For the study of industrial catalysis, it is critical to establish the understanding of the relationship between the nanoscale morphology/chemistry and the overall performance. This is indeed very challenging due to the fact that the industrial catalysis is usually hierarchically structured and chemically heterogeneous. With the developments in synchrotron based spectro-microscopic techniques, the nanoscale visualization of catalytic materials under operating conditions has been demonstrated [1]. Synchrotron also facilitates multi-model approach that could provide complimentary information and could help to construct a more systematic understanding of the material’s behavior [3]. These novel developments open vast scientific opportunities in the field of catalysis research.<br/>One major bottle neck in this field is in the scientific big data mining aspect. When coupled with the <i>in-situ/operando</i> experimental capabilities, the efficient and robust data mining methods could potentially help to identify chemical species that maybe small in quantity and short in life time, but critical in their functionality.<br/>In this presentation, I will review the state-of-the-art developments in both the synchrotron based spectro-microscopy and the associated big data mining methods. Case studies in the field of Fischer-Tropsch [1] and Fluid Catalytic Cracking [2-3] will be presented to demonstrate the strength of data mining approach that is capable of extracting scientifically important information from the imaging data and providing key insights into the degradation mechanisms.

Scientific data mining in nanoscale visualization of catalysis at work

255th National Meeting (2018)

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