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Cited by 571

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

A Hybrid Quantum Dot−Antibody Fragment Fluorescence Resonance Energy Transfer-Based TNT Sensor

Goldman, Ellen R., Medintz, Igor L., Whitley, Jessica L., Hayhurst, Andrew, Clapp, Aaron R., Uyeda, H. Tetsuo, Deschamps, Jeffrey R., Lassman, Michael E., Mattoussi, Hedi·2005·Journal of the American Chemical Society

Abstract

We demonstrate the use of luminescent QDs conjugated to antibody fragments to develop solution-phase nanoscale sensing assemblies, based on fluorescence resonance energy transfer (FRET) for the specific detection of the explosive 2,4,6-trinitrotoluene (TNT) in aqueous environments. The hybrid sensor consists of anti-TNT specific antibody fragments attached to a hydrophilic QD via metal-affinity coordination. A dye-labeled TNT analogue prebound in the antibody binding site quenches the QD photoluminescence via proximity-induced FRET. Analysis of the data collected at increasing dye-labeled analogue to QD ratios provided an insight into understanding how the antibody fragments self-assemble on the QD. Addition of soluble TNT displaces the dye-labeled analogue, eliminating FRET and resulting in a concentration-dependent recovery of QD photoluminescence. Sensor performance and specificity were evaluated.

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Crossref · 10.1021/ja043677l

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Metadata

Authors
Goldman, E. R., Medintz, I. L., Whitley, J. L., Hayhurst, A., Clapp, A. R., Uyeda, H. T., Deschamps, J. R., Lassman, M. E., & Mattoussi, H.
Venue
Journal of the American Chemical Society
Published
2005-04-13
Volume
127
Issue
18
Pages
6744-6751
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W2066569554
Cited by
571
References
1
FWCI
29.02
Type
journal article
Language
en
Open access
closed

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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.