Citation: Yuan Qi, Yang Hao, Xie Miao, Cheng Tao. Theoretical Research on the Electroreduction of Carbon Dioxide[J]. Acta Physico-Chimica Sinica, ;2021, 37(5): 201004. doi: 10.3866/PKU.WHXB202010040 shu

Theoretical Research on the Electroreduction of Carbon Dioxide

Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou 215123, Jiangsu Province, China

Corresponding author: Cheng Tao, tcheng@suda.edu.cn
Received Date: 19 October 2020
Revised Date: 10 November 2020
Accepted Date: 30 November 2020
Available Online: 10 December 2020
Fund Project: the National Natural Science Foundation of China 21975148the Natural Science Foundation of Jiangsu Higher Education Institutions SBK20190810The project was supported by the National Natural Science Foundation of China (21975148) and the Natural Science Foundation of Jiangsu Higher Education Institutions (SBK20190810)

Converting CO₂ into value-added products via sustainable energy, such as electrical energy, has several advantages. First, it is one of the most promising routes to close the carbon loop and plays a crucial role in significantly reducing the CO₂ concentration in the atmosphere. Second, it can utilize CO₂ as a valuable industry reactant that can store energy by converting electrical energy to chemical energy. Although the CO₂ reduction reaction has been studied for more than three decades, the sluggish kinetics remain a bottleneck, which requires a highly efficient catalyst. However, none of the reported catalysts meets the requirements for any practical application due to low activity and poor selectivity. To rationally design a more efficient CO₂ reduction catalyst, understanding the reaction mechanism is crucial. Although it is challenging to experimentally capture and characterize the reactive intermediates, atomic modeling serves as an alternative for providing an understanding of the elementary reactions on a microscale. Significant progress has been made in understanding the reaction mechanism using multiscale simulations. In this study, important progress in revealing the reaction mechanism of CO₂ reduction using computational simulation in recent years is summarized. First, the advances in simulation methods for electrochemical reactions are introduced, and the advantages and disadvantages of various methods are compared. Second, the detailed reaction mechanism of CO₂ reduction to various major products, such as CO, CH₄, and C₂H₄, and minor products, such as ethanol and acetate, are disused. Different results obtained from different approximations are compared, while a mechanism that can better explain the existing experimental results is recommended. Third, the operando technique, such as ambient pressure X-ray photoelectron spectroscopy, is disused. The operando analysis results are direct evidence to validate the theoretically proposed reaction pathway. In turn, the theoretical predictions can help resolve the experimental spectrum, which is usually too complex to refer to a reference system. The combination of theory and operando experiments should be one of the most promising directions in determining the reaction mechanism. Fourth, novel synthesis strategies are discussed. These new ideas are beneficial for simplifying the synthesis process or increasing the diversity of products. Finally, the recent progress in the application of machine learning to big data for CO₂ reduction is discussed. These new powerful tools may play a crucial role in reaction mechanism studies. Overall, in the study of electrochemical reaction mechanism, theoretical simulation can provide the reaction details and energy information of elementary reactions at the atomic level. Therefore, in the study of electrochemical reaction mechanism of carbon dioxide, the microscopic mechanism that the experiment cannot provide is supplemented. On the one hand, it explains the existing experimental phenomena; however, on the other hand, it provides new insights for the study of reaction mechanism. On this basis, the use of new research paradigms, such as high-throughput computing and machine learning, provides new ideas for a rational design for accelerating material development.
- Carbon dioxide,
- Electrochemistry,
- Reduction reaction,
- Theoretical simulation,
- Heterogeneous catalysis
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沈阳化工大学材料科学与工程学院沈阳 110142