The DOI matches its Crossref record.
Proof of Single-Molecule Sensitivity in Surface Enhanced Raman Scattering (SERS) by Means of a Two-Analyte Technique
Abstract
A method is proposed to pin down unambiguous proof for single-molecule sensitivity in surface enhanced Raman spectroscopy (SERS). The simultaneous use of two analyte molecules enables a clear confirmation of the single (or few)-molecule nature of the signals. This method eliminates most of the uncertainties associated with low dye concentrations in previous experiments. It further shows that single- or few-molecule signals are very common in SERS, both in liquids and on dry substrates.
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
Guerrini, L., & Graham, D. (2012) · Chemical Society Reviews · doi:10.1039/c2cs35118h · Cited by 441
Rigorous justification of the |E|4 enhancement factor in Surface Enhanced Raman Spectroscopy
Le Ru, E. C., & Etchegoin, P. G. (2006) · Chemical Physics Letters · doi:10.1016/j.cplett.2006.03.042 · Cited by 432
Self-citationLarge-Scale Hot Spot Engineering for Quantitative SERS at the Single-Molecule Scale
Chen, H.-Y., Lin, M.-H., Wang, C.-Y., Chang, Y.-M., & Gwo, S. (2015) · Journal of the American Chemical Society · doi:10.1021/jacs.5b09111 · Cited by 426
Surface-enhanced Raman spectroscopy at single-molecule scale and its implications in biology
Wang, Y., & Irudayaraj, J. (2013) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2012.0026 · Cited by 125
Metadata
- Authors
- Le Ru, E. C., Meyer, M., & Etchegoin, P. G.
- Venue
- The Journal of Physical Chemistry B
- Publisher
- American Chemical Society (ACS)
- Published
- 2006-01-10
- Volume
- 110
- Issue
- 4
- Pages
- 1944-1948
- ISSN
- 1520-5207 (ISSN-L)
- 1520-6106
- OpenAlex
- W2090170839
- Cited by
- 550
- References
- 0
- FWCI
- 14.71
- 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: Proof of Single-Molecule Sensitivity in Surface Enhanced Raman Scattering (SERS) by Means of a Two-Analyte Technique. https://app.cochat.ai/papers/10.1021/jp054732v
- The paper
- Le Ru, E. C., Meyer, M., & Etchegoin, P. G. (2006). Proof of Single-Molecule Sensitivity in Surface Enhanced Raman Scattering (SERS) by Means of a Two-Analyte Technique. The Journal of Physical Chemistry B. https://doi.org/10.1021/jp054732v
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.