Project Grant 2437420

Award Date 7/1/25
Completion Date 6/30/29
Dollars Obligated $218K
Federal Grant Program
47.083
Assistance Type
Project Grant
Place of Performance
Brookings, SD 57007, USA
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This $932,864 Project Grant awarded by the National Science Foundation (NSF) Integrative Activities program (CFDA 47.083) supports a collaborative research initiative led by the South Dakota School of Mines and Technology (SDSM&T). The project aims to advance germanium (Ge)-based material research and detector technology using artificial intelligence (AI) and innovative fabrication techniques. The key products and services to be delivered include: Developing AI-driven Ge crystal growth and...
This Project Grant award, provided by the National Science Foundation (NSF) Integrative Activities program (CFDA 47.083), supports a collaborative research initiative led by the University of South Dakota (USD) to advance germanium (Ge)-based detector technology. The $337,678 award, effective July 1, 2025 through June 30, 2029, aims to enhance the detection of low-energy particles and improve the accuracy of medical imaging. The project will leverage artificial intelligence (AI) and innovative...
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This Project Grant award, totaling $218,423, was provided by the National Science Foundation's (NSF) Integrative Activities program (CFDA 47.083) to South Dakota State University (SDSU) from July 1, 2025 to June 30, 2029.

The grant supports a collaborative research initiative to advance germanium (Ge) material research and detector technology using artificial intelligence (AI) and innovative fabrication techniques. Led by SDSU, this statewide project includes six South Dakota institutions and two industry partners. The key objectives are to enhance the detection of low-energy particles for applications in experimental neutrino physics, dark matter search, and high-precision medical imaging. The project will develop Ge internal charge amplification technology and optimize Ge purification, crystal growth, and detector fabrication using AI-based algorithms. This research aims to enable more accurate detection of terahertz radiation from low-mass dark matter and neutrinos, as well as facilitate high-precision medical imaging for earlier diagnoses and more accurate radiation therapy. The initiative will also evaluate the impact of advanced imaging on healthcare in local communities and foster scientific, technical, and economic innovation through early career faculty development and expanded laboratory capabilities.

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