Shape-Engineered CO<sub>2 </sub>electroreduction over Silver Nanostructures
Bibliographic record
Abstract
CO2 makes up the largest portion of greenhouse gases (GHG) which are primarily responsible for climate change and global warming. The concept of utilizing CO2 as a feedstock fuel has drawn attention in research communities worldwide [1]. Thus, an innovative energy-efficient CO2 conversion system has therefore become a critical step towards the ultimate goal of CO2 capture and utilization. Electrochemical CO2 reduction reaction (CO2RR) is regarded as one of the most promising methods for CO2 conversion due to the increased availability of low-cost renewable energy. Currently, the major issues associated with electrochemical CO2RR include a broad distribution of the products, low catalytic activity, large required overpotentials, collectively leading to the low conversion efficiencies, high energy consumption and insufficient catalyst durability. Therefore, it is desirable to develop new highly active and selective catalysts capable of efficiently converting CO2 into high-value fuels at ambient temperature. For crystalline catalysts, the particle size [2-3] and surface crystallography (grain boundary [4] and vacancies [5]) are important factors in determining the catalytic activity, reaction products, reaction kinetics and selectivity [6-7]. However, there have been limited studies on CO2RR over metal NPs regarding the influence of particle shape, this warrants further exploration since the presence of edge and corner sites varies as the shape changes. Shape control has received extensive attentions for Ag with particular emphasis on triangular Ag nanoplate (Tri-Ag-NP) because of their unique structure-related optical properties and potential applications. This study demonstrates a predominant shape-dependent electrocatalytic reduction of CO2 to CO on triangular silver nanoplates (Tri-Ag-NPs) in 0.1 M KHCO3. Compared with similarly sized Ag nanoparticles (SS-Ag-NPs) and bulk Ag, Tri-Ag-NPs exhibited an enhanced current density and significantly improved Faradaic efficiency (96.8%) and energy efficiency (61.7%). Additionally, CO starts to be observed at an ultralow overpotential of 96 mV, further confirming the superiority of Tri-Ag-NPs as a catalyst for CO2RR towards CO formation. Density functional theory (DFT) calculations reveal that the significantly enhanced electrocatalytic activity and selectivity at lowered overpotential originate from the shape-controlled structure. This not only provides the optimum edge-to-corner ratio, but also dominates at the facet of Ag (100) where it requires lower energy to initiate the rate-determining step. This study demonstrates a promising approach to tune electrocatalytic activity and selectivity of metal catalysts for CO2RR by creating optimal facet and edge site through shape-controlled synthesis. References D.R. Feldman, W.D. Collins, P.J. Gero, M.S. Torn, E.J. Mlawer and T.R. Shippert, Nature, 519, 339 ( 2010). C. Kim, H.S. Jeon, T. Eom, M.S. Jee, H. Kim, C.M. Friend, B.K. Min and Y.J. Hwang, J. Am. Chem. Soc., 137, 13844 (2015). W. Zhu, R. Michalsky, O. Metin, H. Lv, S. Guo, C.J. Wright, X. Sun, A.A. Peterson and S. Sun, J. Am. Chem. Soc., 135, 16833 ( 2013). X. Feng, K. Jiang, S. Fan and M.W. Kanan, J. Am. Chem. Soc., 137, 4606 (2015). Q. Tang, Y. Lee, D.Y. Li, W. Choi, C.W. Liu, D. Lee and D.E. Jiang, J. Am. Chem. Soc., 139, 9728 ( 2017). S. Liu, X.Z. Wang, H. Tao, T. Li, Qi Liu, Z. Xu, X.Z. Fu, J.L. Luo, Nano Energy, 45, 456 (2018). S. Liu, H. Tao, L. Zeng, Q. Liu, Z. Xu, Q. Liu, J.L. Luo, J. Am. Chem. Soc., 139, 2160 (2017).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".