The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
The Degree of Rate Control: A Powerful Tool for Catalysis Research
Abstract
High Resolution Image Download MS PowerPoint Slide The “degree of rate control” (DRC) is a mathematical approach for analyzing multistep reaction mechanisms that has proven very useful in catalysis research. It identifies the “rate-controlling transition states and intermediates” (i.e., those whose DRCs are large in magnitude). Even in mechanisms with over 30 intermediates and transition states, these are generally just a few distinct chemical species whose energies, if they could be independently changed, would achieve a faster net reaction rate to the product of interest. For example, when there is a single “rate-determining step”, the DRC for its transition state (TS) is 1, which means (by definition) that if this TS’s energy could be decreased by k B T (where k B is Boltzmann’s constant and T is temperature), the net rate would increase by a factor of e . Because the (relative) energies of these key adsorbed intermediates and transition states can be adjusted by modifying the catalyst or solvent, or even a reactant’s molecular structure, the DRC values provide important ideas for catalyst improvement. The species with large DRCs are also the ones whose energetics must be most accurately measured or calculated to achieve an accurate kinetic model for any reaction mechanism. A tutorial on DRC analysis, the calculation of DRCs, and examples of the applications of DRCs in catalysis research is presented here. Applications of DRC analysis include the following: clarifying reaction kinetics, improving the accuracy of computational models, improving reaction conditions, improving choice of oxidant in selective oxidation, incorporation in algorithms which calculate net reaction rates of multistep mechanisms without solving the differential equations involved, and high-throughput computational screening of catalyst materials. Because DRC values can be determined experimentally, a full microkinetic model is not required to take advantage of DRC analysis.
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
Towards the computational design of solid catalysts
Nørskov, J. K., Bligaard, T., Rossmeisl, J., & Christensen, C. H. (2009) · Nature Chemistry · doi:10.1038/nchem.121 · Cited by 4,161
Computational high-throughput screening of electrocatalytic materials for hydrogen evolution
Greeley, J., Jaramillo, T. F., Bonde, J., Chorkendorff, I., & Nørskov, J. K. (2006) · Nature Materials · doi:10.1038/nmat1752 · Cited by 4,097
Alloys of platinum and early transition metals as oxygen reduction electrocatalysts
Greeley, J., Stephens, I. E. L., Bondarenko, A. S., Johansson, T. P., Hansen, H. A., Jaramillo, T. F., Rossmeisl, J., Chorkendorff, I., & Nørskov, J. K. (2009) · Nature Chemistry · doi:10.1038/nchem.367 · Cited by 3,203
Density functional theory in surface chemistry and catalysis
Nørskov, J. K., Abild-Pedersen, F., Studt, F., & Bligaard, T. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1006652108 · Cited by 2,611
From the Sabatier principle to a predictive theory of transition-metal heterogeneous catalysis
Medford, A. J., Vojvodic, A., Hummelshøj, J. S., Voss, J., Abild-Pedersen, F., Studt, F., Bligaard, T., Nilsson, A., & Nørskov, J. K. (2015) · Journal of Catalysis · doi:10.1016/j.jcat.2014.12.033 · Cited by 2,254
How to Conceptualize Catalytic Cycles? The Energetic Span Model
Kozuch, S., & Shaik, S. (2011) · Accounts of Chemical Research · doi:10.1021/ar1000956 · Cited by 1,859
The Brønsted–Evans–Polanyi relation and the volcano curve in heterogeneous catalysis
Bligaard, T., Nørskov, J. K., Dahl, S., Matthiesen, J., Christensen, C. H., & Sehested, J. (2004) · Journal of Catalysis · doi:10.1016/j.jcat.2004.02.034 · Cited by 1,713
Abild-Pedersen, F., Greeley, J., Studt, F., Rossmeisl, J., Munter, T. R., Moses, P. G., Skúlason, E., Bligaard, T., & Nørskov, J. K. (2007) · Physical Review Letters · doi:10.1103/physrevlett.99.016105 · Cited by 1,678
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
Universality in Heterogeneous Catalysis
Nørskov, J. K., Bligaard, T., Logadottir, A., Bahn, S., Hansen, L. B., Bollinger, M., Bengaard, H., Hammer, B., Sljivancanin, Z., Mavrikakis, M., et al. (2002) · Journal of Catalysis · doi:10.1006/jcat.2002.3615 · Cited by 1,400
Cited by
Huang, J., Sheng, H., Ross, R. D., Han, J., Wang, X., Song, B., & Jin, S. (2021) · Nature Communications · doi:10.1038/s41467-021-23390-8 · Cited by 707
Electrocatalysis in Alkaline Media and Alkaline Membrane-Based Energy Technologies
Yang, Y., Peltier, C. R., Zeng, R., Schimmenti, R., Li, Q., Huang, X., Yan, Z., Potsi, G., Selhorst, R., Lu, X., et al. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00331 · Cited by 544
Microkinetic Modeling: A Tool for Rational Catalyst Design
Motagamwala, A. H., & Dumesic, J. A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00394 · Cited by 485
Machine learning for heterogeneous catalyst design and discovery
Goldsmith, B. R., Esterhuizen, J., Liu, J., Bartel, C. J., & Sutton, C. (2018) · AIChE Journal · doi:10.1002/aic.16198 · Cited by 444
Computational Approach to Molecular Catalysis by 3d Transition Metals: Challenges and Opportunities
Vogiatzis, K. D., Polynski, M. V., Kirkland, J. K., Townsend, J., Hashemi, A., Liu, C., & Pidko, E. A. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00361 · Cited by 423
Metadata
- Authors
- Campbell, C. T.
- Venue
- ACS Catalysis
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-03-21
- Volume
- 7
- Issue
- 4
- Pages
- 2770-2779
- ISSN
- 2155-5435 (ISSN-L)
- OpenAlex
- W2593663142
- Cited by
- 572
- References
- 53
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The Degree of Rate Control: A Powerful Tool for Catalysis Research. https://app.cochat.ai/papers/10.1021/acscatal.7b00115
- The paper
- Campbell, C. T. (2017). The Degree of Rate Control: A Powerful Tool for Catalysis Research. ACS Catalysis. https://doi.org/10.1021/acscatal.7b00115
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.
