The DOI matches its Crossref record.
Plasmon Near-Field Coupling in Metal Dimers as a Step toward Single-Molecule Sensing
Abstract
In this study, we report on ultrasensitive protein detection with lithographically prepared plasmonic nanostructures. We have engineered optical nanosensors by the combined approach of negative resist, electron beam lithography, and reactive ion etching to form highly reproducible arrays of gold dimers in which the near-field coupling in their subwavelength gap enables for scaling the sensing volume down to the single-protein scale. In good agreement with recent theoretical predictions, the dimer geometry offers enhanced sensitivity compared to isolated particles for the detection of both small organic molecules and proteins. Beyond, by exploiting size exclusion, we are capable of monitoring the number of proteins able to bind across the gap region through the precise engineering of the structures coupled to the selective binding of a surface-assembled monolayer and covalent attachment of the protein.
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
Cited by
Patra, P. P., Chikkaraddy, R., Tripathi, R. P. N., Dasgupta, A., & Kumar, G. V. P. (2014) · Nature Communications · doi:10.1038/ncomms5357 · Cited by 1,635
Plasmonic coupling in noble metal nanostructures
Jain, P. K., & El-Sayed, M. A. (2010) · Chemical Physics Letters · doi:10.1016/j.cplett.2010.01.062 · Cited by 963
Single-Molecule Plasmon Sensing: Current Status and Future Prospects
Taylor, A. B., & Zijlstra, P. (2017) · ACS Sensors · doi:10.1021/acssensors.7b00382 · Cited by 366
LSPR Chip for Parallel, Rapid, and Sensitive Detection of Cancer Markers in Serum
Aćimović, S. S., Ortega, M. A., Sanz, V., Berthelot, J., Garcia-Cordero, J. L., Renger, J., Maerkl, S. J., Kreuzer, M. P., & Quidant, R. (2014) · Nano Letters · doi:10.1021/nl500574n · Cited by 310
Self-citation
Metadata
- Authors
- Aćimović, S. S., Kreuzer, M. P., González, M. U., & Quidant, R.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2009-04-22
- Volume
- 3
- Issue
- 5
- Pages
- 1231-1237
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2004731748
- Cited by
- 361
- References
- 0
- FWCI
- 17.27
- 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: Plasmon Near-Field Coupling in Metal Dimers as a Step toward Single-Molecule Sensing. https://app.cochat.ai/papers/10.1021/nn900102j
- The paper
- Aćimović, S. S., Kreuzer, M. P., González, M. U., & Quidant, R. (2009). Plasmon Near-Field Coupling in Metal Dimers as a Step toward Single-Molecule Sensing. ACS Nano. https://doi.org/10.1021/nn900102j
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.