Welcome to the Hensley Catalysis Lab at Stevens Institute of Technology! Our research addresses the knowledge-gap in our ability to connect the in situ chemical composition and structure of heterogeneous catalysts to the selective activation of chemical bonds. This addresses two of the grand challenges for heterogeneous catalysis in energy and sustainability this century: (1) production of fuels and value-added chemicals from biomass and plastic waste and (2) accelerated capture and conversion of carbon dioxide. In both of these challenges, there is a critical need for fundamental, nanoscale insight into the surface reactions, accounting for the coverage and configuration dependent interactions between adsorbates and surface components that lead to overlayer formation and catalyst reconstruction under working conditions. Without an accurate, nanoscale picture of the dynamic interplay between adsorbates, catalyst surface structure and composition, and performance, the kinetic and mechanistic properties of multi-component catalysts cannot be effectively and precisely connected to the composition and structure of active sites formed during reaction. This in turn severely limits opportunities for the rational design of complex catalyst materials.

This research group directly tackles this critical need by combining multi-scale computational simulations (i.e. density functional theory, Monte Carlo, molecular dynamics, etc.) with data science techniques (i.e. cluster expansions, regression modeling, machine learning, etc.) to (1) sample the catalytically relevant configuration space for multi-component surfaces under working conditions, (2) identify the rate limiting transition states across a range of multi-component catalysts, and (3) design multi-component surfaces for targeted bond activation. This approach establishes the physically accessible catalytically active sites and enables quantitative connections between nanoscale interactions and experimentally measurable surface structure, composition, and catalytic performance. Our ultimate goal is to discover nanoscale parameters that obviate the need for further experiments because, simply put, we already know the answer.