Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Surface-enhanced Raman Scattering Substrates Based on Nanometre Scale Structures on Butterfly Wings

Not Accessible

Your library or personal account may give you access

Abstract

Surface-enhanced Raman scattering (SERS) has received a great deal interest as an analytical tool due to its potential for obtaining Raman signals from single molecules. Many methods for preparing SERS-active substrate have been reported. These range from nano-particle based methods, which lack reproducibility, to highly reproducible nano-arrays requiring time consuming and costly preparation. We show that highly reproducible SERS can be achieved by applying a metallic coating to the brightly coloured regions of the graphium weiskei butterfly wing. Electron microscopy reveals the wing exhibit nanostructures with comparable dimensions to the roughness scale of SERS substrates. SERS measurements performed on wings coated with 60 nm of silver display enhancement factors of approximately 107 with no apparent background contribution from the wing. To demonstrate effectiveness and reproducibility the substrate is coated with a monoclonal antibody.

© 2007 SPIE

PDF Article
More Like This
Bowtie Nanoantennas as Substrates for Electrochemical Surface-Enhanced Raman Scattering (SERS)

F. Jäckel, A.A. Kinkhabwala, and W.E. Moerner
FThF3 Frontiers in Optics (FiO) 2007

Surface-Enhanced Raman Spectroscopy using silver impregnated polycarbonate substrates

L. Lagonigro, A. C. Peacock, P. J. A. Sazio, T. Hasell, P. D. Brown, and S. M. Howdle
CE1_4 The European Conference on Lasers and Electro-Optics (CLEO/Europe) 2007

Fiber Surface Enhanced Raman Scattering (SERS) Sensors based on a Double Substrate “Sandwich” Structure

Chao Shi, Claire Gu, Debraj Ghosh, Leo Seballos, Shaowei Chen, and Jin Z. Zhang
CMJJ1 Conference on Lasers and Electro-Optics (CLEO:S&I) 2008

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.