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Lim, Kang Rui Garrick

Disentangling the effects of nanoparticle proximity and size on thermal catalytic performance is challenging with traditional synthetic methods. Here, we adopt a modular raspberry-colloid-templating approach to tune the average interparticle distance, while preserving all other physicochemical characteristics, including nanoparticle size. By controlling the metal loading and placement of pre-formed nanoparticles within a 3D macroporous support and using the hydrogenation of benzaldehyde to benzyl alcohol and toluene as our probe reaction, we report that increasing the interparticle distance (12 to 21 nm) substantially enhances selectivity towards benzyl alcohol (54 to 99%) without compromising catalytic activity or stability. Combining electron tomography, kinetic evaluation, and simulations, we show that interparticle distance modulates the local benzyl alcohol concentration profile between active sites, consequently affecting benzyl alcohol readsorption, which promotes hydrogenolysis to toluene. Our results illustrate the relevance of proximity effects as a mesoscale tool to control the adsorption of intermediates and hence, catalytic performance.

Nanoparticle (NP) size and proximity are two physical descriptors applicable to practically all NP-supported catalysts. However, with conventional catalyst design, independent variation of these descriptors to investigate their individual effects on thermocatalysis remains challenging. Using a raspberry-colloid-templating approach, we synthesized a well-defined catalyst series comprising Pd12Au88 alloy NPs of three distinct sizes and at two different interparticle distances. We show that NP size and interparticle distance independently control activity and selectivity, respectively, in the hydrogenation of benzaldehyde to benzyl alcohol and toluene. Surface-sensitive spectroscopic analysis indicates that the surfaces of smaller NPs expose a greater fraction of reactive Pd dimers, compared to inactive Pd single atoms, thereby increasing intrinsic catalytic activity. Computational simulations reveal how a larger interparticle distance improves catalytic selectivity by diminishing the local benzyl alcohol concentration profile between NPs, thus suppressing its readsorption and consequently, undesired formation of toluene. Accordingly, benzyl alcohol yield is maximized using catalysts with smaller NPs separated by larger interparticle distances, overcoming activity–selectivity trade-offs. This work exemplifies the high suitability of the modular raspberry-colloid-templating method as a model catalyst platform to isolate individual descriptors and establish clear structure–property relationships, thereby bridging the materials gap between surface science and technical catalysts.