Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 313

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

Synthesis of novel hierarchical graphene/polypyrrole nanosheet composites and their superior electrochemical performance

Xu, Chaohe, Sun, Jing, Gao, Lian·2011·Journal of Materials Chemistry

Abstract

Novel hierarchical graphene/polypyrrole (GNS/PPy) nanosheet composites have been successfully fabricated for the first time based on an in situ chemical oxidation polymerization method. Sheet-like PPy with a thickness of 20–50 nm grown on the surface of the composites of the GNS/PPy polymerized at an early stage due to the dominated role of the sheet-like GNS and residue glucose in the synthesis process. Supercapacitors based on the hierarchical composite exhibit large electrochemical capacitance (318.6 F g−1) at a scan rate of 2 mV s−1. They also show superior electrochemical rate capability and cyclic stability. The capacitance retained ∼95% (132.9 F g−1) after 1000 cycles at a scan rate of 100 mV s−1. Accordingly, the hierarchical GNS/PPy nanosheet composites are potential electrode materials for electrochemical supercapacitors.

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 · 10.1039/c1jm11275a

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

Show all 1 references

Cited by

See all 313

Metadata

Authors
Xu, C., Sun, J., & Gao, L.
Venue
Journal of Materials Chemistry
Published
2011
Volume
21
Issue
30
Pages
11253
ISSN
  • 0959-9428 (ISSN-L)
  • 1364-5501
OpenAlex
W1989507675
Cited by
313
References
1
FWCI
8.83
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: Synthesis of novel hierarchical graphene/polypyrrole nanosheet composites and their superior electrochemical performance. https://app.cochat.ai/papers/10.1039/c1jm11275a
The paper
Xu, C., Sun, J., & Gao, L. (2011). Synthesis of novel hierarchical graphene/polypyrrole nanosheet composites and their superior electrochemical performance. Journal of Materials Chemistry. https://doi.org/10.1039/c1jm11275a

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.