Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 382

The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.

Polydopamine-Coated, Nitrogen-Doped, Hollow Carbon–Sulfur Double-Layered Core–Shell Structure for Improving Lithium–Sulfur Batteries

Zhou, Weidong, Xiao, Xingcheng, Cai, Mei, Yang, Li·2014·Nano Letters

Abstract

To better confine the sulfur/polysulfides in the electrode of lithium-sulfur (Li/S) batteries and improve the cycling stability, we developed a double-layered core-shell structure of polymer-coated carbon-sulfur. Carbon-sulfur was first prepared through the impregnation of sulfur into hollow carbon spheres under heat treatment, followed by a coating polymerization to give a double-layered core-shell structure. From the study of scanning transmission electron microscopy (STEM) images, we demonstrated that the sulfur not only successfully penetrated through the porous carbon shell but also aggregated along the inner wall of the carbon shell, which, for the first time, provided visible and convincing evidence that sulfur preferred diffusing into the hollow carbon rather than aggregating in/on the porous wall of the carbon. Taking advantage of this structure, a stable capacity of 900 mA h g(-1) at 0.2 C after 150 cycles and 630 mA h g(-1) at 0.6 C after 600 cycles could be obtained in Li/S batteries. We also demonstrated the feasibility of full cells using the sulfur electrodes to couple with the silicon film electrodes, which exhibited significantly improved cycling stability and efficiency. The remarkable electrochemical performance could be attributed to the desirable confinement of sulfur through the unique double-layered core-shell architectures.

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/nl502238b

Cites

42 of 42 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

Cites

Show all 42 references

Cited by

Metadata

Authors
Zhou, W., Xiao, X., Cai, M., & Yang, L.
Venue
Nano Letters
Published
2014-08-29
Volume
14
Issue
9
Pages
5250-5256
ISSN
  • 1530-6984 (ISSN-L)
  • 1530-6992
OpenAlex
W2332640839
Cited by
382
References
42
FWCI
28.30
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.