The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Chip-Based Scanometric Detection of Mercuric Ion Using DNA-Functionalized Gold Nanoparticles
Abstract
We have developed a chip-based scanometric method for the detection of mercuric ion (Hg (2+)). This method takes advantage of the cooperative binding and catalytic properties of DNA-functionalized gold nanoparticles and the selective binding of a thymine-thymine mismatch for Hg (2+). The limit of detection of this assay in buffer and environmentally relevant samples (lake water) is 10 nM (2 ppb) Hg (2+), which is the U.S. Environmental Protection Agency (EPA) limit of [Hg (2+)] for drinkable water and 1 order of magnitude lower than previous colorimetric assays. This assay is capable of discriminating Hg (2+) from 15 other environmentally relevant metal ions. The method is attractive for potential point-of-use applications due to its high throughput, convenient readout, and portability.
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
The toxicology of mercury—current exposures and clinical manifestations
Clarkson, T. W., Magos, L., & Myers, G. J. (2003) · New England Journal of Medicine · doi:10.1056/nejmra022471 · Cited by 1,766
Cited by
Sulfur and Its Role In Modern Materials Science
Boyd, D. A. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201604615 · Cited by 512
Li, T., Dong, S., & Wang, E. (2009) · Analytical Chemistry · doi:10.1021/ac900188y · Cited by 473
Zhu, Z., Su, Y., Li, J., Li, D., Zhang, J., Song, S., Zhao, Y., Li, G., & Fan, C. (2009) · Analytical Chemistry · doi:10.1021/ac9010809 · Cited by 431
Detection and Spatial Mapping of Mercury Contamination in Water Samples Using a Smart-Phone
Wei, Q., Nagi, R., Sadeghi, K., Feng, S., Yan, E., Ki, S. J., Caire, R., Tseng, D., & Ozcan, A. (2014) · ACS Nano · doi:10.1021/nn406571t · Cited by 400
Gold nanoparticle probes for the detection of mercury, lead and copper ions
Lin, Y.-W., Huang, C.-C., & Chang, H.-T. (2011) · The Analyst · doi:10.1039/c0an00652a · Cited by 379
Metadata
- Authors
- Lee, J.-S., & Mirkin, C. A.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2008-07-30
- Volume
- 80
- Issue
- 17
- Pages
- 6805-6808
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2033043994
- Cited by
- 210
- References
- 1
- FWCI
- 9.06
- 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.