This $431,454 Project Grant award from the National Science Foundation's (NSF) Division of Chemistry was provided to The Johns Hopkins University and the University of Massachusetts, Lowell to study the design of catalytic materials for electrochemical synthesis of organonitrogen compounds. The overarching goal is to develop a fundamental understanding of electrochemical carbon-nitrogen coupling using metal-organic frameworks called boron imidazolate frameworks (BIFs) as the catalyst platform....
This $770,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program supports fundamental research on singly dispersed metal-atom catalysts conducted by researchers at the University of Central Florida, University of California Riverside, and Brewton Parker College. The overarching goal is to develop a comprehensive understanding of the factors controlling the activity and selectivity of these catalysts for hydrogen production from methanol...
This Project Grant award from the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) provides $643,347.00 to the University of Alabama-Tuscaloosa to develop computational tools that empower the design of efficient molecular catalysts. The key products and services to be delivered under this 5-year award include: Using high-performance computational screening to identify optimal catalyst structures, compositions, and kinetics to increase activity, selectivity, and...
This National Science Foundation (NSF) Project Grant award, funded through the Mathematical and Physical Sciences program (CFDA 47.049), provides $424,350 to Duquesne University from September 1, 2023 to August 31, 2026. The project aims to design and discover energy-efficient, environmentally friendly, and cost-effective metal-free catalysts for the activation and conversion of small molecules, such as converting carbon dioxide to useful chemicals and fuels. The research approach combines...
The University of Chicago was awarded a $500,000 project grant from the National Science Foundation Division of Chemistry to develop multifunctional metal-organic frameworks for cooperative catalysis. The grant was awarded on August 1, 2021 with a completion date of July 31, 2024. The funding supports research under the NSF's Mathematical and Physical Sciences program (CFDA 47.049), which aims to strengthen the nation's scientific enterprise through advancing knowledge and understanding of major...
The National Science Foundation (NSF) has awarded a $535,830 Project Grant under its Mathematical and Physical Sciences (CFDA 47.049) program to Professor Charles Musgrave and his research team at the University of Colorado-Boulder. The goal of this 3-year grant, with a period of performance from September 1, 2024 to August 31, 2027, is to accelerate the discovery and design of novel electrocatalysts for carbon-free fertilizer synthesis using renewable energy. The researchers will leverage...
The National Science Foundation awarded a $431,091 Project Grant to the University of Massachusetts under the Engineering federal grant program (CFDA 47.041) to develop computational tools for rational design of nanoporous catalysts for carbonylation reactions. The award period is from September 1, 2022 through August 31, 2027. The project aims to discover effective porous solid-acid catalysts as a technologically and environmentally appealing alternative to rare metal catalysts and corrosive...
This $650,000 Project Grant awarded by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) funds an industrial-academic collaboration between Princeton University and the Merck Catalysis Laboratory. The goal is to develop new sustainable catalytic methods for the large-scale synthesis of pharmaceutical drugs using earth-abundant metals like iron, cobalt, and nickel instead of rare precious metals. The research aims to identify viable catalytic...
The University of California, San Diego will develop new classes of solid, tunable catalytic materials for important chemical transformations under a $525,000 National Science Foundation project grant in the Mathematical and Physical Sciences program (CFDA 47.049). Professor Seth Cohen and his research team will create metal-organic frameworks using phosphorus-derived ligands linked to palladium, rhodium, and iridium metal centers. They will modify these materials to promote cross-coupling and...
This Project Grant award of $350,000 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports collaborative research by professors at the University of Wisconsin-Madison, University of Missouri-Kansas City, and Colorado State University. The goal is to study new approaches to catalysis and electrochemistry for the synthesis of biaryl molecules, which are useful in polymers and agriculture. The researchers will use analytical and computational...
This $350,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports a collaborative research effort between Johns Hopkins University and the University of Massachusetts Lowell. The goal is to design and develop novel metal-organic framework (MOF) catalysts for electrochemical synthesis of commercially valuable organonitrogen compounds like urea, acetamide, and N-methylamines.
The project has three key objectives: (1) identify structure-function relationships between MOF electronics and carbon-nitrogen coupling, (2) observe reaction intermediates using in situ vibrational spectroscopy, and (3) develop physics-informed machine learning models to guide the discovery of new MOF catalysts. By leveraging the tunability of MOFs, the researchers aim to gain fundamental insights into catalytic mechanisms and explore the scope of electrochemical C-N coupling with abundant carbon and nitrogen feedstocks. In addition to the research, the project includes educational initiatives like a video series showcasing diverse paths to chemical research and a new course on applied machine learning for computational catalysis.