Optimization of Magnetoplasmonic ε-Near-Zero Nanostructures Using a Genetic Algorithm.

Area of Science:

• Nanophotonics and Plasmonics
• Biomedical Engineering
• Materials Science

Background:

• Magnetoplasmonic nanostructures offer high-resolution sensing and miniaturization potential for biosensors.
• Perimittivity-near-zero (ε-near-zero) effects enable efficient light-to-plasmon coupling, simplifying device integration.
• Current limitations include time-consuming numerical simulations due to the lack of analytical phase-matching conditions.

Purpose of the Study:

• To develop a rapid design mechanism for magnetoplasmonic ε-near-zero nanostructures.
• To optimize transverse magneto-optical Kerr effect (TMOKE) signals and magnetoplasmonic sensing performance.
• To overcome the computational bottleneck in designing these advanced sensing platforms.

Main Methods:

• Implementation of a genetic algorithm (GA) for automated nanostructure design.
• Optimization of magnetoplasmonic nanostructures for enhanced TMOKE and sensing capabilities.
• Utilizing a standard dual-core CPU for rapid computational analysis.

Main Results:

• The GA successfully designed magnetoplasmonic ε-near-zero sensing platforms in minutes.
• Achieved a sensitivity exceeding 56°/RIU and a figure of merit around 10², surpassing previous methods.
• Demonstrated the GA's efficiency, completing designs in 2-5 minutes.

Conclusions:

• The developed GA provides a fast and efficient method for designing high-performance magnetoplasmonic ε-near-zero sensors.
• This approach significantly accelerates the development cycle for integrated (bio)sensing devices.
• The optimized platforms show promise for advanced, miniaturized biosensing applications.