CAREER: Understand and Regulate Surface Segregation of High-Entropy Oxides for Enhanced Catalysis

NON-TECHNICAL SUMMARY This research project is generating new knowledge regarding the atomic structures of high entropy oxides, which are special, and extremely thin metal films containing five or more positively charged atoms, which are known as “cations”. These extremely thin high entropy oxides outperform conventional metal oxides in many ways, including as electrocatalysts, which simply means they are really good at accelerating electrochemical reactions in things like solid oxide fuel cells. This also means high entropy oxides can serve as more effective agents to facilitate other chemical reactions, such as converting water into hydrogen peroxide. This proposal hypothesizes performance of high entropy oxides as electrocatalysts depends heavily on surface structures which can differ significantly from the microstructures found inside. This research is studying how different cations are distributed in high entropy oxides with varying compositions, while also exploring effective approaches to accurately predict their response. This project is chiefly focused on precisely controlling and understanding how different surface structures impact the electrocatalytic performance, and providing guidance on how to utilize surface behaviors to achieve enhanced electrocatalysis. In addition to scientific advancement and technological development, this project is integrating research and education to cultivate future professionals with interdisciplinary skills, with the goal of generating a positive and lasting impact on STEM industries in the vicinity. TECHNICAL SUMMARY Mechanistic understanding and precise control of surface segregation are critical for regulating interfacial behavior during electrocatalysis among high entropy oxides. This project is bridging the gap by synergistically integrating computational and experimental approaches, including but not limited to density functional theory calculations, Metropolis Monte Carlo simulations, synthesis and characterization of high entropy oxides, electrochemistry, machine learning, and microkinetic modeling. These combined research efforts are generating atomistic insights into the surface segregation behavior of high entropy oxides for the purposes of developing effective frameworks for screening optimal formulations across multidimensional compositional space, and providing general guidance on how to rationally design them for optimized energy conversion. This research is unlocking the vast design space of high entropy oxides, while providing opportunities to develop electrocatalysts with improved stability, activity, and selectivity. Additionally, this work is replacing passive learning in classrooms with engaging environments that help students develop skills in critical thinking and effective communication. The interdisciplinary nature of this project is contributing to the training of students and to growing professionals in the science and engineering workforce with the skills needed to tackle the most pressing scientific challenges of local STEM industries both today and tomorrow. 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: 2542701 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT,01002930DB NSF RESEARCH & RELATED ACTIVIT,01003031DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Xiang He | Institution: Florida Institute of Technology, MELBOURNE, FL | Award Amount: $391,948 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2542701 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2542701.html