Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 159

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

Hierarchically Porous Carbon Encapsulating Sulfur as a Superior Cathode Material for High Performance Lithium–Sulfur Batteries

Xu, Guiyin, Ding, Bing, Nie, Ping, Shen, Laifa, Dou, Hui, Zhang, Xiaogang·2014·ACS Applied Materials & Interfaces

Abstract

Lithium-sulfur (Li-S) batteries are deemed to be a promising energy storage device for next-generation high energy power system. However, insulation of S and dissolution of lithium polysulfides in the electrolyte lead to low utilization of sulfur and poor cycling performance, which seriously hamper the rapid development of Li-S batteries. Herein, we reported that encapsulating sulfur into hierarchically porous carbon (HPC) derived from the soluble starch with a template of needle-like nanosized Mg(OH)2. HPC has a relatively high specific surface area of 902.5 m(2) g(-1) and large total pore volume of 2.60 cm(3) g(-1), resulting that a weight percent of sulfur in S/HPC is up to 84 wt %. When evaluated as cathodes for Li-S batteries, the S/HPC composite has a high discharge capacity of 1249 mAh g(-1) in the first cycle and a Coulombic efficiency as high as 94% with stable cycling over prolonged 100 charge/discharge cycles at a high current density of 1675 mA g(-1). The superior electrochemical performance of S/HPC is closely related to its unique structure, exhibiting the graphitic structure with a high developed porosity framework of macropores in combination with mesopores and micropores. Such nanostructure could shorten the transport pathway for both ions and electrons during prolonged cycling.

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/am4038728

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

Cites

Cited by

See all 159

Metadata

Authors
Xu, G., Ding, B., Nie, P., Shen, L., Dou, H., & Zhang, X.
Venue
ACS Applied Materials & Interfaces
Published
2013-12-23
Volume
6
Issue
1
Pages
194-199
ISSN
  • 1944-8244 (ISSN-L)
  • 1944-8252
OpenAlex
W2089132742
Cited by
159
References
1
FWCI
12.38
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.