A proxy for oxygen storage capacity from high-throughput screening and automated data analysis

Area of Science:

• Materials Science
• Catalysis
• Chemical Engineering

Background:

• Oxygen storage and release are crucial in heterogeneous catalysis, particularly in Mars-van Krevelen mechanisms.
• Direct measurement of oxygen storage capacity (OSC) is a bottleneck due to its time-consuming and difficult-to-parallelize nature.
• Accelerating the discovery of high-performance oxygen storage catalysts is essential for advancing catalytic processes.

Purpose of the Study:

• To develop a high-throughput synthesis and characterization workflow for rare-earth doped ceria-zirconia oxygen storage catalysts.
• To create a predictive model for OSC based on data from automated characterization techniques.
• To identify key material properties that correlate with high OSC performance.

Main Methods:

• A robotic-based co-precipitation synthesis route was employed to create material libraries.
• Sequentially automated powder X-ray diffraction (PXRD), Raman spectroscopy, and thermogravimetric analysis (TGA) were used for characterization.
• A predictive model for OSC was developed using data from fast-to-measure analytical techniques.

Main Results:

• Automated data extraction facilitated rapid trend identification.
• The developed OSC prediction model incorporated variables from rapid analytical techniques.
• Predicted OSC values showed strong agreement with experimental observations on an independent validation set.
• Identified material properties serve as effective proxies for OSC performance.

Conclusions:

• The developed high-throughput workflow and predictive model significantly accelerate the discovery of high-capacity oxygen storage materials.
• The study highlights the potential of using fast analytical techniques for predicting catalytic performance.
• This approach can be extended to discover candidate catalysts for other heterogeneous transformations.