The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Adsorption and diffusion of water on graphene from first principles
Abstract
Water monomer adsorption on graphene is examined with state-of-the-art electronic structure approaches. The adsorption energy determinations on this system from quantum Monte Carlo and the random-phase approximation yield small values of 100 meV. These benchmarks provide a deeper understanding of the reactivity of graphene that may underpin the development of improved more approximate methods enabling the accurate treatment of more complex processes at wet-carbon interfaces. As an example, we show how dispersion-corrected density functional theory, which we show gives a satisfactory description of this adsorption system, predicts that water undergoes ultra-fast diffusion on graphene at low temperatures.
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
Lee, C., Yang, W., & Parr, R. G. (1988) · Physical Review B · doi:10.1103/physrevb.37.785 · Cited by 100,874
Cited by
Heterostructures for Electrochemical Hydrogen Evolution Reaction: A Review
Zhao, G., Rui, K., Dou, S. X., & Sun, W. (2018) · Advanced Functional Materials · doi:10.1002/adfm.201803291 · Cited by 1,422
Random-phase approximation and its applications in computational chemistry and materials science
Ren, X., Rinke, P., Joas, C., & Scheffler, M. (2012) · Journal of Materials Science · doi:10.1007/s10853-012-6570-4 · Cited by 610
Lao, M., Li, P., Jiang, Y., Pan, H., Dou, S. X., & Sun, W. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.107231 · Cited by 290
Metadata
- Authors
- Ma, J., Michaelides, A., Alfè, D., Schimka, L., Kresse, G., & Wang, E.
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2011-07-01
- Volume
- 84
- Issue
- 3
- ISSN
- 1098-0121 (ISSN-L)
- 1550-235X
- OpenAlex
- W1990891738
- Cited by
- 273
- References
- 1
- FWCI
- 9.61
- 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: Adsorption and diffusion of water on graphene from first principles. https://app.cochat.ai/papers/10.1103/physrevb.84.033402
- The paper
- Ma, J., Michaelides, A., Alfè, D., Schimka, L., Kresse, G., & Wang, E. (2011). Adsorption and diffusion of water on graphene from first principles. Physical Review B. https://doi.org/10.1103/physrevb.84.033402
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.
