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.

该奖项支持理论，计算和数据密集型研究和教育，旨在使用计算和数据密集型方法来帮助开发用于分离气体的聚合物膜。PI将开发计算工具，模拟由长链分子组成的聚合物制成的材料。这些工具将与其他理论，以数据为中心的方法和计算方法结合使用，以促进对聚合物材料的理解，并为当前和新兴技术应用的聚合物膜开发稳健的设计方法。这项工作的重点是使用聚合物膜来清洁不必要污染物的气流，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最近成功地招募高中和本科生进行夏季研究的推动下，PI将继续努力在本科和研究生两级招募女性和少数民族学生。一个重点将是为高中生提供机会，在这个项目中进行夏季研究。在过去成功的鼓舞下，这些高中生将积极鼓励追求本科教育，导致可能的职业生涯在STEM。技术总结该奖项支持理论，计算和数据密集型的研究，和教育，以促进对聚合物膜的理解。对于气体分离应用有效的聚合物膜具有重量轻且易于加工的附加优点。聚合物膜材料已经有了许多进展，但其中大部分都是经验性设计的。为了开发设计策略，需要对控制这些材料的分子传输、溶解度以及因此的渗透性和选择性的微观机制进行定量理解。PI将使用计算机模拟和数据密集型方法来解决该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）数据密集型方法能否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能否用于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，在橡胶状聚合物的情况下，渗透性的溶质尺寸依赖性与在其玻璃状类似物中发现的情况“相反”。这是一个总趋势吗？如果是，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？和（iii）在气体分离的背景下，当纳米颗粒与聚合物物理混合时，纳米颗粒的作用是什么？如果纳米粒子具有选择性，这种现象会受到什么影响？在这种情况下，纳米颗粒在聚合物玻璃老化中的作用是什么？该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

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.