Iso-Nanozymes of Atomically Precise Copper Nanoclusters

Abstract

Nanozymes, a promising class of enzyme mimics based on nanostructures, have attracted considerable research interest. However, in sharp contrast to the structural precision of natural enzymes, most nanozymes are poorly defined structurally. The absence of nanozyme systems that mimic natural isoenzymes—which catalyze similar reactions despite slight differences in their chemical structures—has particularly hindered the understanding of their structure–performance relationships. Such nanozyme analogues, termed iso-nanozymes, remain largely unexplored. Here, we report the first pair of iso-nanozymes. Two analogous copper nanoclusters—[Cu₃₂(SC₂H₅)₁₆(PPh₃)₈Cl₉]⁺ (Cu₃₂) and [Cu₃₀(SC₂H₅)₁₆(PPh₃)₆Cl₉]⁺ (Cu₃₀)—were synthesized and structurally characterized. Single-crystal X-ray diffraction analysis reveals that Cu₃₀ possesses an identical metal framework and ligand types as Cu₃₂, with a comparable ligand distribution. The only structural difference is the absence of two PPh₃Cu⁺ units in Cu₃₀, which results in a substantial enhancement of its catalytic performance in the horseradish peroxidase-mimicking reaction. Under identical conditions, the specific activity (SA) of the Cu₃₀ nanozyme is approximately 6.5 times higher than that of Cu₃₂. Density functional theory calculations indicate that the notable difference in the SA between the two cluster nanozymes is attributed to variations in adsorption energies, which stem from their different geometric and electronic structures. This study not only introduces the novel concept of iso-nanozymes using atomically precise metal nanoclusters, but also establishes a model system for investigating the critical influence of nanozyme structure, down to the atomic level, on catalytic efficiency. These findings are anticipated to inspire further research interest in atomically precise metal nanoclusters within the nanozyme community.

Keywords

catalysts / crystal structure / iso-nanozyme / metal clusters / structure–performance relationships

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