2D High‐Entropy Phosphorus Chalcogenides for Efficient Solar‐Driven CO 2 Reduction to Ethylene

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Authors: Shicheng Luo, Ruihuan Duan, Baorong Xu, Bijun Tang, Honghui Ou, He Li, Bo Lin, Zheng Liu, Guidong Yang
Cited by: 2
References: 64

Abstract

ABSTRACT Solar‐driven CO 2 reduction to value‐added ethylene (C 2 H 4 ) is considered as a promising and mild approach for storing solar energy into chemical bonds in fuels and chemicals, yet the thermodynamic obstacles related to CO 2 activation and C─C coupling significantly limit the practical application of this approach. Developing high‐entropy materials (HEMs), featuring multi‐principal elements and high configurational entropy, has emerged as a topic of considerable interest for addressing the aforesaid challenge. Herein, an emerging 2D high‐entropy phosphorus chalcogenide (HEPC), Cu(CrVInFeMnNi)P 2 S 6 , is rationally developed as a multifunctional photocatalyst via integrating multiple cations into the frame of CuCrP 2 S 6 . The Cu site in the HEPC serves as the dominant active center for activating CO 2 and achieving C─C coupling for solar‐driven CO 2 to C 2 H 4 . Besides, the multi‐metal matrix of Cr, V, In, Fe, Mn, and Ni sites leads to a multi‐site integrated electron‐donation effect in HEPC, where these different metal sites form a d‐band gradient arrangement in HEPC as well as act as the auxiliary electron‐donating centers for increasing the charge density of the Cu site and significantly boosting C─C coupling. As a result, Cu(CrVInFeMnNi)P 2 S 6 achieves an ultrahigh apparent quantum yield (AQY) of 7.4% at 475 nm for solar‐driven CO 2 to C 2 H 4 (a superior C 2 H 4 selectivity of 71%) under the sacrificial‐agent‐free condition, outperforming the vast majority of state‐of‐the‐art photocatalysts. This work pioneers the application of high‐entropy phosphorus chalcogenides in catalysis and provides a new idea for the development of efficient multifunctional materials.

Fields of Study & Subjects

CO2 Reduction Techniques and Catalysts Advanced Photocatalysis Techniques Machine Learning in Materials Science

Publication details

Journal / source
Advanced Materials
Publisher
Wiley
Volume
38
Issue
18
Pages
e19961–e19961
Publication date
1 March 2026
Language
EN