Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 234

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Synthesis and sensor applications of MoS2-based nanocomposites

Zhang, Wensi, Zhang, Panpan, Su, Zhiqiang, Wei, Gang·2015·Nanoscale

Abstract

Molybdenum disulfide (MoS2) is a typical layered transition-metal dichalcogenide material, which has aroused a great deal of interest in the past few years. Recently, more and more attention has been focused on the synthesis and applications of MoS2-based nanocomposites. In this review, we aimed to present a wider view of the synthesis of various MoS2-based nanocomposites for sensor and biosensor applications. We highlighted the potential methods like self-assembly, hydrothermal reaction, chemical vapour deposition, electrospinning, as well as microwave and laser beam treatments for the successful preparation of MoS2-based nanocomposites. On the other hand, three representative types of detection devices fabricated by the MoS2-based nanocomposites, field-effect transistor, optical, and electrochemical sensors, were introduced in detail and discussed fully. The relationships between the sensing performances and the special nanostructures within the MoS2-based nanocomposites were presented and discussed.

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/c5nr06121k

Cites

7 of 7 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 7 references

Cited by

See all 234

Metadata

Authors
Zhang, W., Zhang, P., Su, Z., & Wei, G.
Venue
Nanoscale
Published
2015
Volume
7
Issue
44
Pages
18364-18378
ISSN
  • 2040-3364 (ISSN-L)
  • 2040-3372
OpenAlex
W2137592170
Cited by
234
References
7
FWCI
10.13
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 and sensor applications of MoS2-based nanocomposites. https://app.cochat.ai/papers/10.1039/c5nr06121k
The paper
Zhang, W., Zhang, P., Su, Z., & Wei, G. (2015). Synthesis and sensor applications of MoS2-based nanocomposites. Nanoscale. https://doi.org/10.1039/c5nr06121k

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.