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.

非技术摘要该奖项支持理论、计算和数据密集型研究和教育，旨在利用计算和数据密集型方法帮助开发由聚合物制成的用于分离气体的膜。 PI 将开发计算工具，对由长链分子组成的聚合物制成的材料进行建模。这些工具将与其他理论、以数据为中心的计算方法结合使用，以增进对聚合物材料的理解，并为当前和新兴技术应用开发稳健的聚合物膜设计方法。这项工作的一个亮点集中在使用聚合物膜来清洁气流中不必要的污染物，例如从气流中去除腐蚀性硫基化合物，以减轻长距离泵送压裂气体时的管道腐蚀。这些研究活动与广泛的教育活动相结合。在 PI 最近成功招募高中生和本科生进行暑期研究的推动下，PI 将继续努力招募本科生和研究生级别的女性和少数族裔学生。重点是为高中生提供在该项目中进行夏季研究的机会。受到过去成功的鼓舞，这些高中生将被积极鼓励接受本科教育，从而在 STEM 领域从事可能的职业。技术摘要该奖项支持理论、计算和数据密集型研究以及教育，以增进对聚合物膜的理解。聚合物膜对于气体分离应用非常有效，并且具有重量轻且易于加工的额外优点。聚合物膜材料已经取得了许多进展，但其中大多数都是凭经验设计的。为了制定设计策略，需要定量了解控制分子传输、溶解度以及这些材料的渗透性和选择性的微观机制。 PI 将使用计算机模拟和数据密集型方法来解决本主题中一些最重要的未解决问题。该奖项支持主要使用分子动力学模拟和机器学习方法来解决三个重要问题的研究：（i）数据密集型方法是否可以应用于设计感兴趣的膜材料？更具体地说，数据挖掘和机器学习是否可以用于预测渗透率和选择性，从而预测膜气体分离性能的上限相关性？ (ii)已经发现，在橡胶状聚合物的情况下，渗透性对溶质尺寸的依赖性与在它们的玻璃状类似物中发现的“相反”。这是一个总体趋势吗？如果是，那么从以筛分为主的玻璃态趋势到可能由溶解度效应驱动的橡胶态行为的转变是如何发生的？ (iii) 当纳米颗粒在气体分离中与聚合物物理混合时，它们的作用是什么？如果纳米颗粒具有选择性，这种现象会受到怎样的影响？在这种情况下，纳米粒子在聚合物玻璃老化中发挥什么作用？该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.