Surface Enhanced Raman Spectroscopy Analysis Research Articles

Urine-based SERS and multivariate statistical analysis for identification of non-muscle-invasive bladder cancer and muscle-invasive bladder cancer.

Bladder cancer (BC) is an epidemiological urologic malignancy that continues to increase each year. Early diagnosis and prognosis monitoring is always significant in clinical practice, especially in distinguishing non-muscle-invasive bladder cancer (NMIBC) from muscle-invasive bladder cancer (MIBC), due to the various depths of tumor invasion related to different therapeutic schedules and recurrence rates. Common diagnostic approaches are too invasive or generally inefficient in accuracy and specificity. In this work, a totally non-invasive and cost-effective method is established by investigating urine samples using surface-enhanced Raman spectroscopy (SERS) and multivariate statistical analysis. The comparison of urine SERS spectra shows the intensities of characteristic peaks for DNA/RNA, hypoxanthine, albumin, D-( +)-galactosamine, fatty acids, and some amino acids are distinguishable in BC occurrence and invasion progression. A PLS-LDA-based two-step binary classification scheme is performed on urine SERS spectra and the diagnostic accuracies were 97.7% and 96.3% for healthy individuals versus BC patients and NMIBC versus MIBC patients, respectively. Moreover, the impact of urine SERS spectral lengths in reaching high-precision recognition of BC is investigated. The results show that the Raman peaks at 803, 893, 1139, 1375, and 1466cm-1 play an essential role in correctly categorizing healthy control, NMIBC, and MIBC patients, and SERS spectra ranges from 400 to 1600cm-1 are enough for this identification task. These findings provide a sensitive, label-free, rapid, and totally non-invasive way for assessment of invasion depth of BC to its early diagnosis and prognosis monitoring, as well as valuable insights for selecting reasonable spectral range to enhance the measurement efficiency especially in large-scale sample datasets.

Rapid diagnosis and recurrence prediction of choledocholithiasis disease using raw bile with machine learning assisted SERS

Surface-enhanced Raman spectroscopy (SERS) analysis based on body fluids has been widely applied in disease diagnose. Choledocholithiasis is a widespread and often recurrent digestive system disease, with limited data on factors predicting its formation and reappearance. Bile contains many components that could provide valuable diagnostic information; however, the current diagnosis of biliary disease by SERS focuses on detecting specific component in the bile, overlooking the complex interplay and correlations among multiple factors that could be crucial for accurate diagnosis. This work directly obtained multi-component SERS spectral information of raw bile from 46 patients. Characteristic information was extracted from bile SERS spectra using Principal Component Analysis (PCA), revealing variations in the content of characteristic components associated with different choledocholithiasis types and their recurrence frequency. Pearson correlation analysis was also introduced to reveal the interactions of primary active substances pertinent to choledocholithiasis diagnosis. The efficacy of PCA and Support Vector Machine (SVM) models in classifying stone types, presented an accuracy of 99.2 %. Furthermore, the interaction patterns among SERS characteristic components in choledocholithiasis recurrence frequency were revealed, and with the support of SVM, the prediction for different recurrence rates reached an accuracy of 95.2 %. Overall, this work demonstrates that integrating SERS with machine learning can support disease diagnosis and the interpretation of correlations among multiple components, facilitating elucidating the disease mechanisms.

NiO/AgNPs nanowell enhanced SERS sensor for efficient detection of micro/nanoplastics in beverages

The ubiquity of plastic products has led to an increased exposure to micro and nano plastics across diverse environments, presenting a novel class of pollutants with substantial health implications. Emerging research indicates their capacity to infiltrate human organs, posing risks of tissue damage and carcinogenesis. Given the prevalent consumption of beverages as a primary vector for these plastics' entry into the human system, there is an imperative need for the advancement of precise detection methodologies in liquids. In this study, we introduce a substrate comprising a Nickel Oxide (NiO) nanosheet array decorated with Silver Nanoparticles (AgNPs) for the Surface-Enhanced Raman Spectroscopy (SERS) analysis of micro//nano plastics. This configuration, leveraging a unique nanowell architecture alongside silver plasmonic enhancement, demonstrates unparalleled sensitivity and repeatability in signal, facilitating the accurate quantification of these contaminants. Through the application of a portable Raman apparatus, this study successfully identifies prevalent micro/nano plastics including polystyrene (PS), polyethylene (PE), and polypropylene (PP), achieving detection sensitivities of 5 μg/mL, 25 μg/mL, and 25 μg/mL, respectively. Moreover, the substrate's efficacy extends to the detection of PS within commonly consumed beverages such as water, milk, and liquor with sensitivities of 25 μg/mL, 50 μg/mL, and 50 μg/mL, respectively. These findings highlight the substrate's potential as an expedient and effective sensor for the real-time monitoring of micro/nano plastic pollutants.

Label-free surface-enhanced Raman spectroscopy analysis method for liquid biopsy and its application in serum-based lung cancer diagnosis and classification

Liquid biopsy offers promise for the diagnosis of malignant tumors or their precancerous lesions at the subclinical stage, which is crucial for improving the survival rate of cancer. Label-free surface-enhanced Raman spectroscopy (SERS) has become an emerging detection method in liquid biopsy. The accuracy of SERS-based diagnosis relies on both precise measurement and effective data analysis. In our previous study, a large laser spot SERS method was proposed for the precise detection of complex liquids. In this work, a logical Raman data analysis method was developed, to address the lack of Raman characteristics and biological significance in current SERS-based diagnosis data analysis. The measured Raman data is classified to obtain a weighting matrix of classification criteria, and this matrix is used to establish a material correspondence with the Raman spectra, extract key Raman peaks, determine corresponding biomolecules and biological processes, thus enabling a logical analysis of the data. This method has been used to analyze the SERS data of lung cancer, it can not only show exceptional performance in diagnosing lung cancer and differentiating small cell lung cancer, but also obtain the Raman diagnostic and classification criteria with clear numerical values and reasonable biological significance, demonstrating its great potential value in precise biosensing.

Low-cost preparation of 3D-Au@SiO2 substrates for surface-enhanced Raman spectroscopy and finite difference time domain enhancement analysis

Thin films (thickness of ∼2 to 4 μm) comprised of SiO2 particles (300–400 nm) were deposited on glass slides using a screen-printing approach. After annealing, films were functionalized with polyethyleneimine (PEI) to facilitate the adsorption of 3 to 5 nm Au nanoparticles (Au NPs) on the SiO2 particle films, and the resulting Au@SiO2 substrates were used to evaluate their potential for Surface-Enhanced Raman Spectroscopy (SERS) applications. Two types of supported silica particles, monodisperse solid silica (s-SiO2) and textured silica (t-SiO2), were used to investigate the impact of the SiO2 surface properties on the formation of Au NPs hotspots. Monodisperse s-SiO2 particles were synthesized using the Stöber method, while textured surface materials were obtained by incorporating cetrimonium bromide (CTAB) into the solution at a concentration below the critical micelle concentration.SERS studies were conducted using Rhodamine 6G (R6G) as a target molecule. For Au@s-SiO2, the SERS signal results showed a reasonable spatial reproducibility with relative standard deviation values (RSD ∼5 to 16% for studies on six different samples), in agreement with literature values (RSD ∼10 %). In the case of t-SiO2, the spatial reproducibility was also good in some slides with low% RSD between 7 and 12 %, but in other substrates with values as high as 20 % to 50 % RSD. Also, a lower slide-to-slide %RSD value, 26 % vs. 34 %, was found for the s-SiO2 sample.For the Au@t-SiO2 substrates, the estimated calibration sensitivity and the calculated limit of quantification (LoQ) for R6G were 0.392 (log (peak area) vs. log ([R6G]) and 0.381 μM, respectively, close to the visual practical limit. For Au@s-SiO2, the sensitivity was lower, 0.305 vs 0.392, and the LoQ was 0.016 μM, which is lower than the visual practical limit 0.1 μM. To put these results in context, SiO2 and Au@SiO2 films were characterized using different methods, including profilometry, high resolution scanning and transmission electron microscopy (HR-SEM and TEM), UV–visible and Raman spectroscopy.In addition, finite-difference time domain (FDTD) simulations were performed to understand the relationship between substrate morphology and SERS enhancement. Image processing and 3D modelling software were used to convert the HR-SEM images of the SERS substrates into FDTD simulation-compatible models. The simulations showed that the Au@s-SiO2 substrate had a greater average plasmon resonance enhancement over the Au@t-SiO2 substrate, in good agreement with the LoQ differences between substrates, ∼0.02 μM for Au@ s-SiO2 vs. ∼0.4 μM for Au@t-SiO2.

Unlocking the potential of biofilm-forming plant growth-promoting rhizobacteria for growth and yield enhancement in wheat (Triticum aestivum L.)

Plant growth-promoting rhizobacteria (PGPR) boost crop yields and reduce environmental pressures through biofilm formation in natural climates. Recently, biofilm-based root colonization by these microorganisms has emerged as a promising strategy for agricultural enhancement. The current work aims to characterize biofilm-forming rhizobacteria for wheat growth and yield enhancement. For this, native rhizobacteria were isolated from the wheat rhizosphere and ten isolates were characterized for plant growth promoting traits and biofilm production under axenic conditions. Among these ten isolates, five were identified as potential biofilm-producing PGPR based on in vitro assays for plant growth-promoting traits. These were further evaluated under controlled and field conditions for their impact on wheat growth and yield attributes. Surface-enhanced Raman spectroscopy analysis further indicated that the biochemical composition of the biofilm produced by the selected bacterial strains includes proteins, carbohydrates, lipids, amino acids, and nucleic acids (DNA/RNA). Inoculated plants in growth chamber resulted in larger roots, shoots, and increase in fresh biomass than controls. Similarly, significant increases in plant height (13.3, 16.7%), grain yield (29.6, 17.5%), number of tillers (18.7, 34.8%), nitrogen content (58.8, 48.1%), and phosphorus content (63.0, 51.0%) in grains were observed in both pot and field trials, respectively. The two most promising biofilm-producing isolates were identified through 16 s rRNA partial gene sequencing as Brucella sp. (BF10), Lysinibacillus macroides (BF15). Moreover, leaf pigmentation and relative water contents were significantly increased in all treated plants. Taken together, our results revealed that biofilm forming PGPR can boost crop productivity by enhancing growth and physiological responses and thus aid in sustainable agriculture.

Combining surface-enhanced Raman spectroscopy (SERS) and paper spray mass spectrometry (PS-MS) for illicit drug detection

There is an ongoing effort in the US illicit drug market to make new psychoactive compounds more potent and addictive. Due to continuous chemical modifications, many fentanyl analogs are developed and mixed with more traditional illicit drugs, such as cocaine and heroin. Detecting fentanyl and fentanyl analogs in these illicit drug mixtures has become more crucial because of the increased potency and associated health risks. Most confirmatory procedures require time-consuming and expensive, highly sophisticated laboratory equipment and experimental procedures, which can delay critical information that might save a victim or find a suspect. In this study, we propose miniaturizing and accelerating this process by combining surface-enhanced Raman spectroscopy (SERS) analysis and paper spray mass spectrometry (PS-MS). For this aim, dual-purposed paper substrates were developed through soaking in Au/Ag nanostars suspensions. These novel, in-house prepared paper SERS substrates showed stability for up to four weeks with and without the presence of drug compounds. Fentanyl analogs with similar SERS spectra were differentiated by coupling with PS-MS. The limit of detection (LOD) for fentanyl on the paper substrates is 34 μg/mL and 0.32 μg/mL for SERS and PS-MS, respectively. Fentanyl and fentanyl analogs show selective SERS enhancement that helped to detect trace amounts of these opioids in heroin and cocaine street samples. In short, we propose the combination of SERS/PS-MS by using modified paper substrates to develop cost-effective, sensitive, rapid, portable, reliable, and reproducible methods to detect illicit drugs, especially trace amounts of fentanyl and fentanyl analogs in illicit drug mixtures. The combination of these two category A techniques allows for the identification of illicit drugs according to the SWGDRUG guidelines.

Self-assembled core-shell nanoparticles with embedded internal standards for SERS quantitative detection and identification of nicotine released from snus products.

Surface enhanced Raman spectroscopy (SERS) is a unique analytical technique with excellent performance in terms of sensitivity, non-destructive detection and resolution. However, due to the randomness and poor repeatability of hot spot distribution, SERS quantitative analysis is still challenging. Meanwhile, snus is a type of tobacco product that can release nicotine and other components in the mouth without burning, and the rapid detection technique based on SERS can reliably evaluate the amount of nicotine released from snus, which is of great significance for understanding its characteristics and regulating its components. Herein, the strategy was proposed to solve the feasibility of SERS quantitative detection based on self-assembled core-shell nanoparticles with embedded internal standards (EIS) due to EIS signal can effectively correct SERS signal fluctuations caused by different aggregation states and measurement conditions, thus allowing reliable quantitative SERS analysis of targets with different surface affinity. By means of process control, after the Au nanoparticles (Au NPs) were modified with 4-Mercaptobenzonitrile (4-MBN) as internal standard molecules, Ag shell with a certain thickness was grown on the surface of the AuNP@4-MBN, and then the Au@4-MBN@Ag NPs were used to regulate and control the assembly of liquid-liquid interface. The high-density nano-arrays assembled at the liquid-liquid interface ensure high reproducibility as SERS substrates, and which could be used for SERS detection of nicotine released from snus products. In addition, time-mapping research shows that this method can also be used to dynamically monitor the release of nicotine. Moreover, such destruction-free evaluation of the release of nicotine from snus products opens up new perspectives for further research about the impact of nicotinoids-related health programs.

Determination of Trace Levels of Fenbendazole in Milk, Yogurt and Curd Using Surface Enhanced Raman Spectroscopy (SERS)

A simple, rapid and sensitive surface enhanced Raman spectroscopy (SERS) method is described for the determination of fenbendazole (FBZ) residues in milk and fermented dairy products using yogurt and curd as examples. An extraction method was used to purify the samples from interfering components of the matrix. 5-Nitrothiabendazole, which has similar physicochemical properties, was used as an internal standard. The detection limits for milk were 1.8 nM, 8.8 nM for yogurt, and 13.5 nM for curd. Using partial least squares (PLS), a calibration model was built for the determination of fenbendazole from 100 to 1000 nM. The root-mean square error of calibration (RMSEC) and the root-mean square error of prediction (RMSEP) were 39.4 and 46.5 nM, respectively. The recovery rate was from 97% to 120% and the relative standard deviation was less than 5.0%. The difference between this technique from the widely used liquid chromatography–mass spectrometry (LC–MS) method is in the reduction in time for preliminary sample preparation and significantly lower costs for reagents and consumables. The article examines the influence of order when mixing reagents in a SERS experiment, which is an important tool for optimizing the sensitivity and reproducibility of the method. It was shown for the first time that when various alkaline earth metal chlorides were used for agglomeration of silver nanoparticles (AgNPs), a change in the ratio of peak intensities of the two analytes in the solution was observed. This phenomenon may be used to increase the selectivity of SERS analysis of multicomponent samples.

Surface-enhanced Raman spectroscopy analysis of menstrual cycle: From biochemical changes to diagnostics of vaginal infections

Analysis of vaginal fluid is essential for the diagnosis of cervical cancer, various infections and the increasingly common infertility as well. Surface-enhanced Raman spectroscopy (SERS), which is an ultrasensitive technique, provides information from the molecular level of the analysed system and together with chemometric methods, can be a perfect diagnostic tool. However, the variety of processes naturally occurring during the menstrual cycle has a definite impact on the biochemistry of vaginal swabs. This study adopted SERS to track the biochemical changes recorded in spectra of analysed clinical entities during two menstrual cycles. Using Partial Least Squares Regression (PLSR) technique, we demonstrated the spectral information typical for three types of body fluid: menstrual blood (MB), menstrual spotting (MS) and vaginal fluid (VF). The hormonal changes during the menstrual cycle can be reflected in SERS spectra and allow distinction between the fertile and infertile phases. Significantly larger data separation was observed due to infection rather than hormonal fluctuation. These results give a firm foundation for the diagnosis of vaginal infections regardless of the menstrual cycle’s day.

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

Surface-functionalized SERS platform for deep learning-assisted diagnosis of Alzheimer's disease

Early diagnosis of Alzheimer's disease is crucial to stall the deterioration of brain function, but conventional diagnostic methods require complicated analytical procedures or inflict acute pain on the patient. Then, label-free Surface-enhanced Raman spectroscopy (SERS) analysis of blood-based biomarkers is a convenient alternative to rapidly obtain spectral information from biofluids. However, despite the rapid acquisition of spectral information from biofluids, it is challenging to distinguish spectral features of biomarkers due to interference from biofluidic components. Here, we introduce a deep learning-assisted, SERS-based platform for separate analysis of blood-based amyloid β (1–42) and metabolites, enabling the diagnosis of Alzheimer's disease. SERS substrates consisting of Au nanowire arrays are fabricated and functionalized in two characteristic ways to compare the validity of different Alzheimer's disease biomarkers measured on our SERS system. The 6E10 antibody is immobilized for the capture of amyloid β (1–42) and analysis of its oligomerization process, while various self-assembled monolayers are attached for different dipole interactions with blood-based metabolites. Ultimately, SERS spectra of blood plasma of Alzheimer's disease patients and human controls are measured on the substrates and classified via advanced deep learning techniques that automatically extract informative features to learn generalizable representations. Accuracies up to 99.5% are achieved for metabolite-based analyses, which are verified with an explainable artificial intelligence technique that identifies key spectral features used for classification and for deducing significant biomarkers.

Characterization of Illicit Drugs by Surface-Enhanced Raman Spectroscopy (SERS) and Principal Component Analysis (PCA)

Addictive, illicit drugs pose a high threat of relapse. The deaths related to illicit drugs have substantially increased in the last decade. Globally, the illicit drug trade poses a significant challenge to public health and law enforcement. It is essential to utilize advanced analytical methodologies that can characterize illegal substances precisely to monitor these drugs. Surfaced-enhanced Raman spectroscopy (SERS) has become a valuable tool, providing distinct capabilities for analyzing illegal substances. Surface enhancement enables far more sensitive detection and identification of illicit drugs by suppressing the fluorescence and enhancing the Raman signals. In this regard, a simple method with limited sample processing has been established to characterize illicit drugs that include methamphetamine (MAMP) (ice), bhang, marijuana (cannabis), opium, and diacetylmorphine (heroin). Moreover, principal component analysis (PCA) was employed for the identification of the characteristic SERS spectral features of these drugs. This article examines the potential application of SERS in the characterization of illicit substances, focusing on its ability to contribute significantly to law enforcement and forensic investigation.

Raman and Surface Enhanced Raman spectroscopy analysis of breast cancer cell lines with different HER2 expression profiles

Assessing HER2 expression in breast cancer cells holds significant diagnostic and prognostic importance. Traditional methods like immunohistochemistry and in situ hybridization suffer from low sensitivity and misclassification rates. In this frame, techniques such as vibrational microscopies can ensure, together with low costs and analytical speed, both high accuracy and precision. Herein, we propose a combined Raman and SERS approach for characterizing 4 breast cancer cell lines and normal cells with varying HER2 expression levels. We show that Raman spectroscopy offers a promising alternative, providing unique molecular fingerprints for cell types based on their biochemical signatures. Its non-invasive nature and ability to detect subtle changes in cellular metabolism make it ideal for cancer cell analysis. Coupled with machine learning techniques like PCA and LDA, Raman spectroscopy can classify different breast cancer subcategories accurately. Surface Enhanced Raman Scattering (SERS) further enhances sensitivity, allowing the detection of single molecules like HER2 receptors. Overall, our results enable fast screening of cancer subpopulation in terms of HER2 concentration and macromolecule cell content. Integration of Raman spectroscopy with SERS offers precise identification and opens avenues for personalized therapies.

Utilization of microdroplets as optical lenses for surface-enhanced Raman spectroscopy (SERS) enhancement on localized silver nanoparticle-decorated porous silicon substrates

We demonstrate the use of a microdroplet as an optical lens positioned on a localized silver nanoparticle-decorated porous silicon (LocAg-PS) surface for enhanced surface-enhanced Raman spectroscopy (SERS) analysis.

Aptamer-aided plasmonic nano-urchins for reporter-free surface-enhanced Raman spectroscopy analysis of cortisol.

Cortisol is a vital glucocorticoid hormone reflecting stress levels and related disease processes. In this study, we report an aptamer-functionalized plasmonic nano-urchin (α-FeOOH@Au-aptamer)-aided cortisol-capturing and surface-enhanced Raman spectroscopy (SERS) analysis approach. The designed α-FeOOH@Au-aptamer exhibits a well-patterned plasma structure, which combines the good SERS enhancement ability of reduced nanogaps between the Au plasma and the hot spot-favored structure of anisotropic tips from α-FeOOH urchins, with the high affinity of the aptamer towards cortisol molecules. The α-FeOOH@Au-aptamer achieved reporter-free SERS quantification for cortisol with good sensitivity (limit of detection <0.28 μmol L-1), robust salt (1.0 mol per L NaCl) and protein (5.0 mg per mL bovine serum protein) tolerance, favorable reproducibility, as well as good reusability. We further demonstrated the good cortisol-capturing ability and SERS efficacy of the α-FeOOH@Au-aptamer profiling in the serum and urine samples. Our approach provides an alternative tool for cortisol analysis and a reference strategy for report-free SERS detection of small molecules.

Electrochemical surface-enhanced Raman spectroscopy analysis of malachite green on gold substrates

The pH-sensitivity and electroactivity of malachite green (MG), a cationic dye, can potentially affect the surface-enhanced Raman spectroscopy (SERS) profile during electrochemical SERS (EC-SERS) analysis. An EC-SERS investigation was performed by acquiring dynamic SERS profiles of MG on Au SERS substrates at various applied potentials to elucidate the electrochemistry of MG at the molecular level. The appropriate EC-SERS potential window of MG, mass and electron transfer modes, and the number of electrons and protons in the redox process were determined by cyclic voltammetry. The EC-SERS parameters (pH and type of supporting electrolyte) were optimized. The highest SERS signal was achieved by chronoamperometry SERS (CA-SERS) and CV-SERS at an applied potential of 0.6 V (vs. Ag/AgCl) using 0.1 M phosphate buffer at pH 4 as the supporting electrolyte. Plausible mechanisms for electrooxidation and SERS signal enhancement were proposed. The oxidized form of MG, conformational changes, and evolution of the adsorption orientation were successfully elucidated using CV-SERS. This study provides molecular-level insight into the adsorption orientation of MG and its oxidized form, which involves aromatic rings with a tilted upright orientation at positive applied potentials. The mechanism of SERS signal enhancement is expounded, which is valuable for developing EC-SERS-based MG sensing.

The Label-Free Detection and Identification of SARS-CoV-2 Using Surface-Enhanced Raman Spectroscopy and Principal Component Analysis.

The World Health Organization (WHO) declared in a May 2023 announcement that the COVID-19 illness is no longer categorized as a Public Health Emergency of International Concern (PHEIC); nevertheless, it is still considered an actual threat to world health, social welfare and economic stability. Consequently, the development of a convenient, reliable and affordable approach for detecting and identifying SARS-CoV-2 and its emerging new variants is crucial. The fingerprint and signal amplification characteristics of surface-enhanced Raman spectroscopy (SERS) could serve as an assay scheme for SARS-CoV-2. Here, we report a machine learning-based label-free SERS technique for the rapid and accurate detection and identification of SARS-CoV-2. The SERS spectra collected from samples of four types of coronaviruses on gold nanoparticles film, fabricated using a Langmuir-Blodgett self-assembly, can provide more spectroscopic signatures of the viruses and exhibit low limits of detection (<100 TCID50/mL or even <10 TCID50/mL). Furthermore, the key Raman bands of the SERS spectra were systematically captured by principal component analysis (PCA), which effectively distinguished SARS-CoV-2 and its variant from other coronaviruses. These results demonstrate that the combined use of SERS technology and PCA analysis has great potential for the rapid analysis and discrimination of multiple viruses and even newly emerging viruses without the need for a virus-specific probe.

Enrichment-Sensing All-in-One Strategy Integrated in the La(OH)3-Au@AgNPs Substrate for Rapid Surface-Enhanced Raman Spectroscopy Analysis of Purine Components.

Improving the speediness of complex sample analysis has attracted much research interest in analytical science. In this work, an enrichment-sensing all-in-one strategy was presented for rapid surface-enhanced Raman spectroscopy (SERS) analysis of purine components by using the La(OH)3-Au@AgNPs nanocomposite. Two-dimensional (2D) La(OH)3 nanosheets with nanothickness and accessible active sites not only acted as efficient media for the rapid enrichment of analytes but also provided flat planes for the intensive decoration of Au@AgNPs nanoparticles to amplify the SERS signals of adsorbed analytes. The nanocomposite could realize the rapid enrichment-sensing of purine components in 1 min, including mercaptopurine, thioguanine, adenine, and purine. Subsequently, the surface adsorption behaviors were explored by density functional theory and the enhancement mechanisms were simulated by the finite-difference time-domain method. Moreover, the nanocomposite also exhibited good SERS performances with relative standard deviations (RSDs) of uniformity less than 6.5% (n = 23), RSDs of batch-to-batch stability less than 7.3% (n = 9), and long-term stability over 9 weeks with RSDs within 6.6%. Finally, the enrichment-sensing strategy was applied for the rapid SERS analysis of two projects: mercaptopurine in tablets and adenine in beers with detection limits of 6.0 and 0.76 μg/L and spiked recoveries of 90.9-100 and 84.2-101%, respectively. Benefiting from the high-performance enrichment medium and closely packed plasmonic nanoparticles, the enrichment-sensing all-in-one strategy possesses great potential for rapid on-site detection in food safety and pharmaceutical analysis.

SERS sensing of Metanil yellow in turmeric solution using self-organized nanoparticle arrays grown on Ion beam patterned soda-lime glass

Metanil yellow is a potential carcinogen that is commonly used as a food adulterant in turmeric powder. In this study, we detected Metanil Yellow from turmeric powder solution using a Surface Enhanced Raman Spectroscopy (SERS) substrate based on nano patterned soda-lime glass. Low amplitude ripples with an average wavelength around 65 nm was produced on commercially available soda-lime glass using low energy argon ion beam irradiation. Large area SERS templates were produced by growing self-organized ordered Ag nanoparticles on the rippled soda-lime glass. The optical analysis of the produced substrates shows the anisotropic plasmonic response and better Localized Surface Plasmon Resonance (LSPR) activity than Ag nanoparticles grown on rippled Si substrate. A biaxial layer consisting of Lorentz and Drude oscillators describes the real and imaginary parts of the dielectric function. The SERS analysis using Crystal Violet (CV) molecules demonstrates a five-fold improvement in the efficiency of silver grown on glass ripples than silver grown on Si ripples. We have tested the substrate for detecting Metanil yellow up to 10-6 M concentration in the water. Further, using the substrate, up to 10-4 M concentration of Metanil Yellow is detected from the turmeric solution.