Surface-enhanced Raman Scattering Spectra Research Articles

Automated identification of pesticide mixtures via machine learning analysis of TLC-SERS spectra

Identification of components in pesticide mixtures has been a major challenge in spectral analysis. In this paper, we assembled monolayer Ag nanoparticles on Thin-layer chromatography (TLC) plates to prepare TLC-Ag substrates with mixture separation and surface-enhanced Raman scattering (SERS) detection. Spectral scans were performed along the longitudinal direction of the TLC-Ag substrate to generate SERS spectra of all target analytes on the TLC plate. Convolutional neural network classification and spectral angle similarity machine learning algorithms were used to identify pesticide information from the TLC-SERS spectra. It was shown that the proposed automated spectral analysis method successfully classified five categories, including four pesticides (thiram, triadimefon, benzimidazole, thiamethoxam) as well as a blank TLC-Ag data control. The location of each pesticide on the TLC plate was determined by the intersection of the information curves of the two algorithms with 100 % accuracy. Therefore, this method is expected to help regulators understand the residues of mixed pesticides in agricultural products and reduce the potential risk of agricultural products to human health and the environment.

Detection of surface enhanced Raman scattering active hotspot using near field scanning optical microscopy

Hotspots are high-intensity electromagnetic zones that form, for example, at the interstitials of plasmonic nanoaggregates, resulting in a considerable rise in the enhancement factor. However, it is inevitable to achieve specific nanometric geometry as well as a suitable technique to capture the details of hotspots. We report near-field surface-enhanced Raman scattering (SERS) spectroscopy of a well-defined gold nanoaggregate of a few nanoparticles adsorbed with a small number of target analytes. A spectrally and spatially resolved SERS measurement setup using an aperture near-field scanning optical microscope (a-NSOM) facilitated the direct observation of localized electromagnetic (EM) fields at the interstitials through SERS. Correlated optical image and corresponding nanometric geometry were captured through the home-built a-NSOM setup. Near-field SERS spectra were recorded at different sites of interest. It was evident that the interstitial positioned at the center of the tetramer provided the most intense Raman scattering, implying the possibility of a SERS-active hotspot therein. SERS bands of the spectrum of the Raman-active dye Rhodamine 6G recorded at the same hotspot coincided well with those reported so far. It was noteworthy that most of the SERS bands in such scenery got enhanced. Such direct observation with high spatial resolution is indispensable to understanding the origin of localized EM fields at “hotspots” and the EM enhancement factor in the SERS process. A finite-difference time-domain (FDTD) analysis was carried out to validate the results.

A sandwich-structured multifunctional platform based on self-assembled Ti3C2Tx@Au NPs films, antibiotics, and silent region SERS probe for the capture, determination, and drug resistance analysis of Gram-positive bacteria.

A multifunctional surface-enhanced Raman scattering (SERS) platform integrating sensitive detection and drug resistance analysis was developed for Gram-positive bacteria. The substrate was based on self-assembled Ti3C2Tx@Au NPs films and capture molecule phytic acid (IP6) to achieve specific capture of Gram-positive bacteria and different bacteria were analyzed by fingerprint signal. It had advantages of good stability and homogeneity (RSD = 8.88%). The detection limit (LOD) was 102CFU/mL for Staphylococcus aureus and 103CFU/mL for MRSA, respectively. A sandwich structure was formed on the capture substrate by signal labels prepared by antibiotics (penicillin G and vancomycin) and non-interference SERS probe molecules (4-mercaptobenzonitrile (2223cm-1) and 2-amino-4-cyanopyridine (2240cm-1)) to improve sensitivity. The LOD of Au NPs@4-MBN@PG to S. aureus and Au NPs@AMCP@Van to MRSA and S. aureus were all improved to 10 CFU/mL, with a wide dynamic linear range from 108 to 10CFU/mL (R2 ≥ 0.992). The SERS platform can analyze the drug resistance of drug-resistant bacteria. Au NPs@4-MBN@PG was added to the substrate and captured MRSA to compare the SERS spectra of 4-MBN. The intensity inhomogeneity of 4-MBN at the same concentrations of MRSA and the nonlinearity at the different concentrations of MRSA revealed that MRSA was resistant to PG. Finally, the SERS platform achieved the determination of MRSA in blood. Therefore, this SERS platform has great significance for the determination and analysis of Gram-positive bacteria.

Source Tracing of Kidney Injury via the Multispectral Fingerprint Identified by Machine Learning-Driven Surface-Enhanced Raman Spectroscopic Analysis.

Early diagnosis of drug-induced kidney injury (DIKI) is essential for clinical treatment and intervention. However, developing a reliable method to trace kidney injury origins through retrospective studies remains a challenge. In this study, we designed ordered fried-bun-shaped Au nanocone arrays (FBS NCAs) to create microarray chips as a surface-enhanced Raman scattering (SERS) analysis platform. Subsequently, the principal component analysis (PCA)-two-layer nearest neighbor (TLNN) model was constructed to identify and analyze the SERS spectra of exosomes from renal injury induced by cisplatin and gentamycin. The established PCA-TLNN model successfully differentiated the SERS spectra of exosomes from renal injury at different stages and causes, capturing the most significant spectral features for distinguishing these variations. For the SERS spectra of exosomes from renal injury at different induction times, the accuracy of PCA-TLNN reached 97.8% (cisplatin) and 93.3% (gentamicin). For the SERS spectra of exosomes from renal injury caused by different agents, the accuracy of PCA-TLNN reached 100% (7 days) and 96.7% (14 days). This study demonstrates that the combination of label-free exosome SERS and machine learning could serve as an innovative strategy for medical diagnosis and therapeutic intervention.

Nano-architected titania/gold@GO nanosheets for sonocatalytic degradation of azo dyes in textile wastewater

Environmental pollution is creating serious risks to both human health and the atmosphere which has been a major threat on a global scale. Therefore, it is necessary to focus on the evolution of the environment and trace contaminants for effective and reliable detection and degradation methods. Photochemical methods such as Surface-Enhanced Raman Scattering (SERS) and sono-catalytic degradation can be used to accelerate the detection and degradation of toxic dyes. In the present work, a Gold/Titania/Graphene oxide nanocomposite (ATG) was investigated as a capable dual substrate for SERS sensing and sono-catalytic degradation of pollutants. The nanosize distribution (6–12 nm) of ATG was synthesized by a solvothermal route. X-ray diffraction pattern illustrates the anatase phase of the titanium dioxide. Sonocatalytic degradation was aided using Raman spectroscopy. The effectiveness of ATG as a sono-catalytic substrate and SERS sensor was first evaluated using commercial dyes, such as malachite green and Bismarck brown. Furthermore, research has been conducted on the harmful elements in textile effluents (TE). The NN and C-N bands found in the SERS spectra of TE indicates the presence of carcinogenic azo-dye impurities. The enhancement factor was observed to be 5.77×1012 with the limit of detection (LOD) as 6.0×10−12M. Additionally, the sono-catalysis of ATG was evaluated for the elimination of these dye pollutants in TE. Following sono-catalysis by ATG, a high degradation rate constant of approximately 8.44 ×10−3/min was noted in TE with 89 % of degradation efficiency. The enhanced SERS detection, sono-catalytic degradation of synthetic dyes and effluent stem from the combined action of’ plasmonic characteristics of Au NPs, anatase TiO2, and the π-π stacking characteristic of GO.

Linear Vibronic Coupling Approach for Surface-Enhanced Raman Scattering: Quantifying the Charge-Transfer Enhancement Mechanism.

The outstanding amplification observed in surface-enhanced Raman scattering (SERS) is due to several enhancement mechanisms, and standing out among them are the plasmonic (PL) and charge-transfer (CT) mechanisms. The theoretical estimation of the enhancement factors of the CT mechanism is challenging because the excited-state coupling between bright plasmons and dark CT states must be properly introduced into the model to obtain reliable intensities. In this work, we aim at simulating electrochemical SERS spectra, considering models of pyridine on silver clusters subjected to an external electric field E⃗ that represents the effect of an electrode potential Vel. The method adopts quantum dynamical propagations of nuclear wavepackets on the coupled PL and CT states described with linear vibronic coupling models parametrized for each E⃗ through a fragment-based maximum-overlap diabatization. By presenting results at different values of E⃗, we show that indeed there is a relation between the population transferred to the CT states and the total scattered intensity. The tuning and detuning processes of the CT states with the bright PLs as a function of the electric field are in good agreement with those observed in experiments. Finally, our estimations for the CT enhancement factors predict values in the order of 105 to 106, meaning that when the CT and PL states are both in resonance with the excitation wavelength, the CT and PL enhancements are comparable, and vibrational bands whose intensity is amplified by different mechanisms can be observed together, in agreement with what was measured by typical experiments on silver electrodes.

Cation Chain Length of Nonhalogenated Ionic Liquids Matters in Enhancing SERS of Cytochrome c on Zr-Al-Co-O Nanotube Arrays.

Surface-enhanced Raman scattering (SERS) is a remarkably powerful analytical technique enabling trace-level detection of biological molecules. The interaction of a probe molecule with the SERS substrate shows important distinctions in the SERS spectra, providing inherent fingerprint information on the probe molecule. Herein, nonhalogenated phosphonium-based ionic liquids (ILs) containing cations with varying chain lengths were used as trace additives to amplify the interaction between the cytochrome c (Cyt c) and Zr-Al-Co-O (ZACO) nanotube arrays, strengthening the SERS signals. An increased enhancement factor (EF) by 2.5-41.2 times compared with the system without ILs was achieved. The improvement of the SERS sensitivity with the introduction of these ILs is strongly dependent on the cation chain length, in which the increasing magnitude of EF is more pronounced in the system with a longer alkyl chain length on the cation. Comparing the interaction forces measured by Cyt c-grafted atomic force microscopy (AFM) probes on ZACO substrates with those predicted by the extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory, the van der Waals forces became increasingly dominant as the chain length of the cations increased, associated with stronger Cyt c-ZACO XDLVO interaction forces. The major contributing component, van der Waals force, stems from the longer cation chains of the IL, which act as a bridge to connect Cyt c and the ZACO substrate, promoting the anchoring of the Cyt c molecules onto the substrate, thereby benefiting SERS enhancement.

Honeycomb-Inspired Surface-Enhanced Raman Scattering Microarray for Large-Area Automated Testing of Urease in Saliva Samples.

Surface-enhanced Raman scattering (SERS) technology, as an important analytical tool, has been widely applied in the field of chemical and biomedical sensing. Automated testing is often combined with biochemical analysis technologies to shorten the detection time and minimize human error. The present SERS substrates for sample detection are time-consuming and subject to high human error, which are not conducive to the combination of SERS and automated testing. Here, a novel honeycomb-inspired SERS microarray is designed for large-area automated testing of urease in saliva samples to shorten the detection time and minimize human error. The honeycomb-inspired SERS microarray is decorated with hexagonal microwells and a homogeneous distribution of silver nanostars. Compared with the other four common SERS substrates, the optimal honeycomb-inspired SERS microarray exhibits the best SERS performance. The RSD of 100 SERS spectra continuously collected from saliva samples is 6.56%, and the time of one detection is reduced from 5 min to 10 s. There is a noteworthy linear relationship with a R2 of 0.982 between SERS intensity and urease concentration, indicating the quantitative detection capability of the urease activity in saliva samples. The honeycomb-inspired SERS microarray, combined with automated testing, provides a new way in which SERS technology can be widely used in biomedical applications.

Rapid and Precise Differentiation and Authentication of Agricultural Products via Deep Learning-Assisted Multiplex SERS Fingerprinting.

Accurate and rapid differentiation and authentication of agricultural products based on their origin and quality are crucial to ensuring food safety and quality control. However, similar chemical compositions and complex matrices often hinder precise identification, particularly for adulterated samples. Herein, we propose a novel method combining multiplex surface-enhanced Raman scattering (SERS) fingerprinting with a one-dimensional convolutional neural network (1D-CNN), which enables the effective differentiation of the category, origin, and grade of agricultural products. This strategy leverages three different SERS-active nanoparticles as multiplex sensors, each tailored to selectively amplify the signals of preferentially adsorbed chemicals within the sample. By strategically combining SERS spectra from different NPs, a 'SERS super-fingerprint' is constructed, offering a more comprehensive representation of the characteristic information on agricultural products. Subsequently, utilizing a custom-designed 1D-CNN model for feature extraction from the 'super-fingerprint' significantly enhances the predictive accuracy for agricultural products. This strategy successfully identified various agricultural products and simulated adulterated samples with exceptional accuracy, reaching 97.7% and 94.8%, respectively. Notably, the entire identification process, encompassing sample preparation, SERS measurement, and deep learning analysis, takes only 35 min. This development of deep learning-assisted multiplex SERS fingerprinting establishes a rapid and reliable method for the identification and authentication of agricultural products.

Coherent Electron-Vibron Interactions in Surface-Enhanced Raman Scattering (SERS).

In this Letter we identify coherent electron-vibron interactions between near-resonant and nonresonant electronic levels that contribute beyond standard optomechanical models for off-resonant or resonance surface-enhanced Raman scattering (SERS). By developing an open-system quantum model using first molecular interaction principles, we show how the Raman interference of both resonant and nonresonant contributions can provide several orders of magnitude modifications of the SERS peaks with respect to former optomechanical models and over the fluorescence backgrounds. This cooperative optomechanical mechanism allows for generating an enhancement of nonclassical photon pair correlations between Stokes and anti-Stokes photons, which can be detected by photon-counting measurements. Our results demonstrate Raman enhancements and suppressions of coherent nature that significantly impact the standard estimations of the optomechanical contribution from SERS spectra and their quantum mechanical observable effects.

Ascorbate functionalized Au@AgNPs SERS sensor combined random frog-partial least squares for the prediction of chloramphenicol in milk

The residue of chloramphenicol (CAP) in milk is a great threat to human health. Therefore, the study designed a simple ultrasensitive label-free surface-enhanced Raman scattering (SERS) sensing system for milk employing ascorbate functionalized core-shell gold-silver nanoparticles (Au@AgNPs) synthesized with 2.1 of 10 mM AgNO3 coupled chemometric method. Au@AgNPs with an enhancement factor of 6.99 × 106 was applied to acquire the SERS spectrum of CAP and showed linearity in the range of 0.0001–1000 µg/mL with 1st derivative random frog-partial least squares (RF-PLS) model. Three chemometric models were comparatively applied, among them, the RF-PLS model showed the best results and achieved a correlation coefficient in calibration = 0.985 and prediction = 0.9783 with prediction error (RMSEP) = 0.475 µg/mL with a limit of detection of 2.73 × 10−5 µg/mL. Moreover, recovery of intra- and inter-day analysis was 88–94.38% with a relative standard deviation (RPD) of < 10%. Furthermore, high-performance liquid chromatography (HPLC) analysis was conducted to compare the results with the developed method, and an insignificant (p > 0.05) difference was observed, indicating that the constructed method has good accuracy for the prediction of CAP in milk.

Dendritic Ag@c-Si structure for influenza A virus detection by SERS and machine learning

The combination of Surface-enhanced Raman Scattering (SERS) method and machine learning algorithms has been successfully demonstrated for the detection and differentiation of influenza A virus in buffer medium. Dendritic Ag nanostructures on single-crystal Si wafer have been proposed as SERS-active substrates, which are fabricated using a low-cost and reproducible AgNO3 reduction method. Depending on the time of Ag deposition, structures of different morphologies were obtained. These structures were studied by spectroscopic ellipsometry (SE). To analyze the spectra obtained by SE, we used the Bruggeman effective medium approximation to determine the volume fraction and height of the metal, the Drude model to describe free electrons, the Tauc-Lorentz model to describe the influence of interband transitions, as well as Lorentz model and Gauss model to describe localized surface plasmon resonance (LSPR). SERS was achieved through LSPR excitation and the presence of “hot spots” in the regions around the tips and in the spaces between close-packed dendritic Ag nanostructures. The sample with the most developed surface is the most promising for SERS, since the spectral position of the LSPR is at 658 nm, which is close to the excitation wavelength of the He–Ne laser. The SERS spectra of influenza A virus in buffer medium are difficult to distinguish, so a machine learning algorithms (principal component analysis and support vector machine) were used to directly classify them. For the sample with the most developed morphology, the detection accuracy of influenza A virus was 76.6 ± 4.2 % at a total protein concentration in the test analyte of 300 μg/ml (according to the Lowry method).

Beyond single-molecule chemistry for electrified interfaces using molecule polaritons

Abstract This review introduces the preparation, observation, and characterization of systems of molecule polaritons. We summarize recent progress by our group on the control of molecular properties in the spaces of various optical cavities, such as plasmonic metal nanostructures and Fabry–Perot mirrors. After stating our motivations, we introduce topics on single-molecule observations based on surface-enhanced Raman scattering (SERS) spectroscopy. Our original findings are not limited to the ultrasensitive detection of molecules/materials, but include unique resonant enhancements of SERS through exotic electronic excitation processes at electrified interfaces. Comprehensive analyses of SERS spectra provide novel routes to electronic excitation using plasmonic metal nanostructures. This behavior leads to the distinct photoenergy conversion based on excited electrons and holes with anomalous electrochemical potentials. Such unique resonant electronic excitation emerges as a large optical force that can be used to manipulate small single molecules on surfaces in solution, even at room temperature. We also introduce systems for examining unique interactions between molecules and the optical modes of cavities, from single molecules to molecular ensembles. Plasmonic surface lattices and Fabry–Perot mirrors allow the formation of electronic and vibrational strong coupling states, respectively, showing unique properties of molecule polaritons, even under dark conditions, i.e. without photoillumination.

Two-Dimensional SERS Sensor Array for Identifying and Visualizing the Gas Spatial Distributions of Two Distinct Odor Sources

The spatial distribution of gas emitted from an odor source provides valuable information regarding the composition, size, and localization of the odor source. Surface-enhanced Raman scattering (SERS) gas sensors exhibit ultra-high sensitivity, molecular specificity, rapid response, and large-area detection. In this paper, a SERS gas sensor array was developed for visualizing the spatial distribution of gas evaporated from benzaldehyde and 4-ethylbenzaldehyde odor sources. The SERS spectra of the gas were collected by scanning the sensor array using an automatic detection system. The non-negative matrix factorization algorithm was employed to extract feature and concentration information at each spot on the sensor array. A heatmap image was generated for visualizing the gas spatial distribution using concentration information. Gaussian fitting was applied to process the image for localizing the odor source. The size of the odor source was estimated using the processed image. Moreover, the spectra of benzaldehyde, 4-ethylbenzaldehyde, and their gas mixture were simultaneously detected using one SERS sensor array. The feature information was recognized using a convolutional neural network with an accuracy of 98.21%. As a result, the benzaldehyde and 4-ethylbenzaldehyde odor sources were identified and visualized. Our research findings have various potential applications, including odor source localization, environmental monitoring, and healthcare.

Investigation of selective SERS enhancement mechanism of Au nanospheres and Au nanorods based on 2T2D-SERS correlation spectroscopy

The selective enhancement mechanism in surface-enhanced Raman scattering (SERS) is demonstrated. Two different types of single nanoparticles (Au nanosphere and Au nanorod) were used to investigate the role of the localized surface plasmon resonance (LSPR) in SERS spectra by using the two-trace two-dimensional (2T2D) correlation spectroscopy. The SERS intensities of three probe molecules, 4-mercaptobenzoic acid (4-MBA), 4-aminothiophenol (4-ATP), and 4-bromobenzenethiol (4-BBT), respectively, were enhanced but slightly different when adsorbed on Au nanospheres and Au nanorods. 2T2D correlation SERS spectra clearly showed that even with the same shape of Au nanoparticles, the main factors influencing the SERS enhancement can vary depending on the specific type of SERS tags used. Such subtle difference could not be clearly identified by the conventional spectral analysis. This result sheds light on potential applications of 2T2D correlation spectroscopy. For 4-MBA molecules, the a1 and b2 modes are mainly affected by the Au nanospheres and Au nanorods. For 4-ATP molecules, the a1 and b2 modes related to C–S stretching combined with C–C stretching band are mainly affected by Au nanorods and Au nanospheres. For 4-BBT molecules, the a1 and b2 modes of C–C (aromatic ring) stretching band are mainly affected by Au nanorods and Au nanospheres. This study offers valuable insights into the relationship between nanoparticle shape and SERS enhancement.

Label-Free Surface-Enhanced Raman Scattering Detection of Fire Blight Pathogen Using a Pathogen-Specific Bacteriophage.

Fire blight is one of the most devastating plant diseases, causing severe social and economic problems. Herein, we report a novel method based on label-free surface-enhanced Raman scattering (SERS) combined with an Erwinia amylovora-specific bacteriophage that allows detecting efficiently fire blight bacteria E. amylovora for the first time. To achieve the highest SERS signals for E. amylovora, we synthesized and compared plasmonic nanoparticles (PNPs) with different sizes, i.e., bimetallic gold core-silver shell nanoparticles (Au@AgNPs) and monometallic gold nanoparticles (AuNPs) and utilized the coffee-ring effect for the self-assembly of PNPs and enrichment of fire blight bacteria. Furthermore, we investigated the changes in the SERS spectra of E. amylovora after incubation with an E. amylovora-specific bacteriophage, and we found considerable differences in the SERS signals as a function of the bacteriophage incubation time. The results indicate that our bacteriophage-based label-free SERS analysis can specifically detect E. amylovora without the need for peak assignment on the SERS spectra but simply by monitoring the changes in the SERS signals over time. Therefore, our facile method holds great potential for the label-free detection of pathogenic bacteria and the investigation of viral-bacterial interactions.

Synergistic enhancement of ultrahigh SERS activity via Cu2O@Ag Core-Shell structure for accurate label-free identification of breast tumor subtypes

Conventional methodology for accurate identification of breast tumor subtypes is complex and time-consuming. Label-free SERS (surface-enhanced Raman scattering) enables rapid detection of biomolecules and provides intrinsic fingerprint information of analytes. Exploring reliable SERS bioprobes with high sensitivity and repeatability is essential for efficient typing of cancer cells. In this study, a novel core-shell noble metal-semiconductor Cu2O@Ag heterostructure with uniform morphology and outstanding SERS activity for label-free detection has been proposed. The Cu2O@Ag SERS substrates exhibit exceptional sensitivity to detect 4 nitrophenthiol (4NTP) at extreme low concentration (10−15 M), and the SERS spectra demonstrate excellent selectivity and repeatability (RSD ≈ 8.84%). The enhancement mechanism is attributed to the synergistic enhancement of photoinduced charge transfer and boosted surface plasmon resonance effect, confirmed by reduced fluorescence lifetime of methylene blue on SERS substrates and enhanced electromagnetic field intensity via finite-difference time-domain computational simulation. The ultrahigh sensitivity and good signal stability endow Cu2O@Ag SERS bioprobe potential for accurate detection of four breast cancer cells subtypes. The rich fingerprint information from SERS spectra is processed using linear differentiation analysis, revealing a high accuracy of 93.3% for subtypes classification. These results demonstrate an excellent SERS bioprobe with high sensitivity and repeatability for precision diagnosis has been developed.

Trace level detection of melamine and cyanuric acid extracted from pet liquid food (milk) using a SERS Au nanogap substrate

This study reported an application of Au nanogap substrates for surface-enhanced Raman scattering (SERS) measurements to quantitatively analyze melamine and its derivative products at trace levels in pet liquid food (milk) combined with a waveband selection approach, namely variable importance in projection (VIP). Six different concentrations of melamine, cyanuric acid, and melamine combined with cyanuric acid were created, and SERS spectra were acquired from 550 to 1620cm−1. Detection was possible up to 200 pM for melamine-contaminated samples, and 400 pM concentration detection for other two groups. The VIP-PLSR models obtained correlation coefficient (R2) values of 0.997, 0.985, and 0.981, with root mean square error of prediction (RMSEP) values of 18.492 pM, 19.777 pM, and 15.124 pM for prediction datasets. Additionally, partial least square discriminant analysis (PLS-DA) was used to classify both pure and different concentrations of spiked samples. The results showed that the maximum classification accuracy for melamine was 100%, for cyanuric acid it was 96%, and for melamine coupled with cyanuric acid it was 95%. The results obtained clearly demonstrated that the Au nanogap substrate offers low-concentration, rapid, and efficient detection of hazardous additive chemicals in pet consuming liquid food.

Three-dimensional hotspot structures constructed from nanoporous gold with a V-cavity and gold nanoparticles for surface-enhanced Raman scattering.

The intensity and sensitivity of surface-enhanced Raman scattering (SERS) spectra are highly dependent on the consistency and homogeneity of the nanomaterials. In this study, we developed a large-area three-dimensional (3D) hotspot substrate with good homogeneity and reproducibility in SERS signals. The substrate is based on the synergistic structures of nanoporous gold (NPG) and gold nanoparticles (AuNPs). NPG was combined with a periodic V-shaped nanocavity array to create nanoporous gold with a V-cavity (NPGVC) array featuring uniform hotspots. A nanoporous gold V-shaped resonant cavity (NPGVRC) structure was developed by incorporating AuNPs into the NPGVC array. The coupling action between the AuNPs and NPGVC resulted in a SERS-enhanced electromagnetic field with 3D hotspot distribution. The strategic incorporation of NPG and V-cavity array significantly expanded the surface area available for analyte adsorption and interaction with AuNPs. Using rhodamine 6G (R6G) and malachite green (MG) as probe molecules, the SERS performance was investigated, and the NPGVRC substrate not only showed excellent enhancement with the limit of detection as low as 10-11 M, but also presented good homogeneity. NPGVRC was then used for biological detection of the influenza A virus, where we acquired and examined the characteristic SERS spectra of two spike proteins. It is demonstrated that there is significant potential for our proposed SERS platform to be used in biosensors.

Noble metal-free SERS: mechanisms and applications.

Surface-enhanced Raman scattering (SERS) is a very important tool in vibrational spectroscopy. The coupling of nanomaterials induces local surface plasmon resonance (LSPR), which contributes greatly to SERS. Due to its remarkable sensitivity in trace detection, SERS has gained prominence in the fields of catalysis, biosensors, drug tracking, and optoelectronic devices. SERS activity is believed to be closely related to the LSPR and charge transfer (CT) of the material. Noble metal nanostructures have been commonly used as SERS-active substrates due to their strong local electric fields and relatively mature preparation, application, and enhancement mechanisms. In recent years, SERS research based on semiconductor materials has attracted significant attention because semiconductor materials have advantages such as repeatable preparation, simple pretreatment, stable SERS spectra and superior biocompatibility, stability, and reproducibility. Semiconductor-based SERS has the potential to enrich SERS theory and applications. Thus, the development of semiconductor materials will introduce a new epoch for SERS-based research. In this review, we outline the two main kinds of semiconductor SERS-active substrates: inorganic and organic semiconductor SERS-active substrates. We also provide an overview of the SERS mechanism for different kinds of materials and SERS-based applications.