Zhao Receives NSF CAREER Award To Advance Ammonia Production Research

ChE Assistant Professor Qing Zhao received a $618,100 NSF CAREER Award for “Computational Characterization of Reaction Mechanisms and Catalytic Microenvironments in Redox-Mediated Ammonia Electrosynthesis.”
Qing Zhao, assistant professor of chemical engineering, received a $618,100 National Science Foundation CAREER Award to investigate fundamental chemistry in an electrochemical-based ammonia production process using advanced computational modeling tools and AI.
The goal is to create a more efficient and environmentally friendly ammonia production process. Zhao says the fundamental insights can be adapted to the production of other chemicals, and, ultimately, the developed computational modeling tools could be applied to other electrochemical processes in the area of energy and sustainability as well.
Ammonia is a critical component in the production of fertilizer, making it essential to the agricultural industry. Currently, it is primarily produced by burning nitrogen and hydrogen gas at high temperatures and high pressures, a process that is powered by electricity produced from fossil fuels. This process releases carbon dioxide and other pollutants into the atmosphere.
Clean energy sources, such as solar and wind, can generate electricity to drive an electrochemical process to produce ammonia. The process is facilitated by a lithium-based electrolyte supported on electrode, which with the formed solid electrolyte interface layer acts as a catalyst to prompt the reaction. It does not require high heat—it can actually occur at room temperature—and it does not produce carbon dioxide. Despite those advantages, the process is not yet as efficient as the traditional fossil-fuel method. Further, the chemical reactions and reaction microenvironments are complex and not easily understood.
Zhao’s research project, “Computational Characterization of Reaction Mechanisms and Catalytic Microenvironments in Redox-Mediated Ammonia Electrosynthesis,” will leverage a combination of advanced computational modeling tools and AI techniques to understand the chemistry at atomic and electronic level during a clean-energy powered electrochemical process. With this modeling, she can determine the reaction pathways and the roles of electrolyte and electrode in catalyzing the reaction to improve efficiency.
“Currently, the energy efficiency of ammonia electrosynthesis doesn’t meet the goal set by the Department of Energy,” Zhao says. “To improve that, we want to have a better understanding of the fundamental chemistry. Insights such as how to optimize the electrolyte and catalyst will further improve efficiencies to meet the DOE goal.”
The computational modeling will focus on a lithium-mediated nitrogen reduction reaction, known as Li-NRR. The computation will leverage multiple electronic structure theory calculations, including density functional theory (DFT), embedded correlated wavefunction theory (ECW), ab initio molecular dynamics (AIMD), and active machine learning.
Zhao will collaborate with the California Institute of Technology to validate the research results obtained through the computational modeling.
As part of this grant, Zhao will provide a research and outreach program, Computation and Catalysis (ComCatalysis), which will include a five-hour workshop and several summer research internship opportunities for high school students in the Greater Boston area.
Longer term, Zhao says the research discoveries could be applied to applications broadly in chemistry. “In general, the chemical insights and developed computational tools could be applied to many other areas in energy and sustainability,” Zhao says.
Abstract Source: NSF