Predicting NO_x Catalysis by Quantifying Ce³⁺ from Surface and Lattice Oxygen

Area of Science:

• Materials Science
• Catalysis
• Surface Chemistry

Background:

• Ceria-based catalysts are vital for nitrogen oxide (NOx) reduction.
• Predictive design of ceria catalysts is hindered by a lack of understanding of defect-related surface properties.
• Existing methods struggle to quantify defects in complex ceria systems, including mixed oxides.

Purpose of the Study:

• To introduce a novel magnetic-based technique for quantifying defects on nanoceria surfaces.
• To establish the link between quantified defects and the catalytic performance in NOx reduction.
• To enable predictive design of ceria-based catalysts by understanding defect-reactivity relationships.

Main Methods:

• Utilized the magnetic response of Ce3+ and adsorbed molecular oxygen on nanoceria surfaces.
• Quantified defect types and numbers on various ceria nanoparticle shapes (rods, cubes, spheres).
• Evaluated catalyst performance for the reduction of nitric oxide (NO) by carbon monoxide (CO) under varying oxygen partial pressures.

Main Results:

• Demonstrated that magnetism quantifies defects, enabling prediction of NOx catalysis.
• Found that enhanced reactivity in nanocubes and nanorods is due to easier defect generation, not surface termination.
• Showed the method's applicability to complex, industrially relevant alumina-supported ceria mixed oxides.

Conclusions:

• Surface localized excess Ce3+ ion areal density is the key parameter for designing efficient NO reduction catalysts.
• The magnetic quantification of defects provides crucial insights for tailoring ceria catalyst performance.
• This technique overcomes limitations in characterizing ill-defined, mixed-oxide catalysts.