Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 385

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

2D MoN‐VN Heterostructure To Regulate Polysulfides for Highly Efficient Lithium‐Sulfur Batteries

Ye, Chao, Jiao, Yan, Jin, Huanyu, Slattery, Ashley D., Davey, Kenneth, Wang, Haihui, Qiao, Shi‐Zhang·2018·Angewandte Chemie International Edition

Abstract

Lithium‐sulfur batteries hold promise for next‐generation batteries. A problem, however, is rapid capacity fading. Moreover, atomic‐level understanding of the chemical interaction between sulfur host and polysulfides is poorly elucidated from a theoretical perspective. Here, a two‐dimensional (2D) heterostructured MoN‐VN is fabricated and investigated as a new model sulfur host. Theoretical calculations indicate that electronic structure of MoN can be tailored by incorporation of V. This leads to enhanced polysulfides adsorption. Additionally, in situ synchrotron X‐ray diffraction and electrochemical measurements reveal effective regulation and utilization of the polysulfides in the MoN‐VN. The MoN‐VN‐based lithium‐sulfur batteries have a capacity of 708 mA h g−1 at 2 C and a capacity decay as low as 0.068 % per cycle during 500 cycles with sulfur loading of 3.0 mg cm−2.

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 · 10.1002/anie.201810579

Cites

1 of 1 references verified0 citation errors · 0 not checkable

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 385

Metadata

Authors
Ye, C., Jiao, Y., Jin, H., Slattery, A. D., Davey, K., Wang, H., & Qiao, S.
Venue
Angewandte Chemie International Edition
Publisher
Wiley
Published
2018-11-07
Volume
57
Issue
51
Pages
16703-16707
ISSN
  • 1433-7851 (ISSN-L)
  • 1521-3773
OpenAlex
W2896718875
Cited by
385
References
1
FWCI
19.00
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: 2D MoN‐VN Heterostructure To Regulate Polysulfides for Highly Efficient Lithium‐Sulfur Batteries. https://app.cochat.ai/papers/10.1002/anie.201810579
The paper
Ye, C., Jiao, Y., Jin, H., Slattery, A. D., Davey, K., Wang, H., & Qiao, S. (2018). 2D MoN‐VN Heterostructure To Regulate Polysulfides for Highly Efficient Lithium‐Sulfur Batteries. Angewandte Chemie International Edition. https://doi.org/10.1002/anie.201810579

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.