Nanoporous Intermetallic Cu<sub>3</sub>Sn/Cu Hybrid Electrodes as Efficient Electrocatalysts for Carbon Dioxide Reduction

Wan, Wu‐Bin; Zhou, Yitong; Zeng, Shu‐Pei; Shi, Hang; Yao, Ruiqi; Wen, Zi; Lang, Xing‐You; Jiang, Qing

doi:10.1002/smll.202100683

Cited by 26 publications

(11 citation statements)

References 53 publications

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“…Finally, on the basis of machine learning predictions, Al 0.6 Cu 3.4 (100) and AlCu 3 (211) surfaces are specified to be highly selective for hydrocarbons. In addition, the studies of Cu 3 Sn and CuZn in CO 2 RR have been reported, which proves the potential of these bimetallic alloys and further verifies our prediction results.…”

Section: Resultssupporting

confidence: 87%

Prediction of Carbon Dioxide Reduction Catalyst Using Machine Learning with a Few-Feature Model: WLEDZ

Xing

Zhang

et al. 2022

J. Phys. Chem. C

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Cu-based alloy catalysts are widely used in the field of carbon dioxide reduction reaction (CO2RR), due to the good selectivity and low overpotential. In order to achieve efficient exploration of alloy catalysts for CO2RR, a machine learning (ML) model, based on a gradient boosting regression (GBR) algorithm, is developed. By implementing a rigorous feature selection process, the dimensionality of feature space is reduced from thirteen to five, including work function (W), local electronegativity (Loc_EN), electronegativity (EN), interplanar spacing (D), and atomic number (Z), which is referred to as the WLEDZ model. The few-feature model has a high performance as that with many features, and the ML model successfully and rapidly predicts the adsorption energy of the key intermediates (HCOO, CO, and COOH) in the CO2RR process. In addition, eight Cu-based bimetallic catalysts are predicted with highly promising alternatives. This demonstrates that the WLEDZ few-feature ML model can screen highly promising bimetallic alloy for CO2RR and can also be used for the design of other types of catalysts.

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Section: Resultssupporting

confidence: 87%

Prediction of Carbon Dioxide Reduction Catalyst Using Machine Learning with a Few-Feature Model: WLEDZ

Xing

Zhang

et al. 2022

J. Phys. Chem. C

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“…The definition of ordered intermetallic structure is the solid state compound with the metallic bond, defined stoichiometry, and ordered crystal structure. [84] The performance of intermetallic Sn-based catalysts is also strongly correlated to the composition. Wan process (Figure 4a,b).…”

Section: Atomic Structure Engineeringmentioning

confidence: 99%

Nanostructure Engineering of Sn‐Based Catalysts for Efficient Electrochemical CO₂ Reduction

Ren

Miao

Ren

et al. 2022

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safe limit 350 ppm), [7] which increased to 414 ppm in 2020, [3,8] with an annual growth rate of ≈1.57 ppm. This value will probably reach 570 ppm by 2100 without proper regulation. [9] Such high concentration of CO 2 in the atmosphere have triggered a series of ecological and environmental issues, including climate change, glacier melting, and sea levels rising, [7,8,10,11] which threaten the sustainable development of humans. Significant endeavors have been devoted to impede the increase of CO 2 concentration, which includes the exploitation of renewable green energy to alleviate the dependence on fossil fuels, [7,12,13] CO 2 sequestration, [14] and mimicking the natural photosynthesis process by converting CO 2 and H 2 O into valuable chemical products to close the carbon cycle. [15,16] Artificial photosynthesis in particular has attracted considerable research interest over the past few decades, since it offers the possibility of producing chemical products that traditionally rely on fossil fuels using waste CO 2 . [15,17] Various technologies have been implemented in artificial photosynthesis process, such as biocatalysis, [18,19] thermocatalysis, [20,21] photocatalysis, [22][23][24][25][26] electrocatalysis, [27][28][29] as well as photoelectrocatalysis. [30] Electrochemical CO 2 reduction (ECO 2 R) has attracted considerable attention due to its unique advantages, which include the relatively mild operating conditions; the tunable product selectivity and reaction path by regulating reaction Excessive anthropogenic CO 2 emission has caused a series of ecological and environmental issues, which threatens mankind's sustainable development. Mimicking the natural photosynthesis process (i.e., artificial photosynthesis) by electrochemically converting CO 2 into value-added products is a promising way to alleviate CO 2 emission and relieve the dependence on fossil fuels. Recently, Sn-based catalysts have attracted increasing research attentions due to the merits of low price, abundance, non-toxicity, and environmental benignancy. In this review, the paradigm of nanostructure engineering for efficient electrochemical CO 2 reduction (ECO 2 R) on Sn-based catalysts is systematically summarized. First, the nanostructure engineering of size, composition, atomic structure, morphology, defect, surficial modification, catalyst/ substrate interface, and single-atom structure, are systematically discussed. The influence of nanostructure engineering on the electronic structure and adsorption property of intermediates, as well as the performance of Sn-based catalysts for ECO 2 R are highlighted. Second, the potential chemical state changes and the role of surface hydroxides on Sn-based catalysts during ECO 2 R are introduced. Third, the challenges and opportunities of Sn-based catalysts for ECO 2 R are proposed. It is expected that this review inspires the further development of highly efficient Sn-based catalysts, meanwhile offer protocols for the investigation of Sn-based catalysts.

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“…CO 2 RR FE for (B) V o -CuO(Sn) and (C) V o -CuO, CuO electrodes. (D) The FE CO and partial current density of V o -CuO(Sn) compared with other CO 2 -to-CO catalysts. (E) Stability measurement of V o -CuO(Sn), V o -CuO, CuO(Sn), and CuO samples at −0.53 V versus RHE in CO 2 -saturated 0.1 M KHCO 3 aqueous solution.…”

Section: Resultsmentioning

confidence: 99%

Sn Dopants with Synergistic Oxygen Vacancies Boost CO₂ Electroreduction on CuO Nanosheets to CO at Low Overpotential

et al. 2022

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Using the electrochemical CO 2 reduction reaction (CO 2 RR) with Cu-based electrocatalysts to achieve carbonneutral cycles remains a significant challenge because of its low selectivity and poor stability. Modulating the surface electron distribution by defects engineering or doping can effectively improve CO 2 RR performance. Herein, we synthesize the electrocatalyst of V o -CuO(Sn) nanosheets containing oxygen vacancies and Sn dopants for application in CO 2 RR-to-CO. Density functional theory calculations confirm that the incorporation of oxygen vacancies and Sn atoms substantially reduces the energy barrier for *COOH and *CO intermediate formation, which results in the high efficiency, low overpotential, and superior stability of the CO 2 RR to CO conversion. This electrocatalyst possesses a high Faraday efficiency (FE) of 99.9% for CO at a low overpotential of 420 mV and a partial current density of up to 35.22 mA cm −2 at −1.03 V versus reversible hydrogen electrode (RHE). The FE CO of V o -CuO(Sn) could retain over 95% within a wide potential area from −0.48 to −0.93 V versus RHE. Moreover, we obtain long-term stability for more than 180 h with only a slight decay in its activity. Therefore, this work provides an effective route for designing environmentally friendly electrocatalysts to improve the selectivity and stability of the CO 2 RR to CO conversion.

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Nanoporous Intermetallic Cu₃Sn/Cu Hybrid Electrodes as Efficient Electrocatalysts for Carbon Dioxide Reduction

Cited by 26 publications

References 53 publications

Prediction of Carbon Dioxide Reduction Catalyst Using Machine Learning with a Few-Feature Model: WLEDZ

Prediction of Carbon Dioxide Reduction Catalyst Using Machine Learning with a Few-Feature Model: WLEDZ

Nanostructure Engineering of Sn‐Based Catalysts for Efficient Electrochemical CO₂ Reduction

Sn Dopants with Synergistic Oxygen Vacancies Boost CO₂ Electroreduction on CuO Nanosheets to CO at Low Overpotential

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Nanoporous Intermetallic Cu3Sn/Cu Hybrid Electrodes as Efficient Electrocatalysts for Carbon Dioxide Reduction

Cited by 26 publications

References 53 publications

Prediction of Carbon Dioxide Reduction Catalyst Using Machine Learning with a Few-Feature Model: WLEDZ

Prediction of Carbon Dioxide Reduction Catalyst Using Machine Learning with a Few-Feature Model: WLEDZ

Nanostructure Engineering of Sn‐Based Catalysts for Efficient Electrochemical CO2 Reduction

Sn Dopants with Synergistic Oxygen Vacancies Boost CO2 Electroreduction on CuO Nanosheets to CO at Low Overpotential

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Nanoporous Intermetallic Cu₃Sn/Cu Hybrid Electrodes as Efficient Electrocatalysts for Carbon Dioxide Reduction

Nanostructure Engineering of Sn‐Based Catalysts for Efficient Electrochemical CO₂ Reduction

Sn Dopants with Synergistic Oxygen Vacancies Boost CO₂ Electroreduction on CuO Nanosheets to CO at Low Overpotential