Reconciling experimental catalytic data stemming from structure sensitivity

Experimental data have long served as a valuable resource for model validation and identification of the active site. Yet, literature kinetics data often exhibit significant differences among laboratories for the same catalyst and reaction, but the reasons have remained elusive. Here, we exploit if we can rationalize (most of) this variation through catalyst structure sensitivity. We introduce a methodology to build a structure-descriptor-based microkinetic model and investigate the relations between nanoparticle structure and reaction kinetics using the complete methane oxidation on Pt as a model reaction and literature data mining. A volcano-like rate is observed with an optimum coordination number. Unlike common expectations, smaller particles have very low reactivity because of carbon poisoning. Interestingly, most of the data variation can be successfully traced to structure sensitivity. This methodology also enables rapid prediction of kinetic performance and active site determination for designing optimal catalyst structures. It can also serve as a data quality tool to assess experimental outliers. Additional reasons for data variability are discussed.

Automated summary

In this study, a structure-descriptor-based microkinetic model was built to investigate the relationship between nanoparticle structure and reaction kinetics. It was found that a volcano-like rate with an optimum coordination number exists during the methane oxidation on Pt. This methodology enables rapid prediction of kinetic performance, active site determination, and assessment of data quality for experimental outliers.