Numerical method for high accuracy index of refraction estimation for spectro-angular surface plasmon resonance...

Area of Science:

• Optics and Photonics
• Materials Science
• Nanotechnology

Background:

• Surface Plasmon Resonance (SPR) is a label-free technique for detecting refractive index changes.
• Traditional SPR methods often struggle with simultaneous high resolution and large dynamic range.
• Existing algorithms may have limitations in signal-to-noise ratio (SNR) and robustness to data noise.

Purpose of the Study:

• To develop a novel algorithm for accurate refractive index change estimation.
• To improve resolution and dynamic range in SPR measurements.
• To enhance the signal-to-noise ratio compared to conventional methods.

Main Methods:

• An eigenvector analysis based algorithm was developed.
• The algorithm processes 2-D reflectance/dispersion images from spectro-angular SPR systems.
• Simulations were performed with varying levels of noise (up to six bits on 16-bit data).

Main Results:

• The algorithm accurately estimates refractive index changes.
• High resolution and a large dynamic range are achieved simultaneously.
• Simulated noisy data yielded an error of less than 10^-8 refractive index units.
• Experimental measurements demonstrated a significantly higher SNR than the standard 1-D weighted centroid dip finding algorithm.

Conclusions:

• The eigenvector analysis algorithm offers superior performance for SPR-based refractive index sensing.
• This method provides a robust and high-fidelity approach for analyzing SPR data.
• The technique is suitable for applications requiring precise and sensitive refractive index measurements.