Surface-enhanced Raman Scattering Spectra Research Articles

Plasmonic nanorod probes' journey inside plant cells for in vivo SERS sensing and multimodal imaging.

Nanoparticle-based platforms are gaining strong interest in plant biology and bioenergy research to monitor and control biological processes in whole plants. However, in vivo monitoring of biomolecules using nanoparticles inside plant cells remains challenging due to the impenetrability of the plant cell wall to nanoparticles beyond the exclusion limits (5-20 nm). To overcome this physical barrier, we have designed unique bimetallic silver-coated gold nanorods (AuNR@Ag) capable of entering plant cells, while conserving key plasmonic properties in the near-infrared (NIR). To demonstrate cellular internalization and tracking of the nanorods inside plant tissue, we used a comprehensive multimodal imaging approach that included transmission electron microscopy (TEM), confocal fluorescence microscopy, two-photon luminescence (TPL), X-ray fluorescence microscopy (XRF), and photoacoustics imaging (PAI). We successfully acquired SERS signals of nanorods in vivo inside plant cells of tobacco leaves. On the same leaf samples, we applied orthogonal imaging methods, TPL and PAI techniques for in vivo imaging of the nanorods. This study first demonstrates the intracellular internalization of AuNR@Ag inside whole plant systems for in vivo SERS analysis in tobacco cells. This work demonstrates the potential of this nanoplatform as a new nanotool for intracellular in vivo biosensing for plant biology.

Nanoscale Investigation of DNA Demethylation in Leukemia Cells by Means of Ultrasensitive Vibrational Spectroscopy.

DNA methylation is a crucial epigenetic hallmark of cancer development but the experimental methods able to prove nanoscale modifications are very scarce. Over time, Raman and its counterpart, surface-enhanced Raman scattering (SERS), became one of the most promising techniques capable to investigate nanoscale modifications of DNA bases. In our study, we employed Raman/SERS to highlight the differences between normal and leukemia DNA samples and to evaluate the effects of a 5-azacytidine treatment on leukemia cells. To obtain spectral information related to DNA base modifications, a DNA incubation step of 4 min at 94 °C, similar to the one performed in the case of RT-PCR experiments, was conducted prior to any measurements. In this way, reproducible Raman/SERS spectra were collected for all genomic DNA samples. Our Raman results allowed discrimination between normal and cancer DNAs based on their different aggregation behavior induced by the distinct methylation landscape present in the DNA samples. On the other hand, the SERS spectra collected on the same DNA samples show a very intense vibrational band located at 1008 cm-1 assigned to a rocking vibration of 5-methyl-cytosine. The intensity of this band strongly decreases in cancer DNA due to the modification of the methylation landscape occurring in cancers. We believe that under controlled experimental conditions, this vibrational band could be used as a powerful marker for demonstrating epigenetic reprogramming in cancer by means of SERS.

Ultrasensitive Optical Fingerprinting of Biorelevant Molecules by Means of SERS-Mapping on Nanostructured Metasurfaces.

The most relevant technique for portable (on-chip) sensors is Surface Enhanced Raman Scattering (SERS). This strategy crashes in the case of large (biorelevant) molecules and nano-objects, whose SERS spectra are irreproducible for "homeopathic" concentrations. We suggested solving this problem by SERS-mapping. We analyzed the distributions of SERS parameters for relatively "small" (malachite green (MG)) and "large" (phthalocyanine, H2Pc*) molecules. While fluctuations of spectra for "small" MG were negligible, noticeable distribution of spectra was observed for "large" H2Pc*. We show that the latter is due to a random arrangement of molecules with respect to "hot spot" areas, which have limited sizes, thus amplifying the lines corresponding to vibrations of different molecule parts. We have developed a method for engineering low-cost SERS substrates optimized for the best enhancement efficiency and a measurement protocol to obtain a reliable Raman spectrum, even for a countable number of large molecules randomly distributed over the substrate.

Spectroscopic analysis of mushrooms by surface-enhanced Raman scattering (SERS)

BackgroundMushrooms have always been considered an important source of food and biologically active compounds with several medicinal properties. In recent years, different methods were used to study the quality and chemical composition of mushrooms. Among these, Fourier transform infrared (FT-IR) and FT-Raman spectroscopy techniques have been successfully applied to identify different mushroom species. However, the structural biomolecule components existing in the mycelium or in the fruiting bodies may produce strong fluorescence emission that overlaps the Raman radiation, thus avoiding their analyses by Raman. SERS spectroscopy is a powerful technique which uses metal nanoparticles (NPs) to enhance the Raman signal of molecules adsorbed on the NPs surface. In addition, SERS is able to quench the macromolecule florescence. In this work, we have employed silver nanoparticles in order to get mushroom fingerprints based on SERS as quick procedure to analyze and identify different chemical compounds from the fruiting bodies of six edible and/or medicinal mushrooms: Lentinula edodes, Ganoderma lucidum, Pleurotus cornucopiae, Pleurotus ostreatus, Tuber aestivum and Tuber magnatum.ResultsSERS analyses performed directly on fruiting body fragments produced characteristic spectra for each species. One group of mushrooms (L. edodes, G. lucidum, T. aestivum and T. magnatum) was dominated by the bands of nucleic acids; and the other one (P. cornucopiae and P. ostreatus), by the bands of pigments such as melanins; carotenoids; azafilones; polyketides; and flavonoids located in the cell wall. Additionally, bands corresponding to cell wall polysaccharides, particularly chitosan and 1,3-β D-glucan, were identified in the extracts of P. cornucopiae, P. ostreatus and L. edodes. No signal of cell wall polysaccharides was found in G. lucidum extract. Raman mapping of the analyzed samples was useful in tracking the spatial distribution of the marker bands. Moreover, the principal component analysis (PCA) carried out on the acquired SERS spectra, allows to discriminate the analyzed mushroom species.ConclusionsThe SERS technique has the ability to generate a strong Raman signal from mushroom fruiting bodies using Ag-NPs deposited directly on intact, untreated mushroom tissues. Using this methodology, commonly applied laboratory time-consuming methods can be avoided or bypassed as well as analysis time can be reduced.Graphical

Radiation mediated oxidation processes for the conversion of 4-aminothiophenol in to 4, 4′-dimercaptoazobenzene

Gold (Au) nanoparticles of average size ∼ 4 nm were in-situ synthesized and simultaneously embedded on SiO2 nanospheres in a single step process by irradiating a solution (HAuCl4, PVP and SiO2 nanospheres) with 6 MeV electrons at a fluence of 1.0 × 1015 e−/cm2. Substrates were prepared by coating silicon wafers with the solution of SiO2 nanospheres embedded with Au nanoparticles and used for recording Surface-enhanced Raman scattering (SERS) spectra of 4-aminothiophenol (4-ATP) and 4-ATP along with NaOH with a Raman spectrometer. The appearance of characteristic SERS peaks of both 4-ATP and 4, 4′-DMAB in Raman spectra confirmed conversion of a fraction of 4-ATP into 4, 4′-DMAB in the presence of Au nanoparticles, and the intensity of SERS peaks increased in the presence of NaOH. In the recorded SERS spectra, the prominent peaks at around 1140 cm−1, 1390 cm−1 and 1438 cm−1 were observed and assigned respectively to β(C–H), ν(NN) and ν(NN) chemical bonds, which are called b2 modes. The results of the present work revealed that in the presence of Au nanoparticles, the OH− radicals produced from NaOH play an important role in the conversion of 4-ATP into 4, 4′-DMAB, and enhancing the intensities of b2 peaks. The Au nanoparticles of size ≤5 nm have inherent nanoscale defects and therefore surface with nanoscale roughness. All these together greatly enhanced electric field which brought out the strong Raman intensity and catalytic oxidation reaction leading to conversion of 4-ATP into 4,4′-DMAB on the Au–SiO2 substrates.

SERS spectra of a bioactive carboximidamide derivative at different concentrations: Experimental and DFT investigations

In the current study (Z)-2-(2-oxoindolin-3-ylidene)hydrazine-1-carboximidamide hydrochloride's (OHC) Infrared (IR), Raman and surface-enhanced Raman scattering (SERS) spectra at various concentrations were presented experimentally and using density functional theory (DFT) simulations. Ag6, OHC and OHC-Ag6 systems’ structures were initially optimized. The OHC-Ag6 complex exhibits charge transfer interaction (CTI) between Ag and the OHC, according to frontier orbital molecular (FMO) analysis. The CTI under investigation was confirmed by molecular electrostatic potential (MEP) analysis. The molecule is chemisorbed on Ag6 in a tilted manner through the lone pair of atoms, according to SERS investigation. With changes in concentration, the interaction of OHC with metal also changes, resulting in orientation changes. Additionally, there was a strong correlation between theoretical findings and experimental values. Therefore, the current discovery opens the door for engineering robust SERS active substrates useful for creating OHC-related biosensors.

Identification of antibiotic residues in aquatic products with surface-enhanced Raman scattering powered by 1-D convolutional neural networks

Universal and fast antibiotic residues detection technology is imperative for the control of food safety in aquatic products. However, accurate surface-enhanced Raman scattering (SERS) quantitative detection of complicated samples is still a challenge. A recognition method powered by deep learning and took advantage of the unique fingerprint information merits of SERS was proposed. Herein, the spectra were collected by Ag nanofilm SERS substrate prepared by self-assembly of Ag nanoparticles on water/oil interface. A SERS-based database of commonly used antibiotics in aquatic products was set up, which is suitable for employed as input data for learning and training. The results show that the five types of antibiotics are successfully distinguished through principal component analysis (PCA) and each antibiotic in every type was successfully distinguished. Furthermore, one-dimensional convolutional neural networks (1-D CNN) was used to distinguish the antibiotics, and the results show that all the test samples were correctly predicted by 1-D CNN model. The results of this research suggest the great potential of the combination of SERS spectra with deep learning as a method for rapid and highly accurate identification of antibiotic residues in aquatic products.

Binding mechanism and SERS spectra of 5-fluorouracil on gold clusters.

The adsorption behaviour of the 5-fluorouracil (5FU) on small gold clusters Au N with N = 6, 8, 20 was evaluated by means of density functional theory using the PBE-D3 functional in combination with a mixed basis set, i.e. cc-pVDZ-PP for gold atoms and cc-pVTZ for non-metal elements. The binding energies between 5FU and gold clusters were determined in the range of 16-24 and 11-19kcal/mol in gas-phase and aqueous media, respectively. The corresponding Gibbs energies were found to be around -7 to -10kcal/mol in vacum and sigificantly reduced to -1 to -6kcal/mol in water solution, indicating that both the association and dissociation processes are likely spontaneous. An analysis on the charge density difference tends to confirm the existence of a charge transfer from the 5FU molecule to Au atoms. Analysis of the surface-enhanced Raman scattering (SERS) spectra of 5FU adsorbed on the Au surfaces shows that the stretching vibrations of N-H and C=O bonds play a major role in the SERS phenomenon. A mechanism for the drug releasing from the gold surfaces is also proposed. The process is triggered by either the low pH in cancerous tumors or the presence of cysteine residues in protein matrices.

Exploring Intensity Distributions and Sampling in SERS-Based Immunoassays.

Surface-enhanced Raman scattering (SERS) is a sensitive, widely used spectroscopic technique. However, SERS is perceived as poorly reproducible and insufficiently robust for standard applications in analytical chemistry. Here, we demonstrated that reliable SERS immunoassay quantification at low concentrations (pM range) can be achieved by careful experimental design and appropriate data analysis statistics. A SERS-based immunoassay for IgG in human serum (3.1-50.0 ng mL-1 or 20.6-333 pM) was developed as a proof of concept. Calibration curves were created using the population median of the band area, centered at 592 cm-1, of a SERS reporter (Nile Blue A). Histograms of 7200 SERS spectra show lognormal distributions. SEM images of the sensor platform confirm a correlation between the number of SERS probes (ERLs) at the surface and the SERS intensity response. The IgG immunosensor reported here presented a limit of detection of 1.11 ng mL-1 or 7.39 pM and a limit of quantification of 9.04 ng mL-1 or 60.30 pM, within a 95% confidence level. The % error of the predicted versus the actual response of a quality control (QC) sample was 0.13%. The percent error of the QC sample decreases exponentially with the number of measurements. Randomly selected spatially separated measurements provided lower QC % error than a larger number of measurements that were closely spaced. We propose that it is necessary to describe the measured populations using an appropriate sample size for good statistics and consider the interrogation of sufficiently large and well-separated areas of the sensor surface to achieve a reliable sampling.

Probe-Free Identification of RNA Virus Variants with Point Mutations by Surface-Enhanced Raman Spectroscopy.

As observed in the COVID-19 pandemic, RNA viruses continue to rapidly evolve through mutations. In the absence of effective therapeutics, early detection of new severely pathogenic viruses and quarantine of infected people are critical for reducing the spread of the viral infections. However, conventional detection methods require a substantial amount of time to develop probes specific to new viruses, thereby impeding immediate response to the emergence of viral pathogens. In this study, we identified multiple types of viruses by obtaining the spectral fingerprint of their surface proteins with probe-free surface-enhanced Raman scattering (SERS). In addition, the SERS-based method can remarkably distinguish influenza virus variants with several surface protein point mutations from their parental strain. Principal component analysis (PCA) of the SERS spectra systematically captured the key Raman bands to distinguish the variants. Our results show that the combination of SERS and PCA can be a promising tool for rapid detection of newly emerging mutant viruses without a virus-specific probe.

Rapid synthesis of MoS2–Ag nanocomposites via photoreduction for optical tuning and surface-enhanced Raman scattering applications

Semiconductor–metal nanocomposites have been widely investigated to modify the intrinsic properties of materials used for optoelectronic devices and sensing applications. In this study, a method for rapid synthesis of MoS2–Ag nanocomposites via laser-assisted photoreduction was proposed. For the photoreduction process, we used AgNO3 solution as a metal source. Under laser irradiation, Ag ions were easily reduced on MoS2 by photo-generated electrons from MoS2. The optical properties of MoS2–Ag nanocomposites were easily controlled by simple adjustment of the photoreduction time. To investigate the surface-enhanced Raman scattering (SERS) effect of the MoS2–Ag nanocomposites, the SERS spectra of methylene blue (MB) on MoS2–Ag nanocomposites were measured, and the nanocomposites were found to enhance the Raman scattering intensity of MB up to ∼106. Therefore, the laser-assisted photoreduction method has great potential for rapid synthesis and optical tuning of semiconductor–metal nanocomposites.

Equilibria of semi-volatile isothiazolinones between air and glass surfaces measured by gas chromatography and Raman spectroscopy

Trace amounts of semi-volatile organic compounds (SVOCs) of the two isothiazolinones of 2-methylisothiazol-3(2H)-one (MIT) and 2-octyl-4-isothiazolin-3-one (OIT) were detected both in the air and on glass surfaces. Equilibria of SVOCs between air and glass were examined by solid phase microextraction-gas chromatography/mass spectrometry (SPME-GC/MS). Surface to air distribution ratios of Ksa for MIT and OIT were determined to be 5.10 m and 281.74 m, respectively, suggesting more abundant MIT in the gas phase by a factor of ∼55. In addition, a facile method of silver nanocube (AgNC)-assisted surface-enhanced Raman scattering (SERS) has been developed for the rapid and sensitive detection of MIT and OIT on glass surfaces. According to MIT and OIT concentration-correlated SERS intensities of Raman peaks at ∼1585 cm−1 and ∼1125 cm−1, respectively. Their calibration curves have been obtained in the concentration ranges between 10−3 to 10−10 M and 10−3 to 10−11 M with their linearity of 0.9986 and 0.9989 for MIT and OIT, respectively. The limits of detection (LODs) of the two isothiazolinones were estimated at 10−10 M, and 10−11 M for MIT and OIT, respectively. Our results indicate that AgNC-assisted SERS spectra are a rapid and high-ultrasensitive method for the quantification of MIT and OIT in practical applications. The development of analytical methods and determination of the Ksa value obtained in this study can be applied to the prediction of the exposure to MIT and OIT from various chemical products and dynamic behaviors to assess human health risks in indoor environments.

Understanding chemical enhancements of surface-enhanced Raman scattering using a Raman bond model for extended systems.

In this work, we extend a previously developed Raman bond model to periodic slab systems for interpreting chemical enhancements of surface-enhanced Raman scattering (SERS). The Raman bond model interprets chemical enhancements as interatomic charge flow modulations termed Raman bonds. Here, we show that the Raman bond model offers a unified interpretation of chemical enhancements for localized and periodic systems. As a demonstration of the Raman bond model, we study model systems consisting of CO and pyridine molecules on Ag clusters and slabs. We find that for both localized and periodic systems, the dominant Raman bonds are distributed near the molecule-metal interface and, therefore, the chemical enhancements are determined by a common Raman bond pattern. The effects of surface coverage, thickness, and roughness on the chemical enhancements have been studied, which shows that decreasing surface coverage or creating surface roughness increases chemical enhancements. In both of these cases, the inter-fragment charge flow connectivity is improved due to more dynamic polarization at the interface. The chemical enhancement is shown to scale with the inter-fragment charge flow to the fourth power. Since the inter-fragment charge flow is determined by the charge transfer excitation energy, the Raman bond model is connected to the transition-based analysis of chemical enhancements. We also show that the SERS spectra of localized and periodic systems normalized by inter-fragment charge flows can be unified. In summary, the Raman bond model offers a unique framework for understanding SERS spectra in terms of Raman bond distributions and offers a connection between localized and periodic model systems of SERS studies.

Deep Learning-Based Multicapturer SERS Platform on Plasmonic Nanocube Metasurfaces for Multiplex Detection of Organophosphorus Pesticides in Environmental Water.

In situ rapid detection of contaminants in environmental water is crucial for protecting the ecological environment and human health; however, it is always hindered by the complexity of sample matrices, trace content, and unknown species. Herein, we demonstrate a deep learning-based multicapturer surface-enhanced Raman scattering (SERS) platform on plasmonic nanocube metasurfaces for multiplex determination of organophosphorus pesticides (OPPs) residues. Poly(vinylpyrrolidone), 4-mercaptobenzoic acid, and l-cysteine are assembled on Ag nanocubes (AgNCs) and act as capturers to chemically define OPPs. Meanwhile, the OPPs-captured AgNCs efficiently close the interparticle distance and generate plasmonic metasurfaces, guaranteeing ultrasensitive and reproducible SERS analysis. Furthermore, by strategically combining all capturer-OPP SERS spectra, comprehensive "combined-SERS spectra" are reconstructed to enhance spectral variations of each OPP. Based on the combined-SERS spectra, a deep learning model is trained to predict OPPs, which significantly improve the qualitative and quantitative analysis accuracy. We successfully identified multiple OPPs in farmland, river, and fishpond water using this strategy. The whole detection procedure requires only 30 min, including sampling, SERS measurements, and deep learning analyses. This combination of a multicapturer SERS platform with the deep learning algorithm creates a rapid and reliable analytical strategy for multiplex detection of target molecules, providing a potential paradigm shift for environment-related research.

Serum-based surface-enhanced Raman spectroscopy combined with PCA-RCKNCN for rapid and accurate identification of lung cancer

Early and precise diagnosis of lung cancer is critical for a better prognosis. However, it is still a challenge to develop an effective strategy for early precisely diagnose and effective treatments. Here, we designed a label-free and highly accurate classification serum analytical platform for identifying mice with lung cancer. Specifically, the microarray chip integrated with Au nanostars (AuNSs) array was employed to measure the surface-enhanced Raman scattering (SERS) spectra of serum of tumor-bearing mice at different stages, and then a recognition model of SERS spectra was constructed using the principal component analysis (PCA)-representation coefficient-based k-nearest centroid neighbor (RCKNCN) algorithm. The microarray chip can realize rapid, sensitive, and high-throughput detection of SERS spectra of serum. RCKNCN based on the PCA-generated features successfully differentiated the SERS spectra of serum of tumor-bearing mice at different stages with a classification accuracy of 100%. The most prominent spectral features for distinguishing different stages were captured in PCs loading plots. This work not only provides a practical SERS chip for the application of SERS technology in cancer screening, but also provides a new idea for analyzing the feature of serum at the spectral level.

Conductance Modulation in an α-Terthiophene Molecular Junction Characterized by Surface-Enhanced Raman Scattering

We fabricate α-terthiophene single-molecule junctions (SMJs) and characterize their conductive states by the simultaneous measurements of surface-enhanced Raman scattering (SERS) and electronic conductance. The α-terthiophene SMJ showed broad conductance distribution due to the different portions of the multiple aromatic rings connected to the electrodes. SERS spectra obtained by the simultaneous measurement showed the vibrational energy of the C−C stretching mode depends on the conductance. The Raman spectra calculated by the density-functional theory (DFT) explain the tendency of the vibrational energy shift for typical junction structures. Therefore, the SERS measurement and DFT calculation have revealed that conductance modulation originates from the change in the connecting point of the α-terthiophene SMJ. These findings provide efficient information for building molecule-based circuits and lead to molecule-based potentiometry with aromatic rings.

Single-model multi-tasks deep learning network for recognition and quantitation of surface-enhanced Raman spectroscopy.

Surface-enhanced Raman scattering (SERS) spectroscopy analysis has long been the central task of nanoscience and nanotechnology to realize the ultrasensitive recognition/quantitation applications. Recently, the blooming of artificial intelligence algorithms provides an edge tool to revolutionize the spectroscopy analysis, especially for multiple substances analysis and large-scale data handling. In this study, a single-model multi-tasks deep learning network is proposed to simultaneously achieve the qualitative recognition and quantitative analysis of SERS spectroscopy. The SERS spectra of two kinds of hypoglycemic drugs (PHE, ROS) and the corresponding mixtures are collected, respectively, with the concentration grade from 10-4 M to 10-8 M. Based on the SERS spectroscopy dataset, the loss functions and hyperparameters of the multi-tasks classifications model are optimized, and the recognition accuracies are tested by simulation experiments. It is demonstrated that the accuracy rates of qualitative and quantitative analysis can reach up to 99.0% and 98.4%, respectively. Moreover, the practical feasibility of this multi-tasks model is demonstrated by using it to achieve qualitative and quantitative analysis of PHE and ROS in complex serum matrix. Overall, this single-model multi-tasks deep learning network shows significant potential for the recognition and quantitation of SERS spectroscopy, which provides the algorithmic and experimental basis for large-scale and multiple substances SERS spectra analysis.

PCA-TLNN-based SERS analysis platform for label-free detection and identification of cisplatin-treated gastric cancer

Serum analysis is crucial for favourable prognosis of gastric cancer (GC) and for improving patient survival rates. However, it remains a challenge to develop an effective strategy to accurately identify differences in gastric cancer before and after treatment to guide efficacy evaluation. In this study, we combined surface-enhanced Raman scattering (SERS) with principal component analysis (PCA)-two-layer nearest neighbour (TLNN) to propose a promising serum analytical platform for label-free detection of cisplatin-treated GC mice. A microarray chip fabricated from Au nano-hexagon (AuNH) substrates was employed to measure the SERS spectra of the serum of GC mice at different treatment stages, and then a model for recognition of SERS spectra was constructed using a PCA-TLNN algorithm. The results revealed that the microarray chip exhibited superior portability, SERS activity, stability, and uniformity. Through PCA-TLNN, the GC mice at different treatment stages were successfully segregated, and several key spectral features for distinguishing different treatment stages were captured. The established PCA-TLNN model achieved satisfactory results, with an accuracy of over 97.5%, a sensitivity of over 90%, and a specificity of over 96.7%. Label-free serum SERS in combination with multivariate analysis could serve as a potential technique for the clinical diagnosis and staging of treatments.

Raman spectroscopy combined with deep learning for rapid detection of melanoma at the single cell level

Melanoma is an aggressive and metastatic skin cancer caused by genetic mutations in melanocytes, and its incidence is increasing year by year. Understanding the gene mutation information of melanoma cases is very important for its precise treatment. The current diagnostic methods for melanoma include radiological, pharmacological, histological, cytological and molecular techniques, but the gold standard for diagnosis is still pathological biopsy, which is time consuming and destructive. Raman spectroscopy is a rapid, sensitive and nondestructive detection method. In this study, a total of 20,000 Surface-enhanced Raman scattering (SERS) spectra of melanocytes and melanoma cells were collected using a positively charged gold nanoparticles planar solid SERS substrate, and a classification network system based on convolutional neural networks (CNN) was constructed to achieve the classification of melanocytes and melanoma cells, wild-type and mutant melanoma cells and their drug resistance. Among them, the classification accuracy of melanocytes and melanoma cells was over 98%. Raman spectral differences between melanocytes and melanoma cells were analyzed and compared, and the response of cells to antitumor drugs were also evaluated. The results showed that Raman spectroscopy provided a basis for the medication of melanoma, and SERS spectra combined with CNN classification model realized classification of melanoma, which is of great significance for rapid diagnosis and identification of melanoma.

Challenges in surface-enhanced Raman scattering detection of pesticide carbendazim and ways to overcome

Considerable details in the sampling procedure for the detection of the pesticide carbendazim by surface-enhanced Raman scattering (SERS) were developed. Experimental parameters and recommendations were selected to minimize side processes of carbendazim photodestruction as well as to reduce the background signal from the silver substrate. It was found from the comparison of experimental and density functional theory calculations data that the position of the SERS bands in the carbendazim spectrum depends on the nature of the applied solvent due to different orientations of carbendazim with respect to the surface of silver nanoparticle film. Adsorption from a chloroform solution is preferable to form an aqueous solution for SERS because random adsorption of molecules on a silver surface provides robust carbendazim identification, displaying a larger number of bands in the SERS spectrum, a higher “signal-to-noise” ratio, and then the processes of carbendazim photodestruction slow down.