Cu <sup>+</sup> ‐Cu <sup>0</sup> Cluster Pairs in Defective MOFs for Highly Synergistic CO <sub>2</sub> Hydrogenation to Methanol
Abstract
ABSTRACT Precise arrangement of multivalent Cu sites for the synchronized activation of CO 2 and H 2 in selective methanol synthesis remained challenging. Herein, we designed spatially arrayed Cu + ‐Cu 0 cluster pairs confined within UiO‐66 (U66) to achieve spatiotemporally directed CO 2 hydrogenation to methanol. This architecture was constructed by creating missing‐linker defects within U66 using L‐ascorbic acid (LA), followed by sequential anchoring of high‐density, adjacent Cu + ‐Cu 0 cluster pairs (≈1:1 ratio) to yield a Cu/U66(LA‐Cu) catalyst. Under reaction conditions of 220°C and 3 MPa, this catalyst achieved a methanol space‐time yield of 688.1 mg·g −1 ·h −1 and an ultra‐high methanol selectivity (97.0%). Its unity‐methanol selectivity was 1.06–1.77 times that of high‐stability oxide materials. Meanwhile, catalyst's stability of 200 h was significantly prolonged compared to high‐selectivity MOF‐based catalysts that operated stably for 60–175 h. Mechanistic studies revealed that Cu + ‐Cu 0 cluster pairs within defective U66 not only enabled partitioned synergistic activation of CO 2 and H 2 , but established an efficient electron/hydrogen transfer pathway. This facilitated the migration of active hydrogen species to CO 2 adsorption sites, where adsorbed CO 2 was converted into CO intermediates and underwent deep hydrogenation. This structural design synergistically enhanced compatibility between H 2 dissociation and CO 2 hydrogenation steps, enabling reaction to proceed efficiently along the RWGS + CO‐hydro pathway.
Article Details
Authors (14)
Xuehui Jia
Weige Su
Changheng Zhong
Guangxi Key Laboratory of AI‐Driven Zero‐Carbon Technology Key Laboratory of New Low‐Carbon Green Chemical Technology Education Department of Guangxi Zhuang Autonomous Region School of Chemistry and Chemical Engineering Guangxi University Nanning People's Republic of China
Zuxue Bai
Guangxi Key Laboratory of AI‐Driven Zero‐Carbon Technology Key Laboratory of New Low‐Carbon Green Chemical Technology Education Department of Guangxi Zhuang Autonomous Region School of Chemistry and Chemical Engineering Guangxi University Nanning People's Republic of China
Jingyu Bao
Guangxi Key Laboratory of AI‐Driven Zero‐Carbon Technology Key Laboratory of New Low‐Carbon Green Chemical Technology Education Department of Guangxi Zhuang Autonomous Region School of Chemistry and Chemical Engineering Guangxi University Nanning People's Republic of China
Yan Zhang
Xin Yu
BGI Research, Qingdao, China.
Jianmin Chen
Zhaowei Jia
Guangxi Key Laboratory of AI‐Driven Zero‐Carbon Technology Key Laboratory of New Low‐Carbon Green Chemical Technology Education Department of Guangxi Zhuang Autonomous Region School of Chemistry and Chemical Engineering Guangxi University Nanning People's Republic of China
Ruimeng Wang
Guangxi Key Laboratory of AI‐Driven Zero‐Carbon Technology Key Laboratory of New Low‐Carbon Green Chemical Technology Education Department of Guangxi Zhuang Autonomous Region School of Chemistry and Chemical Engineering Guangxi University Nanning People's Republic of China
Liqin Zhou
Guangxi Key Laboratory of AI‐Driven Zero‐Carbon Technology Key Laboratory of New Low‐Carbon Green Chemical Technology Education Department of Guangxi Zhuang Autonomous Region School of Chemistry and Chemical Engineering Guangxi University Nanning People's Republic of China
Jiguang Deng
State Key Laboratory of Materials Low-Carbon Recycling, College of Materials Science and Engineering
Zhongxing Zhao
Zhenxia Zhao
School of Chemistry and Chemical Engineering, Guangxi Key Laboratory of AI-Driven Zero-Carbon Technologies, Key Laboratory of New Low-carbon Green Chemical Technology Education Department of Guangxi Zhuang Autonomous Region