(656g) Towards Design of Active Site Requirements of Selective Hydrogenation of Different Functionalities

Selective hydrogenation of certain functionalities in molecules or of certain molecules in mixed streams are important in the chemical industry. By targeting specific functionalities unique to molecules, it is possible remove impurities in a stream without modifying the relevant reactants. For example, selective hydrogenation of acetylene to ethylene can remove the acetylene impurity in ethylene prior to polymerization before the acetylene can poison the catalyst. Currently in industry, Pd-based bimetallic catalysts are used to catalyze the selective hydrogenation of acetylene.

Bimetallic catalysts are often used to improve selectivity performance compared to single component catalysts. However, surface segregation of randomly distributed alloys is unavoidable in reaction conditions, making selective hydrogenation reactions elusive. Intermetallics compounds can offer tunable site electronics and ensemble structure for selective hydrogenation catalysis. By tuning the composition of intermetallic compounds, it is possible to determine the site requirements needed for different functionalities. Our group successfully utilized Pd-Zn γ-brass intermetallic compounds to demonstrate the site requirement of ethylene hydrogenation. Herein, we expanded our intermetallic materials to those in Materials Project to investigate the site requirement to other functionalities for selective hydrogenation.

In this talk, we will present how we use DFT and machine learning to identify key descriptors of catalytic surfaces that promote the selective hydrogenation of certain functionalities, while deterring the selective hydrogenation of other functionalities. The adsorption of probe molecules on varying bimetallic surfaces with different active site ensembles will be calculated and collated into a database. Descriptors, such as active site nuclearity and geometry and bimetallic surface composition, will be examined through a machine learning algorithm to identify which properties from the surfaces influenced the adsorption of different functionalities. By comparing the differences in descriptors for different functionalities, it is possible to identify what active site ensemble is needed to selectively hydrogenate a certain functionality.