Critical Factors Controlling Gas Separations by Polymeric Membranes

控制聚合物膜气体分离的关键因素

• 批准号: 1829655
• 负责人: Sanat Kumar
• 金额: $ 24万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1829655&HistoricalAwards=false
• 关键词: Critical; Factors; Controlling; Gas; Separations

NONTECHNICAL SUMMARYThis award supports theoretical, computational, and data-intensive research, and education with an aim to use computation and data-intensive approaches to help develop membranes made of polymer to be used to separate gases. The PI will develop computational tools modelling materials made of polymers which are composed of long-chain molecules. These tools will be used in conjunction with other theoretical, data-centric, and computational methods to advance understanding of polymeric materials and to develop robust design methodologies for polymer membranes for current and emerging technological applications. A highlight of the work focuses on using polymer membranes to clean gas streams of unnecessary pollutants, for example removing corrosive sulfur-based compounds from gas streams to mitigate pipeline corrosion when fracked gases are pumped over long distances. These research activities are coupled to extensive educational activities. Driven by the PI's recent success in recruiting high school and undergraduate students for summer research, the PI will continue his efforts to recruit women and minority students at both the undergraduate and graduate levels. A focus will be to provide high school students with opportunities to perform summer research in this project. Encouraged by past successes, these high-school students will be actively encouraged to pursue undergraduate education leading to possible careers in STEM.TECHNICAL SUMMARYThis award supports theoretical, computational, and data-intensive research, and education to advance understanding of polymeric membranes. Polymeric membranes, which are efficient for gas separation applications, have the added advantages of being lightweight and easily processable. There have been many advances in polymer membrane materials, but most of these have been empirically designed. To develop design strategies, there is need for a quantitative understanding of the microscopic mechanisms controlling molecular transport, solubility, and hence permeability and selectivity of these materials. The PI will use computer simulations and data-intensive approaches to target some of the most important unresolved questions in this topic.This award supports research which will primarily use molecular dynamics simulations and machine learning methodologies to address three important questions: (i) Can data-intensive methods be applied to design membrane materials of interest? More specifically, can data mining and machine learning be used to predict permeability and selectivity, and thus the upper bound correlation for membrane gas separation performance? (ii) It has been found that the solute size dependence of permeability in the case of rubbery polymers is "opposite" to that found in their glassy analogs. Is this a general trend, and if so, how does the transition from glassy trends, dominated by sieving, to rubbery behavior, which is probably driven by solubility effects, occur? and (iii) What is the role of nanoparticles when they are physically mixed with polymers in the context of gas separation? How is this phenomenon affected if the nanoparticles are made selective? What is the role of nanoparticles in aging of polymer glasses in this context?This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

非技术摘要这一奖项支持理论，计算和数据密集型研究，并旨在使用计算和数据密集型方法来帮助开发由聚合物制成的膜，用于分离气体。 PI将开发由由长链分子组成的聚合物制成的计算工具建模材料。这些工具将与其他理论，以数据为中心和计算方法结合使用，以提高对聚合物材料的理解，并为当前和新兴技术应用开发强大的聚合物膜设计方法。这项工作的重点是使用聚合物膜清洁不必要的污染物的气流，例如，在长距离泵送压裂气体时，将基于腐蚀性的基于硫磺的硫化合物从气流中降低管道腐蚀。这些研究活动与广泛的教育活动相结合。在PI最近在招募高中和本科生进行夏季研究方面的成功的驱动下，PI将继续努力招募本科和研究生水平的妇女和少数民族学生。重点是为高中学生提供在该项目中进行夏季研究的机会。在过去的成功的鼓励下，这些高中生将被积极地鼓励从事本科教育，从而导致STEM的职业。技术摘要这一奖项支持理论，计算和数据密集型研究以及教育，以提高对聚合物膜的理解。有效的气体分离应用的聚合膜具有轻巧且易于加工的额外优势。聚合物膜材料取得了许多进步，但其中大多数是经验设计的。为了制定设计策略，需要定量了解控制分子运输，溶解度以及这些材料的渗透性和选择性的微观机制。 PI将使用计算机仿真和数据密集型方法来针对本主题中一些最重要的未解决问题。该奖项支持将主要使用分子动力学模拟和机器学习方法的研究来解决三个重要问题：（i）（i）数据密集型方法可以应用于设计的膜材料吗？更具体地说，可以使用数据挖掘和机器学习来预测渗透性和选择性，从而预测膜气体分离性能的上限相关性？ （ii）已经发现，在橡胶聚合物的情况下，渗透性的溶质尺寸依赖性与其玻璃类似物中的溶质大小相反。这是一个普遍的趋势，如果是这样，从筛分，筛分，可能是由溶解度效应驱动的橡胶行为的过渡如何发生？ （iii）当纳米颗粒与聚合物在气体分离的背景下与聚合物物理混合时，纳米颗粒的作用是什么？如果使纳米颗粒具有选择性，该现象将如何影响？在这种情况下，纳米颗粒在聚合物眼镜衰老中的作用是什么？该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响标准，被认为值得通过评估来获得支持。

项目成果

Designing exceptional gas-separation polymer membranes using machine learning

• DOI: 10.1126/sciadv.aaz4301
• 发表时间: 2020-05-01
• 期刊: SCIENCE ADVANCES
• 影响因子: 13.6
• 作者: Barnett, J. Wesley;Bilchak, Connor R.;Kumar, Sanat K.
• 通讯作者: Kumar, Sanat K.