用分子信息场理论加速预测区块共聚物库的相位行为
Charles Li1, Elizabeth A Murphy2,3, Stephen J Skala2,4
1Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, California 93106, United States.
这项研究引入了一种多尺度建模方法来预测聚合物溶液相图,减少埃迪森的配方设计. 该方法使用原子模拟来参数化粗粒度模型以准确地预测结构和相位行为.
科学领域:
- 聚合物科学与工程
- 材料科学
- 计算化学
背景情况:
- 聚合物解决方案在消费者护理,治疗和涂料等多种应用中至关重要.
- 预测这些复杂系统的自我组装和相位行为是由于长时间/长度尺度和化学特异性而具有挑战性.
- 目前的配方设计在很大程度上依赖于经验,试错 (爱迪逊的) 方法.
研究的目的:
- 开发一种系统的,预测性的聚合物溶液配方计算方法.
- 准确预测在溶液中的双块聚合物的完整温度-度相图.
- 在配方设计中减少依赖埃迪森的方法.
主要方法:
- 一种结合原子分子动力学模拟和粗粒度场理论模型的多尺度建模策略.
- 原子模拟用于参数化粗粒度模型.
- 粗粒模拟有效地探索长时间和长度尺度以确定结构和相位行为.
主要成果:
- 通过小角度X射线散射验证,准确预测溶液中的模拟二块聚合物的完整温度-度相图.
- 多尺度方法成功地克服了模拟自组装的传统方法的局限性.
- 证明了溶液结构和相位行为的严格确定.
结论:
- 提出的多尺度建模方法为埃迪森的配方设计提供了一个系统和可预测的替代方案.
- 这种方法有可能极大地加快新型配方的选.
- 它可以在广泛的聚合物应用中指导组件选择和组合优化.
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