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Plasmonic Nanocavities-based Aperiodic crystal for Protein-Protein Recognition SERS sensors

M. Rippa
  • Institute of Applied Sciences and Intelligent Systems “E. Caianiello” of CNR, 80072 Pozzuoli, Italy
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ R. Castagna
  • Institute of Applied Sciences and Intelligent Systems “E. Caianiello” of CNR, 80072 Pozzuoli, Italy
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ M. Pannico / P. Musto / E. Bobeico / J. Zhou / L. Petti
  • Corresponding author
  • Institute of Applied Sciences and Intelligent Systems “E. Caianiello” of CNR, 80072 Pozzuoli, Italy
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2017-07-05 | DOI: https://doi.org/10.1515/odps-2017-0007


The revelation of protein-protein interactions is one of the main preoccupations in the field of proteomics. Nanoplasmonics has emerged as an attractive surface-based technique because of its ability to sense protein binding under physiological conditions in a label-free manner. Here, we present a detailed experimental study of the use of aperiodic photonic nanocavities for plasmonic Surface Enhanced Raman Scattering (SERS) protein detection and recognition. The plasmonic crystal is designed on a 2D Thue-Morse array configuration. The SERS nanosensor is coated with a proper self-assembled monolayer to covalently bind Bovine Serum Albumin that is a well known model to study biological (specifically, protein) systems. The performance of the nanosensor is assessed by recording a new Raman (SERS) peak in the fingerprint region and by a giant enhancement of the SERS signal intensity, both reported and discussed.

Keywords: Photonic Crystals; Plasmonics; Sensors; Nanofabrication; Nanolithography; Raman; Surface Enhanced Raman Scattering (SERS); Localized Surface Plasmonic Resonance (LSPR); Metamaterials


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About the article

Received: 2017-01-28

Revised: 2017-04-28

Accepted: 2017-05-23

Published Online: 2017-07-05

Published in Print: 2017-06-27

Citation Information: Optical Data Processing and Storage, Volume 3, Issue 1, Pages 54–60, ISSN (Online) 2084-8862, DOI: https://doi.org/10.1515/odps-2017-0007.

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© 2017. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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