Artificial intelligence and machine learning for plasmonic and surface-enhanced sensing

Area of Science:

• Optics and Photonics
• Materials Science
• Analytical Chemistry

Background:

• Plasmonic sensing utilizes surface plasmons in nanomaterials for sensitive detection.
• Techniques include surface-enhanced Raman scattering (SERS), metal-enhanced fluorescence (MEF), and surface plasmon resonance (SPR).
• Applications span biomedical, environmental, and food safety sectors.

Purpose of the Study:

• To review the integration of artificial intelligence (AI) and machine learning (ML) in plasmonic sensing.
• To explore AI/ML's role in advancing sensor design, material characterization, and data analysis.
• To highlight applications benefiting from AI/ML augmentation in plasmonic sensing.

Main Methods:

• Review of existing literature on plasmonic sensing principles.
• Exploration of AI/ML methodologies applicable to sensor development and data interpretation.
• Case studies illustrating AI/ML impact on plasmonic sensing performance.

Main Results:

• AI/ML tools offer significant potential for optimizing plasmonic sensor design and synthesis.
• Machine learning improves signal processing and image analysis for enhanced sensitivity and selectivity.
• AI/ML integration leads to more robust and accurate plasmonic sensing systems.

Conclusions:

• AI/ML integration is a key trend poised to revolutionize plasmonic sensing.
• Future directions involve further synergy between AI/ML and plasmonic nanomaterials for advanced analytical tools.
• This synergy promises enhanced capabilities for diverse real-world applications.