| NSF Org: |
CHE Division Of Chemistry |
| Recipient: |
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| Initial Amendment Date: | September 7, 2021 |
| Latest Amendment Date: | July 26, 2022 |
| Award Number: | 2137575 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Anne-Marie Schmoltner
CHE Division Of Chemistry MPS Directorate for Mathematical and Physical Sciences |
| Start Date: | September 15, 2021 |
| End Date: | November 30, 2022 (Estimated) |
| Total Intended Award Amount: | $248,613.00 |
| Total Awarded Amount to Date: | $248,613.00 |
| Funds Obligated to Date: |
FY 2022 = $0.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
1601 E MARKET ST GREENSBORO NC US 27411 (336)334-7995 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
NC US 27411-0001 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
OFFICE OF MULTIDISCIPLINARY AC, BROADENING PARTICIPATION |
| Primary Program Source: |
010V2122DB R&RA ARP Act DEFC V |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.049 |
ABSTRACT
This award is funded in part under the American Rescue Plan Act of 2021 (Public Law 117-2). In this project, funded by the Mathematical and Physical Sciences Directorate and the Chemistry Division, Professor Bo Wang and his students at North Carolina Agricultural & Technical State University (NCAT) will develop tools for analysis of chemical data that would be easier to apply for non-specialists. The data are related to environmental and health studies and will be obtained using Nuclear Magnetic Resonance (NMR) methods. The novel way of data processing will use artificial intelligence (AI) methods. This research could have significant impacts on health monitoring by providing fast analysis of complicated data sets. The research is likely to inspire students at the PI's institution, which is a Historically Black College or University (HBCU). Prof. Wang?s plans for broadening participation in STEM by underrepresented minority (URM) students include introducing students to applied data science and AI methods, which are very marketable job skills but are not taught regularly at the undergraduate level. Opportunities for independent interdisciplinary research projects will be provided and a new course will be developed.
The identification of metabolites will be improved using AI techniques applied to analysis of 1D and 2D NMR data. NMR has high instrument reliability, but there is a common problem of data overfitting. This new method will provide improved ways for extracting peaks in complex spectra. Training data are freely available from published literature and Prof. Wang?s own studies, and the method will be tested with data recorded by Professor Wang?s group. Methods developed will help make metabolomics and other sophisticated NMR-based research accessible to a broader range of scientists. This research will be integrated with education through course and curriculum development, and the introduction of an educational website. Summer workshops will also be conducted.
ucted.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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