Optical fibre sensors using graphene-based materials: A review

Hernaez, Miguel ORCID: https://orcid.org/0000-0001-7878-4704, Zamarreño, Carlos R., Melendi-Espina, Sonia ORCID: https://orcid.org/0000-0002-1083-3896, Bird, Liam R., Mayes, Andrew G. and Arregui, Francisco J. (2017) Optical fibre sensors using graphene-based materials: A review. Sensors, 17 (1). ISSN 1424-8220

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Abstract

Graphene and its derivatives have become the most explored materials since Novoselov and Geim (Nobel Prize winners for Physics in 2010) achieved its isolation in 2004. The exceptional properties of graphene have attracted the attention of the scientific community from different research fields, generating high impact not only in scientific journals, but also in general-interest newspapers. Optical fibre sensing is one of the many fields that can benefit from the use of these new materials, combining the amazing morphological, chemical, optical and electrical features of graphene with the advantages that optical fibre offers over other sensing strategies. In this document, a review of the current state of the art for optical fibre sensors based on graphene materials is presented.

Item Type:	Article
Additional Information:	This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
Uncontrolled Keywords:	optical fibre sensors,graphene,graphene oxide,reduced graphene oxide,carbon materials,thin films,nanostructured coatings
Faculty \ School:	Faculty of Science > School of Chemistry (former - to 2024) Faculty of Science > School of Mathematics (former - to 2024)
UEA Research Groups:	Faculty of Science > Research Groups > Physical and Analytical Chemistry (former - to 2017) Faculty of Science > Research Groups > Chemistry of Materials and Catalysis Faculty of Science > Research Groups > Sustainable Energy Faculty of Science > Research Groups > Materials, Manufacturing & Process Modelling
Depositing User:	Pure Connector
Date Deposited:	17 Jan 2017 00:07
Last Modified:	21 Nov 2024 03:19
URI:	https://ueaeprints.uea.ac.uk/id/eprint/62117
DOI:	10.3390/s17010155

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