March 05, 2020
Recycling of transition metals from spent batteries and fuel cells typically involves high-temperature processes, the optimization of which is expensive and requires specialized reactors. This two-year NSF project with a total budget of ~$1.4M aims at laying the foundation for the computational prediction of chemical and electrochemical reactions at high temperatures. Together with collaborators at the University of Illinois at Urbana-Champaign and at the University of Minnesota, Urban's group will develop a machine-learning approach for the prediction of high-temperature phase diagrams.
Read more: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940290
