Artificial intelligence and machine learning for plasmonic and surface-enhanced sensing
Ailsa Geddis, Hannah Williams, Saba Bashir, Jason Malenfant, Caroline Dubois, Louis Hamlet, Jean‐François Masson
Abstract
Plasmonic sensing is a vibrant field where the optical properties of surface plasmons are exploited to create analytical sensors for biomedical, environmental and food safety applications, among others. Upon irradiation of light on a plasmon-active nanomaterial, the enhancement of the electric field leads to augmented scattering, absorption and luminescence of molecules in, respectively, surface-enhanced Raman scattering (SERS), surface-enhanced infrared absorption (SEIRA) and metal-enhanced fluorescence (MEF) and to highly sensitive refractometric sensors with surface plasmon resonance (SPR) and localised surface plasmon resonance (LSPR). The advent of a new generation of artificial intelligence (AI) and machine learning (ML) tools provides an opportunity to further advance the design, synthesis and characterisation of plasmonic materials, improve signal processing and image analysis in plasmonic sensing experiments and to design sensors with better sensitivity, selectivity and robustness. The review will first build basic knowledge in plasmonic sensing and AI/ML, before discussing opportunities for AI/ML-augmented sensor design and data analysis, and then discuss applications where AI/ML provided added benefits in plasmonic sensing. The review will conclude with a perspective on where the field is trending.