Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 322

The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.

Metal ions doped carbon quantum dots: Synthesis, physicochemical properties, and their applications

Lin, Liping, Luo, Yaxin, Tsai, Peiyu, Wang, Jiajing, Chen, Xi·2018·TrAC Trends in Analytical Chemistry

Abstract

Abstract As an efficient method to improve the intrinsic properties of carbon quantum dots (CQDs), doping CQDs with heteroatoms including nonmetallic atoms and metal ions recently receive great attention. Since the summary about the achievements of CQDs doping with nonmetallic atoms has been carried out. In this minireview, we will overview the state-of-the-art knowledge of metal ions doped-CQDs (M-CQDs). The synthetic routes for M-CQDs, the novel physicochemical properties originating from their unique morphology and constitute, and their applications in environmental, biological and energy-related areas will be summarized and highlighted. The review aims to provide new insights into the exploration of the methods in order to improve the functionality of M-CQDs, the relationship between newly novel physicochemical properties and metal ions doping, as well as their possible applications in future.

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 · 10.1016/j.trac.2018.03.015

Cites

38 of 38 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 38 references

Metadata

Authors
Lin, L., Luo, Y., Tsai, P., Wang, J., & Chen, X.
Venue
TrAC Trends in Analytical Chemistry
Publisher
Elsevier BV
Published
2018-06
Volume
103
Pages
87-101
ISSN
0165-9936 (ISSN-L)
OpenAlex
W2795317146
Cited by
322
References
38
FWCI
7.50
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: Metal ions doped carbon quantum dots: Synthesis, physicochemical properties, and their applications. https://app.cochat.ai/papers/10.1016/j.trac.2018.03.015
The paper
Lin, L., Luo, Y., Tsai, P., Wang, J., & Chen, X. (2018). Metal ions doped carbon quantum dots: Synthesis, physicochemical properties, and their applications. TrAC Trends in Analytical Chemistry. https://doi.org/10.1016/j.trac.2018.03.015

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.