Electric Fields and Solvents as Dual Knobs for Selectivity in Methane-to-Methanol Conversion

Professor Samuel Odoh of the University of Nevada, Reno is studying how electric fields and solvents can work together inside tiny porous materials called zeolites to improve the conversion of methane gas into methanol, a useful liquid fuel and chemical building block. Methane is an abundant component of domestic natural gas that is often wasted or flared during extraction and transport. By converting this gas into transportable liquid methanol directly at the source, this project will capture a highly valuable resource, provide new economic opportunities, and strengthen resilient supply chains across the United States. To achieve this goal, the research will employ advanced computer simulations, facilitated by artificial intelligence (AI), to discover how applying gentle electric fields can steer chemical reactions toward producing methanol rather than unwanted byproducts. Beyond the scientific advancements, the initiative will actively train the next generation of scientists by providing mentorship, research internships, and a hands-on Computational Bootcamp for high school and college students from in the Reno-Tahoe area. Professor Samuel Odoh of the University of Nevada, Reno is developing a solvent-explicit, field-aware, and uncertainty-quantified computational framework to investigate how external electric fields applied in capacitor-mode geometries will improve methane-to-methanol conversion selectivity in copper-exchanged zeolite CHA. A central part of this work will be the deployment of AI and machine-learning interatomic potentials for high-throughput, field-free exploration of complex pore microenvironments. The study will map how device-grade electric fields shift the distributions of cation sitings and solvent hydrogen-bond networks without perturbing intrinsic C-H activation barriers. The central hypothesis is that these field-conditioned microenvironments will act as a thermodynamic and kinetic selectivity valve, heavily favoring methoxy rebound and methanol desorption over competitive over-oxidation pathways. Free-energy barriers will be integrated into a deterministic microkinetic network allowing validation of the predictive models against operando spectroscopic experiments. 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.