Collaborative Research: Atomistic Resolution and Design of Plasmonic Catalysts for Ammonia Synthesis

In this project, Professors Junwei Lucas Bao of Boston College and Jennifer A. Dionne of Stanford University are studying how visible light and tiny particles of gold and ruthenium could help produce ammonia under gentler conditions. Ammonia will remain essential for fertilizer and global food production, and it may also be useful for storing and moving hydrogen in future technologies. However, today’s main method for producing ammonia requires very high heat and pressure and consumes large amounts of energy. This project explores whether sunlight-like visible light could power small metal particles to help nitrogen and hydrogen gases react under much milder conditions. The team will combine computer models, quantum theory development, laboratory experiments, powerful microscopes, and data tools to learn which particle shapes and surface features will work best. Physics-driven artificial intelligence (AI) will help the team find patterns in large sets of experimental and computer-generated data and choose the most promising particles and reaction conditions to test next. The results could help guide new ways to make fertilizer and other important chemicals with less extreme heat and pressure. The project will also train students at Boston College and Stanford University in chemistry, materials science, computer modeling, data science, and AI. Through online workshops, hands-on demonstrations, high-school learning activities, undergraduate research opportunities, and public art-science activities, the project would help students and broader communities understand how light, chemistry, tiny materials, and computer tools could work together in modern chemical research. Professors Junwei Lucas Bao of Boston College and Jennifer A. Dionne of Stanford University are studying how visible light and gold-ruthenium nanoparticles could help produce ammonia under gentler conditions. This project will combine catalyst discovery experiments, advanced imaging, high-accuracy quantum chemistry, and data-driven modeling to understand how visible light could enable ammonia synthesis on gold-ruthenium nanoparticles under mild conditions. The project will address a central challenge in catalysis: real catalyst particles are not flat, static surfaces, but 3D structures whose atoms can rearrange during reaction. Experimental measurements will identify promising catalyst compositions, measure their performance under light, and reveal how their atomic structures change during ammonia synthesis. These experimentally measured structures will then be used directly in high-accuracy quantum-chemical calculations to study how nitrogen and hydrogen react on realistic catalyst surfaces. By connecting measured catalyst structures with accurate calculations of reaction steps, the research team will determine which structural and electronic features would make ammonia formation easier under illumination. AI methods will integrate experimental measurements, atomic-scale structural information, and quantum-chemical descriptors to predict improved catalyst structures and reaction conditions for further testing. The expected outcome will be a broadly useful strategy for designing light-driven catalysts based on real, dynamic catalyst structures rather than simplified models. This approach could advance the fundamental understanding of plasmonic catalysis and guide future methods for producing ammonia and other important chemicals under less demanding conditions. 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.