Surface Enhanced Raman Spectroscopy Analysis Research Articles

Fabrication and Applications of 3D Nanoarchitectures for Advanced Electrocatalysts and Sensors.

For the last few decades, nanoscale materials and structures have been extensively studied and developed, making a huge impact on human sustainability. For example, the introduction of nanostructures has brought substantial development in electrocatalysts and optical sensing applications. However, there are still remaining challenges that need to be resolved to further improve their performance, reliability, and cost-effectiveness. Herein, long-range ordered 3D nanostructures and their design principles are introduced with an emphasis on electrocatalysts for energy conversion and plasmonic nanostructures for optical sensing. Among the various fabrication techniques, sequential solvent-injection-assisted nanotransfer printing is suggested as a practical fabrication platform for tunable long-range ordered 3D nanostructures composed of ultrahigh-resolution building blocks. Furthermore, the importance of understanding and controlling the 3D design parameters is discussed to realize more efficient energy conversion as well as effective surface-enhanced Raman spectroscopy analyses, suggesting new solutions for clean energy and healthcare issues.

A mathematical approach to deal with nanoparticle polydispersity in surface enhanced Raman spectroscopy to quantify antineoplastic agents

Antineoplastic agents are, for most of them, highly toxic drugs prepared at hospital following individualized prescription. To protect patients and healthcare workers, it is important to develop analytical tools able to identify and quantify such drugs on a wide concentration range. In this context, surface enhanced Raman spectroscopy (SERS) has been tested as a specific and sensitive technique. Despite the standardization of the nanoparticle synthesis, a polydispersity of nanoparticles in the suspension and a lack of reproducibility persist. This study focuses on the development of a new mathematical approach to deal with this nanoparticle polydispersity and its consequences on SERS signal variability through the feasibility of 5-fluorouracil (5FU) quantification using silver nanoparticles (AgNPs) and a handled Raman spectrophotometer. Variability has been maximized by synthetizing six different batches of AgNPs for an average size of 24.9 nm determined by transmission electron microscopy, with residual standard deviation of 17.0%. Regarding low performances of the standard multivariate data processing, an alternative approach based on the nearest neighbors were developed to quantify 5FU. By this approach, the predictive performance of the 5FU concentration was significantly improved. The mean absolute relative error (MARE) decreased from 16.8% with the traditional approach based on PLS regression to 6.30% with the nearest neighbors approach (p-value < 0.001). This study highlights the importance of developing mathematics adapted to SERS analysis which could be a step to overcome the spectral variability in SERS and thus participate in the development of this technique as an analytical tool in quality control to quantify molecules with good performances, particularly in the pharmaceutical field.

Protein Quantification and Imaging by Surface-Enhanced Raman Spectroscopy and Similarity Analysis.

Protein quantification techniques such as immunoassays have been improved considerably, but they have several limitations, including time‐consuming procedures, low sensitivity, and extrinsic detection. Because direct surface‐enhanced Raman spectroscopy (SERS) can detect intrinsic signals of proteins, it can be used as an effective detection method. However, owing to the complexity and reliability of SERS signals, SERS is rarely adopted for quantification without a purified target protein. This study reports an efficient and effective direct SERS‐based immunoassay (SERSIA) technique for protein quantification and imaging. SERSIA relies on the uniform coating of gold nanoparticles (GNPs) on a target‐protein‐immobilized substrate by simple centrifugation. As centrifugation induces close contact between the GNPs and target proteins, the intrinsic signals of the target protein can be detected. For quantification, the protein levels in a cell lysate are estimated using similarity analysis between antibody‐only and protein‐conjugated samples. This method reliably estimates the protein level at a sub‐picomolar detection limit. Furthermore, this method enables quantitative imaging of immobilized protein at a micrometer range. Because this technique is fast, sensitive, and requires only one type of antibody, this approach can be a useful method to detect proteins in biological samples.

Silver nanoflower-coated paper as dual substrate for surface-enhanced Raman spectroscopy and ambient pressure mass spectrometry analysis.

Paper-based analytical devices (PADs) have encountered a wealth of applications in recent years thanks to the numerous advantages of paper as a support. A silver nanoflower (AgNF) modified paper-based dual substrate for both surface-enhanced Raman spectroscopy (SERS) and ambient pressure paper spray mass spectrometry (PS-MS) was developed. AgNFs were immobilized on nylon-coated paper modified with silver and ethylenediamine. The developed substrate was characterized via scanning electron microscopy and infrared spectroscopy. The densely packed nanoscale petals of the AgNFs lead to a large number of so-called hot spots at their overlapping points, which result in an enhancement of the Raman signal. In addition, the presence of the AgNFs produces an increase in the sensitivity of the mass spectrometric analysis as compared with bare paper and nylon/Ag-coated paper. The dual substrate was evaluated for the identification and quantification of ketoprofen in aqueous standards as well as human saliva from healthy volunteers. The method enables the determination of ketoprofen with a limit of detection and limit of quantification via PS-MS of 0.023 and 0.076mgL-1, respectively, with a relative standard deviation (RSD) of 3.4% at a concentration of 0.1mgL-1. This dual substrate enables the simple and fast detection of ketoprofen with minimal sample preparation, providing complementary Raman and mass spectrometric information. Graphical abstract.

Surface-enhanced Raman spectroscopy of neonicotinoid insecticide imidacloprid, assisted by gold and silver nanostructures

Advanced silver and gold nanostructures were fabricated on alumina ceramic by thermal deposition technique. They were applied as active substrates for surface-enhanced Raman spectroscopy analyses of the neonicotinoid insecticides Nuprid 200 SP and Warrant 700 WG, both containing imidacloprid, in quantities much smaller than usually applied in the agricultural medicine. The efficiency of the fabricated structures was simulated by finite difference time domain (FDTD) method. The limit of detection of imidacloprid was estimated to be about ≈0.05 mM. The detection of small amounts or residue of harmful pollutants was forwarded to bearing on the environment and especially on the honey bees’ health.

Quantitative Surface-Enhanced Raman Spectroscopy Analysis through 3D Superlattice Arrays of Au Nanoframes with Attomolar Detection.

This paper reports a methodology for synthesizing and ordering gold nanoframes into three-dimensional (3D) arrays with a controlled thickness, leading to homogeneous plasmonic superstructures, with which quantitative analysis via surface-enhanced Raman spectroscopy (SERS) has been successfully demonstrated. Because this preparation method allows for systematic control of nanoframe film thickness and the resulting 3D plasmonic superstructure, which exhibits a unique nanoporous network of hot-spots, detection limits down to 10-18 M, corresponding to ≈6000 molecules, have been measured. Compared to analogous solid nanoparticle superstructures, the nanoframe superstructures with their unique nanoporous architecture effectively dissipate the heat inevitably generated by laser excitation during measurement, effectively suppressing the formation of carbonaceous materials and therefore their accompanying fluorescence interference, especially important for low concentration detection.

Label-free serum detection of Trichinella spiralis using surface-enhanced Raman spectroscopy combined with multivariate analysis

Based on blood serum surface-enhanced Raman spectroscopy (SERS) analysis, this paper proposed a simple and unlabeled non-invasive serum detection for T. spiralis infection. Serum samples were collected and analyzed from 40 rats at 0 days post infection (dpi) (normal rats), 19 uninfected rats, and 16 rats infected with T. spiralis at 28 dpi, using SERS measurements. Multivariate statistical techniques, such as linear discriminant analysis (LDA) and principal components analysis (PCA), were used to analyze and identify the obtained blood serum SERS spectra. The diagnosis algorithms, based on PCA-LDA, achieved a diagnostic sensitivity of 87.5%, a specificity of 94.7%, and an accuracy of 91.4% for separating the samples infected with T. spiralis from the control samples. This exploratory study demonstrated that colloidal Ag NPs-based SERS serum analysis technique combined with PCA-LDA has a great potential in improving the detection of T. spiralis infection and onsite screening.

Label-free screening of intrahepatic cholangiocarcinoma cells using electroporation assisted surface-enhanced Raman scattering combined with statistical algorithms

Analysis of biochemical components in living cells is essential for early screening of intrahepatic cholangiocarcinoma (ICC). In this paper, we investigated the feasibility of using electroporation assisted surface-enhanced Raman spectroscopy and multivariate analysis methods to discriminate ICC cells (HCCC-9810 cell line) from normal liver cells (Lo2 cell line) with the aim to develop a label-free and fast method for ICC cell screening. Electroporation technology was employed to deliver silver nanoparticles into living cells and then high quality SERS spectra were successfully measured from 100 ICC cells and 100 normal liver cells. There were significant differences in SERS spectra between ICC cells and normal cells, which indicated special changes in the content of biomolecules including nucleic acids, proteins and lipid in different cell lines. Based on the principal component analysis (PCA) and linear discriminant analysis (LDA) algorithms, high diagnostic sensitivity (97%), specificity (96%) and accuracy (96.5%) were achieved for distinguishing ICC cells and normal cells. This exploratory work demonstrates that the electroporation-assisted SERS technique combined with PCA-LDA diagnostic algorithms has great potential for detecting and screening of cancer cell.

Gold nanoflowers grown in a porous Si/SiO2 matrix: The fabrication process and plasmonic properties

Highly branched metal nanostructures are of great interest for creating highly sensitive sensors of various molecules through the use of surface-enhanced Raman scattering. However, the method of manufacturing such nanostructures is often complex and difficult to reproduce. Herein, we report a simple, one-pot synthesis of flower-like gold nanostructures (AuNFs) in a porous Si/SiO2 matrix for potential surface enhanced Raman spectroscopy (SERS) applications. Templates covered with 500-nm diameter AuNFs with rough coral-like petals were obtained through a simple electroless reduction of gold(III) chloride salt in diluted hydrofluoric acid inside pores in the Si/SiO2 layer, which were obtained by swift heavy ion track technology. Detailed SERS analysis showed that single nanoflowers are capable of enhancing Raman signals and can be used as independent nanosensors. Having a few AuNFs in close proximity to each other enables more “hot spots” to be formed with an even greater increase in signal intensity. Using such substrates, reliable detection of 4-mercaptopyridine could be achieved down to 0.01 µM concentration. Finally, we theoretically demonstrate that the AuNFs are characterized by a wide-band plasmonic mode as well as by hot spots in the electromagnetic near-field distribution, which can result in a high enhancement factor for Raman scattering.

Surface-Enhanced Raman Spectroscopy (SERS) of Neonicotinoid Insecticide Thiacloprid Assisted by Silver and Gold Nanostructures.

This study expresses our results on surface-enhanced Raman spectroscopy (SERS) analyses of neonicotinoid insecticide thiacloprid, i.e., Calypso 480 SC, in quantities much smaller than usually applied in the agricultural medicine. Advanced Ag and Au nanostructures created by the thermal deposition technique on Al2O3 ceramic were applied as active substrates for SERS analyses. The minimum concentration of thiacloprid detected was 380 µM and the enhancement factor was estimated to be about 3 × 103. The intensity of the SERS peaks increased by an order of magnitude after pulsed laser annealing of the films and formation of nanoparticle arrays and the enhancement factor reached ≈104, respectively. The proposed study has direct bearing on the environment and human health by detection of small amounts or residue of harmful pollutants using a relatively cheap and easy method to produce active SERS substrates.

Highly Sensitive and Rapid Surface Enhanced Raman Spectroscopic (SERS) Determination of Thiram on the Epidermis of Fruits and Vegetables Using A Silver Nanoparticle-Modified Fibrous Swab

A surface enhanced Raman spectroscopy (SERS) substrate with high sensitivity for the rapid determination of pesticide residues was developed by combining silver nanoparticles (AgNPs) and a fibrous swab. The SERS substrate was prepared simply by forming a SERS-active area on the section of fibrous swab and centrifuge tube with a soak-absorb approach. Rhodamine 6 G (R6G), a common SERS probe molecule, was used for performance characterization of the prepared SERS substrate that was shown to have high sensitivity, good stability and good reproducibility. Due to its good flexibility, the prepared SERS substrate was used to wipe contaminated surfaces for residue extraction. High sensitivity for detecting trace-level pesticides was verified for thiram. The limits of detection (LODs) for thiram on apple, grape and eggplant epidermis were calculated to be as low as 0.5313, 0.5768, and 0.5495 ng·cm−2, respectively, nearly 2 orders of magnitude below the maximum residue limit (MRL) for pesticide in food issued by the National Food Safety Standard of China. In addition, there was a linear relationship between the SERS intensity and the concentration of thiram in these three samples, indicating the good potential of this substrate for quantitative SERS analysis.

Design and Simple Assembly of Gold Nanostar Bioconjugates for Surface-Enhanced Raman Spectroscopy Immunoassays.

Immunoassays using Surface-Enhanced Raman Spectroscopy are especially interesting on account not only of their increased sensitivity, but also due to its easy translation to point-of-care formats. The bases for these assays are bioconjugates of polyclonal antibodies and anisotropic gold nanoparticles functionalized with a Raman reporter. These bioconjugates, once loaded with the antigen analyte, can react on a sandwich format with the same antibodies immobilized on a surface. This surface can then be used for detection, on a microfluidics or immunochromatographic platform. Here, we have assembled bioconjugates of gold nanostars functionalized with 4-mercaptobenzoic acid, and anti-horseradish peroxidase antibodies. The assembly was by simple incubation, and agarose gel electrophoresis determined a high gold nanostar to antibody binding constant. The functionality of the bioconjugates is easy to determine since the respective antigen presents peroxidase enzymatic activity. Furthermore, the chosen antibody is a generic immunoglobulin G (IgG) antibody, opening the application of these principles to other antibody-antigen systems. Surface-Enhanced Raman Spectroscopy analysis of these bioconjugates indicated antigen detection down to 50 µU of peroxidase activity. All steps of conjugation were fully characterized by ultraviolet-visible spectroscopy, dynamic light scattering, -Potential, scanning electron microscopy, and agarose gel electrophoresis. Based on the latter technique, a proof-of-concept was established for the proposed immunoassay.

High accuracy detection of malignant pleural effusion based on label-free surface-enhanced Raman spectroscopy and multivariate statistical analysis.

The present study aims to diagnose malignant pleural effusion (MPE) based on the identification of distinctive Raman spectra bands. The tests on 83 pleural effusion (PE) samples including 32 benign PE (BPE) and 51 MPE were performed based on rapid and label-free surface-enhanced Raman spectroscopy (SERS). The TiO2 photo-catalyzed Ag NPs were used as SERS substrate. And the SERS spectra of BPE and MPE were compared and diagnosed through orthogonal partial least squares discriminant analysis (OPLS-DA). The diagnosis results showed that the sensitivity and specificity can reach 92.2% and 93.8%, respectively, based on leave-one-out cross validation. And the area under curve values of MPE was 0.985. This study demonstrated an accurate way of combining Raman spectra of PE with OPLS-DA to identify MPE and BPE.

Multi-functional SERS substrate: Collection, separation, and identification of airborne chemical powders on a single device

Due to its extreme sensitivity and fingerprint specificity, surface enhanced Raman spectroscopy (SERS) is a powerful tool for substance identification. Developments in portable low-cost SERS substrates and handheld Raman spectrometers enable SERS analysis at sample origin, with great potential benefit to field-work applications in numerous disciplines. This study reports a procedure which incorporates sample collection, isolation, and SERS identification of airborne solids on a single inexpensive substrate. This procedure, vacuum filtration-paper chromatography–SERS (VF-PC-SERS), utilizes a porous filter paper decorated with plasmonic nanoparticles which we call nanopaper. The porous fiber structure facilitates both the vacuum filter powder capture and the isolation of components by paper chromatography, while the nanoplasmonic coating enhances Raman signal. One potentially high-impact application of VF-PC-SERS is field analysis of hazardous or illicit materials. This study demonstrates a proof-of-concept for VF-PC-SERS using powdered rhodamine 6G (R6G) dispersed in air, resulting in 100% detection accuracy (true positive rate) at R6G levels as low as 0.6 mg/m3. Analysis of R6G contaminated with topsoil or lactose resulted in specific identification of R6G in powder mixtures containing as little as 0.1 wt. % R6G. This study demonstrates the feasibility of VF-PC-SERS as a safer procedure to identify hazardous substances at the point of sample origin.

Optimal ELM-RBF model and SERS Analysis of Saliva for Classification of NS1.

Extreme Learning Machine (ELM) with Radial Basis Function (RBF) Kernel has demonstrated strong capability in pattern recognition and classification problems. NS1 is a biomarker for flavivirus related diseases, where current detection methods are serum based and hence invasive. Our previous work has captured NS1 molecular fingerprint in saliva using Surface Enhanced Raman Spectroscopy (SERS) that could amount to non-invasive detection method. SERS is an improved Raman spectroscopic technique, which can amplify spectral intensity by 103 to l07 times, to yield usable spectra of low concentration NS1 in saliva. The spectra produced contain 1801 features for each of the 284 samples collected. Principal Component Analysis (PCA) transforms a high dimensional data to a lower dimension principal components (PCs), at no sacrifice of important information of the original data. Both termination criteria of PCA and kernel parameters of ELM have effect on performance of the classifier models. This paper aims to unravel an optimal ELM-RBF classifier model for classification of NS1 salivary SERS spectra. Performance of a total of 864 classifier models are examined and compared in terms of [accuracy, kappa, precision, sensitivity and specificity]. Results show that CPV- and EOC-ELM-RBF classifier models are on par and outperform the Scree-ELM-RBF classifier models.

Composition of the Biofilm Matrix of Cutibacterium acnes Acneic Strain RT5.

In skin, Cutibacterium acnes (former Propionibacterium acnes) can behave as an opportunistic pathogen, depending on the strain and environmental conditions. Acneic strains of C. acnes form biofilms inside skin–gland hollows, inducing inflammation and skin disorders. The essential exogenous products of C. acnes accumulate in the extracellular matrix of the biofilm, conferring essential bacterial functions to this structure. However, little is known about the actual composition of the biofilm matrix of C. acnes. Here, we developed a new technique for the extraction of the biofilm matrix of Gram-positive bacteria without the use of chemical or enzymatic digestion, known to be a source of artifacts. Our method is based on the physical separation of the cells and matrix of sonicated biofilms by ultracentrifugation through a CsCl gradient. Biofilms were grown on the surface of cellulose acetate filters, and the biomass was collected without contamination by the growth medium. The biofilm matrix of the acneic C. acnes RT5 strain appears to consist mainly of polysaccharides. The following is the ratio of the main matrix components: 62.6% polysaccharides, 9.6% proteins, 4.0% DNA, and 23.8% other compounds (porphyrins precursors and other). The chemical structure of the major polysaccharide was determined using a nuclear magnetic resonance technique, the formula being →6)-α-D-Galp-(1→4)-β-D-ManpNAc3NAcA-(1→6)-α-D-Glcp-(1→4)-β-D-ManpNAc3NAcA-(1→3)-β-GalpNAc-(1→. We detected 447 proteins in the matrix, of which the most abundant were the chaperonin GroL, the elongation factors EF-Tu and EF-G, several enzymes of glycolysis, and proteins of unknown function. The matrix also contained more than 20 hydrolases of various substrata, pathogenicity factors, and many intracellular proteins and enzymes. We also performed surface-enhanced Raman spectroscopy analysis of the C. acnes RT5 matrix for the first time, providing the surface-enhanced Raman scattering (SERS) profiles of the C. acnes RT5 biofilm matrix and biofilm biomass. The difference between the matrix and biofilm biomass spectra showed successful matrix extraction rather than simply the presence of cell debris after sonication. These data show the complexity of the biofilm matrix composition and should be essential for the development of new anti-C. acnes biofilms and potential antibiofilm drugs.

Surface-enhanced raman spectroscopy: A non invasive alternative procedure for early detection in childhood asthma biomarkers in saliva.

The early detection of bronchial inflammation in asthma, through a non-invasive, simple method and under a subclinical state, could lead to a more effective control of this condition. The aim of this study was to identify biomarkers of bronchial inflammation in the saliva of children with asthma through immunoassay and Surface Enhanced Raman Spectroscopy (SERS). We conducted an analytical cross-sectional study in 44 children ages 6-12; the diagnosis of asthma was made according to Global Initiative for Asthma (GINA) standards. The children's saliva was analyzed by immunoassay for the quantification of 37 cytokines, as well as SERS analysis in a confocal Raman microscope at 785 nm. We found a significant association between bronchial obstruction and IL-8 (p = 0.004), IL-10 (p = 0.008) and sCD163 (p = 0.003). The Raman spectra showed significant amplification in the region of 760 to 1750 cm-1. The Principal Component Analysis and Linear Discriminant Analysis (PCA-LDA) method has a sensitivity of 85%, specificity of 82% and an accuracy of 84% for the diagnosis of asthma. These results demonstrate the presence of a subclinical inflammatory state, suggestive of bronchial remodeling in the population studied. The SERS method is a potential tool for identifying bronchial inflammation and its endotype, allowing for a highly sensitive and specific diagnosis.

Graphene Quantum Dots Wrapped Gold Nanoparticles with Integrated Enhancement Mechanisms as Sensitive and Homogeneous Substrates for Surface-Enhanced Raman Spectroscopy.

Rational engineering of highly stable and Raman-active nanostructured substrates is still urgently in demand for achieving sensitive and reliable surface-enhanced Raman spectroscopy (SERS) analysis in solution phase. Herein, monodisperse N-doping graphene quantum dots wrapped Au nanoparticles (Au-NGQD NPs) were facilely prepared, and further their applications as substrates in SERS-based detection and cellular imaging have been explored. The as-prepared Au-NGQD NPs exhibit superior long-term stability and biocompatibility, as well as large enhancement capability due to the integration of electromagnetic and chemical enhancements. The practical applicability of the Au-NGQD NPs was verified via the direct SERS tests of several kinds of aromatics in solution phase. Finite-difference time-domain simulations in combination with density functional theory calculation were also successfully used to explain the enhancement mechanism. Furthermore, the Au-NGQD NPs were conjugated with 4-nitrobenzenethiol (4-NBT, as reporter) and 4-mercaptophenylboronic acid (MPBA, as targeting element) to construct the MPBA/4-NBT@Au-NGQD probes, which could specifically recognize glycan-overexpressed cancer cells through SERS imaging on a cell surface. The prepared Au-NGQDs show great potential as superior SERS substrates in solution phase for on-site Raman detection.

Label-free diagnosis of lung cancer with tissue-slice surface-enhanced Raman spectroscopy and statistical analysis.

Despite the rapid development of medical science, the diagnosis of lung cancer is still quite challenging. Due to the ultrahigh detection sensitivity of surface-enhanced Raman spectroscopy (SERS), SERS has a broad application prospect in biomedicine, especially in the field of tumor blood detection. Although Raman spectroscopy can diagnose lung cancer through tissue slices, its weak cross sections are problematic. In this study, silver nanoparticles (AgNPs) were added to the surface of lung tissue slices to enhance the Raman scattering signals of biomolecules. The electromagnetic field distribution of AgNPs prepared was simulated using the COMSOL software. SERS obtained from the slices reflected the difference in biochemical molecules between normal (n = 23) and cancerous (n = 23) lung tissues. Principal component-linear discriminate analysis (PCA-LDA) was utilized to classify lung cancer and healthy lung tissues. The receiver operating characteristic curve gave the sensitivity (95.7%) and specificity (95.7%) of the PCA-LDA method. This study sheds new light on the general applicability of SERS analysis of tissue slices in clinical trials.

Identification of Newly Emerging Influenza Viruses by Detecting the Virally Infected Cells Based on Surface Enhanced Raman Spectroscopy and Principal Component Analysis.

Rapid diagnosis and quarantine of influenza virus mutant-infected people is critical to contain the fatal viral infection spread because effective antiviral drugs are normally not available. Conventional methods, however, cannot be used for the diagnosis because these methods need predefined labels, likely also unavailable for just emerging viruses. Here, we propose label-free identification of cells infected with different influenza viruses based on surface-enhanced Raman spectroscopy (SERS) and principal component analysis (PCA). Viral envelope proteins that are displayed on the surface of cells after infection of influenza viruses were targeted for this identification. Cells that expressed the envelope proteins of A/WSN/33 H1N1 or A/California/04/2009 H1N1 influenza viruses produced distinct SERS signals. Cells that displayed combinations of the envelope proteins from these two viral variants, an indication of emergence of a new virus, also generated characteristic SERS patterns. However, the cell's own surface proteins often hindered the identification of virally infected cells by producing SERS peaks similar to viral ones. PCA of the obtained SERS patterns could effectively capture the virus-specific signal components from the jumbled SERS peaks. Our study demonstrates a potential of combination of SERS and PCA to identify newly emerging influenza viruses through sensing the cells infected with the viruses.