Correlative XRM-FIB/SEM Study of Autocatalysts

Often when trying to correlate performance to rational catalyst design, several observations, measurements and iterative refinements are necessary.  Moreover, in industry, this must be done rapidly due to high turnover. This agile approach requires fast characterization of materials in a corelative way using a methodology aided by machine learning and automation. The most general workflow for a monolith sample consists of segmenting out features such as catalyst, substrate and pores from images and then performing measurements on these features. Both parts of the workflow present several challenges. In this study we show how ZEISS products are used successfully and delivering the information needed.