Rare-earth metals are an indispensable part of today’s high-tech consumer products. As demand for them increases, so does the need for enhancing a new frontier in extracting those metals from electronic waste – a more environmentally friendly way than taking them directly from the ground. 

Cyclone Engineer Ratul Chowdhury is applying data-driven research to make innovations in that new process.

Chowdhury, an assistant professor of chemical and biological engineering, will work with a team of collaborators led by the Department of Energy’s Lawrence Livermore National Laboratory, together with Penn State and industrial partners. The project is funded through the Critical Materials Innovation Hub (CMI). The CMI is an Energy Innovation Hub led by Ames National Laboratory with support from the DOE’s Advanced Materials and Manufacturing Technologies Office (AMMTO).

Part of the new rare-earth metals extraction technology revolves around using the protein Lanmodulin (LanM), which binds to rare-earth metals and aids in their separation from various sources, such as electronic wastes and magnets. Since the United States needs to import much of its supply of rare-earth metals, developing alternative and unconventional sources for them has become an important objective. 

It has been estimated that LanM is nearly a billion times better at binding to rare-earth elements than other metals. Chowdhury’s team is developing methods to design and experimentally test variants of LanM to further enhance its abilities in extraction. 

“We are using data-driven design and dynamic simulation to accurately capture the role of geometry and chemistry of proteins that drive interactions with these critical metals,” said Chowdhury. “This analysis will be applied in designing a selective protein-based extraction agent for rare-earth metals. We are manipulating the LanM protein to further improve its binding ability. We are also working toward enhancing its stability in various pH and temperature conditions for industrial extraction.”

Graduate student Hannah Gates from Chowdhury’s research group has contributed to the preliminary data, and she will build and refine these computational tools and incorporate experimental validation data for her Ph.D. thesis.