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Computational Design and Experimental Validation of Enzyme Mimicking Cu-Based Metal-Organic Frameworks for the Reduction of CO2 into C2 Products: C-C Coupling Promoted by Ligand Modulation and the Optimal Cu-Cu Distance.
Mao, Xin; Gong, Wanbing; Fu, Yang; Li, Jiayi; Wang, Xinyu; O'Mullane, Anthony P; Xiong, Yujie; Du, Aijun.
Afiliação
  • Mao X; School of Chemistry and Physics and Centre for Materials Science, Queensland University of Technology, Gardens Point Campus, Brisbane 4001, Australia.
  • Gong W; Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, National Synchrotron Radiation Laboratory, School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, Anhui 230026, China.
  • Fu Y; Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, Ministry of Education Engineering Research, Centre of Thin Film Photoelectronic Technology, Renewable Energy Conversion and Storage Centre, Nankai University,
  • Li J; Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, National Synchrotron Radiation Laboratory, School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, Anhui 230026, China.
  • Wang X; Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, National Synchrotron Radiation Laboratory, School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, Anhui 230026, China.
  • O'Mullane AP; School of Chemistry and Physics and Centre for Materials Science, Queensland University of Technology, Gardens Point Campus, Brisbane 4001, Australia.
  • Xiong Y; Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, National Synchrotron Radiation Laboratory, School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, Anhui 230026, China.
  • Du A; School of Chemistry and Physics and Centre for Materials Science, Queensland University of Technology, Gardens Point Campus, Brisbane 4001, Australia.
J Am Chem Soc ; 145(39): 21442-21453, 2023 Oct 04.
Article em En | MEDLINE | ID: mdl-37748045
ABSTRACT
While extensive research has been conducted on the conversion of CO2 to C1 products, the synthesis of C2 products still strongly depends on the Cu electrode. One main issue hindering the C2 production on Cu-based catalysts is the lack of an appropriate Cu-Cu distance to provide the ideal platform for the C-C coupling process. Herein, we identify a lab-synthesized artificial enzyme with an optimal Cu-Cu distance, named MIL-53 (Cu) (MIL= Materials of Institute Lavoisier), for CO2 conversion by using a density functional theory method. By substituting the ligands in the porous MIL-53 (Cu) nanozyme with functional groups from electron-donating NH2 to electron-withdrawing NO2, the Cu-Cu distance and charge of Cu can be significantly tuned, thus modulating the adsorption strength of CO2 that impacts the catalytic activity. MIL-53 (Cu) decorated with a COOH-ligand is found to be located at the top of a volcano-shaped plot and exhibits the highest activity and selectivity to reduce CO2 to CH3CH2OH with a limiting potential of only 0.47 eV. In addition, experiments were carried out to successfully synthesize COOH-decorated MIL-53(Cu) to prove its high catalytic performance for C2 production, which resulted in a -55.5% faradic efficiency at -1.19 V vs RHE, which is much higher than the faradic efficiency of the benchmark Cu electrode of 35.7% at -1.05 V vs RHE. Our results demonstrate that the biologically inspired enzyme engineering approach can redefine the structure-activity relationships of nanozyme catalysts and can also provide a new understanding of the catalytic mechanisms in natural enzymes toward the development of highly active and selective artificial nanozymes.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article