The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Colorimetric Detection of Urine Glucose Based ZnFe2O4 Magnetic Nanoparticles
Open accessAbstract
In this paper, we discovered that ZnFe(2)O(4) magnetic nanoparticles (MNPs) possess intrinsic peroxidase-like activity. ZnFe(2)O(4) MNPs exhibit several advantages such as high catalytic efficiency, good stability, monodispersion, and rapid separation over other peroxidase nanomimetics and horseradish peroxidase (HRP). ZnFe(2)O(4) MNPs were used as a colorimetric biosensor for the detection of urine glucose. This method is simple, inexpensive, highly sensitive, and selective for glucose detection using glucose oxidase (GOx) and ZnFe(2)O(4) MNPs with a linear range from 1.25 × 10(-6) to 1.875 × 10(-5) mol L(-1) with a detection limit of 3.0 × 10(-7) mol L(-1). The color change observable by the naked eyes based on the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) is the principle for the sensing of urine glucose level.
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.
Integrity signals
Registry
Cites
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.
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
Intrinsic peroxidase-like activity of ferromagnetic nanoparticles
Gao, L., Zhuang, J., Nie, L., Zhang, J., Zhang, Y., Gu, N., Wang, T., Feng, J., Yang, D., Perrett, S., et al. (2007) · Nature Nanotechnology · doi:10.1038/nnano.2007.260 · Cited by 7,483
Self-citation
Cited by
Nirala, N. R., Abraham, S., Kumar, V., Bansal, A., Srivastava, A., & Saxena, P. S. (2015) · Sensors and Actuators B: Chemical · doi:10.1016/j.snb.2015.04.091 · Cited by 180
Lekha, S., & M, S. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.2993591 · Cited by 105
Metadata
- Authors
- Su, L., Feng, J., Zhou, X., Ren, C., Li, H., & Chen, X.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-06-22
- Volume
- 84
- Issue
- 13
- Pages
- 5753-5758
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2083036660
- Cited by
- 503
- References
- 1
- FWCI
- 14.77
- Type
- journal article
- Language
- en
- Open access
- green
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: Colorimetric Detection of Urine Glucose Based ZnFe2O4 Magnetic Nanoparticles. https://app.cochat.ai/papers/10.1021/ac300939z
- The paper
- Su, L., Feng, J., Zhou, X., Ren, C., Li, H., & Chen, X. (2012). Colorimetric Detection of Urine Glucose Based ZnFe2O4 Magnetic Nanoparticles. Analytical Chemistry. https://doi.org/10.1021/ac300939z
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.
