Size-Dependent Graphene Support for Decorating Gold Nanoparticles as a Catalyst for Hydrogen Evolution Reaction with Machine Learning-Assisted Prediction

Size-dependent two-dimensional (2D) materials (e.g., graphene) have been recently used to improve their performance in various applications such as membrane filtration, energy storage, and electrocatalysts. It has also been demonstrated that 2D nanosheets can be one of the promising support materials for decorating nanoparticles (NPs). However, the optimum nanosheet size (lateral length and thickness) for supporting NPs has not yet been explored to enhance their catalytic performance. Herein, we elucidate the mechanism behind size-dependent graphene (GP) as a support due to which gold nanoparticles (AuNPs) are used as an active catalyst for the hydrogen evolution reaction (HER). Surprisingly, the decoration of AuNPs increased with the increasing nanosheet size, counter to what is widely reported in the literature (high surface area for smaller nanosheet size). We found that a large graphene nanosheet (lGP; similar to 800 nm) used as the AuNP support (lGP/AuNPs) exhibited superior performance for the HER with long-term stability. The lGP/AuNPs with a suitable content of AuNPs provides a low overpotential and a small Tafel slope, being lower than that of other reported carbon-based HER electrocatalysts. This results from highly exposed active sites of well-dispersed AuNPs on lGP giving high conductivity. The laminar structure of the stacked graphene nanosheets and the high wettability of the lGP/AuNPs electrode surface also play crucial roles in enhancing electrolytes for penetration in the electrode, suggesting a highly electrochemical surface area. Moreover, machine learning (Random Forest) was also used to reveal the essential features of the advanced catalytic material design for catalyst-based applications.

Automated summary

This study elucidates the mechanism behind size-dependent graphene as a support for gold nanoparticles and shows that larger graphene nanosheets exhibit superior performance and stability in catalytic reactions. Machine learning was also used to identify key features for advanced catalytic material design.