Direct Observation of Reactant, Intermediate, and Product Species for Nitrogen Oxide-Selective Catalytic Reduction on Cu-SSZ-13 Using In Situ Soft X-ray Spectroscopy

JOURNAL OF PHYSICAL CHEMISTRY C(2022)

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摘要
Catalytic processes have supported the develop-ment of myriad beneficial technologies, yet our fundamental understanding of the complex interactions between reaction intermediates and catalyst surfaces is still largely undefined for many reactions. Experimental analyses have generally been limited to investigation of catalyst materials or a subset of functional groups as indirect probes of the critical surface-bound intermediate species and reaction mechanisms. A more direct approach is to probe the intermediate species themselves, but this requires direct study of the local chemical environment of light elements. In this work, we use soft X-ray emission spectroscopy (XES) and a custom-designed in situ reactor cell to directly observe and characterize the electronic structure of reactant, intermediate, and product species under reaction conditions. Specifically, we employ N K XES to probe the interaction of various nitrogen species with a Cu-SSZ-13 catalyst during selective catalytic reduction of nitrogen oxides (NO and NO2) by ammonia (NH3-SCR), a reaction that is critical for the removal of NOx pollutants formed in combustion reactions. This work reveals a novel spectral feature for all spectra measured with flowing NO gas present, which we attribute to the interaction of NO with the catalyst. We find that introducing both NO and O2 gases (compared to only NO) increases the interaction of NO with Cu-SSZ-13. Adsorption of NH3 leads to a more pronounced spectral signal compared to NO adsorption. For the standard NH3-SCR reaction, we observe a strong N2 signal, comprising 30% of the total spectral intensity. These results demonstrate the vast potential of this technique to provide direct, novel insights into the complex interactions between reaction intermediates and the active sites of catalysts, which may guide advanced knowledge-based optimization of these processes.
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