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Updated: Jun 30, 2026

Surface Enhanced Raman Spectroscopy Detection of Biomolecules Using EBL Fabricated Nanostructured Substrates
Published on: March 20, 2015
A practical method to fabricate gold substrates for surface-enhanced Raman spectroscopy
Ratna Tantra1, Richard J C Brown, Martin J T Milton
1National Physical Laboratory, Analytical Sciences, Hampton Road, Teddington, Middlesex TW11 0LW, United Kingdom. ratna.tantra@npl.co.uk
A new, practical method for creating surface-enhanced Raman spectroscopy (SERS) substrates uses readily available materials. This dip-coating technique offers consistent performance comparable to traditional methods for chemical analysis.
Area of Science:
- Materials Science
- Spectroscopy
- Nanotechnology
Background:
- Surface-enhanced Raman spectroscopy (SERS) requires specialized substrates for sensitive chemical detection.
- Current methods for SERS substrate fabrication can be time-consuming and inconsistent.
Purpose of the Study:
- To develop a practical and reproducible method for fabricating SERS substrates using commercially available materials.
- To evaluate the performance and consistency of the newly developed SERS substrates.
Main Methods:
- Dip-coating poly-L-lysine derivatized microscope slides in a gold colloidal suspension.
- Characterization of colloid deposition using scanning electron microscopy (SEM).
- Evaluation of SERS performance and reproducibility using Rhodamine 6G and Isoniazid.
Main Results:
- Successful fabrication of SERS substrates with performance comparable to traditional methods.
- Demonstrated reproducibility across multiple substrate locations with low variability (<10% RSD for some peaks).
- Observed differential susceptibility of spectral peaks to variability, linked to chemisorption and local enhancement effects.
Conclusions:
- The dip-coating method provides a practical, time-efficient, and reproducible approach to SERS substrate fabrication.
- The developed substrates exhibit reliable SERS performance for chemical detection.
- Understanding spectral variability aids in optimizing SERS analysis and interpreting results.
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