Improving catalyst activity, stability, and selectivity is critical for hydrogen and carbon-negative energy technologies. Strong metal–support interactions (SMSI), where thin oxide layers partially cover metal nanoparticles, can be used to tune catalyst performance. While dense oxide layers may block active sites, they can improve selectivity by suppressing side reactions. Microporous oxide layers can enhance both activity and stability, and 1D oxide structures help passivate defects, improving long-term durability in harsh electrochemical environments such as fuel cells.
Improving catalytic activity, stability and selectivity are the major goals for the hydrogen energy and carbon negative efforts toward net-zero emission. The strong metal-support interaction (SMSI) phenomenon is a classic concept in heterogeneous catalysis, which is characterized by the encapsulation of metal nanoparticles by ultrathin oxide films. While full encapsulation would deadly suppress catalytic activity, it could be taken advantage of to develop intelligent catalysts with unique selectivity, stability and catalytic activity for electrochemical energy conversion (including oxygen cycle, carbon cycle and nitrogen cycle). Those goals can be achieved through a fundamental understanding of the preferential formation of the following type of interfaces: [1] 2D dense oxide/metal interfaces, [2] 2D microporous oxide/metal interfaces, [3] 1D oxide/metal interfaces. While forming 2D dense oxides will block active sites, preferential blocking may tune the selectivity and eliminate byproducts. On the other hand, forming 2D microporous oxides/metal interfaces may lead to bi-functional catalysts with improved activity and stability. Also, defect passivation through the preferential formation of 1D oxides is crucial for the long-term performance of electrocatalysts used under harsh electrochemical environments, e.g., in fuel cells.
The oxygen evolution reaction (OER) is the main bottleneck in water electrolysis and metal–air batteries. Because OER occurs under highly oxidative aqueous conditions, the active catalyst phases are poorly understood, limiting rational catalyst design. This project aims to identify the active phases, reaction centers, and mechanisms of transition-metal oxyhydroxide OER catalysts. Advanced error-cancellation strategies and simulated annealing will be used to accurately model strongly correlated oxides and locate active structures. These atomic-scale models will be linked to experiments through spectroscopy simulations (e.g., XAS), and combined with machine learning to design multifunctional active sites that break conventional scaling relationships.
Modeling aqueous electrocatalysis is more complex than gas–solid catalysis due to the effects of solvent, solutes, pH, and electrode potential at electrified interfaces. This project combines ab initio molecular dynamics with advanced energy-level alignment and potential-control methods to accurately model water–solid interfaces. These methods will capture key double-layer features such as hydrogen bonding, cation effects, and electrode potential. The resulting insights will enable detailed studies of complex electrochemical reactions and guide the design of durable, efficient, and selective catalysts for energy-relevant electrocatalysis.