Advancing SERS Applications of 2D Materials through the Interplay of Rational Design and Structure-Property Relationships

Abstract

Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive analytical tool for molecular investigations, particularly in biological systems. While metal nanoparticles (NPs) have been widely explored for SERS, their performance depends on size, shape, and crystal structure. However, their Raman scattering efficiency is low, limiting applications. To overcome these challenges, 2D materials have emerged as promising SERS substrates due to their high surface area, charge transfer capabilities, stability, and tunable optical properties. Their biocompatibility makes them ideal for chemical and biomedical applications, including microfluidic systems, drug delivery, and in vivo diagnostics. This review comprehensively examines the development, structural characteristics, and plasmonic integration of 2D materials in SERS. It highlights design considerations, structural optimization using machine learning (ML), and material performance. ML-driven approaches enable precise tuning of 2D materials' optical, electrical, and chemical properties, enhancing biosensing capabilities. Computational algorithms facilitate the detection of ultra-low concentrations of biomolecules such as deoxyribonucleic acid (DNA), proteins, and metabolites. ML also offers powerful tools for data analysis, material optimization, and automated sensing, significantly advancing SERS applications. The synergy between ML and 2D materials opens new avenues for high-performance biosensing and analytical technologies.