SERS Spectra Research Articles

Complex evaluation of Raman spectra using morphological filtering: Algorithms, software implementation, and experimental verification of baseline correction, peak recognition, and cosmic ray removal in SERS spectra of designer drugs

AbstractWe discuss a full‐scale treatment of real‐life Raman spectra with pronounced artifacts via mathematical morphology: Baseline correction, peak recognition, and cosmic ray removal are provided using a single kernel. The suggested treatment is peak‐oriented, and its novelty lies in utilization of contact points between a spectrum and its morphological mapping to provide robust peak recognition via low‐order morphological operators. Reaching a compromise between complicated iterative processing and requiring tedious input from the user, the provided algorithms permit effective unsupervised evaluation of individual spectra as well as Raman maps and time or depth series. The method is demonstrated on SERS identification of selected designer drugs.

Porous Au/AAO: A simple and feasible SERS substrate for dynamic monitoring and mechanism analysis of DNA oxidation

Herein, a simple strategy was proposed to acquire an orderly and controllable periodic structure. Firstly, the optimal parameters of porous anodic aluminum oxide (AAO) were screened out by FDTD solutions. Combined with magnetron sputtering of gold nano-film, the Au/AAO SERS substrate was thus fabricated. The enhancement factor (EF) was calculated to be 4.87 × 106, and SERS spectra with good reproducibility were also acquired. The oxidization process of plasmid DNA was thus monitored under the conditions of low detection power and short integration time. Fenton's reagent was used as oxidizing reagent, and Na2SO4 was applied for the internal standard in SERS analysis. The changes of I657/I998, I723/I998, I1587/I998, I1648/I998 were studied over time. DNA oxidation product 8-OHdG was also detected in this process. Then the relationship between the relative peak intensity ratios and the base structure change in DNA was established. The conclusions of SERS spectral analysis were verified by agarosegelelectrophoresis, DNA oxidative damage gel detection and colorimetric detection of 8-OHdG. The mechanism analysis of DNA oxidation process was thus studied. The simple and feasible detection strategy proposed could extend the application of SERS technology in the process monitoring of photosensitive or other unstable biological samples.

Surface-Enhanced Raman Spectroscopy (SERS) for characterization SARS-CoV-2

We used SERS with silver nanoparticles (AgNPs) as the active substrate to develop a, simple, quick, and accurate method for the detection and characterization SARS-CoV-2 without the need for RNA isolation and purification. Inactivated SARS-CoV-2 was used. The SERS signals were more than 105times enhanced than the normal Raman (NR) spectra. The SERS spectra of SARS-CoV-2 fingerprint revealed pronounced intensity signals of nucleic acids; aromatic amino acid side chains: 1007 cm−1 (Phe marker), 1095 cm−1 (CN and PO2− markers), 1580 cm−1 (Tyr, Trp markers). Vibrations of the protein main chain: 1144 cm−1 (CN and NH2 markers), 1221 cm−1 (CN and NH markers), 1270 cm−1 (NH2 marker), 1453 cm−1 (CHCH2 marker). All of these biomolecules could be adsorbed on the AgNPs surface's dense hot patches. The intensity of the SERS band varied with the concentration of SARS-CoV-2, with a virus detection limit of less than 103 vp/mL and RSDs of 20 %.

Adsorption of 4-Mercapto Pyridine with Gold Nanoparticles Embedded in the Langmuir–Blodgett Film Matrix of Stearic Acid: SERS, XPS Studies Aided by Born–Oppenheimer on the Fly Dynamics, Time–Resolved Wavelet Transform Theory, and DFT

This paper reports the adsorptive behavior of the 4-mercaptopyridine (4MPy) molecule soaked in gold nanoparticles (AuNPs) that remain embedded in the bilayer Langmuir–Blodgett (LB) film matrix of stearic acid (SA) for various soaking times (STs). The as-fabricated substrate proved to be an efficient SERS sensing platform that can sense the analyte 4MPy molecules at trace concentrations of ∼1.0 × 10–9 M. The XPS study not only reveals the adsorption of 4Mpy molecules with AuNPs via a sulfur atom but also suggests partial degradation of the analyte molecule upon adsorption. This observation is further substantiated from the SERS spectral profile, which shows unusual broadening of the enhanced Raman bands of the molecule at higher STs. The experimental observations are supported by Born–Oppenheimer on-the-fly molecular dynamics (BO-OF-MD), time-resolved wavelet transform theory (WT), and the DFT calculations based on adcluster models. Selective enhancements of Raman bands in the SERS spectra further suggest the involvement of charge transfer (CT) interaction to the overall enhancements of Raman bands of the analyte molecule. The molecule → CT contribution has been estimated from electron density difference calculations and the corresponding CT distance; the amount of CT is also envisaged.

New Insights into the Multivariate Analysis of SER Spectra Collected on Blood Samples for Prostate Cancer Detection: Towards a Better Understanding of the Role Played by Different Biomolecules on Cancer Screening: A Preliminary Study.

Simple SummaryIn recent years, research on biofluids using Raman and SERS has expanded dramatically, indicating the enormous promise of this technology as a high-throughput tool for identifying cancer and other disorders. In the investigations thus far, researchers have concentrated on a specific illness or condition, but the techniques employed to acquire experimental spectra prevent direct comparison of the data. This necessitates comparative research of a variety of diseases and an increase in scientific cooperation to standardize experimental conditions. In our study, positive results were reached by applying a combined SERS multivariate analysis (MVA) to the urgent problem of prostate cancer diagnosis that was directly linked to real-world settings in healthcare. Moreover, in comparison to the prostate-specific antigen (PSA) test, which has a high sensitivity but limited specificity, our combined SERS-MVA method has greater specificity, which may assist in preventing the overtreatment of patients.It is possible to obtain diagnostically relevant data on the changes in biochemical elements brought on by cancer via the use of multivariate analysis of vibrational spectra recorded on biological fluids. Prostate cancer and control groups included in this research generated almost similar SERS spectra, which means that the values of peak intensities present in SERS spectra can only give unspecific and limited information for distinguishing between the two groups. Our diagnostic algorithm for prostate cancer (PCa) differentiation was built using principal component analysis and linear discriminant analysis (PCA-LDA) analysis of spectral data, which has been widely used in spectral data management in many studies and has shown promising results so far. In order to fully utilize the entire SERS spectrum and automatically determine the most meaningful spectral features that can be used to differentiate PCa from healthy patients, we perform a multivariate analysis on both the entire and specific spectral intervals. Using the PCA-LDA model, the prostate cancer and control groups are clearly distinguished in our investigation. The separability of the following two data sets is also evaluated using two alternative discrimination techniques: principal least squares discriminant analysis (PLS-DA) and principal component analysis—support vector machine (PCA-SVM).

Surface-enhanced Raman spectroscopy for drug discovery: peptide-RNA binding.

The ever-growing demand for new drugs highlights the need to develop novel cost- and time-effective techniques for drug discovery. Surface-enhanced Raman spectroscopy (SERS) is an emerging ultrasensitive and label-free technique that allows for the efficient detection and characterization of molecular interactions. We have recently developed a SERS platform for detecting a single protein molecule linked to a gold substrate (Almehmadi et al. Scientific Reports 2019). In this study, we extended the approach to probe the binding of potential drugs to RNA targets. To demonstrate the proof of concept, two 16-amino acid residue peptides with close primary structures and different binding affinities to the RNA CUG repeat related to myotonic dystrophy were tested. Three-microliter solutions of the RNA repeat with these peptides at nanomolar concentrations were probed using the developed approach, and the binding of only one peptide was demonstrated. The SER spectra exhibited significant fluctuations along with a sudden strong enhancement as spectra were collected consecutively from individual spots. Principal component analysis (PCA) of the SER spectral datasets indicated that free RNA repeats could be differentiated from those complexed with a peptide with 100% accuracy. The developed SERS platform provides a novel opportunity for label-free screening of RNA-binding peptides for drug discovery. Schematic representation of the SERS platform for drug discovery developed in this study.

Development of a SERS based cancer diagnosis approach employing cryosectioned thyroid tissue samples on PDMS

An efficient SERS based novel analytical approach named Cryosectioned-PDMS was developed systematically and evaluated applying on 64 thyroid biopsy samples. To utilize thyroid biopsy samples, a 20-μl volume of h-AgNPs suspension was dropped on a 5-μm thick cryosectioned biopsy specimen placed on the PDMS coated glass slide. The SERS spectra from a 10 × 10 points array acquired by mapping 22.5 μm × 22.5 μm sized area from suspended dried droplets placed on the tissue surface. The probability of correctly predicted performance for diagnosis of malignant, benign and healthy tissues was resulted in the accuracy of 100 % for the spectral bands at 667, 724, 920, 960, 1052, 1096, 1315 and 1457 cm−1 using PCA-fed LDA machine learning. The Cryosectioned-PDMS biophotonic approach with PCA-LDA predictive model demonstrated that the vibrational signatures can accurately recognize the fingerprint of cancer pathology from a healthy one with a simple and fast sample preparation methodology.

Assessing the effect of different pH maintenance situations on bacterial SERS spectra.

Phenotyping of bacteria with vibrational spectroscopy has caught much attention in bacteria-related research. It is known that many factors could affect this process. Among them, solution pH maintenance is crucial, yet its impact on the bacterial SERS spectra is surprisingly neglected. In this work, we focused on two situations related to pH maintenance: the effect of the same buffer on the SERS spectra of bacteria under different pH values, and the influence of different buffers on the SERS spectra of bacteria under the same pH value. Specifically, Britton-Robison (BR) buffer was used to evaluate the effect of pH value on bacteria SERS spectra thanks to its wide pH range. Four different buffers, namely BR buffer, acetate buffer, phosphate buffer, and carbonate buffer, were used to illustrate the impact of buffer types on SERS spectra of bacteria. The results showed that the intensity and number of characteristic peaks of the SERS spectra of Gram-negative (G -) bacteria changed more significantly than Gram-positive (G +) bacteria with the change of pH value. Furthermore, compared with phosphate buffer and carbonate buffer, BR buffer could bring more characteristic SERS bands with better reproducibility, but slightly inferior to acetate buffer. In conclusion, the influence of the pH and types of the buffer on the SERS spectra of bacteria are worthy of further discussion.

Label-free detection of bladder cancer and kidney cancer plasma based on SERS and multivariate statistical algorithm

In this study, we mainly aimed to investigate the diagnostic potential of surface-enhanced Raman spectroscopy for bladder cancer and kidney cancer which are the most common cancers of the urinary system, and evaluate the classification ability of three statistical algorithms: principal component analysis-linear discriminate analysis (PCA-LDA), partial least square-random forest (PLS-RF), and partial least square-support vector machine (PLS-SVM). The plasma of 26 bladder cancer patients, 38 kidney cancer patients and 39 normal subjects was mixed with the same volume of silver nanoparticles, respectively, and then high-quality SERS signal was obtained. The SERS spectra in the range of 400–1800 cm−1 were compared and analyzed. There were some significant differences in SERS peak intensity, which may reflect the changes in the content of some biomacromolecules in the plasma of cancer patients. Based on the three algorithms of PCA-LDA, PLS-RF and PLS-SVM, the classification accuracy of SERS spectra of plasma from cancer patients and normal subjects was 98.1%, 100% and 100%, respectively.In addition, the classification accuracy of the three diagnostic algorithms to classify the SERS spectra of bladder cancer and kidney cancer was 81.3%, 91.7%, and 98.4%, respectively. This exploratory work demonstrates that SERS combined with PLS-SVM algorithm has superior performance for clinical screening of bladder cancer and kidney cancer through peripheral plasma.

Surface-Enhanced Raman Spectroscopy of Pretreated Plasma Samples Predicts Disease Recurrence in Muscle-Invasive Bladder Cancer Patients Undergoing Neoadjuvant Chemotherapy and Radical Cystectomy.

ObjectiveTo explore the value of surface-enhanced Raman spectroscopy analysis of pretreated plasma samples in prediction of bladder cancer (BCa) recurrence after neoadjuvant chemotherapy (NAC) and radical cystectomy (RC).Patients and MethodsSERS was used to analyze plasma samples collected before biopsy and treatment in BCa patients undergoing NAC and RC. The value of clinicopathological parameters and distinctive SERS peaks in the prediction of disease recurrence were analyzed in Cox regression proportional hazard analysis and Log rank test. Principal component analysis and linear discriminant analysis (PCA-LDA) were employed to process spectral data and construct diagnostic algorithms.ResultsA total of 88 patients with 440 plasma SERS spectra were collected. The SRES spectra from recurrent patients were compared with patients who remained recurrence free. The SERS demonstrated higher levels of circulating free nucleic acid components in recurrent population, which is represented by significantly higher intensities at SERS peaks of 725 cm−1, 1328 cm−1 and 1455 cm−1. The SERS also detected significantly lower levels of tryptophan shown as lower significantly intensities at the 1558 cm−1, which is proved to be an independent predictor of BCa recurrence. The addition of SERS peaks of 1558 cm−1 to classic clinicopathological predictors including pathological tumor stage, lymph node metastasis and pathological downstaging can significantly enhance the power of the predictive model from 0.66 to 0.76 in the area under curve (AUC) of receiver operating characteristic (ROC) curves. Meanwhile, the PCA-LDA diagnostic model based on SERS spectra reveals a high accuracy of 85.2% in prediction of disease recurrence and the AUC of 0.92 in the ROC curve. When validated in the leave-one-out cross-validation method, the accuracy of the model remained 84.1%.ConclusionWe show that SERS analysis of plasma before NAC treatment can accurately classify patients with different risks of disease recurrence after surgery and improve the power of clinicopathological predictive models, thus refining clinical decision-making.

Proximity hybridization induced rolling circle amplification for label-free SERS detection of the depression marker human apolipoprotein A4

A new label-free method was developed for SERS detection of human apolipoprotein A4. Rolling circle amplification (RCA) was used, which could induce the production of AuNPs (poly adenine and adsorption gold nanoparticles). When there were two DNA labeled antibodies and target protein, MB1 (molecular beacon 1) was unfolded and the substrate was modified in the homogeneous solution, and the proximate complex was formed. The unfolded molecular beacon worked as a primer in the hybridization with the RCA template to start RCA, which could produce many long sequences of DNA containing amounts of adenines. The AuNPs were bound with the long-repeated adenine in the RCA product, causing accumulation of AuNPs on the surface of the electrode. It was indicated that the spectral characteristics of adenine at 736 cm−1 strongly dominated the SERS spectrum of DNA. Adenine worked as an internal marker for detecting human apolipoprotein A4 by using label-free SERS method. When the conditions were optimal, the detection of human apolipoprotein A4 was carried out from 10 pg mL−1 to 1000 ng mL−1, and the detection limit was low (4.1 pg mL−1). Meanwhile, the specificity was also excellent because the antibody could specifically bind with the corresponding antigen. In addition, since adenine was dominant in SERS spectra and the affinity between AuNPs and poly adenine was high, the detection procedure could be performed without any sophisticated modification. This method might provide a promising strategy for diagnosis in clinical practice.

Differential surface partitioning for an ultrasensitive solid-state SERS sensor and its application to food colorant analysis

Solid-state SERS sensors are desirable point-of-care tools due to their portability. However, the level of SERS sensitivity achieved in liquid phase is rarely duplicated in the solid phase. We report herein the fabrication of a SERS sensor using alumina beads as the solid support and demonstrate its high SERS sensitivity with the model analyte 4-aminophenyl disulfide (4-APDS). The key to sensitivity is a hydrophilic-hydrophobic surface gradient constructed by sequentially coating with the surfactant cetyltrimethylammonium bromide and fluorous 1H,1H,2H,2H-perfluorooctyltriethoxysilane. The surface gradient, together with chloride etching, allows the detection of 4-APDS at the low concentration of 10−15 M. The practicality of the sensor beads is evidenced by successfully tracking the SERS fingerprints of five food colorant standards in the SERS spectra of a popular candy product. These SERS sensor beads are easy to prepare, convenient to use, and highly responsive as a SERS platform for the analysis of colorants.

SERS liquid biopsy in breast cancer. What can we learn from SERS on serum and urine?

SERS analysis of biofluids, coupled with classification algorithms, has recently emerged as a candidate for point-of-care medical diagnosis. Nonetheless, despite the impressive results reported in the literature, there are still gaps in our knowledge of the biochemical information provided by the SERS analysis of biofluids. Therefore, by a critical assignment of the SERS bands, our work aims to provide a systematic analysis of the molecular information that can be achieved from the SERS analysis of serum and urine obtained from breast cancer patients and controls. Further, we compared the relative performance of five different machine learning algorithms for breast cancer and control samples classification based on the serum and urine SERS datasets, and found comparable classification accuracies in the range of 61–89%. This result is not surprising since both biofluids show striking similarities in their SERS spectra providing similar metabolic information, related to purine metabolites. Lastly, by carefully comparing the two datasets (i.e., serum and urine) we show that it is possible to link the misclassified samples to specific metabolic imbalances, such as carotenoid levels, or variations in the creatinine concentration.

Multiplex sensing of complex mixtures by machine vision analysis of TLC-SERS images

Thin layer chromatography in tandem with surface-enhanced Raman scattering (TLC-SERS) has demonstrated tremendous potentials as a new analytical chemistry tool to detect a wide range of substances from real-world samples. However, it still faces significant challenges of multiplex sensing from complex mixtures due to the imperfect separation by TLC and the resulting interference of SERS detection. In this article, we propose a multiplex sensing method of complex mixtures by machine vision analysis of the scanning image of the TLC-SERS results. Briefly, various pure substances in solution and the complex mixture solution are separated by TLC followed by one-dimensional SERS scanning of the entire TLC plate, which generates TLC-SERS images of all target substances along the chromatography path. After that, a machine vision method is employed to extract the template images from the TLC-SERS images of pure substance solutions. Finally, we apply a feature point matching strategy based on the Winner-take-all principle, which matches the template image of each pure substance with the mixture image to confirm the existence and derive the position of each target substance in the TLC plate, respectively. Our experimental results based on the mixture solution of five different substances show that the proposed machine vision analysis is highly selective, sensitive and does not require artificial analysis of the SERS spectra. Therefore, we envision that the proposed machine vision analysis of the TLC-SERS imaging is an objective, accurate, and efficient method for multiplex sensing of trace level of target substances from complex mixtures.

Vibrational Study (Raman, SERS, and IR) of Plant Gallnut Polyphenols Related to the Fabrication of Iron Gall Inks.

FT-Raman, FTIR, and SERS spectra of the structurally related gallnut polyphenols tannic acid, gallic acid, pyrogallol, and syringic acid are reported in this work aiming at performing a comparative assignation of the bands and finding specific marker features that can identify these compounds in complex polyphenol mixtures. Tannic and gallic acids are the principal components in oak gallnuts, and they can be found in iron gall inks. The different functional groups existing in these molecules and their spatial distribution lead to slight changes of the vibrations. The Raman spectra are dominated by bands corresponding to the ring vibrations, but the substituents in the ring strongly affect these vibrations. In contrast, the FTIR spectra of these molecules are dominated by the peripheral oxygen-containing substituents of the aromatic ring and afford complementary information. SERS spectroscopy can be used to analyze trace amounts of these compounds, but the spectra of these polyphenols show strong changes in comparison with the Raman spectra, indicating a strong interaction with the metal. The most significant modification observed in the SERS spectra of these compounds is the weakening of the benzene 8a ring vibration and the subsequent intensification of the 19a mode of the benzene ring. This mode is also more intense in the FTIR spectra, and its intensification in the SERS spectra could be related to a drastic change in the molecular polarizability associated with the interaction of the polyphenol with the metal in Ag NPs.

Qualitative and quantitative detection and identification of two benzodiazepines based on SERS and convolutional neural network technology.

Drug abuse is a global social issue of concern. As the drug market expands, there is an urgent need for technological methods to rapidly detect drug abuse to meet the needs of different situations. Here, we present a strategy for the rapid identification of benzodiazepines (midazolam and diazepam) using surface-enhanced Raman scattering (SERS) combined with neural networks (CNN). The method uses a self-assembled silver nanoparticle paper-based SERS substrate for detection. Then, a SERS spectrum intelligent recognition model based on deep learning technology was constructed to realize the rapid and sensitive distinction between the two drugs. In this work, a total of 560 SERS spectra were collected, and the qualitative and quantitative identification of the two drugs in water and a beverage (Sprite) was realized by a trained convolutional neural network (CNN). The predicted concentrations for each scenario could reach 0.1-50 ppm (midazolam in water), 0.5-50 ppm (midazolam in water and diazepam in Sprite), and 5-150 ppm (diazepam in Sprite), with a strong coefficient of determination (R2) larger than 0.9662. The advantage of this method is that the neural network can extract data features from the entire SERS spectrum, which makes up for information loss when manually identifying the spectrum and selecting a limited number of characteristic peaks. This work clearly clarifies that the combination of SERS and deep learning technology has become an inevitable development trend, and also demonstrates the great potential of this strategy in the practical application of SERS.

Raman spectra and DFT calculations of thiophenol molecules adsorbed on a gold surface.

We report the calculation of Raman modes of thiophenol molecules adsorbed on a real gold surface. The calculated Raman spectra strongly depend on the absorption configuration of the molecule on the metallic surface, a feature that should be carefully taken into account in the interpretation of the surface enhanced Raman spectra (SERS). The calculated Raman spectra are compared with experimental SERS measurements, the best accordance being obtained for a tilted configuration of the absorbed molecule. The present study supports the necessary combination of computational approaches with SERS measurements to predict the type of molecular adsorption configurations on metallic surfaces.

地熱発電プラントにおける酸性熱水からのシリカ付着低減のためのa-C:H合成法の検討

In this study, in order to control the effects of hydrogen content and carbon network structure in the outermost layer of a-C:H film on silica scaling adhesion from geothermal steam, the effects of substrate bias during deposition on the hydrogen content and carbon network structure in the outermost layer of a-C:H were investigated. For this purpose, outermost layer and bulk layer of a-C:H were analyzed by surface-enhanced Raman spectroscopy and conventional Raman spectroscopy, respectively. Silica adhesion simulation tests were performed on a-C:H films deposited at substrate biases of -400 and -700 V by using DC plasma CVD using methane gas as a source gas. The a-C:H film deposited at 400 - V had a smaller amount of silica adhesion than that of the a-C:H film deposited at -700 V. The SERS spectra for the a–C:H films showed an increase in the amount of hydrogen and a decrease in the sp2/sp3 ratio in the outermost layer due to a decrease in the substrate bias, and thus we interpreted that these differences caused the decrease of the silica adhesion.

SERS Investigations of 2-methoxy-2-methylpropylisonitrile (MIBI) and Its Technetium Complex

"Technetium radiopharmaceuticals remain the most widely available and cost-effective compounds used in nuclear medicine imaging. However, exact information regarding the chemical structure of these compounds is scarce. In this study we present a spectroscopic investigation of Hexakis (2-methoxy-2-methylpropylisonitrile) (MIBI) Technetium (99mTc) (99mTc-sestamibi). Surface Enhanced Raman Scattering spectra of the ligand and the Technetium sestamibi complex were recorded. For the later, the measurements were recorded in solution as administered to patients and at different moments from the time of preparation in order to monitor its stability. The information provided by the vibrational characterization of the MIBI ligand allowed us to understand spectroscopic features pertaining to the SERS spectra of the Technetium complex. Keywords: Technetium; radiopharmaceuticals; SPECT; MIBI; SERS. "

Microwave-assisted polycrystalline Ag/AgO/AgCl nanocomposites synthesis using banana corm (rhizome of Musa sp.) extract: Characterization and antimicrobial studies

The rapid emergence of multidrug-resistant microbes has turned into the main threat to public health worldwide. The preparation and application of nanomaterial-based antimicrobials have increased to address this problem with great significance. This study has investigated the bio-fabrication of polycrystalline Ag-NCs using BCE as a reducer and stabilizer under microwave irradiation. Ag-NCs formations were optimized using varying concentrations of BCE and silver acetate. The XRD confirmed the incidence of phases of nanostructured crystals of Ag/AgO/AgCl. The average crystallite sizes were calculated to be 22.19, 36.65, and 36.65 nm for Ag, AgO, and AgCl, respectively. SERS spectra revealed the vibrational modes of the Ag and Ag-O stretch bands. FTIR spectra have indicated the incidence of chemical fingerprints for Ag-NCs and BCE biomolecules. SEM and HR-TEM micrographs exhibited the almost spherical to the oval shape of ∼50 nm. SAED pattern attested to silver crystallinity in agreement with XRD analysis. The MIC values of the Ag-NCs against Escherichia coli, Staphylococcus aureus, Aspergillus brasiliensis, and Rhizopus stolonifer were observed to be 120, 60, 200, and 100 µg/ml, respectively. This study has demonstrated the facile method to prepare polycrystalline Ag-NCs with appropriate structural and morphological characteristics for antimicrobial applications.