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$Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing$

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On-chip complex refractive index detection at multiple wavelengths for selective sensing.

Raghi S El Shamy¹, Mohamed A Swillam², Xun Li³

¹Faculty of Engineering, Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, L8S 4L8, Canada.

Scientific Reports

|June 6, 2022

Summary

This summary is machine-generated.

This study introduces a novel on-chip optical sensor using a micro-ring resonator (MRR) to detect the complex refractive index of multiple substances. The method enables selective sensing and accurate concentration determination of mixtures.

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Area of Science:

Photonics and Optical Sensing
Nanotechnology
Chemical Sensing

Background:

Selective sensing often relies on complex surface functionalization or limited absorption spectroscopy.
Existing on-chip methods struggle with sensitivity and specificity, particularly for non-biological media.
Detecting the complex refractive index (n and k) offers a unique fingerprint for substance identification.

Purpose of the Study:

To develop an on-chip method for selective detection of complex refractive index at multiple wavelengths.
To overcome limitations of surface functionalization and absorption spectroscopy for on-chip sensing.
To enable accurate identification and quantification of substances in multi-element media.

Main Methods:

Utilized a single silicon-on-insulator (SOI) micro-ring resonator (MRR) for broadband operation (1.46–1.6 µm).
Detected multiple resonances (11) across the operating range to measure refractive index dispersion.
Determined real (n) and imaginary (k) parts of the refractive index from resonance wavelength and absorption coefficient.

Main Results:

Achieved high accuracy in detecting real (n) and imaginary (k) parts of the refractive index (avg. error < 0.0047% and 1.65%, respectively).
Successfully identified four substances: methanol, ethanol, propanol, and water.
Determined concentration composition of multi-element media with 97.4% accuracy using least squares method.

Conclusions:

The proposed MRR-based method provides a selective and sensitive on-chip sensing solution.
This technique overcomes limitations of traditional optical sensing methods for complex media.
The developed sensor is suitable for identifying individual components and quantifying mixtures in real-time.