Surface-enhanced Raman spectroscopy: benefits, trade-offs and future developments

Abstract

Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopy technique with sensitivity down to the single molecule level that provides fine molecular fingerprints, allowing for direct identification of target analytes. Extensive theoretical and experimental research, together with continuous development of nanotechnology, has significantly broadened the scope of SERS and made it a hot research field in chemistry, physics, materials, biomedicine, and so on. However, SERS has not been developed into a routine analytical technique, and continuous efforts have been made to address the problems preventing its real-world application. The present minireview focuses on analyzing current and potential strategies to tackle problems and realize the SERS performance necessary for translation to practical applications.

Fig. 1. Schematic diagrams of (a) LSPR effect: metal conductive electrons are excited into collective oscillations generating an electromagnetic field highly localized in the metal–dielectric interface when irradiated with light; (b) nanoparticle–molecule interaction: leading to the mutual excitation of the Raman polarizability (red thin arrow) from the local EM field (green arrow) and generating the enhanced Raman signal of molecule (thick red arrow). Reproduced with permission from ref. 14 Copyright 2016 Nature Publishing Group.

Fig. 2. Schematic diagrams of (a) the limited penetration depth of a laser beam through a dried sample (especially to one with a high concentration of fluorescent dyes), due to the strong absorption at the laser wavelength; (b) the waist profile of the laser beam in solution, the corresponding confocal depth (z) and the Raman intensity–depth profile calculated from the integrated intensity of the 520.7 cm⁻¹ for a single crystal Si wafer; reprinted with permission from ref. 7. Copyright 2009 Springer; (c) the excitation configuration during SERS measurement, where the number of target analytes contributing to SERS is determined by the surface coverage of adsorbed targets rather than the concentration in solution, and the average SERS signal from multiple spots may report a more consistent EF value due to the non-uniform distribution of molecules over the SERS substrate.

Fig. 3. Scheme of diverse surface-affinity ligands, which immobilize analytes via either physisorption or chemisorption (covalent bond) interactions: electrostatic modification drives the adsorption of analytes on the surface of nanostructures by electrostatic force; molecular steric effects by porous materials like MOFs allow the selective permeation of small molecules via the molecular sieving effect; host–guest recognition by macrocycles or MIP captures a wide variety of guest molecules with a high affinity; biological recognition with biological polymers like DNA or peptides has a high affinity to diverse analytes with sizes ranging from small molecules to macromolecules or even cells; and chemical derivatization by a small bridge molecule on the surface with the functional group reacts with the analyte.

Fig. 4. Scheme of three strategies for pushing the limit of quantitative detection. (a) Direct extraction of the semi-quantification linear range (red line) from the plot of SERS intensity versus analyte concentration (black curve). (b) Improved quantification by introducing an internal standard with the simultaneous adsorption mode. (c) Core-molecule-shell mode with the internal standard embedded between the core and shell of nanoparticle to avoid the potential competitive adsorption between the internal standard and analyte.

Fig. 5. The three-step guideline towards the quantitative SERS analysis of real samples showing the purpose, strategy, and key points. The first step is to realize reliable qualitative analysis and optimize the detection sensitivity. The second step involves the creation of a working curve with the desired accuracy and the utilization of internal standards to further improve the accuracy. The third step is to deal with real samples enabled by sample pretreatments and data analyses.