A Computational-Experimental Investigation of the Mechanisms Responsible for the Enhanced CO<sub>2</sub> Electrochemical Reduction of Dendritic Sn<sub>1</sub>Pb<sub>3</sub> Alloy
Bibliographic record
Abstract
The electrochemical conversion of CO 2 into useful chemicals and fuels provides a means to recycle CO 2 and achieve carbon balance.[1, 2] There are several value-added products that can be obtained from the electroreduction of CO 2. Formic acid/formate is one of the useful products which has a strong market.[3] Formic acid/formate is widely used in several industrial sectors including pharmaceutical synthesis, pulp and paper production, textile finishing, as additive in animal feeds and as deicing agent.[4] In addition, formic acid/formate has been identified as a potentially hydrogen carrier and as a fuel for direct formate fuel cells (DFFC).[5, 6] Years of research have shown that various metals including Pb, Hg, Bi, and Sn produce HCOOH as a major product (high Faradic efficiency) during the electroreduction of CO 2 .[7-9] However, large onset potentials and high overpotentials should be applied when large current densities are achieved. Several investigations revealed that increasing the electrochemical active surface area of catalysts is effective to decrease the overpotenial.[10-12] However, Reducing the onset potential for CO 2 electroreduction calls for more subtle manipulation of the catalyst composition since it is determined by the adsorption energies of reaction intermediates. Alloying with appropriate elements can affect the CO 2 reduction onset potential. In this study, dendritic Sn-Pb alloy and Pb is investigated for CO 2 electroreduction. The performances of these materials for the CO 2 electroreduction will be evaluated. The DFT calculation results will be presented to explain the better performances of dendritic Sn 1 Pb 3 alloy. References [1] G. Centi, E.A. Quadrelli, S. Perathoner, Energy Environ. Sci., 6 (2013) 1711-1731. [2] J.-P. Jones, G.K.S. Prakash, G.A. Olah, Isr. J. Chem., 54 (2014) 1451-1466. [3] E.V. Kondratenko, G. Mul, J. Baltrusaitis, G.O. Larrazábal, J. Pérez-Ramírez, Energy Environ. Sci., 6 (2013) 3112-3135. [4] J. Hietala, A. Vuori, P. Johnsson, I. Pollari, W. Reutemann, H. Kieczka, Formic Acid. In Ullmann's Encyclopedia of Industrial Chemistry, (Ed.). doi:10.1002/14356007.a12_013.pub3, Wiley-VCH Weinheim, 2016, pp. 1-22. [5] R. Francke, B. Schille, M. Roemelt, Chem. Rev., 118 (2018) 4631-4701. [6] M. Aresta, A. Dibenedetto, A. Angelini, Chem. Rev., 114 (2014) 1709-1942. [7] W. Zhang, Y. Hu, L. Ma, G. Zhu, Y. Wang, X. Xue, R. Chen, S. Yang, Z. Jin, Adv. Sci., 5 (2018) 1700275. [8] J. Gong, L. Zhang, Z.J. Zhao, Angew. Chem. Int. Ed. Engl., 129 (2017) 11482–11511. [9] J. Qiao, Y. Liu, F. Hong, J. Zhang, Chem. Soc. Rev., 43 (2014) 631-675. [10] C. Rogers, W.S. Perkins, G. Veber, T.E. Williams, R.R. Cloke, F.R. Fischer, J. Am. Chem. Soc., 139 (2017) 4052-4061. [11] M. Fan, S. Garbarino, G.A. Botton, A.C. Tavares, D. Guay, J. Mater. Chem. A, 5 (2017) 20747-20756. [12] A. Dutta, C.E. Morstein, M. Rahaman, A. Cedeño López, P. Broekmann, ACS Catal., 8 (2018) 8357-8368.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".