Novel palladium-based catalysts for petrochemical industry: synthesis and characterization under relevant industrial conditions
From 26.03.2019 till 31.12.2020
Grant holder: Aram Bugaev
Members: Oleg Usoltsev, Alina Skorynina, Elizaveta Kamyshova
Palladium-based nanocatalysts are widely applied in the important reactions in the petrochemical industry tp process organic raw materials into products of higher economic value. Even small changes in the catalytic activity and selectivity have a significant economic impact, that is why a rational design of new catalysts for a specific industrial tasks, is a problem of exceptional scientific and practical importance. Despite significant progress in the synthesis and diagnosis of palladium-based nanocatalysts, a mechanistic understanding of the catalytic reactions on their surfaces has not yet been achieved, and the preparation of new effective materials is usually done by trial and error approach. The analysis of literature data in the field of nanoscale palladium materials allows us to identify the following key problems in this area. First, most of the classical methods of studying the structure, such as X-ray diffraction, turn out to be uninformative to describe surface phenomena and when studying nano-objects. Secondly, the methods traditionally used to study the surface, including scanning electron microscopy, photoelectron and Auger spectroscopy, require either vacuum conditions or are carried out at low partial pressure of reagents, which complicates the extrapolation of the results to the real industrial concentrations, pressures and temperatures. Finally, the analysis of experimental data is hampered by the variance of shape, size, and various structural parameters common for nanoscale samples.
This project proposes an integrated approach to determining the fundamental relationships of the structure and catalytic activity of palladium nanocatalysts based on the use of advanced methods of chemical synthesis to produce model catalysts, state-of-the-art in situ and operando techniques based on megascale research facilities under realistic industrial conditions, as well as introducing original big data analysis methods and machine learning algorithms to solve problems in the field material science.