AI-Guided Biology for Recovering Critical Minerals
Critical minerals like cobalt and rare earth elements are essential for various energy technologies and advanced manufacturing. However, recovering them from dilute sources such as mine tailings or wastewater is costly and inefficient with conventional methods.
Researchers at Ames National Laboratory are developing MagSynth, an AI-guided synthetic biology platform to capture these materials. By combining synthetic biology, machine learning, and physics-based molecular modeling, the project researchers will create modular systems that efficiently extract valuable elements from low-concentration sources.
In the first year, the team will demonstrate proof-of-concept by with a focus on cobalt recovery. In the future, the platform will be expanded to recover a broader range of critical minerals, supporting secure domestic supply chains and reducing the negative impacts from traditional mining.
This research is supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Biological Systems Science Division. Ames National Laboratory is operated for the U.S. Department of Energy by Iowa State University under Contract No. DEAC02-07CH11358.
Principal Investigator: Peng Xu
Co-PIs: Ratul Chowdhury, Larry Halverson, Marit Nilsen-Hamilton, Long Qi
Staff Scientists: Pierre Palo, Gennady Pogorelko, Lee Bendickson
Post Docs: Ashley Paulson, Gregory Curtin, Tian You, Karuna Anna Sajeevan