Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 178

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Ultrasensitive Flow Injection Chemiluminescence Detection of DNA Hybridization Using Signal DNA Probe Modified with Au and CuS Nanoparticles

Zhang, Shusheng, Zhong, Hua, Ding, Caifeng·2008·Analytical Chemistry

Abstract

A novel and sensitive flow injection chemiluminescence assay for sequence-specific DNA detection based on signal amplification with nanoparticles (NPs) is reported in the present work. The "sandwich-type" DNA biosensor was fabricated with the thiol-functionalized capture DNA first immobilized on an Au electrode and hybridized with one end of target DNA, the other end of which was recognized with a signal DNA probe labeled with CuS NPs and Au NPs on the 3'- and 5'-terminus, respectively. The hybridization events were monitored by the CL intensity of luminol-H2O2-Cu(2+) after the cupric ions were dissolved from the hybrids. We demonstrated that the incorporation of Au NPs in this sensor design significantly enhanced the sensitivity and the selectivity because a single Au NP can be loaded with hundreds of signal DNA probe strands, which were modified with CuS NPs. The ratios of Au NPs, signal DNA probes, and CuS NPs modified on the gold electrode were approximately 1/101/103. A preconcentration process of cupric ions performed by anodic stripping voltammetry technology further increased the sensor performance. As a result of these two combined effects, this DNA sensor could detect as low as femtomolar target DNA and exhibited excellent selectivity against two-base mismatched DNA. Under the optimum conditions, the CL intensity was increased with the increase of the concentration of target DNA in the range of 2.0 x 10(-14)-2.0 x 10(-12) M. A detection limit of 4.8 x 10(-15) M target DNA was achieved.

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 · 10.1021/ac800847r

Cites

3 of 3 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

Cited by

See all 178

Metadata

Authors
Zhang, S., Zhong, H., & Ding, C.
Venue
Analytical Chemistry
Published
2008-08-30
Volume
80
Issue
19
Pages
7206-7212
ISSN
  • 0003-2700 (ISSN-L)
  • 1520-6882
OpenAlex
W2083594284
Cited by
178
References
3
FWCI
7.44
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.