Tuning Silver Nanostructures for Electrochemical CO<sub>2</sub> Reduction to Value-Added Fuels
Bibliographic record
Abstract
When combined with the use of renewable energies, electrochemical CO2 reduction reaction (CO2RR) is an attractive way to alleviate the greenhouse gas effects and the related environmental issues.[1] However, the inertness of CO2 itself, the sluggish multi-electron transfer kinetics and the competitive hydrogen evolution reaction (HER) during CO2RR render the simultaneous achievement of desirable catalytic activity and product selectivity on most electrocatalysts difficult.[2-3] CO2RR on most electrode surfaces requires large overpotential due to the poor catalytic activity. The rising costs of metals, in particular, noble metals such as Ag, Au and Pd, are the main hindrance toward large-scale application. Thus, the enhanced performance is desirable upon loading a certain or even lower level of noble metal nanostructures as compared to their counterparts for CO2RR. To this end, substantial experimental and theoretical efforts have been devoted to surface engineering by introducing grain boundary, oxide-reduction or oxygen plasma treatments. These have all previously been recognized to considerably improve CO2RR catalytic activity. As a critical structural feature, the morphology (e.g., unique architecture and/or size effects), can also greatly affect the catalytic activities of metal nanostructures, which can be achieved through purposefully tailoring energetically favourable low-coordinated atoms over various morphologies. Morphology control of size effect has been confirmed to greatly influence the catalytic activity for CO2RR over Au,[4] Ag[5] and Pd[6] NPs. However, there have been limited experimental and theoretical investigations of morphology control on the effects of both unique architecture and size, especially the nanostructures which are wholly enclosed by energetically favourable specific facets. With these new materials, the quantities of metals and the associated costs required to achieve a certain level of catalytic efficiency may be reduced significantly. To this end, various Ag nanostructures were successfully synthesized, and it is found that these Ag nanostructures enclosed by energetically favourable facets were impressively efficient and stable for CO2RR toward CO formation, accompanied with high CO selectivity in a broad potential window. The considerably enhanced catalytic activity and selectivity toward CO2RR were systematically rationalized through analyzing the density functional theory (DFT) calculations, the percentages of various catalytically active sites and how these specific Ag nanostructures affecting the active sites as well as the partial density of states (PDOS). References [1] L. Zhang, Z.J. Zhao, T. Wang, J. Gong, Chem. Soc. Rev.;47, 5423 (2018). [2] M. Asadi, K. Kim, C. Liu, A. V. Addepalli, P. Abbasi, P. Yasaei, P. Phillips, A. Behranginia, J. M. Cerrato, R. Haasch, Science, 353, 467 (2016). [3] W. Zhang, Q. Qin, L. Dai, R. Qin, X. Zhao, X. Chen, D. Ou, J. Chen, T. T. Chuong, B. Wu, Angew. Chem. Int. Ed., 57, 9475 (2018). [4] W. Zhu, R. Michalsky, O. Metin, H. Lv, S. Guo, C. J. Wright, X. Sun, A. A. Peterson, S. Sun, J. Am. Chem. Soc., 135, 16833 (2013). [5] C. Kim, H. S. Jeon, T. Eom, M. S. Jee, H. Kim, C. M. Friend, B. K. Min, Y. J. Hwang, J. Am. Chem. Soc., 137, 13844 (2015). [6] D. Gao, H. Zhou, J. Wang, S. Miao, F. Yang, G. Wang, J. Wang, X. Bao, J. Am. Chem. Soc., 137, 4288 (2015).
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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 | 0.001 |
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".