(252e) Raspberry-Colloid-Templating As a Model Thermocatalytic Platform

Nanoparticle (NP)-supported heterogeneous catalysts play a central role in the production of more than 90% of chemicals manufactured globally. The performance (activity, selectivity, and stability) of these catalysts is predicated on a variety of descriptors related to the NPs, support material, and the interactions between them. However, current preparative methods towards these catalysts often do not permit independent changes to these coupled factors, thereby hindering the understanding of the role of each individual descriptor on catalytic performance. To unequivocally derive structure-property relationships, we draw bioinspiration from the morpho butterfly, in combination with our expertise in colloidal synthesis, assembly, and sol-gel chemistry, to devise a raspberry-colloid templating (RCT) platform. The modular RCT platform enables independent combinatorial variations of the material’s building blocks and their organization, thereby affording numerous degrees of freedom for optimizing the material’s functional properties, from the nanoscale to the macroscale. Furthermore, the RCT method confers high thermomechanical stability by partially embedding NPs within the support, while retaining high reactant accessibility. Using the RCT strategy, we illustrate how collective NP properties, such as NP proximity and spatially disparate localization, can be independently controlled without concomitant changes to other catalytic descriptors that would otherwise confound catalytic analyses. We highlight the unique suitability of the modular RCT platform as a well-defined model catalyst platform to independently isolate and tune potential catalytic descriptors to unambiguously derive structure–property relationships that bridge surface science studies to technical catalysts.