Experimental Data Variability in Heterogeneous Catalytic Reactions

Published experimental data have long served as a valuable resource for assessing catalytic performance, facilitating catalyst discovery and understanding complicated reaction mechanisms. However, the reported literature data are heterogeneous in nature and often exhibit large differences for identical systems. This fact complicates meaningful comparisons across sources and hinders the utilization of prior knowledge. In order to alleviate this problem, it is important to identify the causes and interpret the discrepancies. In this work, we develop a creative methodology to do this. Specifically, a descriptor-based microkinetic model is established that provides the possibility to evaluate scenarios at a dramatically reduced computational cost compared to first-principles calculations. We demonstrate this approach for the complete methane oxidation as a case study. Our methodology provides a novel approach to assess and analyze prior published data.