Investigation of CO2 adsorption on MOF porous media using artificial intelligence and advanced computational chemistry

The carbon dioxide (CO2) concentration in the atmosphere has been increasing continuously due to human activities such as burning fossil fuels, which causes global climate change. Conventional CO2 capture strategies are practical, but they are costly and only lower the rate of CO2 emissions to the atmosphere. To decrease the global concentration of CO2 in the atmosphere, CO2 should be directly captured from ambient air with novel separation techniques. Metal-organic frameworks (MOFs) are among the efficient alternatives for CO2 capture, especially in direct air capture (DAC) process. They look like sponges with unique abilities – being able to take up, hold, and release molecules from their pores. Synthesizing all possible MOFs in laboratories is complex, expensive, and time-consuming. The proposed project will integrate artificial intelligence and computational chemistry techniques to design and select suitable MOFs and reveal the active mechanisms during CO2 adsorption/desorption. The artificial intelligence will speed up the pre-screening process of the suitable MOFs for each scenario of CO2 capturing among the large databank of hypothetical MOFs for further experimental and/or simulation evaluations. This process will provide the experimentalists with inexpensive and effective strategies by suggesting most efficient MOFs instead of synthesizing numerous available MOFs.

Faculty Supervisor:

Sohrab Zendehboudi

Student:

Partner:

Advanced CERT Canada

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Memorial University of Newfoundland

Program: