Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 233

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Ratiometric optical oxygen sensing: a review in respect of material design

Feng, Yan, Cheng, Jinghui, Zhou, Li, Zhou, Xiangge, Xiang, Haifeng·2012·The Analyst

Abstract

The quantitative determination of oxygen concentration is essential for a variety of applications ranging from life sciences to environmental sciences. Optical oxygen sensing allows non-invasive measurements with biological objects, parallel monitoring of multiple samples, and imaging. In general, ratiometric optical oxygen sensing is more desirable, due to its advantages of selectivity, insensitivity to ambient or scattered light, and elimination of instrumental fluctuation. Moreover, it can provide the perceived colour change, which would be useful not only for the ratiometric method of detection but also for rapid visual sensing. Mainly focusing on material design for ratiometric measurement, this review describes the overall progress made in the past ten years on ratiometric optical ground-state triplet oxygen sensing and offers a critical comparison of various methods reported in the literature. It also provides a development blueprint for ratiometric optical oxygen sensing.

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/c2an35907c

Cites

2 of 2 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 2 references

Cited by

See all 233

Metadata

Authors
Feng, Y., Cheng, J., Zhou, L., Zhou, X., & Xiang, H.
Venue
The Analyst
Published
2012
Volume
137
Issue
21
Pages
4885
ISSN
  • 0003-2654 (ISSN-L)
  • 1364-5528
OpenAlex
W1998775281
Cited by
233
References
2
FWCI
12.33
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: Ratiometric optical oxygen sensing: a review in respect of material design. https://app.cochat.ai/papers/10.1039/c2an35907c
The paper
Feng, Y., Cheng, J., Zhou, L., Zhou, X., & Xiang, H. (2012). Ratiometric optical oxygen sensing: a review in respect of material design. The Analyst. https://doi.org/10.1039/c2an35907c

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.