Developments of SERS: From Fundaments to Applications

Abstract

Surface-enhanced Raman scattering (SERS) spectroscopy has evolved into an influential analytical technique within contemporary spectroscopy, primarily due to its exceptional capacity to amplify inherently weak Raman signals by several orders of magnitude. This extraordinary sensitivity allows the detection of molecules at extremely low concentrations, thus positioning SERS as a pivotal technique across diverse fields such as chemistry, biology, medicine, and environmental science. Central to SERS is the use of plasmonic nanostructures, typically constructed from noble metals like gold (Au) or silver (Ag). These structures facilitate localized surface plasmon resonances (LSPRs), generating significantly enhanced electromagnetic fields at their surfaces. Since its inception in the 1970s, SERS has transitioned from being merely a fundamental spectroscopic curiosity to becoming a robust analytical tool with numerous practical applications, notably in biosensing, medical diagnostics, and forensic science. Despite its profound analytical capabilities, the broader adoption of SERS faces several critical challenges. Issues such as substrate reproducibility, long-term stability (particularly concerning silver-based substrates), and complexity in interpreting spectra, especially within biological and environmental matrices, continue to restrict its universal application. Variations in nanostructured substrates often result in inconsistent Raman enhancements, complicating reproducibility and standardization efforts. Additionally, the susceptibility of silver substrates to oxidative degradation poses further limitations on their applicability in long-term or environmental sensing scenarios. Furthermore, spectral interpretation remains a significant hurdle due to overlapping signals, which can obscure analyte-specific Raman bands, particularly within complex biological or environmental media. Nevertheless, recent advances in nanotechnology, artificial intelligence (AI), and materials science have begun to address these persistent challenges, enabling novel applications and greater reliability in analytical outcomes. In this issue section, we critically examine recent advancements in SERS, specifically focusing on innovative substrate engineering, AI-assisted spectral analysis, and emerging applications in biomedical and environmental fields (Scheme 1). Progress in nanofabrication techniques has significantly enhanced the precision and reproducibility of SERS substrates. Recent studies underscore the importance of substrate geometry in dictating Raman enhancement capabilities. For instance, 202400352 revealed that micro-nanotopographical substrates with hexagonal configurations provide a consistent increase in Raman signal enhancement compared to substrates with triangular or rectangular patterns. These precision-engineered substrates, fabricated through advanced lithography techniques, consistently generate uniform plasmonic “hot spots” that markedly improve the reproducibility and sensitivity of SERS measurements. Consequently, such substrates have demonstrated substantial utility in biomedical applications, exemplified by their successful employment in detecting biomarkers for traumatic brain injuries, thus underscoring their potential clinical relevance. The reliance on conventional plasmonic materials, specifically gold and silver, has well-known limitations, prompting interest in alternative substrate materials. Silver, although effective, suffers significantly from oxidative degradation, thereby limiting its long-term applicability. Gold, while stable, presents economic constraints due to its high cost. To address these issues, semiconductor-based substrates have recently garnered considerable attention. For example, titanium dioxide (TiO₂) nanoparticles, when modified with lithium ions (Li⁺), have shown notable Raman enhancement effects through charge-transfer mechanisms (resonance at the surface). As reported by 202300548, these semiconductor substrates utilize electronic structure tuning, analogous to strategies employed in dye-sensitized solar cells, to achieve Raman enhancement factors (EF) up to 10⁴. Such non-plasmonic substrates offer promising pathways toward cost-effective, stable, and reliable enhanced Raman applications applications, especially valuable in environmentally challenging conditions. Interpreting SERS spectra, particularly in complex biological samples, presents a substantial analytical challenge due to overlapping spectral features and noise. Recent advances in machine learning (ML) and deep learning (DL) have considerably improved data analysis capabilities, enabling efficient extraction of meaningful information from complex spectral datasets. A comprehensive review by 202300664 highlights the successful integration of artificial intelligence (AI) with SERS platforms for rapid identification of antimicrobial-resistant (AMR) bacterial strains, providing significant progress toward addressing the global AMR crisis. By utilizing neural networks trained on extensive spectral libraries, researchers have achieved highly accurate pathogen classification, enhancing early diagnosis and enabling targeted therapeutic interventions. Beyond spectral interpretation, AI integration significantly advances the design and operational efficiency of SERS-based biosensors. Machine learning algorithms now facilitate real-time optimization of experimental parameters, substantially increasing detection sensitivity and specificity. Such adaptive biosensors have tremendous potential for point-of-care diagnostics, where rapid, reliable, and precise analytical capabilities are paramount. SERS has also exhibited substantial promise in the realm of early disease detection, significantly enhancing clinical diagnostics. Recent research by 202400299 introduced a dual-mode biosensor employing plasmonic nanoparticles, specifically gold–silver (Au–Ag) nanocapsules, combined with molecularly imprinted polymers (MIPs) to detect the cancer biomarker L1CAM. This innovative system demonstrated superior sensitivity, achieving a tenfold enhancement over traditional electrochemical detection methods, while effectively minimizing interferences from non-target biomolecules. Complementary to this, foundational studies examining the adsorption of oligonucleotides onto gold nanoparticles 202400067 have provided critical insights relevant to designing genetic mutation detectors, thus highlighting the significant potential of SERS for personalized medicine. The capability of SERS extends beyond biomedical applications into environmental monitoring, where it facilitates the sensitive detection of pollutants, pesticides, and food contaminants. Its specificity and high sensitivity make SERS an ideal analytical tool for trace-level detection of hazardous substances, including heavy metals and organic toxins. Recent developments in portable SERS devices, integrated with cloud-based AI analytical platforms, have opened new possibilities for real-time environmental monitoring. This portability combined with rapid analytical capability positions SERS as an instrumental tool for immediate response to environmental contamination incidents, thus significantly enhancing monitoring and public safety measures. In conclusion, SERS is significantly transitioning from an experimental spectroscopic method to a useful analytical technique with wide-ranging implications. Continuous innovations in substrate engineering, combined with advanced AI-driven data analysis approaches and hybrid sensing technologies, are addressing critical limitations, significantly broadening SERS applicability across diverse fields. As interdisciplinary collaboration intensifies, SERS is increasingly poised to become a foundational analytical technology, providing exceptional sensitivity and versatility to address complex real-world analytical challenges in healthcare, environmental monitoring, and industrial applications. The authors declare no conflict of interest. Xing Yi Ling is a Professor of Chemistry from Nanyang Technological University, Singapore. She received her Ph.D. degree in Chemistry from the University of Twente, the Netherlands, and her postdoctoral research at the University of California, Berkeley. Her research focuses on using nanotechnology for fundamental studies and applications in environmental, healthcare, and catalysis fields. In particular, she is interested in self-assembling shape-controlled noble metal nanoparticles and applications in surface-enhanced Raman scattering (SERS). Ramon A. Alvarez-Puebla is an ICREA Professor at the Universitat Rovira i Virgili in Tarragona. He holds a B.Sc. in Chemistry from Universidad de Navarra and a Ph.D. in Surface Science from Universidad Publica de Navarra, in 2003. He was a postdoc at the University of Windsor and General Motors Corporation, worked as a Research Officer at the National Institute for Nanotechnology of the National Research Council of Canada, and as an Associate Professor at the Universidade de Vigo. In particular, he is interested in nanophotonics, SERS, and their applications in nanomedicine, chemical biology, and photocatalysis.

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