Visualization of the Distance-Dependent Synergistic Interaction in Heterogeneous Dual-Site Catalysis
Understanding the characteristics of interfacial hydroxyl (OH) at the solid/liquid electrochemical interface is crucial for deciphering synergistic catalysis. However, it remains challenging to elucidate the influences of spatial distance between interfacial OH and neighboring reactants on reaction kinetics at the atomic level. Herein, we visualize the distance-dependent synergistic interaction in heterogeneous dual-site catalysis by using ex-situ infrared nanospectroscopy and in situ infrared spectroscopy techniques. These spectroscopic techniques achieve direct identification of the spatial distribution of synergistic species and reveal that OH facilitates the reactant deprotonation process depending on site distances in dual-site catalysts. Via modulating Ir-Co pair distances, we find that the dynamic equilibrium between generation and consumption of OH accounts for high-efficiency synergism at the optimized distance of 7.9 Å. At farther or shorter distances, spatial inaccessibility and resistance of OH with intermediates lead to OH accumulation, thereby diminishing the synergistic effect. Hence, a volcano-shaped curve has been established between the spatial distance and mass activity using formic acid oxidation as the probe reaction. This notion could also be extended to oxophilic metals, like Ir-Ru pairs, where volcano curves and dynamic equilibrium further evidence the universal significance of spatial distances.
Authors
- Jiawei Xue (ORCID: https://orcid.org/0000-0003-3693-4115)
- Qiquan Luo (ORCID: https://orcid.org/0000-0003-1450-2856)
- Tao Ding (ORCID: https://orcid.org/0000-0001-9143-0679)
- Zheng Rong Jiang (ORCID: https://orcid.org/0000-0003-4297-464X)
- Airong Xu (ORCID: https://orcid.org/0000-0002-6601-9754)
- Dong Liu
- Jun Bao (ORCID: https://orcid.org/0000-0002-1812-8999)
- Xiaokang Liu (ORCID: https://orcid.org/0000-0002-4138-3138)
- Tao Yao (ORCID: https://orcid.org/0000-0001-8699-8294)
- Hui Huang (ORCID: https://orcid.org/0000-0002-7494-4552)
- Shuaiwei Jiang
- Tong Liu (ORCID: https://orcid.org/0000-0002-1157-8890)
Institutions
- University of Science and Technology of China (CN)
- Anhui University (CN)
- National University of Defense Technology (CN)
- Hefei Institutes of Physical Science (CN)
- National Synchrotron Radiation Laboratory (CN)
Publication Details
- Journal
- Journal of the American Chemical Society
- Published
- 2024-10-11
- DOI
- https://doi.org/10.1021/jacs.4c10613
- Citations
- 39
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 4.57
Funders
- National Natural Science Foundation of China
- Chinese Academy of Sciences
- China Postdoctoral Science Foundation
- Youth Innovation Promotion Association of the Chinese Academy of Sciences
- University of Science and Technology of China
- National Key Research and Development Program of China
- Fundamental Research Funds for the Central Universities
- Basic and Applied Basic Research Foundation of Guangdong Province