Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 279

The DOI matches its Crossref record.

Predicted Surface Composition and Thermodynamic Stability of MXenes in Solution

Ashton, Michael, Mathew, Kiran, Hennig, Richard G., Sinnott, Susan B.·2016·The Journal of Physical Chemistry C

Abstract

First-principles calculations are used to compare the binding energies of O, OH, and F on two-dimensional, metal carbide and nitride, or MXene, surfaces in order to predict the dependence of the thermodynamic stability of these compounds on their chemical composition. Solvation effects are implicitly included in the calculations to reproduce experimental conditions as closely as possible. The results indicate that all MXene surfaces are saturated with oxygen when exposed to H 2 O/HF solutions at low hydrogen chemical potential, μ H, and that Sc-based MXenes can also be fluorinated in solutions of higher μ H . After investigating the thermodynamic stability of all 54 MXene compounds M n +1 X n O 2 (M = Sc, Ti, V, Cr, Zr, Nb, Mo, Hf, Ta; X = C, N; n = 1, 2, 3), 38 are predicted to have formation energies below 200 meV/atom. Of these, six are predicted to have formation energies below 100 meV/atom, only one of which has been synthesized. Sc-based MXenes are found to be highly stable when their surfaces are terminated with F, which also results in the easiest exfoliation to produce freestanding single layers.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/acs.jpcc.5b11887

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.

A few free checks a day · papers already verified open instantly · results are public

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

Cited by

See all 279

Metadata

Authors
Ashton, M., Mathew, K., Hennig, R. G., & Sinnott, S. B.
Venue
The Journal of Physical Chemistry C
Published
2016-02-05
Volume
120
Issue
6
Pages
3550-3556
ISSN
  • 1932-7447 (ISSN-L)
  • 1932-7455
OpenAlex
W2338599487
Cited by
279
References
0
FWCI
10.33
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: Predicted Surface Composition and Thermodynamic Stability of MXenes in Solution. https://app.cochat.ai/papers/10.1021/acs.jpcc.5b11887
The paper
Ashton, M., Mathew, K., Hennig, R. G., & Sinnott, S. B. (2016). Predicted Surface Composition and Thermodynamic Stability of MXenes in Solution. The Journal of Physical Chemistry C. https://doi.org/10.1021/acs.jpcc.5b11887

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.