The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Electrochemical CO2 Reduction: Classifying Cu Facets
Abstract
For CO 2 reduction reactions, the Cu catalyst is unique, as compared with other metals, because of its ability to produce a wide range of hydrocarbon and oxygenated products. Previously, we have shown that Cu has the unique property of binding CO* without having H* UPD (underpotential deposited). However, the product distribution from Cu depends highly on the exact nature of the Cu surface. In this work, we investigate a series of Cu facets to understand how they affect the product distribution. Some carbon–carbon (CC)-coupled products are statistically shown to highly correlate, while other CC products correlate with C 1 products. We avoid studying the complex reaction network of the CO 2 reduction reaction and focus instead on descriptors for the Cu facet product distribution. The coordination number distribution and the binding energies of the intermediates calculated by density functional theory are investigated as descriptors. The binding energies are close for the Cu facets, and the variation shows that two noncorrelating (orthogonal) binding energies contain up to 70% of the information from the binding energies. Further, structural analysis allows us to uniquely identify the (100)×(110) step sites as specific ethanol-producing sites from the CO 2 reduction reaction
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Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Combining theory and experiment in electrocatalysis: Insights into materials design
Seh, Z. W., Kibsgaard, J., Dickens, C. F., Chorkendorff, I., Nørskov, J. K., & Jaramillo, T. F. (2017) · Science · doi:10.1126/science.aad4998 · Cited by 12,114
The path towards sustainable energy
Chu, S., Cui, Y., & Liu, N. (2017) · Nature Materials · doi:10.1038/nmat4834 · Cited by 4,791
The atomic simulation environment—a Python library for working with atoms
Hjorth Larsen, A., Jørgen Mortensen, J., Blomqvist, J., Castelli, I. E., Christensen, R., Dułak, M., Friis, J., Groves, M. N., Hammer, B., Hargus, C., et al. (2017) · Journal of Physics: Condensed Matter · doi:10.1088/1361-648x/aa680e · Cited by 4,464
How copper catalyzes the electroreduction of carbon dioxide into hydrocarbon fuels
Peterson, A. A., Abild-Pedersen, F., Studt, F., Rossmeisl, J., & Nørskov, J. K. (2010) · Energy & Environmental Science · doi:10.1039/c0ee00071j · Cited by 3,784
Self-citationNew insights into the electrochemical reduction of carbon dioxide on metallic copper surfaces
Kuhl, K. P., Cave, E. R., Abram, D. N., & Jaramillo, T. F. (2012) · Energy & Environmental Science · doi:10.1039/c2ee21234j · Cited by 3,160
Enkovaara, J., Rostgaard, C., Mortensen, J. J., Chen, J., Dułak, M., Ferrighi, L., Gavnholt, J., Glinsvad, C., Haikola, V, Hansen, H. A., et al. (2010) · Journal of Physics: Condensed Matter · doi:10.1088/0953-8984/22/25/253202 · Cited by 2,208
Catalysts and Reaction Pathways for the Electrochemical Reduction of Carbon Dioxide
Kortlever, R., Shen, J., Schouten, K. J. P., Calle-Vallejo, F., & Koper, M. T. M. (2015) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5b01559 · Cited by 2,175
Hori, Y., Wakebe, H., Tsukamoto, T., & Koga, O. (1994) · Electrochimica Acta · doi:10.1016/0013-4686(94)85172-7 · Cited by 2,140
Real-space grid implementation of the projector augmented wave method
Mortensen, J. J., Hansen, L. B., & Jacobsen, K. W. (2005) · Physical Review B · doi:10.1103/physrevb.71.035109 · Cited by 2,072
Cited by
Arán-Ais, R. M., Scholten, F., Kunze, S., Rizo, R., & Roldan Cuenya, B. (2020) · Nature Energy · doi:10.1038/s41560-020-0594-9 · Cited by 771
Electrochemical CO2 reduction (CO2RR) to multi-carbon products over copper-based catalysts
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Zhan, C., Dattila, F., Rettenmaier, C., Bergmann, A., Kühl, S., García-Muelas, R., López, N., & Cuenya, B. R. (2021) · ACS Catalysis · doi:10.1021/acscatal.1c01478 · Cited by 554
Wang, X., Klingan, K., Klingenhof, M., Möller, T., Ferreira de Araújo, J., Martens, I., Bagger, A., Jiang, S., Rossmeisl, J., Dau, H., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-20961-7 · Cited by 373
Self-citationAn, H., Wu, L., Mandemaker, L. D. B., Yang, S., de Ruiter, J., Wijten, J. H. J., Janssens, J. C. L., Hartman, T., van der Stam, W., & Weckhuysen, B. M. (2021) · Angewandte Chemie International Edition · doi:10.1002/anie.202104114 · Cited by 353
Metadata
- Authors
- Bagger, A., Ju, W., Varela, A. S., Strasser, P., & Rossmeisl, J.
- Venue
- ACS Catalysis
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-07-30
- Volume
- 9
- Issue
- 9
- Pages
- 7894-7899
- ISSN
- 2155-5435 (ISSN-L)
- OpenAlex
- W2964386462
- Cited by
- 294
- References
- 32
- FWCI
- 5.98
- Type
- journal article
- Language
- en
- Open access
- closed
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