Abstract
Separation and filtration systems are important in the world, such as for water purification and Lithium recycling. About 1 in 4 people worldwide lack access to clean drinking water. Also, Lithium recycling can facilitate the reuse of valuable materials and reduce the amount of environmentally harmful methods of obtaining them. Graphene oxide nanosheets have been shown as a promising solution to improve filtration while functional groups (FGs) show the potential to improve that further. In this study, we work on finding FGs with the best specific adsorption energies for water purification and lithium recycling using DFT simulations. With the number of functional groups and how computationally expensive DFT calculations can be, we also apply machine learning to help predict the adsorption energies of ions onto the FGs without explicit calculations. So far, we have found several FG candidates and identified a few essential properties to improve the model performance.
Recommended Citation
Potts, Justin M.; Wang, Luqing; Chen, Yiming; and Chan, Maria
(2025)
"Machine learning aided computational study of functional groups for water purification and Li recycle,"
Macalester Journal of Physics and Astronomy: Vol. 13:
Iss.
1, Article 11.
Available at:
https://digitalcommons.macalester.edu/mjpa/vol13/iss1/11