CAREER: Developing Electrode Interfaces for Bioelectrochemical Synthesis
- Institution
- Michigan State University, MI
- Principal investigator
- David Hickey
- Funding
- $550K
- Duration
- 2025–2030
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Abstract
Biochemical reactions often involve the movement of electrons. Often the electrons are transferred between the molecules involved in the reaction. Sometimes, electrons are exchanged with the surrounding environment. Cells have a series of small molecules involved in electron exchange to and from enzymes. These are known as redox cofactors. Reactions that require electrons can be driven by electrons provided by cathodes. The electrons are transferred to the redox cofactors and transported to the enzymes that catalyze electrochemical reactions. A single redox cofactor, nicotinamide adenine dinucleotide (NADH), is used by over half of all redox-active enzymes. Electron transfer from electrodes to NADH is inefficient. The main objectives of this project are to develop new electrode interfaces and design synthetic NADH mimics to enable their selective and rapid regeneration using electricity. In parallel, the project will create new technical training opportunities for formerly incarcerated individuals helping to build a talent pipeline aligned with the needs of advanced biomanufacturing. - The objective is to develop a modular platform for electrochemical regeneration of NADH-like cofactors (mNADHs). These mNADHs would be compatible with a broad range of NADH-dependent oxidoreductase enzymes. The project consists of three synergistic aims. The first is to use thermostable NADH dehydrogenases to create mNADHs that can be regenerated via mediated bioelectrocatalysis. The second is to develop sterically and electronically tuned mNADHs that favor selective formation of enzymatically active 1,4-dihydro species during direct electrochemical reduction. The third is to create electrochemically reversible pyridinium-based redox mediators that mimic NADH function through single-electron transfer to flavin-containing enzymes. Supervised machine learning and density functional theory (DFT) computations will be used to derive quantitative structure–activity relationship (QSAR) models that correlate molecular structure with bioelectrocatalytic performance and guide molecular design. The project integrates experimental electrochemistry, enzymology, and spectroelectrochemical kinetics to elucidate key factors influencing cofactor regeneration efficiency and stability. By generating new mechanistic understanding and tunable redox scaffolds, this work will enable broader electrification of biocatalytic processes. 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.
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