CAREER: Data-Enhanced Multi-Scale Simulations for Advancing SAMs in Interfacial Electrocatalysis

Carbon dioxide (CO2) is released every day from power plants, factories, and vehicles. Managing this waste carbon is an important national challenge. One potential solution is to use electricity to turn CO2 into useful fuels and chemicals. However, today’s methods often have difficulty both capturing CO2 and controlling how it reacts. This limits efficiency and the ability to make more valuable products. This project tackles this challenge by studying new catalysts that can capture and convert CO2 to hydrocarbon fuels. The research focuses on Cu electrocatalysts covered by thin layers of molecules. These molecular layers serve dual purposes of binding CO2 and guiding the electrochemical reactions that produce useful multi-carbon products. The project uses computer simulations, artificial intelligence, machine learning methods, and quantum phenomena to understand how the catalyst structure affects their performance. Research and education are integrated through hands-on student training, K–12 outreach at a local Discovery Museum, a new graduate course in applied machine learning, and open educational content shared through a public YouTube teaching channel. This project investigates hybrid ligand-metal surfaces to better understand how to capture and convert CO2 in a single step. The research uses self-assembled monolayers (SAMs) on copper electrodes as a model system to combine carbon capture and electrochemical conversion. To study this, the project uses a computer simulation framework that combines physics-based machine learning, density functional theory, and microkinetic modeling to simulate how molecules interact with the catalyst surface under realistic chemical conditions. Specifically, the project improves our understanding of electrocatalysis by using computers to test how ligand layers control CO2 binding, identifying the exact chemical steps that turn CO2 into multi-carbon products, and examining how the liquid electrolyte and electric fields work together to control the reaction. Experimental spectroscopy measurements are used to test and confirm these computer predictions, and investigate quantum phenomena at the electrode surface. Ultimately, the project aims to show exactly how these catalyst sites stabilize chemical reactions, identify the key factors that make them efficient, and create design rules for next-generation catalysts. Finally, this new modeling framework is not just for carbon capture and conversion; it can also be used for other green energy technologies, including nitrogen reduction and biomass upgrading. 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. NSF Award ID: 2541271 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Fanglin Che | Institution: Worcester Polytechnic Institute, WORCESTER, MA | Award Amount: $637,801 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2541271 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2541271.html