The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Highly Sensitive and Selective Detection of Nanomolar Ferric Ions Using Dopamine Functionalized Graphene Quantum Dots
Abstract
The good stability, low cytotoxicity, and excellent photoluminescence property of graphene quantum dots (GQDs) make them an emerging class of promising materials in various application fields ranging from sensor to drug delivery. In the present work, the dopamine-functionalized GQDs (DA-GQDs) with stably bright blue fluorescence were successfully synthesized for low level Fe(3+) ions detection. The as-synthesized GQDs are uniform in size with narrow-distributed particle size of 4.5 ± 0.6 nm and high quantum yield of 10.2%. The amide linkage of GQDs with dopamine, confirmed by using XPS and FTIR spectra, results in the specific interaction between Fe(3+) and catechol moiety of dopamine at the interfaces for highly sensitive and selective detection of Fe(3+). A linear range of 20 nM to 2 μM with a detection limit of 7.6 nM is obtained for Fe(3+) detection by DA-GQDs. The selectivity of DA-GQDs sensing probe is significantly excellent in the presence of other interfering metal ions. In addition, the reaction mechanism for Fe(3+) detection based on the complexation and oxidation of dopamine has been proposed and validated. Results obtained in this study clearly demonstrate the superiority of surface functionalized GQDs to Fe(3+) detection, which can pave an avenue for the development of high performance and robust sensing probes for detection of metal ions and other organic metabolites in environmental and biomedical applications.
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
Iron, brain ageing and neurodegenerative disorders
Zecca, L., Youdim, M. B. H., Riederer, P., Connor, J. R., & Crichton, R. R. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1537 · Cited by 1,801
Dopamine-Dependent Iron Toxicity in Cells Derived from Rat Hypothalamus
Paris, I., Martinez-Alvarado, P., Cárdenas, S., Perez-Pastene, C., Graumann, R., Fuentes, P., Olea-Azar, C., Caviedes, P., & Segura-Aguilar, J. (2005) · Chemical Research in Toxicology · doi:10.1021/tx0497144 · Cited by 74
Dopamine complexes of iron in the etiology and pathogenesis of Parkinson’s disease
ARREGUIN, S., NELSON, P., PADWAY, S., SHIRAZI, M., & PIERPONT, C. (2009) · Journal of Inorganic Biochemistry · doi:10.1016/j.jinorgbio.2008.09.007 · Cited by 55
Cited by
A review on graphene-based nanocomposites for electrochemical and fluorescent biosensors
Krishnan, S. K., Singh, E., Singh, P., Meyyappan, M., & Nalwa, H. S. (2019) · RSC Advances · doi:10.1039/c8ra09577a · Cited by 785
Carbon-based quantum particles: an electroanalytical and biomedical perspective
Nekoueian, K., Amiri, M., Sillanpää, M., Marken, F., Boukherroub, R., & Szunerits, S. (2019) · Chemical Society Reviews · doi:10.1039/c8cs00445e · Cited by 294
Carbon Dots as an Effective Fluorescent Sensing Platform for Metal Ion Detection
Yoo, D., Park, Y., Cheon, B., & Park, M.-H. (2019) · Nanoscale Research Letters · doi:10.1186/s11671-019-3088-6 · Cited by 266
Metadata
- Authors
- Dutta Chowdhury, A., & Doong, R.-A.
- Venue
- ACS Applied Materials & Interfaces
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-08-08
- Volume
- 8
- Issue
- 32
- Pages
- 21002-21010
- ISSN
- 1944-8244 (ISSN-L)
- 1944-8252
- OpenAlex
- W2498831167
- Cited by
- 204
- References
- 3
- FWCI
- 10.04
- 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
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.