Collaborative Research: DMREF: GOALI: Discovering Materials for CO2 Capture in the Presence of Water via Integrated Experiment, Modeling, and Theory

The concentration of carbon dioxide in the atmosphere has risen rapidly over the past century, creating significant concerns about global warming and ocean acidification. Carbon capture and sequestration is widely viewed as an essential tool, along with other technologies such as wind and solar energy, for keeping atmospheric CO2 levels from rising further.

This project focuses on developing new materials for selective adsorption of CO2 versus N2. A primary emphasis will be the effect of water on CO2/N2 selectivity and CO2 capacity. The main goal of the proposed work is to develop integrated simulation, theoretical, and experimental methods for understanding the effect of water on CO2/N2 separations in nanoporous materials and to use these tools to speed up the discovery of new materials for CO2 capture.

The development of new materials and technologies that enable cost-effective carbon capture at high-volume point sources is viewed by the International Energy Agency as an essential component of a many-pronged approach to combatting climate change. The project will contribute to the education of graduate and undergraduate students in a highly interdisciplinary project. Web and video-based education and outreach activities will reach a wider audience.

In line with the Materials Genome Initiative, this project will contribute to the development of new strategies for creating metal-organic framework (MOF) materials with programmable structure, by precisely combining pre-assembled building blocks. The project will focus on a few MOF platforms that can be systematically tuned by changing the organic linkers and introducing extra-framework anions, extra-framework cations, and restructured MOF nodes.

These “platform” MOFs are chosen from families of MOFs known to exhibit excellent stability. Optimization of MOF synthesis will be accelerated by use of robotic synthesis tools coupled with machine learning algorithms. Molecular simulation will be used to test new proposed material variations and provide molecular-level insights into observed behavior.

The simulation models will be validated against adsorption data collected in the project, including multicomponent adsorption measurements, which are extremely scarce in the literature. All simulated and experimental adsorption data will be placed in publicly accessible databases. For the most promising materials, single-crystal X-ray studies of molecular siting and arrangements in the pores will be performed using a new technique that does not require synchrotron access.

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.