Mass transport in nanoarray monolithic catalysts: An experimental-theory study

Reducing the mass transfer resistance globally of a catalyst is a key to enhancing the catalytic reaction kinetics and fully utilizing the catalyst activity. Despite the success in tailoring the external mass transfer in the widely studied washcoat monoliths, the internal mass transfer resistance is difficult to be reduced due to the requirement of increasing macroporosity while maintaining high specific surface area and mechanical stability. Therefore, nanostructured array-based monolithic catalysts (nanoarray catalysts) have been developed in the past decade as a promising class of structured catalysts that may complement or substitute washcoat catalysts. This work fundamentally elucidates the enhanced mass transport properties of the nanoarray monolithic catalysts by a combination of experimental measurements and theoretical modeling. Using a low-dimensional model, the relative contributions of resistances were quantified in terms of chemical kinetics, internal and external mass transfers based on a probe model of C2H4 oxidation over the TiO2 supported Pt-based monolithic catalysts. The nanoarray catalysts displayed a lower internal mass transfer resistance than the washcoat counterparts as a result of the high macroporosity and small thickness of nanoarray layers. The nanoarray configuration provides a new pathway towards designing high-performance monolithic reactors and catalysts with low internal diffusion limitations for various gas phase reactions.

Automated summary

Reducing the mass transfer resistance of catalyst is crucial for improving catalytic reaction kinetics and maximizing catalyst activity. Nanoarray catalysts have been developed as a promising alternative to washcoat catalysts, showing lower internal mass transfer resistance due to their high macroporosity and thin layers. This study contributes to understanding the enhanced mass transport properties of nanoarray monolithic catalysts through experimental measurements and theoretical modeling.