The self-supported bimetallic array superstructures were constructed for high-performance coupling electrosynthesis of formate and adipate at the anode and cathode, respectively. Thanks to the unique nanostructure features and the promotion effect of adaptive reactions, the resulting electrocatalysts delivered remarkable performance for both CO2-to-formate and cyclohexanone-to-adipate conversion with high Faradaic efficiencies and current densities.
The coupling electrosynthesis involving CO2 upgrade conversion is of great significance for the sustainable development of the environment and energy but is challenging. Herein, we exquisitely constructed the self-supported bimetallic array superstructures from the Cu(OH)2 array architecture precursor, which can enable high-performance coupling electrosynthesis of formate and adipate at the anode and the cathode, respectively. Concretely, the faradaic efficiencies (FEs) of CO2-to-formate and cyclohexanone-to-adipate conversion simultaneously exceed 90% at both electrodes with excellent stabilities. Such high-performance coupling electrosynthesis is highly correlated with the porous nanosheet array superstructure of CuBi alloy as the cathode and the nanosheet-on-nanowire array superstructure of CuNi hydroxide as the anode. Moreover, compared to the conventional electrolysis process, the cell voltage is substantially reduced while maintaining the electrocatalytic performance for coupling electrosynthesis in the two-electrode electrolyzer with the maximal FEformate and FEadipate up to 94.2% and 93.1%, respectively. The experimental results further demonstrate that the bimetal composition modulates the local electronic structures, promoting the reactions toward the target products. Prospectively, our work proposes an instructive strategy for constructing adaptive self-supported superstructures to achieve efficient coupling electrosynthesis.
Li Liu†, Yingchun He†, Qing Li, Changsheng Cao, Minghong Huang, Dong-Dong Ma*, Xin-Tao Wu, Qi-Long Zhu*
How to cite:
L. Liu, Y. He, Q. Li, C. Cao, M. Huang, D.-D. Ma, X.-T. Wu, Q.-L. Zhu, Exploration 2023, 20230043.