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Cited by 312

The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.

A Review of Gold and Silver Nanoparticle‐Based Colorimetric Sensing Assays

Open access
Sabela, Myalowenkosi, Balme, Sebastien, Bechelany, Mikhael, Janot, Jean‐Marc, Bisetty, Krishna·2017·Advanced Engineering Materials

Abstract

The nanoparticle colorimetric‐based methods have been extensively used for rapid detection, however there are few limitations which can be kept under control or avoided by understanding the crucial parameters involved in these reactions. This review addresses the main parameters that influence colorimetric‐based methods and provides a rational classification of the current approaches, by focusing particularly on gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs). The AgNP and AuNP‐based colorimetric assays can be very efficient and sensitive especially for biomolecule identification and for metal ion detection in environmental screening. Specifically, this review highlights the detection of metal ions through their coordination with nanoparticle stabilizing ligands. The review also addresses various approaches based on label‐free aptasensors to better understand their role as smart colorimetric sensing devices.

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Metadata

Authors
Sabela, M., Balme, S., Bechelany, M., Janot, J., & Bisetty, K.
Venue
Advanced Engineering Materials
Publisher
Wiley
Published
2017-08-03
Volume
19
Issue
12
ISSN
  • 1438-1656 (ISSN-L)
  • 1527-2648
OpenAlex
W2742655952
Cited by
312
References
58
FWCI
7.28
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: A Review of Gold and Silver Nanoparticle‐Based Colorimetric Sensing Assays. https://app.cochat.ai/papers/10.1002/adem.201700270
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Sabela, M., Balme, S., Bechelany, M., Janot, J., & Bisetty, K. (2017). A Review of Gold and Silver Nanoparticle‐Based Colorimetric Sensing Assays. Advanced Engineering Materials. https://doi.org/10.1002/adem.201700270

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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.