Development and optimization of NOx storage and reduction catalysts using statistically guided high-throughput experimentation

The interaction between reaction conditions and catalyst compositions for NOx storage and reduction (NSR) catalysts has been explored using a combination of high-throughput experimentation and experimental design. This was accomplished by varying the concentration of NO and O2, the reducing agent concentration and identity, space velocity, and reactor temperature. All reaction conditions were varied simultaneously for a range of Pt, Ba, and Fe loadings on γ-Al2O3. It was found that most of the reaction conditions investigated affected the saturation NOx storage, the production of nitrous oxide, and the steady state lean NOx reduction. An empirical model was developed using response surface methodology to predict the saturation NOx storage and nitrous oxide production as a function of Pt, Ba, and Fe weight loading. This model was used to optimize the catalytic composition and a comparison between the model predictions and experimental results for the optimized catalysts are given. It was shown that high-throughput experimentation in combination with experimental design leads to more efficient use of experimental resources in addition to a more in depth understanding of catalytic systems.