Phillip Christopher earned his B.S. in chemical engineering from University of California, Santa Barbara in 2006 and his M.S and Ph.D. in chemical engineering from University of Michigan in 2011 working with Prof. Suljo Linic. From 2011-2017 he was an Assistant Professor at University of California, Riverside. In 2017 he moved to the University of California, Santa Barbara where he is a Professor and Vice Chair for Undergraduate Affairs in the Chemical Engineering Department. His research interests are in sustainable chemical conversion, heterogeneous catalysis by supported metals, dynamic behavior of catalysts, and photocatalysis by metal nanostructures. He has been given various awards including the Presidential Early Career Award for Scientists and Engineers, AIChE CRE Division Young Investigator Award, Ipatieff Prize from the ACS, and Paul Emmett Award from the North American Catalysis Society. He also serves as a Senior Editor for ACS Energy Letters.
Supported metal catalysts are used ubiquitously in industrial applications for energy conversion, material/chemical manufacturing, and pollution mitigation. Fundamental research often focuses on elucidating structure-function relationships that connect active site structures and compositions to their reactivities. Relationships that connect active site structure to stability are less well developed. Such insights require appreciation of dynamic structure changes, longer term experimentation, and reactors characterized by gradients in temperatures and chemical potentials. I will highlight two recent research efforts from my group where we have studied the deactivation of supported metal catalysts.
First, I will discuss the deactivation of supported coinage (Cu and Ag) metal catalysts which occurs via sintering due to the low melting points of these metals. We found that the addition of < 1:100 mol fraction of certain dopant metals results in drastic stability enhancement under methanol synthesis reaction conditions A model was developed that proposes the role of dopants as local stabilizers of highly mobile metal atoms. Secondly, I will discuss the deactivation of Rh/TiO2 catalysts under CO2 hydrogenation conditions. Mechanistic studies suggest that deactivation occurs through competing mechanisms as a function of catalyst composition and reaction conditions, motivating the use experimentally trained machine learnt models to predict deactivation behavior. A round robin style experimental campaign was performed across 4 institutions to generate data for this effort. I will discuss our from this effort in the context of experimental uncertainty and the resulting influence on machine learned model development.