UW SER Begins Work on $6 Million NSF EPSCoR Grant with University of Alaska on Sustainable Critical Mineral Recovery

The University of Wyoming School of Energy Resources (SER) has begun work as a subawardee on a major grant from the National Science Foundation (NSF) in collaboration with the University of Alaska Anchorage (UAA) to explore transformative bioprocessing and artificial intelligence (AI)-driven frameworks for sustainable critical mineral recovery.

Funded under the EPSCoR (Established Program to Stimulate Competitive Research) Infrastructure Improvement Program, the $6 million grant project establishes a smart bio-refinery framework with AI-enabled microbial recovery systems to address the nation’s reliance on foreign sources of rare earth elements.

The interdisciplinary research collaboration will integrate microbiology, mineralogy, AI and process engineering to advance critical mineral recovery. The project aims to strengthen U.S. economic and national security through reduced dependence on foreign sources of critical minerals.

Additional partners on the project include the University of Alaska Fairbanks and Montana Technical University.

The UW portion of the project will be led by Tyler Brown, program manager of the minerals team inside SER’s Center for Economic Geology Research. He will primarily be tasked with the characterization, economic analysis and workforce training components of the project.

“We have worked diligently to establish ourselves as the go-to authority in critical mineral characterization, and we are deeply grateful for our robust partnerships with premier institutions,” Brown says. “Our team brings years of specialized experience in characterizing unconventional coal feedstocks, paired with deep expertise in economic analysis, workforce development and social sciences. Supported by strong stakeholder commitment, we are uniquely positioned to advance these vital technologies toward commercialization as their viability is proven.”

The project lead at UAA will use a proprietary bioleaching process to extract critical minerals from diverse feedstocks. By integrating pre- and post-optimization data directly into a machine learning feedback loop, the researchers aim to engineer tailored extraction solutions for varied regional materials. In collaboration with the partner institutions in Alaska and Montana, the team combines regional feedstock research with cutting-edge engineering and AI capabilities.

“This is a unique situation in which each team is bringing a strength to the table to contribute to a holistic approach for critical mineral recovery,” Brown says. “Regional partnerships and pooling expertise across state lines and academic institutions will ultimately allow us to build a resilient, homegrown framework for critical mineral recovery that directly strengthens domestic energy independence.”

The four-year project is one of eight receiving a combined $39.9 million through NSF’s EPSCoR  Infrastructure Improvement Program: Focused EPSCoR Collaborations.