Photocluster catalytic oxidation enabled by structurally precise copper-hydride nanoclusters

The utilization of light energy emerges as a prominent strategy to boost nanocluster catalysis, spurring rapid growth in the field termed photocluster catalysis. In particular, structurally well-defined copper-hydride nanoclusters attract attention as promising photocluster catalysts for organic synthesis. Herein, we report Cu13H6 exhibits excellent activity for photocatalytic α-C–H oxidation, revealing great potential in oxidation chemistry. Cu13H6 is one-pot synthesized and characterized using advanced analytical techniques. Critically, machine learning tentatively identifies hydride positions, reducing dependence on neutron diffraction. The Cu13H6 nanocluster exhibits outstanding activity in the photooxidation of diverse alkyl aromatics and alcohols to ketones under mild conditions. This pathway also supports photocatalytic defluorination, deuteriation, cyclopropane ring-opening and dehydrogenative coupling. This work tentatively determines the structure of Cu13H6 using robust stochastic surface walking global optimization with neural network, and identifies the cluster as a versatile, efficient photocatalyst that broadens photocatalysis applications in organic synthesis. The utilization of light energy has emerged as a prominent strategy to boost copper nanocluster photocatalysis for organic synthesis, but the scope of transformations remains relatively narrow. Here we report the catalytic performance of a copper-hydride nanocluster, Cu13H6, for photocatalytic α-C–H oxidation.

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Journal
Nature Communications
Published
2026-10-09
DOI
https://doi.org/10.1038/s41467-026-78511-y
Primary Topic
Nanocluster Synthesis and Applications
Type
article
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article

Photocluster catalytic oxidation enabled by structurally precise copper-hydride nanoclusters

Yaqi Wang, Takashi Shirai, Nanfeng Zheng, Xiaoyan Sun et al.
Nature Communications
Nanocluster Synthesis and Applications
article

Photocluster catalytic oxidation enabled by structurally precise copper-hydride nanoclusters

Yaqi Wang, Takashi Shirai, Nanfeng Zheng, Xiaoyan Sun, Yunzi Xin, Zhuang Wang, Hui Cun Shen, Cong Fang, Rong Huo, Simin Li, Qi Li, Xiaona Yang
article en

Abstract

The utilization of light energy emerges as a prominent strategy to boost nanocluster catalysis, spurring rapid growth in the field termed photocluster catalysis. In particular, structurally well-defined copper-hydride nanoclusters attract attention as promising photocluster catalysts for organic synthesis. Herein, we report Cu13H6 exhibits excellent activity for photocatalytic α-C–H oxidation, revealing great potential in oxidation chemistry. Cu13H6 is one-pot synthesized and characterized using advanced analytical techniques. Critically, machine learning tentatively identifies hydride positions, reducing dependence on neutron diffraction. The Cu13H6 nanocluster exhibits outstanding activity in the photooxidation of diverse alkyl aromatics and alcohols to ketones under mild conditions. This pathway also supports photocatalytic defluorination, deuteriation, cyclopropane ring-opening and dehydrogenative coupling. This work tentatively determines the structure of Cu13H6 using robust stochastic surface walking global optimization with neural network, and identifies the cluster as a versatile, efficient photocatalyst that broadens photocatalysis applications in organic synthesis. The utilization of light energy has emerged as a prominent strategy to boost copper nanocluster photocatalysis for organic synthesis, but the scope of transformations remains relatively narrow. Here we report the catalytic performance of a copper-hydride nanocluster, Cu13H6, for photocatalytic α-C–H oxidation.

Nature Communications
Xiamen University (CN), Nagoya Institute of Technology (JP), Chinese Academy of Sciences (CN), Inner Mongolia University (CN), Collaborative Innovation Center of Chemistry for Energy Materials (CN), Qingdao Institute of Bioenergy and Bioprocess Technology (CN), China University of Petroleum, East China (CN), University of Chinese Academy of Sciences (CN)
Openalex Percentile: Top 28%
Nanocluster Synthesis and Applications
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