Surface Enhanced Raman Spectroscopy Technique Research Articles

High sensitive setup of gold nanorods substrates for explosives detection using surface enhanced Raman spectroscopy

Surface enhanced Raman spectroscopy (SERS) is a sensitive analytical technique that has been widely utilised for detection of trace levels of organic species. The detection of trace levels of poor vapour pressure molecules such as explosives is a critical challenging due to the low concentration of adsorbed molecules on the surface sites of the SERS substrate. In this work, gold nanorods were fabricated with analyte samples and provided highly sensitivity of SERS detection along with low limit-of-detection (LOD) for explosives down to pM of 2,4- dinitrotoluene (DNT) and nM of both 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) with highly specificity. The sensitivity of the detection was developed by optimising the adsorption of the molecules on nanorods sites, where nanorods and analyte solution were mixed followed by rigorous stirring. This procedure allowed extra molecules to be adsorbed on the nanorods surface, thus, high SERS enhanced has been observed with high specific identification of target probes. This approach showed a powerful method for rapid detection of explosives based SERS technique especially for illicit samples, where the enhancement factor calculated for analytes was in the range 3.17×108 to 2.69×1011.

Single AgNF@ZIF-8 nanoparticle for deep learning assisted SERS detection of gaseous molecule

Random nanogaps between plasmonic nanoparticles formed by the surface tension of solution bring a great challenge in the application of Surface Enhanced Raman Spectroscopy (SERS) technique. Meanwhile, due to its natural nanogaps on the surface, single plasmonic nanoflower exhibits a unique charm for avoiding random gaps in the quantitative analysis of SERS. Herein, a single core-shell plasmonic nanoparticle consisting of an Ag nanoflower (AgNF) core and zeolite imidazolate frame-8 (ZIF-8) shell is synthesized for SERS detection of the gaseous molecule. Benefiting the porous shell of ZIF-8 to capture gaseous molecules and natural nanogaps on the surface of AgNF to enhance Raman intensity, SERS spectra of the gaseous molecule are successfully collected with high sensitivity at 10−6 M, which also exhibit high SERS stability with RSD at 2.29 % for single nanoparticle and 9.86 % for multiple single nanoparticles. In addition, two levels of automatic identification based on deep learning (DL) are also implemented in this work, whose data demonstrate artificial neural network (ANN) algorithm could identify gaseous glutaraldehyde (GA) as typical volatile organic compounds (VOCs) with high accuracy at 97.5 %. This single core-shell plasmonic MOF nanoparticle overcomes the limitations of traditional SERS detection in nanoparticle aggregating and realizes the detection of gaseous molecules, which enlarges the application of SERS technique.

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.

Classification and prediction of Klebsiella pneumoniae strains with different MLST allelic profiles via SERS spectral analysis.

The Gram-negative non-motile Klebsiella pneuomoniae is currently a major cause of hospital-acquired (HA) and community-acquired (CA) infections, leading to great public health concern globally, while rapid identification and accurate tracing of the pathogenic bacterium is essential in facilitating monitoring and controlling of K. pneumoniae outbreak and dissemination. Multi-locus sequence typing (MLST) is a commonly used typing approach with low cost that is able to distinguish bacterial isolates based on the allelic profiles of several housekeeping genes, despite low resolution and labor intensity of the method. Core-genome MLST scheme (cgMLST) is recently proposed to sub-type and monitor outbreaks of bacterial strains with high resolution and reliability, which uses hundreds or thousands of genes conserved in all or most members of the species. However, the method is complex and requires whole genome sequencing of bacterial strains with high costs. Therefore, it is urgently needed to develop novel methods with high resolution and low cost for bacterial typing. Surface enhanced Raman spectroscopy (SERS) is a rapid, sensitive and cheap method for bacterial identification. Previous studies confirmed that classification and prediction of bacterial strains via SERS spectral analysis correlated well with MLST typing results. However, there is currently no similar comparative analysis in K. pneumoniae strains. In this pilot study, 16 K. pneumoniae strains with different sequencing typings (STs) were selected and a phylogenetic tree was constructed based on core genome analysis. SERS spectra (N = 45/each strain) were generated for all the K. pneumoniae strains, which were then comparatively classified and predicted via six representative machine learning (ML) algorithms. According to the results, SERS technique coupled with the ML algorithm support vector machine (SVM) could achieve the highest accuracy (5-Fold Cross Validation = 100%) in terms of differentiating and predicting all the K. pneumoniae strains that were consistent to corresponding MLSTs. In sum, we show in this pilot study that the SERS-SVM based method is able to accurately predict K. pneumoniae MLST types, which has the application potential in clinical settings for tracing dissemination and controlling outbreak of K. pneumoniae in hospitals and communities with low costs and high rapidity.

TiO2–SiO2–Ag electrospun fibers for oxytetracycline detection by SERS

The presence of antibiotic residues such as oxytetracycline (OTC) in wastewater effluents has generated the need to monitor trace concentrations due to the development of bacterial resistance. Surface Enhanced Raman Spectroscopy (SERS) is a promising technique in detection and identification a low concentrations (ng/L) with no sample preparation. A fibrillar support Titania-Silica doped with silver particles was developed in the SERS technique focused on the detection of OTC. The ceramic fibers TiO2SiO2 obtained presented phases of Rutile, Anatase and Amorphous Silica with diameters of 335 ± 76 nm. The fibers were doped with Ag by electrodeposition, showed Ag nanoparticles with sizes from 30 nm to the formation of dendritic structures with sizes greater than 1 μm, depending on the time and voltage applied during synthesis. The support doped with Ag for 30 s at 19 V presented the highest amplification factor (AF) in SERS of 44 for the band at 1278 cm−1.

A SERS nanoswitch based on Cu(II)-mediated dual-ligand AuNPs reversible aggregation for pyrophosphate ions detection

Surface enhanced Raman spectroscopy (SERS) detection was not as accurate and reproducible as fluorometry due to some uncertainties such as operator's focus adjustment and difference between capillaries. Herein, the weak Raman signal (1640 cm−1) from water bending vibration is adopted as a benchmark to improve the practical applicability of Raman detection. Specifically, amygdalin and citrate sodium were applied as reducing and capping agent to synthesize dual-ligand gold nanoparticles (DL-AuNPs). Additionally, the cyano group of amygdalin can attach to the surface of DL-AuNPs and generate a primary SERS signal (2174 cm−1), which can be significantly enhanced by Cu(II) as DL-AuNPs aggregates. More importantly, the strong binding affinity between pyrophosphate ions (PPi) and Cu(II) redisperses DL-AuNPs and decreases the signal intensity, making the DL-AuNPs/Cu(II) SERS sensor promising for the detection of PPi. This proposed method presents a reliable SERS analysis with the stable water background calibration and broaden the application of SERS technique in aqueous solution target monitoring.

Ionic liquid as electrostatic shielding additive for dendrite-free lithium metal battery

Lithium (Li) metal anodes have attracted much attention as their high specific capacity and low electrochemical potential significantly boost the energy density of secondary batteries. Nonetheless, the uncontrolled growth of Li dendrites causing a poor cycle capability restricts the commercialization of Li metal batteries. Herein, an ionic liquid with a long aliphatic chain (N-methyl-N-decyl pyrrolidinium (Pyr1(10)+) bis(trifluoromethanesulphonyl)imide (TFSI−)) is introduced as a non-consuming electrostatic shielding additive into the ether-based electrolyte. The admirable compatibility of Pyr1(10)+ cation with Li metal is verified via electrochemical analysis, and the adsorption of Pyr1(10)+ on the Li-metal/electrolyte interface is confirmed by Surface-enhanced Raman spectroscopy technique. Through redistributing the Li ions on the surface of Li metal, the growth of Li dendrites is suppressed, so as to improve the cycling performance of Li metal anodes in symmetric cells, asymmetric cells and Li||LiFePO4 full cells with low N/P ratio (3:1). This study provides new insights into revealing the electrostatic shielding mechanism of regulating Li metal deposition and prolonging the long-term cycling.

Spectroscopic and computational approach to study the interacting mechanism of drug-adenine complex

The biomolecular complex, Adenine and Rheumatoid Arthritis drug are characterized using vibrational spectroscopy and density functional theory. The spectral characterization of the biomolecule is undertaken by using Fourier transform infrared spectroscopy, Raman and surface-enhanced Raman spectroscopy techniques. The optimization and theoretical calculation of the molecules are performed using the density functional theory method. The frontier molecular orbital, molecular electrostatic potential, natural bond orbital parameters, thermodynamic properties, and first-order hyperpolarizability of the adenine monomer, hydroxychloroquine monomer, and their interacting state are calculated by Becke–Lee–Yang–Parr method with a basis set of 6-311G + (d, p). The Quantum Theory Atoms in Molecules study is performed for investigating the topological parameters, such as the density of electrons, the Laplacian of electron density, and the energy density. The vibrational assignment of the individual and interacting molecules is accomplished through the Vibrational Energy Distribution Analysis program. The theoretical and experimental values show good agreement with one another. The drug-likeliness behavior of the biomolecular complex is also studied. The molecular docking studies of the molecules are carried out against human synovial fluid sPLA2 to determine the best binding sites.

Rapid discrimination of glycogen particles originated from different eukaryotic organisms

There are many commercially available glycogen particles in the market due to their bioactive functions as food additive, drug carrier and natural moisturizer, etc. It would be beneficial to rapidly determine the origins of commercially-available glycogen particles, which could facilitate the establishment of quality control methodology for glycogen-containing products. With its non-destructive, label-free and low-cost features, surface enhanced Raman spectroscopy (SERS) is an attractive technique with high potential to discriminate chemical compounds in a rapid mode. In this study, we applied the combination of SERS technique and machine leaning algorithms on glycogen analysis, which successfully predicted the origins of glycogen particles from a variety of organisms with convolutional neural network (CNN) algorithm plus attention mechanism having the best computational performance (5-fold cross validation accuracy = 96.97 %). In sum, this is the first study focusing on the discrimination of commercial glycogen particles originated from different organisms, which holds the application potential in quality control of glycogen-containing products.

Metal-organic frameworks based surface-enhanced Raman spectroscopy technique for ultra-sensitive biomedical trace detection.

Metal-organic frameworks (MOFs) have attracted widespread interest due to their unique and unprecedented advantages in microstructures and properties. Besides, surface-enhanced Raman scattering (SERS) technology has also rapidly developed into a powerful fingerprint spectroscopic technique that can provide rapid, non-invasive, non-destructive, and ultra-sensitive detection, even down to single molecular level. Consequently, a considerable amount of researchers combined MOFs with the SERS technique to further improve the sensing performance and broaden the applications of SERS substrates. Herein, representative synthesis strategies of MOFs to fabricate SERS-active substrates are summarized and their applications in ultra-sensitive biomedical trace detection are also reviewed. Besides, relative barriers, advantages, disadvantages, future trends, and prospects are particularly discussed to give guidance to relevant researchers.

Sensitive and handy detection of pesticide residue on fruit surface based on single microsphere surface-enhanced Raman spectroscopy technique

Hypothesis: Surface-enhanced Raman spectroscopy (SERS) has become an emerging and reliable tool for detecting pesticide residues due to its high sensitivity, fast testing speed and easy sample handling. SERS active substrates are the key to achieve efficient and sensitive detection. However, for the most widely used noble metal nanoparticles, there are problems of high noble metal nanoparticle usage and random aggregation. The micron-scale Raman spot is focused on multiple randomly aggregated nanoparticles during the test, resulting in poor reproducibility. Therefore, the development of micron-scale cost-effective SERS substrates with good reproducibility and simple detecting method is of great significance in practical detection.Experiments: Through deposition of silver nanoparticles (Ag-NPs) by chemical reduction on the surface of monodisperse sulfonated polystyrene (SPS) microspheres, micron-sized PS@Ag-NPs core–shell microspheres were prepared with excellent SERS activity. After that, two simple protocols (Method I and Method II) were explored for the determination of thiram on apple epidermis.Findings: Based on our developed strategy of the single microsphere SERS technique, we successfully fabricated uniform PS@Ag-NPs substrate with high SERS activity and excellent detection sensitivity. The single microsphere SERS technique possesses the capability of anti-dilutability and the utilization of ultra-low PS@Ag-NPs microsphere dosage, realizing qualitative and quantitative detection of thiram on apple with detection limits far below the standard stipulated by China and the European Union.

Surface-enhanced Raman spectroscopy of polymerase chain reaction (PCR) products of Rifampin resistant and susceptible tuberculosis patients

BackgroundRaman spectroscopy is an effective tool for detecting and discriminating microorganisms that is robust, reliable, and rapid. ObjectivesTo develop a polymerase chain reaction technique (PCR) based on Surface Enhanced Raman Spectroscopy (SERS) technique with principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to assess diagnostic capability of SERS for distinguishing between tuberculosis (TB) positive rifampin resistant and tuberculosis (TB) positive rifampin susceptible samples. MethodsSilver nanoparticles (Ag NPs) were used as SERS substrates and technique was used to distinguish TB positive rifampin (RIF) resistant and TB positive rifampin (RIF) susceptible patients on the basis of characteristic SERS spectral features of their respective PCR products. SERS spectra were acquired from 52 samples of PCR products including 22 samples of TB positive rifampin susceptible, 30 samples of TB positive rifampin resistant and negative control samples. All these samples were collected from individuals of same age. Furthermore, multivariate data analyses techniques such as PCA and PLS-DA were used to assess diagnostic capability of SERS for distinguishing between TB positive rifampin resistant and TB positive rifampin susceptible samples. ResultsPCA is found helpful for successful differentiation among these two groups of spectral data sets. Moreover, PLS-DA provides this classification quantitatively by predicting the class of SERS spectral data set with 73% area under curve, 96% sensitivity, 95.6% specificity and 95% accuracy. ConclusionSERS can be employed for the rapid distinguishing between TB positive rifampin resistant and TB positive rifampin susceptible samples.

SERS detection of DNA cancer with single-base mutation for KRAS-gene

We present a method based on surface enhanced Raman spectroscopy (SERS) to detect DNA KRAS-gene with single-base mutation. The KRAS gene belongs to the oncogenes class of genes. KRAS mutations cause normal cells to become cancerous. The detection utilizes DNA gene probes labeled by CY3 dye (as Raman marker) and plasmonic silver nanoparticles (Ag NPs) deposited on SERS substrates. SERS detect DNA via hybridization to the complementary DNA (target) sequences with single-base mutation. This SERS technique is a powerful tool and has a great potential gives both affectability and selectivity. Also, we utilized the Raman mapping to provide chemical information coupled with spatial information. The mapping image was obtained using scanning with three Raman bands over selected areas of the sample. The limit of detection (LOD) was accomplished down to a concentration 9 nM after the addition of magnesium sulfate (MgSO4) aggregated agent, causing hot spots of the aggregated Ag NPs.

On-Site Detection of SARS-CoV-2 Antigen by Deep Learning-Based Surface-Enhanced Raman Spectroscopy and Its Biochemical Foundations.

A rapid, on-site, and accurate SARS-CoV-2 detection method is crucial for the prevention and control of the COVID-19 epidemic. However, such an ideal screening technology has not yet been developed for the diagnosis of SARS-CoV-2. Here, we have developed a deep learning-based surface-enhanced Raman spectroscopy technique for the sensitive, rapid, and on-site detection of the SARS-CoV-2 antigen in the throat swabs or sputum from 30 confirmed COVID-19 patients. A Raman database based on the spike protein of SARS-CoV-2 was established from experiments and theoretical calculations. The corresponding biochemical foundation for this method is also discussed. The deep learning model could predict the SARS-CoV-2 antigen with an identification accuracy of 87.7%. These results suggested that this method has great potential for the diagnosis, monitoring, and control of SARS-CoV-2 worldwide.

Identifying functioning and nonfunctioning adrenal tumors based on blood serum surface-enhanced Raman spectroscopy.

Adrenal tumors are common tumors in urology and they can be further divided into functioning and nonfunctioning tumors according to whether there is uncommon endocrine function. In clinical practice, the early identification and accurate assessment of adrenal tumors are essential for the guidance of subsequent treatment. However, a nonfunctioning adrenal tumor often lacks obvious clinical symptoms, making it difficult to be timely and precisely diagnosed by conventional examinations. Therefore, a rapid and accurate method for identifying the functioning and nonfunctioning adrenal tumors is urgently required to achieve precise treatment of adrenal tumors. In this study, surface-enhanced Raman spectroscopy was investigated as a diagnostic tool to identify the blood serum samples from healthy volunteers as well as the patients with functioning and nonfunctioning adrenal tumors. Based on the SERS peak analysis, abnormal glycolysis, DNA/RNA, and amino acid metabolites were found to be potential biomarkers for identifying patients with adrenal tumors, while metabolites related to disordered protein catabolism and excessive hormone secretion were expected to further differentiate functioning adrenal tumors from nonfunctioning adrenal tumors. In addition, principal component analysis followed by support vector machine (PCA-SVM) was further applied on those serum SERS measurements, and the classification accuracies of 96.8% and 84.5% were achieved for differentiating healthy group versus adrenal tumor group and functioning adrenal tumor group versus nonfunctioning adrenal tumor group, respectively. The results have demonstrated the prodigious potential of precise adrenal tumor diagnosis by using the blood serum surface-enhanced Raman spectroscopy technique.

A Review on SERS-Based Detection of Human Virus Infections: Influenza and Coronavirus.

The diagnosis of respiratory viruses of zoonotic origin (RVsZO) such as influenza and coronaviruses in humans is crucial, because their spread and pandemic threat are the highest. Surface–enhanced Raman spectroscopy (SERS) is an analytical technique with promising impact for the point–of–care diagnosis of viruses. It has been applied to a variety of influenza A virus subtypes, such as the H1N1 and the novel coronavirus SARS−CoV−2. In this work, a review of the strategies used for the detection of RVsZO by SERS is presented. In addition, relevant information about the SERS technique, anthropozoonosis, and RVsZO is provided for a better understanding of the theme. The direct identification is based on trapping the viruses within the interstices of plasmonic nanoparticles and recording the SERS signal from gene fragments or membrane proteins. Quantitative mono- and multiplexed assays have been achieved following an indirect format through a SERS-based sandwich immunoassay. Based on this review, the development of multiplex assays that incorporate the detection of RVsZO together with their specific biomarkers and/or secondary disease biomarkers resulting from the infection progress would be desirable. These configurations could be used as a double confirmation or to evaluate the health condition of the patient.

Bacillus thuringiensis Cells Selectively Captured by Phages and Identified by Surface Enhanced Raman Spectroscopy Technique.

In this work, the results on the detection and identification of Bacillus thuringiensis (Bt) cells by using surface-enhanced Raman spectroscopy (SERS) are presented. Bt has been chosen as a harmless surrogate of the pathogen Bacillus anthracis (Ba) responsible for the deadly Anthrax disease, because of their genetic similarities. Drops of 200 μL of Bt suspensions, with concentrations 102 CFU/mL, 104 CFU/mL, 106 CFU/mL, were deposited on a SERS chip and sampled after water evaporation. To minimize the contribution to the SERS data given by naturally occurring interferents present in a real scenario, the SERS chip was functionalized with specific phage receptors BtCS33, that bind Bt (or Ba) cells to the SERS surface and allow to rinse the chip removing unwanted contaminants. Different chemometric approaches were applied to the SERS data to classify spectra from Bt-contaminated and uncontaminated areas of the chip: Principal Component Regression (PCR), Partial Least Squares Regression (PLSR) and Data Driven Soft Independent Modeling of Class Analogy (DD-SIMCA). The first two was tested and trained by using data from both contaminated and un-contaminated chips, the last was trained by using data from un-contaminated chips only and tested with all the available data. All of them were able to correctly classify the SERS spectra with great accuracy, the last being suitable for an automated recognition procedure.

Research Progress on Surface-Enhanced Raman Spectroscopy Technique for the Detection of microRNA

Research Progress on Surface-Enhanced Raman Spectroscopy Technique for the Detection of microRNA

Nanostructure-Based Surface-Enhanced Raman Spectroscopy Techniques for Pesticide and Veterinary Drug Residues Screening.

Pesticide and veterinary drug residues in food and environment pose a threat to human health, and a rapid, super-sensitive, accurate and cost-effective analysis technique is therefore highly required to overcome the disadvantages of conventional techniques based on mass spectrometry. Recently, the surface-enhanced Raman spectroscopy (SERS) technique emerges as a potential promising analytical tool for rapid, sensitive and selective detections of environmental pollutants, mostly owing to its possible simplified sample pretreatment, gigantic detectable signal amplification and quick target analyte identification via finger-printing SERS spectra. So theoretically the SERS detection technology has inherent advantages over other competitors especially in complex environmental matrices. The progress in nanostructure SERS substrates and portable Raman appliances will promote this novel detection technology to play an important role in future rapid on-site assay. This paper reviews the advances in nanostructure-based SERS substrates, sensors and relevant portable integrated systems for environmental analysis, highlights the potential applications in the detections of synthetic chemicals such as pesticide and veterinary drug residues, and also discusses the challenges of SERS detection technique for actual environmental monitoring in the future.

Detection of spike protein of SARS-CoV-2 by surface enhanced Raman spectroscopy

The development of rapid detection technique with high sensitivity and high veracity for SARS-CoV-2 is of great importance for the prevention and control of epidemic. In this work, the trace spike protein (S protein) of SARS-CoV-2 in human saliva was detected by surface enhanced Raman spectroscopy (SERS) technique. Significant differences can be found between the Raman spectra of the S protein stained saliva sample and the primitive saliva sample. These results will lay the foundation for the future rapid detection of SARS-CoV-2 by SERS technique.