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Attaching Biological Probes to Silica Optical Biosensors Using Silane Coupling Agents
Published on: May 1, 2012
Periodic plasmonic enhancing epitopes on a whispering gallery mode biosensor.
Stephen Arnold1, Venkata Ramanaiah Dantham, Curtis Barbre
1Department of Applied Physics, Polytechnic Institute of NYU, Brooklyn, New York 11201, USA. SArnold935@aol.com
Optics Express
|November 29, 2012
Summary
This study enhances biosensing by adding gold nanoparticles to whispering gallery mode sensors. This plasmonic approach improves analyte capture and signal interpretation for more sensitive nanoparticle detection.
Area of Science:
- Nanotechnology
- Biosensing
- Plasmonics
Background:
- Whispering gallery mode (WGM) biosensors offer high sensitivity for detecting analytes.
- Current methods face challenges in analyte capture efficiency and signal self-referencing.
- Plasmonic enhancement is a promising strategy to overcome these limitations.
Purpose of the Study:
- To develop a novel WGM biosensor design for enhanced nanoparticle sensing.
- To achieve plasmonic enhancement and self-referencing capabilities.
- To increase the capture rate of analytes using antibody-functionalized gold nanoparticles.
Main Methods:
- Attaching a periodic array of gold nanoparticles (epitopes) to the equator of WGM resonators.
- Utilizing various WGM resonator platforms including silicon/silica rings, disks, and capillaries.
- Designing the optical phase difference between epitopes to be an integer multiple of pi for signal additivity.
Main Results:
- Demonstrated plasmonic enhancement of nanoparticle sensing.
- Achieved self-referencing capabilities for improved signal interpretation.
- Increased analyte capture rate due to antibody-functionalized plasmonic epitopes.
Conclusions:
- The proposed method offers a simple and effective way to enhance WGM biosensor performance.
- This approach is versatile and applicable to various WGM resonator types.
- The design facilitates independent addition of wavelength shifts from individual binding events, simplifying data analysis.
