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  1. Home
  3. Artificial Intelligence And Machine Learning For Plasmonic And Surface-enhanced Sensing.
  1. Home
  3. Artificial Intelligence And Machine Learning For Plasmonic And Surface-enhanced Sensing.

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

Ailsa Geddis1, Hannah Williams1, Saba Bashir1

  • 1Département de Chimie, Institut Courtois, Centre Interdisciplinaire de Recherche sur le Cerveau et L'apprentissage, Quebec Center for Advanced Materials, Regroupement Québécois sur les Matériaux de Pointe, Université de Montréal, C.P. 6128 Succ. Centre-ville, Montréal, Québec, H3C 3J7, Canada. jf.masson@umontreal.ca.

Chemical Society Reviews
|February 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence and machine learning enhance plasmonic sensing. These tools improve sensor design, data analysis, and applications in biomedical and environmental fields.

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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.