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Self-Assembled Nanoparticle Probes for Recognition and Detection of Biomolecules

Maxwell, Dustin J., Taylor, Jason R., Nie, Shuming·2002·Journal of the American Chemical Society

Abstract

Colloidal gold nanocrystals have been used to develop a new class of nanobiosensors that is able to recognize and detect specific DNA sequences and single-base mutations in a homogeneous format. At the core of this biosensor is a 2.5-nm gold nanoparticle that functions as both a nano-scaffold and a nano-quencher (efficient energy acceptor). Attached to this core are oligonucleotide molecules labeled with a thiol group at one end and a fluorophore at the other. This hybrid bio/inorganic construct is found to spontaneously assemble into a constrained arch-like conformation on the particle surface. Binding of target molecules results in a conformational change, which restores the fluorescence of the quenched fluorophore. Unlike conventional molecular beacons with a stem-and-loop structure, the nanoparticle probes do not require a stem, and their background fluorescence increases little with temperature. In comparison with the organic quencher Dabcyl (4,4'-dimethylaminophenyl azo benzoic acid), metal nanoparticles have unique structural and optical properties for new applications in biosensing and molecular engineering.

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Metadata

Authors
Maxwell, D. J., Taylor, J. R., & Nie, S.
Venue
Journal of the American Chemical Society
Published
2002-07-17
Volume
124
Issue
32
Pages
9606-9612
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W1985941691
Cited by
890
References
0
FWCI
10.80
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Self-Assembled Nanoparticle Probes for Recognition and Detection of Biomolecules. https://app.cochat.ai/papers/10.1021/ja025814p
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Maxwell, D. J., Taylor, J. R., & Nie, S. (2002). Self-Assembled Nanoparticle Probes for Recognition and Detection of Biomolecules. Journal of the American Chemical Society. https://doi.org/10.1021/ja025814p

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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.