AI/ML-Driven Surface Plasmon Resonance (SPR) and Spectroscopy: Materials Interfaces and Autonomous Experiments

TL;DR

This review discusses the integration of AI and ML with Surface Plasmon Resonance (SPR) spectroscopy, emphasizing autonomous experiments and self-driving labs for rapid materials discovery and sensor optimization.

Contribution

It introduces the concept of AI-driven autonomous SPR experiments and the development of Self-Driving Labs for advanced materials and sensor research.

Findings

AI enhances data interpretation in SPR sensing

High-throughput experimentation enables quality training data

Autonomous systems accelerate materials discovery

Abstract

This review explores the evolution of Surface Plasmon Resonance (SPR) spectroscopy and sensing, transitioning from fundamental studies of adsorption-desorption kinetics to the sophisticated sensing with Electropolymerized Molecularly Imprinted Polymers (E-MIPs). A significant portion of our previous research focuses on the optical properties, electrochromism of polymer dielectrics, and structure-order correlation in polymer brushes and hierarchical ultrathin films. We then address the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) in data interpretation, culminating in the conceptualization of Self-Driving Labs (SDLs). The importance of generating high-quality training data through high-throughput experimentation (THE) with the SPR is a possibility. These autonomous systems represent the future of materials science, enabling the rapid, closed-loop discovery and optimization of next-generation SPR sensors and analytical methods. This overview highlights the trajectory for integrating conventional experimental design with AI-driven sensing and analytical chemistry across materials and biomedical applications.