This 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.
This 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.
