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Cited by 1,105

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

Single-molecule and single-nanoparticle SERS: from fundamental mechanisms to biomedical applications

Qian, X.-M., Nie, S. M.·2008·Chemical Society Reviews

Abstract

This tutorial review discusses a new class of colloidal metal nanoparticles that is able to enhance the efficiencies of surface-enhanced Raman scattering (SERS) by as much as 10(14)-10(15) fold. This enormous enhancement allows spectroscopic detection and identification of single molecules located on the nanoparticle surface or at the junction of two particles under ambient conditions. Considerable progress has been made in understanding the enhancement mechanisms, including definitive evidence for the single-molecule origin of fluctuating SERS signals. For applications, SERS nanoparticle tags have been developed based on the use of embedded reporter molecules and a silica or polymer encapsulation layer. The SERS nanoparticle tags are capable of providing detailed spectroscopic information and are much brighter than semiconductor quantum dots in the near-infrared spectral window. These properties have raised new opportunities for multiplexed molecular diagnosis and in vivo Raman spectroscopy and imaging.

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Metadata

Authors
Qian, X.-M., & Nie, S. M.
Venue
Chemical Society Reviews
Published
2008
Volume
37
Issue
5
Pages
912
ISSN
  • 0306-0012 (ISSN-L)
  • 1460-4744
OpenAlex
W2091588319
Cited by
1,105
References
3
FWCI
1.69
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Single-molecule and single-nanoparticle SERS: from fundamental mechanisms to biomedical applications. https://app.cochat.ai/papers/10.1039/b708839f
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Qian, X.-M., & Nie, S. M. (2008). Single-molecule and single-nanoparticle SERS: from fundamental mechanisms to biomedical applications. Chemical Society Reviews. https://doi.org/10.1039/b708839f

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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.