The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Computational Approaches to the Chemical Conversion of Carbon Dioxide
Abstract
The conversion of CO 2 into fuels and chemicals is viewed as an attractive route for controlling the atmospheric concentration and recycling of this greenhouse gas, but its industrial application is limited by the low selectivity and activity of the current catalysts. Theoretical modeling, in particular density functional theory (DFT) simulations, provides a powerful and effective tool to discover chemical reaction mechanisms and design new catalysts for the chemical conversion of CO 2, overcoming the repetitious and time/labor consuming trial‐and‐error experimental processes. In this article we give a comprehensive survey of recent advances on mechanism determination by DFT calculations for the catalytic hydrogenation of CO 2 into CO, CH 4, CH 3 OH, and HCOOH, and CO 2 methanation, as well as the photo‐ and electrochemical reduction of CO 2. DFT‐guided design procedures of new catalytic systems are also reviewed, and challenges and perspectives in this field are outlined.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Nanoalloys: From Theory to Applications of Alloy Clusters and Nanoparticles
Ferrando, R., Jellinek, J., & Johnston, R. L. (2008) · Chemical Reviews · doi:10.1021/cr040090g · Cited by 3,690
Coupled-cluster theory in quantum chemistry
Bartlett, R. J., & Musiał, M. (2007) · Reviews of Modern Physics · doi:10.1103/revmodphys.79.291 · Cited by 3,343
Recent advances in catalytic hydrogenation of carbon dioxide
Wang, W., Wang, S., Ma, X., & Gong, J. (2011) · Chemical Society Reviews · doi:10.1039/c1cs15008a · Cited by 3,316
New 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
Carbon capture and storage: how green can black be
Haszeldine, R. S. (2009) · Science · doi:10.1126/science.1172246 · Cited by 2,021
Activity Descriptors for CO 2 Electroreduction to Methane on Transition-Metal Catalysts
Peterson, A. A., & Nørskov, J. K. (2012) · The Journal of Physical Chemistry Letters · doi:10.1021/jz201461p · Cited by 1,667
Identification of Active Gold Nanoclusters on Iron Oxide Supports for CO Oxidation
Herzing, A. A., Kiely, C. J., Carley, A. F., Landon, P., & Hutchings, G. J. (2008) · Science · doi:10.1126/science.1159639 · Cited by 1,530
Turning carbon dioxide into fuel
Jiang, Z., Xiao, T., Kuznetsov, V. L., & Edwards, P. P. (2010) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2010.0119 · Cited by 457
Cited by
Cocatalysts for Selective Photoreduction of CO2into Solar Fuels
Li, X., Yu, J., Jaroniec, M., & Chen, X. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00400 · Cited by 2,304
Ran, J., Jaroniec, M., & Qiao, S. (2018) · Advanced Materials · doi:10.1002/adma.201704649 · Cited by 1,531
Tuning Selectivity of CO2 Hydrogenation Reactions at the Metal/Oxide Interface
Kattel, S., Liu, P., & Chen, J. G. (2017) · Journal of the American Chemical Society · doi:10.1021/jacs.7b05362 · Cited by 1,259
CO 2 Activation over Catalytic Surfaces
Álvarez, A., Borges, M., Corral‐Pérez, J. J., Olcina, J. G., Hu, L., Cornu, D., Huang, R., Stoian, D., & Urakawa, A. (2017) · ChemPhysChem · doi:10.1002/cphc.201700782 · Cited by 412
The reverse water gas shift reaction: a process systems engineering perspective
González-Castaño, M., Dorneanu, B., & Arellano-García, H. (2021) · Reaction Chemistry & Engineering · doi:10.1039/d0re00478b · Cited by 369
Metadata
- Authors
- Cheng, D., Negreiros, F. R., Aprà, E., & Fortunelli, A.
- Venue
- ChemSusChem
- Publisher
- Wiley
- Published
- 2013-05-28
- Volume
- 6
- Issue
- 6
- Pages
- 944-965
- ISSN
- 1864-5631 (ISSN-L)
- 1864-564X
- OpenAlex
- W2025145046
- Cited by
- 179
- References
- 9
- FWCI
- 6.01
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
