Development of high performance catalysts for CO oxidation using data-based modeling

This paper presents a model-aided approach to the development of catalysts for CO oxidation. This is in contrast to the traditional methodology whereby experiments are guided based on experience and intuition of chemists. The proposed approach operates in two stages. To screen a promising combination of active phase, promoter and support material, a powerful “space-filling” experimental design (specifically, Hammersley sequence sampling) was adopted. The screening stage identified Au–ZnO/Al2O3 as a promising recipe for further optimization. In the second stage, the loadings of Au and ZnO were adjusted to optimize the conversion of CO through the integration of a Gaussian process regression (GPR) model and the technique of maximizing expected improvement. Considering that Au constitutes the main cost of the catalyst, we further attempted to reduce the loading of Au with the aid of GPR, while keeping the low-temperature conversion to a high level. Finally we obtained 2.3%Au–5.0%ZnO/Al2O3 with 21 experiments. Infrared reflection absorption spectroscopy and hydrogen temperature-programmed reduction confirmed that ZnO significantly promotes the catalytic activity of Au.

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► Modeling facilitates rapid screening and optimization of catalysts.
► Modeling finds cost-effective catalysts by reducing Au loading while retaining high activity.
► The statistical measure of expected improvement is effective to handle model uncertainty.
► Adding ZnO promotes the activity of Au for CO conversion.