Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 455

The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.

A chemical reduction approach to the synthesis of copper nanoparticles

Open access
Khan, Ayesha, Rashid, Audil, Younas, Rafia, Chong, Ren·2016·International Nano Letters

Abstract

Development of improved methods for the synthesis of copper nanoparticles is of high priority for the advancement of material science and technology. Herein, starch-protected zero-valent copper (Cu) nanoparticles have been successfully synthesized by a novel facile route. The method is based on the chemical reduction in aqueous copper salt using ascorbic acid as reducing agent at low temperature (80 °C). X-ray diffraction, scanning electron microscopy and energy-dispersive X-ray spectroscopy measurements were taken to investigate the size, structure and composition of synthesized Cu nanocrystals, respectively. Average crystallite size of Cu nanocrystals calculated from the major diffraction peaks using the Scherrer formula is about 28.73 nm. It is expected that the outcomes of the study take us a step closer toward designing rational strategies for the synthesis of nascent Cu nanoparticles without inert gas protection.

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 · 10.1007/s40089-015-0163-6

Cites

19 of 19 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 19 references

Cited by

See all 455

Metadata

Authors
Khan, A., Rashid, A., Younas, R., & Chong, R.
Venue
International Nano Letters
Published
2015-11-09
Volume
6
Issue
1
Pages
21-26
ISSN
  • 2008-9295 (ISSN-L)
  • 2228-5326
OpenAlex
W2144807500
Cited by
455
References
19
FWCI
6.00
Type
journal article
Language
en
Open access
hybrid

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: A chemical reduction approach to the synthesis of copper nanoparticles. https://app.cochat.ai/papers/10.1007/s40089-015-0163-6
The paper
Khan, A., Rashid, A., Younas, R., & Chong, R. (2016). A chemical reduction approach to the synthesis of copper nanoparticles. International Nano Letters. https://doi.org/10.1007/s40089-015-0163-6

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.