High throughput experimentation and microkinetic modeling
Date
2006
Authors
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Publisher
University of Delaware
Abstract
In recent years, the use of high-throughput experimentation has grown as a methodology to increase the speed at which experiments have been performed. This speed advantage has been coupled with numerous modeling techniques to guide experiments and gain an efficiency over the traditional Edisonian approach. The techniques used are well suited for optimization studies; however they lack a chemical description of the observed reactions. This thesis explores the coupling the rich data sets of high-throughput experimentation with microkinetic modeling to gain a chemical understanding of the catalytic chemistry. ☐ The catalytic decomposition of ammonia has been proposed as a potential source of COx-free hydrogen. High-throughput experimentation has been used to rapidly collect experimental ammonia decomposition data over Ru, Ir, and Pt catalysts in parallel. ☐ Ru catalysts have shown good agreement to a literature model which includes N*-N* interactions, with the model predicted rate-limiting step as NH − H bond scission. In addition, the most abundant reactive intermediate is predicted to be H* under the reaction conditions studied. Poor agreement is seen with other literature models which neglect intermolecular interactions. This lack of intermolecular interactions results in a prediction of N* saturation of the catalytic surface under experimental reaction conditions. ☐ Using high-throughput data, microkinetic models on Ir and Pt catalysts have been trained. The resulting models utilize coverage dependent activation energies and have a predicted rate-limiting step of NH − H bond scission on Ir and N − H bond scission on Pt. The predicted most abundant reactive intermediate is either NH*3 or H* on both catalysts, depending on reaction conditions.