The evolution of analytical techniques has opened the possibilities of accurate analyte detection through a straightforward method and short acquisition time, leading towards their applicability to identify medical conditions. Surface-enhanced Raman spectroscopy (SERS) has long been proven effective for rapid detection and relies on SERS spectra that are unique to each specific analyte. However, the complexity of viruses poses challenges to SERS and hinders further progress in its practical applications. The principle of SERS revolves around the interaction among substrate, analyte, and Raman laser, but most studies only emphasize the substrate, especially label-free methods, and the synergy among these factors is often ignored. Therefore, issues related to reproducibility and consistency of results, which are crucial for medical diagnosis and are the main highlights of this review, can be understood and largely addressed when considering these interactions. Viruses are composed of multiple surface components and can be detected by label-free SERS, but the presence of non-target molecules in clinical samples interferes with the detection process. Appropriate spectral data processing workflow also plays an important role in the interpretation of results. Furthermore, integrating machine learning into data processing can account for changes brought about by the presence of non-target molecules when analyzing spectral features to accurately group the data, for example, whether the sample corresponds to a positive or negative patient, and whether a virus variant or multiple viruses are present in the sample. Subsequently, advances in interdisciplinary fields can bring SERS closer to practical applications.

Exosomes, nanosized extracellular vesicles containing biomolecular cargo, are increasingly recognized as promising noninvasive biomarkers for cancer diagnosis, particularly for their role in carrying tumor-specific molecular information. Traditional methods for exosome detection face challenges such as complexity, time consumption, and the need for sophisticated equipment. This study addresses these challenges by introducing a novel droplet microfluidic platform integrated with a surface-enhanced Raman spectroscopy (SERS)-based aptasensor for the rapid and sensitive detection of HER2-positive exosomes from breast cancer cells. Our approach utilized an on-chip salt-induced gold nanoparticles (GNPs) aggregation process in the presence of HER2 aptamers and HER2-positive exosomes, enhancing the hot spot-based SERS signal amplification. This platform achieved a limit of detection of 4.5 log10 particles/mL with a sample-to-result time of 5 min per sample. Moreover, this platform has been successfully applied for HER2 status testing in clinical samples to distinguish HER2-positive breast cancer patients from HER2-negative breast cancer patients. High sensitivity, specificity, and the potential for high-throughput screening of specific tumor exosomes make this SERS-based droplet system a potential liquid biopsy technology for early cancer diagnosis.

Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) is an ultrahigh-resolution spectroscopic method for directly obtaining the complex vibrational mode information on individual molecules. SM-SERS offers a wide range of submolecular information on the hidden heterogeneity in its functional groups and varying structures, dynamics of conformational changes, binding and reaction kinetics, and interactions with the neighboring molecule and environment. Despite the richness in information on individual molecules and potential of SM-SERS in various detection targets, including large and complex biomolecules, several issues and practical considerations remain to be addressed, such as the requirement of long integration time, challenges in forming reliable and controllable interfaces between nanostructures and biomolecules, difficulty in determining hotspot size and shape, and most importantly, insufficient signal reproducibility and stability. Moreover, utilizing and interpreting SERS spectra is challenging, mainly because of the complexity and dynamic nature of molecular fingerprint Raman spectra, and this leads to fragmentary analysis and incomplete understanding of the spectra. In this Perspective, we discuss the current challenges and future opportunities of SM-SERS in views of system approaches by integrating molecules of interest, Raman dyes, plasmonic nanostructures, and artificial intelligence, particularly for detecting and analyzing biomolecules to realize the validation and expansion of information space in SM-SERS.

Xylanases are essential hydrolytic enzymes which break down the plant cell wall polysaccharide, xylan composed of D-xylose monomers. Surface-enhanced Raman Spectroscopy (SERS) was utilized for the characterization of interaction of xylanases with xylan at varying concentrations. The study focuses on the application of SERS for the characterization of enzymatic activity of xylanases causing hydrolysis of Xylan substrate with increase in its concentration which is substrate for this enzyme in the range of 0.2% to 1.0%. SERS differentiating features are identified which can be associated with xylanases treated with different concentrations of xylan. SERS measurements were performed using silver nanoparticles as SERS substrate to amplify Raman signal intensity for the characterization of xylan treated with xylanases. Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) were applied to analyze the spectral data to analyze differentiation between the SERS spectra of different samples. Mean SERS spectra revealed significant differences in spectral features particularly related to carbohydrate skeletal mode and O-C-O and C-C-C ring deformations. PCA scatter plot effectively differentiates data sets, demonstrating SERS ability to distinguish treated xylanases samples and the PC-loadings plot highlights the variables responsible for differentiation. PLS-DA was employed as a quantitative classification model for treated xylanase enzymes with increasing concentrations of xylan. The values of sensitivity, specificity, and accuracy were found to be 0.98%, 0.99%, and 100% respectively. Moreover, the AUC value was found to be 0.9947 which signifies the excellent performance of PLS-DA model. SERS combined with multivariate techniques, effectively characterized and differentiated xylanase samples as a result of interaction with different concentrations of the Xylan substrate. The identified SERS features can help to characterize xylanases treated with various concentrations of xylan with promising applications in the bio-processing and biotechnology industries.

Kidney stones are a common urological disease with an increasing incidence worldwide. Traditional diagnostic methods for kidney stones are relatively complex and time-consuming, thus necessitating the development of a quicker and simpler diagnostic approach. This study investigates the clinical screening of kidney stones using Surface-Enhanced Raman Scattering (SERS) technology combined with multivariate statistical algorithms, comparing the classification performance of three algorithms (PCA-LDA, PCA-LR, PCA-SVM). Urine samples from 32 kidney stone patients, 30 patients with other urinary stones, and 36 healthy individuals were analyzed. SERS spectra data were collected in the range of 450-1800 cm-1 and analyzed. The results showed that the PCA-SVM algorithm had the highest classification accuracy, with 92.9 % for distinguishing kidney stone patients from healthy individuals and 92 % for distinguishing kidney stone patients from those with other urinary stones. In comparison, the classification accuracy of PCA-LR and PCA-LDA was slightly lower. The findings indicate that SERS combined with PCA-SVM demonstrates excellent performance in the clinical screening of kidney stones and has potential for practical clinical application. Future research can further optimize SERS technology and algorithms to enhance their stability and accuracy, and expand the sample size to verify their applicability across different populations. Overall, this study provides a new method for the rapid diagnosis of kidney stones, which is expected to play an important role in clinical diagnostics.

Pyocyanin is considered a maker of Pseudomonas aeruginosa (P. aeruginosa) infection. Pyocyanin is among the toxins released by the P. aeruginosa bacteria. Therefore, the development of a direct detection of PYO is crucial due to its importance. Among the different optical techniques, the Raman technique showed unique advantages because of its fingerprint data, no sample preparation, and high sensitivity besides its ease of use. Noble metal nanostructures were used to improve the Raman response based on the surface-enhanced Raman scattering (SERS) technique. Anodic metal oxide attracts much interest due to its unique morphology and applications. The porous metal structure provides a large surface area that could be used as a hard template for periodic nanostructure array fabrication. Porous shapes and sizes could be controlled by controlling the anodization parameters, including the anodization voltage, current, temperature, and time, besides the metal purity and the electrolyte type/concentration. The anodization of aluminum foil results in anodic aluminum oxide (AAO) formation with different roughness. Here, we will use the roughness as hotspot centers to enhance the Raman signals. Firstly, a thin film of gold was deposited to develop gold/alumina (Au/AAO) platforms and then applied as SERS-active surfaces. The morphology and roughness of the developed substrates were investigated using scanning electron microscopy (SEM) and atomic force microscopy (AFM) techniques. The Au/AAO substrates were used for monitoring pyocyanin secreted from Pseudomonas aeruginosa microorganisms based on the SERS technique. The results showed that the roughness degree affects the enhancement efficiency of this sensor. The high enhancement was obtained in the case of depositing a 30 nm layer of gold onto the second anodized substrates. The developed sensor showed high sensitivity toward pyocyanin with a limit of detection of 96 nM with a linear response over a dynamic range from 1 µM to 9 µM.

The electrochemical nitric oxide reduction reaction (NORR), which utilizes water as the sole hydrogen source, has the potential to facilitate ammonia production while concurrently mitigating pollutants. However, limited research has been dedicated to characterizing the structure of interfacial water due to the challenges associated with probing this intricate system, impeding the development of more efficient catalysts for the NORR process. Herein, the Cu2O microcrystals with distinct exposed facets, including {100}, {110}, and {111}, are employed for the model catalysts to investigate interfacial water structure and intermediate species in the NORR process. The results from shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) indicated that the NORR performance in 0.1 M Na2SO4 (with heavy water as the solvent) was positively correlated to the proportion of hydrated Na+ ion water. In addition, a sequence of intermediates from the NORR, including *NOH, *NH, *NH2, and *NH3, was detected by employing a combination of multiple in situ characterization methods. Furthermore, in conjunction with experimental results and theoretical calculations, we revealed the potential reaction pathway of NORR. This study offers novel insights into the NORR mechanism and valuable guidance for the design of high-performance catalysts for ammonia production.

Polylactic acid (PLA) straws hold eco-friendly potential; however, residual diisocyanates used to enhance the mechanical strength can generate carcinogenic primary aromatic amines (PAAs), posing health risks. Herein, we present a rapid, comprehensive strategy to detecting PAAs in 18 brands of food-grade PLA straws and assessing their migration into diverse food simulants. Surface-enhanced Raman spectroscopy was conducted to rapidly screen straws for PAAs. Subsequently, qualitative determination of migrating PAAs into various food simulants (4 % acetic acid, 10 % ethanol, 50 % ethanol) occurred at 70 °C for 2 h using liquid chromatography-mass spectrometry. Three PAAs including 4,4\'-methylenedianiline, 2,4\'-methylenedianiline, and 2,4-diaminotoluene were detected in all straws. Specifically, 2,4-diaminotoluene in 50 % ethanol exceeded specific migration limit of 2 μg/kg, raising safety concerns. Notably, PAAs migration to 10 % and 50 % ethanol surpassed that to 4 % acetic acid within a short 2-hour period. Moreover, PLA straws underwent varying degrees of shape changes before and after migration. Straws with poly(butylene succinate) resisted deformation compared to those without, indicating enhanced heat resistance, while poly(butyleneadipate-co-terephthalate) improved hydrolysis resistance. Importantly, swelling study unveiled swelling effect wasn\'t the primary factor contributing to the increased PAAs migration in ethanol food simulant, as there was no significant disparity in swelling degrees across different food simulants. FT-IR and DSC analysis revealed higher PAAs content in 50 % ethanol were due to highly concentrated polar ethanol disrupting hydrogen bonds and van der Waal forces holding PLA molecules together. Overall, minimizing contact between PLA straws and alcoholic foods is crucial to avoid potential safety risks posed by PAAs.

Surface-enhanced Raman scattering (SERS) has been extensively applied to detect complex analytes due to its ability to enhance the fingerprint signals of molecules around nanostructured metallic surfaces. Thus, it is essential to design SERS-active nanostructures with abundant electromagnetic hotspots in a probed volume according to the dimensions of the analytes, as the analytes must be located in their hotspots for maximum signal enhancement. Herein, we demonstrate a simple method for detecting robust SERS signals from multi-scaled bioanalytes, regardless of their dimensions in the liquid state, through a photothermally driven co-assembly with colloidal plasmonic nanoparticles as signal enhancers. Under resonant light illumination, plasmonic nanoparticles and analytes in the solution quickly assemble at the focused surface area by convective movements induced by the photothermal heating of the plasmonic nanoparticles without any surface modification. Such collective assemblies of plasmonic nanoparticles and analytes were optimized by varying the optical density and surface charge of the nanoparticles, the viscosity of the solvent, and the light illumination time to maximize the SERS signals. Using these light-induced co-assemblies, the intrinsic SERS signals of small biomolecules can be detected down to nanomolar concentrations based on their fingerprint spectra. Furthermore, large-sized biomarkers, such as viruses and exosomes, were successfully detected without labels, and the complexity of the collected spectra was statistically analyzed using t-distributed stochastic neighbor embedding combined with support vector machine (t-SNE + SVM). The proposed method is expected to provide a robust and convenient method to sensitively detect biologically and environmentally relevant analytes at multiple scales in liquid samples.

The contamination of mycotoxins poses a serious threat to global food security, hence the urgent need for simultaneous detection of multiple mycotoxins. Herein, two SERS nanoprobes were synthesized by embedded SERS tags (4-mercaptopyridine, 4MPy; 4-mercaptobenzonitrile, TBN) into the Au and Ag core-shell structure, and each was coupled with the aptamers specific to ochratoxin A (OTA) and zearalenone (ZEN). Meanwhile, a rigid enhanced substrate Indium tin oxide glass/AuNPs/Graphene oxide (ITO/AuNPs/GO) was combined with aptamer functionalized Au@AgNPs via π-π stacking interactions between the aptamer and GO to construct a surface-enhanced Raman spectroscopy (SERS) aptasensor, thereby inducing a SERS enhancement effect for the effective and swift simultaneous detection of both OTA and ZEN. The presence of OTA and ZEN caused signal probes dissociation, resulting in an inverse correlation between Raman signal intensity (1005 cm-1 and 2227 cm-1) and the concentrations of OTA and ZEN, respectively. The SERS aptasensor exhibited wide linear detection ranges of 0.001-20 ng/mL for OTA and 0.1-100 ng/mL for ZEN, with low detection limits (LOD) of 0.94 pg/mL for OTA and 59 pg/mL for ZEN. Furthermore, the developed SERS aptasensor demonstrated feasible applicability in the detection of OTA and ZEN in maize, showcasing its substantial potential for practical implementation.