Surface-enhanced Raman Scattering Spectra Research Articles

Advancing SERS label-free detection of bacteria: Sensing in liquid vs drop-cast

Rapid and reliable detection of antimicrobial-resistant (AMR) bacteria is critical in combatting the growing threat of multi-drug resistance. Among all the available detection techniques, surface-enhanced Raman scattering (SERS) has emerged as a powerful tool for detecting a vast range of molecules and microorganisms, with significant potential for identifying bacterial strains. However, current studies using SERS predominantly utilize a drop-cast method, where a bacterial sample is dried with the SERS substrate to produce SERS spectra of bacteria. While effective, this method is time-consuming and less consistent. In this study, we propose a liquid-based SERS method for the rapid identification and differentiation of bacteria strains. Using gold nanoparticles (AuNPs) as the SERS substrate, we focus on detecting and distinguishing Escherichia coli and Pseudomonas aeruginosa in aqueous samples. Our study investigates multiple variables including nanoparticle size (30, 50, and 80 nm), surface charge/capping agent (negative and positive), and analyte suspension media (water and 0.9% NaCl). We found that fresh bacteria samples in 0.9% NaCl mixed with citrate coated AuNPs with the size of 50 nm yielded optimal parameters for bacteria detection. Comparing drop-cast and liquid methods for SERS detection, we determined that the liquid method provided clearer and more consistent SERS spectra, facilitating effective analysis using Principal Component Analysis (PCA). Our method reliably differentiated E. coli bacterial strains and identified molecular features responsible for their differentiation. Overall, this manuscript presents a reproducible, straightforward, and efficient approach for bacteria detection and differentiation using SERS, contributing to strategies aimed at combating the spread of antibiotic-resistant bacteria.

Comparison of Gap-Enhanced Raman Tags and Nanoparticle Aggregates with Polarization Dependent Super-Resolution Spectral SERS Imaging.

Strongly confined electric fields resulting from nanogaps within nanoparticle aggregates give rise to significant enhancement of surface-enhanced Raman scattering (SERS). Nanometer differences in gap sizes lead to drastically different confined field strengths; so much attention has been focused on the development and understanding of nanostructures with controlled gap sizes. In this work, we report a novel petal gap-enhanced Raman tag (GERT) consisting of a bipyramid core and a nitrothiophenol (NTP) spacer to support the growth of hundreds of small petals and compare its SERS emission and localization to a traditional bipyramid aggregate. To do this, we use super resolution spectral SERS imaging that simultaneously captures the SERS images and spectra while varying the incident laser polarization. Intensity fluctuations inherent of SERS enabled super resolution algorithms to be applied, which revealed subdiffraction limited differences in the localization with respect to polarization direction for both particles. Interestingly, however, only the traditional bipyramid aggregates experienced a strong polarization dependence in their SERS intensity and in the plasmon-induced conversion of NTP to dimercaptoazobenzene (DMAB), which was localized with nanometer precision to regions of intense electromagnetic fields. The lack of polarization dependence (validated through electromagnetic simulations) and surface reactions from the bipyramid-GERTs suggests that the emissions arising from the bipyramid-GERTs are less influenced by confined fields.

Surface‐enhanced Raman scattering of Cis‐(dicyanomethylene) squarate ion on a silver substrate

AbstractThere has been increasing interest in the use of squaraines with different physicochemical properties. However, their applications in nanoplasmonics are limited. For that, detailed information on the interactions of squaraines with plasmonic nanostructures is required. In this work, we have studied the adsorption of the squaraine cis‐dicyanomethylene squarate (CDSQ) on the surface of Ag nanoparticles (AgNP). We have combined normal Raman, surface‐enhanced Raman scattering (SERS), and DFT calculations to get insights into the configuration of its adsorption. DFT calculations have been carried out using simplified models of different adsorption geometries for comparison with the results of the SERS experiments. A comparison between the relative intensities of the normal Raman and SERS spectra indicates that CDSQ is adsorbed perpendicular to the AgNP surface. The predicted spectra suggest that CDSQ is chemically adsorbed on the silver surface through one of its N atoms.

Reuseable Fe3O4@PEI-DTC-Au@Ag magnetic nanocomposites: A versatile and sensitive SERS substrate for food safety assessment

A novel recyclable SERS substrate with magnetic manipulation capability and surface-enhanced Raman scattering (SERS) effect based on Fe3O4@PEI-DTC-Au@Ag (FPDAA) core-shell nanocomposites has been successfully prepared by engineered hydrothermal deposition and seed deposition techniques. The uniformity and SERS efficacy of these substrates, incorporating various Au@Ag core-shell nanocomposites with differing Ag shell thicknesses, were evaluated using 4-aminothiophenol (4-ATP) molecules. The optimal SERS substrate, Fe3O4@PEI-DTC-Au@Ag-3 (FPDAA-3), exhibited an enhancement factor (EF) of up to 1.121×106. This remarkable performance can be attributed to the synergistic effect between the magnetic plasmon of the Fe3O4 nanoparticles (NPs) core and the surface plasmonic resonance properties of Au@Ag core-shell nanocomposites, which create abundant interparticle hotspots within the FPDAA nanocomposites. Furthermore, the FPDAA-3 nanocomposite was employed as a SERS substrate for rapid thiram detection, achieving a minimum detectable concentration of 1×10−9 M. Notably, the relative standard deviation of SERS measurements from 50 randomly selected points was found to be less than 12.13 %, and the SERS spectra intensity showed minimal variation after 5 cycling tests, underscoring the excellent uniformity and recyclability of the FPDAA-3 substrate. The highly reusable and reliable FPDAA-3 nanocomposites enable direct on-site detection of thiram pesticide residues on fruit skins, thereby offering significant implications for food safety assessment and spectroscopic identification.

Evaluating the Kinetics and Molecular Mechanism for Biomimetic Metabolic Activation of PAHs by Surface-Enhanced Raman Scattering Spectroscopy.

Biomimetic cytochrome P450 for chemical activation of environmental carcinogens is an efficient in vitro model for evaluating their mutagenicity and ultimately acquiring the metabolites that cannot be easily accessed by conventional routes of organic synthesis. Different kinds of mutagen derived from polyaromatic hydrocarbons (PAHs) by metalloporphyrin/oxidant model systems have been reported, but the underlying molecular mechanisms are poorly understood. Herein, we have for the first time demonstrated an effective surface-enhanced Raman scattering (SERS) protocol to study the dynamics and biomimetic metabolic behaviors of pyrene (Pyr) in the presence of various oxygen donors. Quantitative information on the relative concentration of Pyr and its metabolites in the biomimetic system can be extracted from the SERS spectra. On the basis of our results, we conclude that the oxidative metabolism of Pyr is highly influenced by the types and concentrations of oxygen donors, leading to the formation of 1-hydroxypyrene and dioxygenated products. Besides, the addition of an appropriate amount of an organic solvent can promote the formation of secondary oxidation products. These results offer valuable insights into the dynamics of PAHs metabolism and the regulation of their metabolic pathways in biomimetic activation. In comparison to traditional liquid chromatography-mass spectrometry, the present SERS approach is more suitable for high-throughput evaluation of the metabolic process and kinetics of PAHs. We anticipate that this approach will enable a more general and comprehensive tracking of metabolic dynamics and molecular mechanisms involved in the biomimetic activation of other xenobiotics, such as procarcinogens, promutagens, and drugs.

Nasopharyngeal carcinoma cell screening based on nuclear targeting Surface-Enhanced Raman Scattering (SERS) detection

BackgroundNasopharyngeal carcinoma (NPC) is a malignant epithelial carcinoma arising from the nasopharyngeal mucosal lining. Diagnosis of NPC at early stage can improve the outcome of patients and facilitate reduction in cancer mortality. The most significant change between cancer cells and normal cells is the variation of cell nucleus. Therefore, accurately detecting the biochemical changes in nucleus between cancer cells and normal cells has great potential to explore diagnostic molecular markers for NPC. Highly sensitive surface-enhanced Raman scattering (SERS) could reflect the biochemical changes in the process of cell cancerization at the molecular level. However, rapid nuclear targeting SERS detection remains a challenge. ResultsA novel and accurate nuclear-targeting SERS detection method based on electroporation was proposed. With the assistance of electric pulses, nuclear-targeting nanoprobes were rapidly introduced into different NPC cells (including CNE1, CNE2, C666 cell lines) and normal nasopharyngeal epithelial cells (NP69 cell line), respectively. Under the action of nuclear localization signaling peptides (NLS), the nanoprobes entering cells were located to the nucleus, providing high-quality nuclear SERS signals. Hematoxylin and eosin (H&E) staining and in situ cell SERS imaging confirmed the excellent nuclear targeting performance of the nanoprobes developed in this study. The comparison of SERS signals indicated that there were subtle differences in the biochemical components between NPC cells and normal nasopharyngeal cells. Furthermore, SERS spectra combined with principal component analysis (PCA) and linear discriminant analysis (LDA) were employed to diagnose and distinguish NPC cell samples, and high sensitivity, specificity, and accuracy were obtained in the screening of NPC cells from normal nasopharyngeal epithelial cells. SignificanceTo the best of our knowledge, this is the first study that employing nuclear-targeting SERS testing to screen nasopharyngeal carcinoma cells. Based on the electroporation technology, nanoprobes can be rapidly introduced into living cells for intracellular biochemical detection. Nuclear-targeting SERS detection can analyze the biochemical changes in the nucleus of cancer cells at the molecular level, which has great potential for early cancer screening and cytotoxicity analysis of anticancer drugs.

Quantitative SERS sensor for mycotoxins with extraction and identification function

The development of new sensors for on-site food toxin monitoring that combine extraction, analytes distinction and detection is important in resource-limited environments. Surface-enhanced Raman scattering (SERS)-based signal readout features fast response and high sensitivity, making it a powerful method for detecting mycotoxins. In this work, a SERS-based assay for the detection of multiple mycotoxins is presented that combines extraction and subsequent detection, achieving an analytically relevant detection limit (∼ 1 ng/mL), which is also tested in corn samples. This sensor consists of a magnetic-core and mycotoxin-absorbing polydopamine-shell, with SERS-active Au nanoparticles on the outer surface. The assay can concentrate multiple mycotoxins, which are identified through multiclass partite least squares analysis based on their SERS spectra. We developed a strategy for the analysis of multiple mycotoxins with minimal sample pretreatment, enabling in situ analytical extraction and subsequent detection, displaying the potential to rapidly identify lethal mycotoxin contamination on site.

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.

Surface‐enhanced Raman scattering spatial fingerprinting decodes the digestion behavior of lysosomes in live single cells

AbstractLysosome, the digestive organelle in eukaryotic cells, plays an important role in the degradation and recirculation of cellular products as well as in maintaining the stability of cellular metabolic microenvironment. Surface‐enhanced Raman scattering (SERS) is a molecular fingerprint technology with high detection sensitivity and photostability, suited for revealing various intracellular molecular information by inducing endocytosis of SERS‐active nanoparticles. However, it remains challenging to selectively extract the molecular information of specific organelles (e.g., lysosomes) from a high‐dimensional spectral set. Herein, we proposed a novel paradigm by combining label‐free SERS spectroscopy with confocal fluorescence imaging to investigate the digestion behavior of lysosomes in cells. The structural similarity algorithm was innovatively introduced and exhibited its effectiveness in screening out the wavenumbers in the SERS spectral set with high correlation with the metabolic behaviors of lysosomes. With comprehensive experiments on HeLa single cells, we captured the intracellular macromolecular digestion phenomenon and discovered the changing pattern of cellular SERS spectra after starvation‐induced autophagy, and analyzed the molecular information within the lysosomes in three‐dimensional space.

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).