Bayesian-optimization-based design of highly active and stable Fe–Cu/SSZ-13 catalysts for the selective catalytic reduction of NOx with NH3

Abstract

Catalysts for the selective catalytic reduction of nitrogen oxides (NO_x) with NH₃ are currently limited by low activity at low temperatures and deactivation under hydrothermal conditions. Herein, we developed a highly active and hydrothermally stable zeolite-based catalyst, Fe–Cu/SSZ-13, using Bayesian optimization (BO). An initial surrogate BO model was constructed and used to identify the optimal Cu and Fe composition through iterative experiments. At each step, the next candidate which optimized the objective function and maximized the acquisition function was selected. The optimized catalyst comprised 2.0 wt% Cu and 2.0 wt% Fe in SSZ-13 zeolite, which was prepared by an incipient wetness impregnation. This catalyst achieved 95.8% NO_x conversion at 250 °C and excellent hydrothermal stability, which outperformed the commercial catalyst. Structural characterization demonstrated that its excellent hydrothermal stability resulted from the effect of optimized loading of Fe co-cation. This study highlights the effectiveness of employing BO to design multicomponent catalysts.