The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Label-free Detection of Influenza Viruses using a Reduced Graphene Oxide-based Electrochemical Immunosensor Integrated with a Microfluidic Platform
Open accessAbstract
Reduced graphene oxide (RGO) has recently gained considerable attention for use in electrochemical biosensing applications due to its outstanding conducting properties and large surface area. This report presents a novel microfluidic chip integrated with an RGO-based electrochemical immunosensor for label-free detection of an influenza virus, H1N1. Three microelectrodes were fabricated on a glass substrate using the photolithographic technique, and the working electrode was functionalized using RGO and monoclonal antibodies specific to the virus. These chips were integrated with polydimethylsiloxane microchannels. Structural and morphological characterizations were performed using X-ray photoelectron spectroscopy and scanning electron microscopy. Electrochemical studies revealed good selectivity and an enhanced detection limit of 0.5 PFU mL−1, where the chronoamperometric current increased linearly with H1N1 virus concentration within the range of 1 to 10 4 PFU mL−1 (R 2 = 0.99). This microfluidic immunosensor can provide a promising platform for effective detection of biomolecules using minute samples.
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Graphene-based nanomaterials and their electrochemistry
Pumera, M. (2010) · Chemical Society Reviews · doi:10.1039/c002690p · Cited by 1,117
Cited by
Toward Nanotechnology-Enabled Approaches against the COVID-19 Pandemic
Weiss, C., Carriere, M., Fusco, L., Capua, I., Regla-Nava, J. A., Pasquali, M., Scott, J. A., Vitale, F., Unal, M. A., Mattevi, C., et al. (2020) · ACS Nano · doi:10.1021/acsnano.0c03697 · Cited by 601
Metadata
- Authors
- Singh, R., Hong, S., & Jang, J.
- Venue
- Scientific Reports
- Published
- 2017-02-15
- Volume
- 7
- Issue
- 1
- Pages
- 42771-42771
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2588315379
- Cited by
- 196
- References
- 1
- FWCI
- 8.39
- Type
- journal article
- Language
- en
- Open access
- gold
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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.
