice model that can be mapped onto high molecular-weight polymers. It will be used to develop quantitative predictions of diblock copolymer melts and of binary homopolymer blends for the purpose of calibrating the chi parameter in experimental systems. Another strategy is field-theoretic simulations (FTS), where the particle-based GCM is transformed into a mathematically equivalent field-based model, which can then be simulated. This approach has been plagued by computational challenges and a so-called ultraviolet divergence, but we have overcome these obstacles to generate a highly efficient FTS algorithm capable of simulating systems that are too complicated for traditional particle-based models. FTS will be used to investigate ternary blends of diblock copolymers with their parent homopolymers, which are well known for producing bicontinuous microemulsions. FTS will also be applied to complicated bottlebrush block copolymers, which are gaining huge attention due to their fast dynamics and large domains.

定量模拟方法将被开发和应用于结构化聚合物熔体，特别是那些涉及嵌段共聚物。这是唯一可能的，因为所有的模型和实验减少到一个标准的高斯链模型（GCM）在高分子量。这种普遍性的深刻含义是，即使是简单的模型也可以通过将两者映射到GCM上来定量地与实验相匹配。 嵌段共聚物很像普通聚合物，只是它们由化学上不同的部分（或嵌段）组成。不同的块通过它们的连接性而分离的趋势导致这些分子的液体（或熔体）自组装成具有纳米尺寸域的结构。将具有不同特性的联合收割机组分组合成明确定义的纳米结构的能力提供了控制材料特性的强有力的方式。此外，有序纳米结构在新兴的纳米技术领域有许多应用。 GCM将聚合物视为由接触力相互作用的细弹性线，其强度由Flory-Huggins chi参数控制。将其他模型或实验映射到GCM上的关键是知道如何校准chi，这是我们在过去几年中取得的巨大进展。虽然GCM支持大多数嵌段共聚物的理论计算，但直接模拟是不切实际的，因此需要其他方法。 一种策略是使用基于粒子的晶格模型，通过将聚合物限制在晶格中来提高模拟效率。虽然晶格是人造的，但由于普遍性，定量预测仍然是可能的。在过去，这些模拟仅限于低分子量聚合物，但我们现在已经开发出一种可以映射到高分子量聚合物的晶格模型。它将被用来开发定量预测的二嵌段共聚物熔体和二元均聚物共混物的目的，在实验系统中校准的chi参数。另一种策略是场论模拟（FTS），其中基于粒子的GCM转换为数学上等效的基于场的模型，然后可以模拟。这种方法一直受到计算挑战和所谓的紫外发散的困扰，但我们已经克服了这些障碍，生成了一个高效的FTS算法，能够模拟传统粒子模型过于复杂的系统。FTS将被用来研究三元共混物的二嵌段共聚物与它们的母体均聚物，这是众所周知的生产双连续微乳液。FTS也将应用于复杂的瓶刷嵌段共聚物，这是获得巨大的关注，由于其快速的动态和大的域。