SERS aptasensor for simultaneous detection of ochratoxin A and zearalenone utilizing a rigid enhanced substrate (ITO/AuNPs/GO) combined with Au@AgNPs.

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.

Examination of primary aromatic amines content in polylactic acid straws and migration into food simulants using SERS with LC-MS.

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.

Label-free detection of kidney stones urine combined with SERS and multivariate statistical algorithm.

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.

In situ surface-enhanced Raman spectroscopy for membrane protein analysis and sensing.

Membrane proteins are involved in a variety of dynamic cellular processes and exploration of the structural basis of membrane proteins is of significance for a better understanding of their functions. In situ analysis of membrane proteins and their dynamics is, however, challenging for conventional techniques. Surface-enhanced Raman spectroscopy (SERS) is powerful in protein structural characterization, allowing for sensitive, in-situ and real-time identification and dynamic monitoring under physiological conditions. In this review, the applications of SERS in probing membrane proteins are outlined, discussed and prospected. It starts with a brief introduction to membrane proteins, SERS theories and SERS-based strategies that commonly-used for membrane proteins. How to assemble phospholipid biolayers on SERS-active materials is highlighted, followed by respectively discussing about direct and indirect strategies for membrane protein sensing. SERS-based monitoring of protein-ligand interactions is finally introduced and its potential in biomedical applications is discussed in detail. The review ends with critical discussion about current challenges and limitations of this research field, and the promising perspectives in both fundamental and applied sciences.

SERS-based microdroplet platform for high-throughput screening of Escherichia coli strains for the efficient biosynthesis of D-phenyllactic acid.

D-Phenyllactic acid (D-PLA) is a potent antimicrobial typically synthesized through chemical methods. However, due to the complexity and large pollution of these reactions, a simpler and more eco-friendly approach was needed. In this study, a strain for D-PLA biosynthesis was constructed, but the efficiency was restricted by the activity of D-lactate dehydrogenase (DLDH). To address this issue, a DLDH mutant library was constructed and the Surface-Enhanced Raman Spectroscopy (SERS) was employed for the precise quantification of D-PLA at the single-cell level. The TB24 mutant exhibited a significant improvement in D-PLA productivity and a 23.03-fold increase in enzymatic activity, which was attributed to the enhanced hydrogen bonding and increased hydrophobicity within the substrate-binding pocket. By implementing multi-level optimization strategies, including the co-expression of glycerol dehydrogenase (GlyDH) with DLDH, chassis cell replacement, and RBS engineering, a significant increase in D-PLA yields was achieved, reaching 128.4 g/L. This study underscores the effectiveness of SERS-based microdroplet high-throughput screening (HTS) in identifying superior mutant enzymes and offers a strategy for large-scale D-PLA biotransformation.

Dual-Mode Sensing of Fe(III) Based on Etching Induced Modulation of Localized Surface Plasmon Resonance and Surface Enhanced Raman Spectroscopy.

Convenient, rapid, highly sensitive and on-site iron determination is important for environmental safety and human health. We developed a sensing system for the detection of Fe(III) in water based on 7-mercapto-4-methylcoumarine (MMC)-stabilized silver-coated gold nanostars (GNS@Ag@MMC), exploiting a redox reaction between the Fe(III) cation and the silver shell of the nanoparticles, which causes a severe transformation of the nanomaterial structure, reverting it to pristine GNSs. This system works by simultaneously monitoring changes in the Localized Surface Plasmon Resonance (LSPR) and Surface-Enhanced Raman Spectroscopy (SERS) spectra as a function of added Fe(III). The proposed sensing system is able to detect the Fe(III) cation in the 1.0 × 10-5-1.5 × 10-4 M range, and its selectivity of the GNS@Ag@MMC sensor toward iron has been verified monitoring the LSPR and the SERS response to other cations with a clear selectivity toward Fe(III).

Contemporary applications of vibrational spectroscopy in plant stresses and phenotyping.

Plant pathogens, including viruses, bacteria, and fungi, cause massive crop losses around the world. Abiotic stresses, such as drought, salinity and nutritional deficiencies are even more detrimental. Timely diagnostics of plant diseases and abiotic stresses can be used to provide site- and doze-specific treatment of plants. In addition to the direct economic impact, this "smart agriculture" can help minimizing the effect of farming on the environment. Mounting evidence demonstrates that vibrational spectroscopy, which includes Raman (RS) and infrared spectroscopies (IR), can be used to detect and identify biotic and abiotic stresses in plants. These findings indicate that RS and IR can be used for in-field surveillance of the plant health. Surface-enhanced RS (SERS) has also been used for direct detection of plant stressors, offering advantages over traditional spectroscopies. Finally, all three of these technologies have applications in phenotyping and studying composition of crops. Such non-invasive, non-destructive, and chemical-free diagnostics is set to revolutionize crop agriculture globally. This review critically discusses the most recent findings of RS-based sensing of biotic and abiotic stresses, as well as the use of RS for nutritional analysis of foods.

SERS analysis of single cells and subcellular components: A review.

SERS is a rapidly advancing and non-destructive technique that has been proven to be more reliable and convenient than other traditional analytical methods. Due to its sensitivity and specificity, this technique is earning its place as a routine and powerful tool in biological and medical studies, especially for the analysis of living cells and subcellular components. This paper reviewed the research progress of single-cell SERS that has been made in the last few years and discussed challenges and future perspectives of this technique. The reviewed SERS platforms have been categorized according to their nature into the following types: (1) colloid-based, substrate-based, or hybrid; (2) ligand-based or ligand-free, and (3) label-based or label-free. The advantages and disadvantages of each type and their potential applications in various fields are thoroughly discussed.

Surface-enhanced Raman spectroscopy for the characterization of xylanases enzyme.

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.

Rapid discrimination between wild and cultivated Ophiocordyceps sinensis through comparative analysis of label-free SERS technique and mass spectrometry.

Ophiocordyceps sinensis is a genus of ascomycete fungi that has been widely used as a valuable tonic or medicine. However, due to over-exploitation and the destruction of natural ecosystems, the shortage of wild O. sinensis resources has led to an increase in artificially cultivated O. sinensis. To rapidly and accurately identify the molecular differences between cultivated and wild O. sinensis, this study employs surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms to distinguish the two O. sinensis categories. Specifically, we collected SERS spectra for wild and cultivated O. sinensis and validated the metabolic profiles of SERS spectra using Ultra-Performance Liquid Chromatography coupled with Orbitrap High-Resolution Mass Spectrometry (UPLC-Orbitrap-HRMS). Subsequently, we constructed machine learning classifiers to mine potential information from the spectral data, and the spectral feature importance map is determined through an optimized algorithm. The results indicate that the representative characteristic peaks in the SERS spectra are consistent with the metabolites identified through metabolomics analysis, confirming the feasibility of the SERS method. The optimized support vector machine (SVM) model achieved the most accurate and efficient capacity in discriminating between wild and cultivated O. sinensis (accuracy = 98.95%, 5-fold cross-validation = 98.38%, time = 0.89s). The spectral feature importance map revealed subtle compositional differences between wild and cultivated O. sinensis. Taken together, these results are expected to enable the application of SERS in the quality control of O. sinensis raw materials, providing a foundation for the efficient and rapid identification of their quality and origin.

Identification of blood at simulated crime scenes using silver nanoparticles with SERS.

The analysis of substances and samples obtained from a crime scene is very important in solving forensic cases. To determine the variables involved in a crime and to expedite the investigation process, the rapid analysis of body fluids in small quantities and within environments containing diverse components is particularly necessary. For this reason, it is of great importance to analyze biological fluids with rapid, noncontaminating, nondestructive, low-cost, and accurate techniques. In recent years, with advancements in laser technology, spectroscopic methods have been introduced as analytical techniques in forensic medicine and chemical studies. This study focuses on surface-enhanced Raman spectroscopy (SERS) to demonstrate the detection of blood samples in simulated crime scenes. To minimize the background signal from fluorescent biomolecules in blood, dilution was performed with two different components and Raman analysis was performed for four different concentrations of blood. In general, a decrease in noise in the spectra was observed as the blood was diluted. Crime scenes consisting of pure blood, blood diluted with ethanol and distilled water (1:2, 1:4, and 1:8), a blood-mineral water mixture, a blood-cherry juice mixture, and silver nanoparticle-added mixtures were simulated, and their spectra were examined. Chemometric analyses of the data were performed. Despite high noise and low peak intensities, blood-identifying signals were detected when examining different blood concentrations. It was observed that silver nanoparticles provided high enhancement of blood peaks thanks to their strong plasmonic properties.

"Partner" cellulose gel with "dialysis" function: Achieve the integration of filtration-enrichment-SERS detection.

Hydrogel and aerogel materials have garnered significant attention in constructing effective surface-enhanced Raman spectroscopy (SERS) substrates due to their excellent adsorption capabilities, high specific surface area, and abundant chemical groups. However, in liquids with complex compositions, non-specific adsorption of macromolecules can lead to surface scaling and pore clogging of the substrate material, limiting the selective enrichment and SERS detection of target molecules. To address this, an innovative aerogel-chimeric hydrogel material (CH@S-CNF/SA/Ag NPs) was developed. The aerogel component, with its high specific surface area and electronegative properties, functions as a SERS "chip" for adsorption and detection of target molecules. Simultaneously, the mesoporous structure of the hydrogel "shell" effectively filters macromolecules from the solution. These CH@S-CNF/SA/Ag NPs were utilized as SERS substrate materials for detecting urine from healthy individuals and patients with chronic kidney disease stage 5 (CKD5). When combined with machine learning algorithms, the detection accuracy reached 99.50%. This work represents a significant advancement in the specific adsorption and SERS detection of small molecules in complex biological samples such as urine and blood.

Ratiometric surface-enhanced Raman spectroscopy detection of 5-hydroxyindole-3-acetic acid based on Au@MIL-125@MIPs substrates.

5-Hydroxyindole-3-acetic acid (5-HIAA) is a molecular marker that can be used in the early diagnosis of carcinoid tumors, and the development of sophisticated 5-HIAA assays is therefore of great importance. Surface-enhanced Raman spectroscopy (SERS) has been widely used for the rapid and sensitive detection of disease biomarkers. Insufficient specificity for tumor markers and poor spectral reproducibility are the bottlenecks in the practical use of SERS technology. In this study, based on MIL-125 surface-loaded gold nanoparticles (Au@MIL-125), a novel strategy was proposed to obtain Au@MIL-125@molecularly imprinted polymers (MIPs) as functional SERS substrates by wrapping a thin MIP shell around the Au@MIL-125 surface for selective separation followed by a 5-HIAA assay. The Raman peak intensity ratio (I865/I1078) was used to quantify 5-HIAA after a SERS spectral calibration with an embedded internal standard (i.e., 4-aminobenzenethiol) to improve the quantitative accuracy. The linear range was from 10-11 to 10-7 M, and the limit of detection (LOD) was 5.45 × 10-13 M. The method of integrating the MIPs with the metal MOF-based nanocomposites was shown to be useful in the analysis of real samples using SERS. The application of SERS for the selective and quantitative detection of analytes in real sample analysis, therefore, has great potential.

Integrating, Validating, and Expanding Information Space in Single-Molecule Surface-Enhanced Raman Spectroscopy for Biomolecules.

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.

Rapid detection of African swine fever virus via SERS probe-modified sandwich hybridization assay.

Rapid and reliable detection method for African swine fever virus (ASFV) is proposed by surface-enhanced Raman spectroscopy (SERS). The ASFV target DNA can be specifically captured by sandwich hybridization between nanomagnetic beads and a SERS probe. Experimental results show that the significant Raman signal of the SERS probe with gold nanoparticles and a molecular reporter DTNB (5,5'-dimercapto-bis (2-nitrobenzoic acid)) can be adopted for detecting the hybridization chain reaction of ASFV DNA. The advantage of the SERS sandwich hybridization assay is the large response range from the single molecule level to 108 copies per mL, which not only can overcome the tedious time required for the amplification reaction but also provides a comparative method to polymerase chain reaction. Furthermore, real samples of African swine fever virus were detected from different subjects of swine fever virus including porcine reproductive respiratory syndrome virus and Japanese encephalitis virus. The proposed biosensor method can rapidly detect ASFV correctly within 15 min as a simple, convenient, low-cost detection approach. The biosensor can be used as a platform for the determination in biological, food, and environmental analytical fields.

Facile coupling of plasmonic Au-NPs on ZnS NFs as a robust SERS substrate for toluidine blue detection and degradation.

BACKGROUND: The robustness and sensitivity of the surface-enhanced Raman spectroscopy (SERS) technique heavily relies on the development of SERS active materials. A hybrid of semiconductor and plasmonic metals is highly effective as a SERS substrate, which enables the trace level detection of various organic pollutants. RESULTS: This approach demonstrates the photodeposition of plasmonic gold nanoparticles (Au-NPs) on the surface of semiconductor-zinc sulfide nanoflowers (ZnS NFs), grown via the hydrothermal route. The synergistic contribution of the charge-transfer phenomenon and localized surface plasmon resonance of the Au-NPs/ZnS NFs makes it an ideal SERS substrate for the detection of organic pollutants, toluidine blue (TB). The proposed material has a high SERS enhancement factor (109), low limit of detection (10-11 M), good reproducibility, selectivity and strong anti-interference ability. Furthermore, the practicability of the Au-NPs/ZnS NFs is explored in real-time water samples, which are obtained with the satisfactory recovery rates. Additionally, the UVC light illumination on the Au-NPs/ZnS NFs has efficiently degraded TB within a time period of 150 min. SIGNIFICANCE AND NOVELTY: These finding demonstrate the significance of the proposed Au-NPs/ZnS NFs for SERS based detection and degradation of organic pollutants in real-time samples, highlighting their potential in monitoring and treating water pollutants in wastewater.

Innovative and versatile surface-enhanced Raman spectroscopy-inspired approaches for viral detection leading to clinical applications: A review.

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.

Establishment of a Raman nanosphere based immunochromatographic system for the combined detection of influenza A and B viruses' antigens on a single T-line.

A simple and rapid system based on Raman nanosphere (R-Sphere) immunochromatography was developed in this study for the simultaneous detection of Influenza A, B virus antigens on a single test line (T-line). Two types of R-Sphere with different characteristic Raman spectrum were used as the signal source, which were labeled with monoclonal antibodies against FluA, FluB (tracer antibodies), respectively. A mixture of antibodies containing anti-FluA monoclonal antibody and anti-FluB monoclonal antibody (capture antibody) was sprayed on a single T-line and goat anti-chicken IgY antibody was coated as a C-line, and the antigen solution with known concentration was detected by the strip of lateral flow immunochromatography based on surface-enhanced Raman spectroscopy (SERS). The T-line was scanned with a Raman spectrometer and SERS signals were collected. Simultaneous specific recognition and detection of FluA and FluB were achieved on a single T-line by analyzing the SERS signals. The findings indicated that the test system could identify FluA and FluB in a qualitative manner in just 15 minutes, with a minimum detection threshold of 0.25 ng ml-1, excellent consistency, and specificity. There was no interference with the other four respiratory pathogens, and it exhibited 8 times greater sensitivity compared to the colloidal gold test strip method. The assay system is rapid, sensitive, and does not require repetitive sample pretreatment steps and two viruses can be detected simultaneously on a single T-line by titrating one sample, which improves detection efficiency, and provide a reference for developing multiplexed detection techniques for other respiratory viruses.

SERS-Based Droplet Microfluidic Platform for Sensitive and High-Throughput Detection of Cancer Exosomes.

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.

Plasmonic Nanotrenches with 1 nm Nanogaps for Surface-Enhanced Raman Scattering-Based Screening of His-Tagged Proteins.

This study represents a highly sensitive and selective approach to protein screening using surface-enhanced Raman scattering (SERS) facilitated by octahedral Au nanotrenches (OANTs). OANTs are a novel class of nanoparticles characterized by narrow, trench-like excavations indented into the eight facets of a Au octahedron. This unique configuration maximizes electromagnetic near-field focusing as the gap distance decreases to ∼1 nm. Owing to geometrical characteristics of the OANTs, near-field focusing can be maximized through the confinement and reflectance of light trapped within the trenches. We used Ni ions and molecular linkers to confer selective binding affinity for His-tagged proteins on the surfaces of the OANTs for SERS-based protein screening. Remarkably, SERS-based protein screening with the surface-modified OANTs yielded outstanding screening capabilities: 100% sensitivity and 100% selectivity in distinguishing His-tagged human serum albumin (HSA) from native HSA. This highlights the significantly enhanced protein screening capabilities achieved through the synergistic combination of SERS and the OANTs.