Preparation of Fe3O4@PDA@Au@GO Composite as SERS Substrate and Its Application in the Enrichment and Detection for Phenanthrene

In this study, highly active Fe3O4@PDA@Au@GO surface-enhanced Raman spectroscopy (SERS) active substrate was synthesized for application in the enrichment and detection of trace polycyclic aromatic hydrocarbons (PAHs) in the environment. The morphology and structure were characterized by transmission electron microscopy (TEM), energy-dispersive spectroscopy (EDS), X-ray diffraction (XRD) and UV–visible absorption spectrum (UV–vis spectra). The effect of each component of Fe3O4@PDA@Au@GO nanocomposites on SERS was explored, and it was found that gold nanoparticles (Au NPs) are crucial to enhance the Raman signal based on the electromagnetic enhancement mechanism, and apart from enriching the PAHs through π–π interaction, graphene oxide (GO) also generates strong chemical enhancement of Raman signals, and polydopamine (PDA) can prevent Au from shedding and agglomeration. The existence of Fe3O4 aided the quick separation of substrate from the solutions, which greatly simplified the detection procedure and facilitated the reuse of the substrate. The SERS active substrate was used to detect phenanthrene in aqueous solution with a detection limit of 10−7 g/L (5.6 × 10−10 mol/L), which is much lower than that of ordinary Raman, it is promising for application in the enrichment and detection of trace PAHs.

Power Spectral Density Analysis for Optimizing SERS Structures

The problem of optimizing the topography of metal structures allowing Surface Enhanced Raman Scattering (SERS) sensing is considered. We developed a model, which randomly distributes hemispheroidal particles over a given area of the glass substrate and estimates SERS capabilities of the obtained structures. We applied Power Spectral Density (PSD) analysis to modeled structures and to atomic force microscope images widely used in SERS metal island films and metal dendrites. The comparison of measured and calculated SERS signals from differing characteristics structures with the results of PSD analysis of these structures has shown that this approach allows simple identification and choosing a structure topography, which is capable of providing the maximal enhancement of Raman signal within a given set of structures of the same type placed on the substrate.

Advances in the Application of Exosomes Identification Using Surface-Enhanced Raman Spectroscopy for the Early Detection of Cancers

Exosomes are small nanoscale vesicles with a double-layered lipid membrane structure secreted by cells, and almost all types of cells can secrete exosomes. Exosomes carry a variety of biologically active contents such as nucleic acids and proteins, and play an important role not only in intercellular information exchange and signal transduction, but also in various pathophysiological processes in the human body. Surface-enhanced Raman Spectroscopy (SERS) uses light to interact with nanostructured materials such as gold and silver to produce a strong surface plasmon resonance effect, which can significantly enhance the Raman signal of molecules adsorbed on the surface of nanostructures to obtain a rich fingerprint of the sample itself or Raman probe molecules with ultra-sensitivity. The unique advantages of SERS, such as non-invasive and high sensitivity, good selectivity, fast analysis speed, and low water interference, make it a promising technology for life science and clinical testing applications. In this paper, we briefly introduce exosomes and the current main detection methods. We also describe the basic principles of SERS and the progress of the application of unlabeled and labeled SERS in exosome detection. This paper also summarizes the value of SERS-based exosome assays for early tumor diagnosis.

Ring Vibrations to Sense Anionic Ibuprofen in Aqueous Solution as Revealed by Resonance Raman

We unravel the potentialities of resonance Raman spectroscopy to detect ibuprofen in diluted aqueous solutions. In particular, we exploit a fully polarizable quantum mechanics/molecular mechanics (QM/MM) methodology based on fluctuating charges coupled to molecular dynamics (MD) in order to take into account the dynamical aspects of the solvation phenomenon. Our findings, which are discussed in light of a natural bond orbital (NBO) analysis, reveal that a selective enhancement of the Raman signal due to the normal mode associated with the C–C stretching in the ring, νC=C, can be achieved by properly tuning the incident wavelength, thus facilitating the recognition of ibuprofen in water samples.

Raman Signal Denoising Using Fully Convolutional Encoder Decoder Network

Raman spectroscopy is a vibrational method that gives molecular information rapidly and non-invasively. Despite its advantages, the weak intensity of Raman spectroscopy leads to low-quality signals, particularly with tissue samples. The requirement of high exposure times makes Raman a time-consuming process and diminishes its non-invasive property while studying living tissues. Novel denoising techniques using convolutional neural networks (CNN) have achieved remarkable results in image processing. Here, we propose a similar approach for noise reduction for the Raman spectra acquired with 10x lower exposure times. In this work, we developed fully convolutional encoder-decoder architecture (FCED) and trained them with noisy Raman signals. The results demonstrate that our model is superior (p-value <0.0001) to the conventional denoising techniques such as the Savitzky-Golay filter and wavelet denoising. Improvement in the signal-to-noise ratio values ranges from 20% to 80%, depending on the initial signal-to-noise ratio. Thus, we proved that tissue analysis could be done in a shorter time without any need for instrumental enhancement.

SERS Platform Based on Hollow-Core Microstructured Optical Fiber: Technology of UV-Mediated Gold Nanoparticle Growth

Surface-enhanced Raman spectroscopy (SERS) is a powerful technique for biosensing. However, SERS analysis has several concerns: the signal is limited by a number of molecules and the area of the plasmonic substrate in the laser hotspot, and quantitative analysis in a low-volume droplet is confusing due to the change of concentration during quick drying. The usage of hollow-core microstructured optical fibers (HC-MOFs) is thought to be an effective way to improve SERS sensitivity and limit of detection through the effective irradiation of a small sample volume filling the fiber capillaries. In this paper, we used layer-by-layer assembly as a simple method for the functionalization of fiber capillaries by gold nanoparticles (seeds) with a mean diameter of 8 nm followed by UV-induced chloroauric acid reduction. We also demonstrated a simple and quick technique used for the analysis of the SERS platform formation at every stage through the detection of spectral shifts in the optical transmission of HC-MOFs. The enhancement of the Raman signal of a model analyte Rhodamine 6G was obtained using such type of SERS platform. Thus, a combination of nanostructured gold coating as a SERS-active surface and a hollow-core fiber as a microfluidic channel and a waveguide is perspective for point-of-care medical diagnosis based on liquid biopsy and exhaled air analysis.

Off-Resonance Gold Nanobone Films at Liquid Interface for SERS Applications

Extensive effort and research are currently channeled towards the implementation of SERS (Surface Enhanced Raman Spectroscopy) as a standard analytical tool as it has undisputedly demonstrated a great potential for trace detection of various analytes. Novel and improved substrates are continuously reported in this regard. It is generally believed that plasmonic nanostructures with plasmon resonances close to the excitation wavelength (on-resonance) generate stronger SERS enhancements, but this finding is still under debate. In the current paper, we compared off-resonance gold nanobones (GNBs) with on-resonance GNBs and gold nanorods (GNRs) in both colloidal dispersion and as close-packed films self-assembled at liquid-liquid interface. Rhodamine 6G (R6G) was used as a Raman reporter in order to evaluate SERS performances. A 17-, 18-, and 55-fold increase in the Raman signal was observed for nanostructures (off-resonance GNBs, on-resonance GNBs, and on-resonance GNRs, respectively) assembled at liquid-liquid interface compared to the same nanostructures in colloidal dispersion. SERS performances of off-resonance GNBs were superior to on-resonance nanostructures in both cases. Furthermore, when off-resonance GNBs were assembled at the liquid interface, a relative standard deviation of 4.56% of the recorded signal intensity and a limit of detection (LOD) of 5 × 10−9 M could be obtained for R6G, rendering this substrate suitable for analytical applications.

Classification of Preeclamptic Placental Extracellular Vesicles Using Femtosecond Laser-fabricated Nanoplasmonic Sensors and Machine Learning

Placental extracellular vesicles (EVs) play an essential role in pregnancy by protecting and transporting diverse biomolecules that aid in fetomaternal communication. However, in preeclampsia, they have also been implicated in contributing to disease progression. Despite their potential clinical value, most current technologies cannot provide a rapid and effective means of differentiating between healthy and diseased placental EVs. To address this, we developed a fabrication process called laser-induced nanostructuring of SERS-active thin films (LINST), which produces nanoplasmonic substrates that provide exceptional Raman signal enhancement and allow the biochemical fingerprinting of EVs. After validating LINST performance with chemical standards, we used placental EVs from tissue explant cultures and demonstrated that preeclamptic and normotensive placental EVs have classifiably distinct Raman spectra following the application of both conventional and advanced machine learning algorithms. Given the abundance of placental EVs in maternal circulation, these findings will encourage immediate exploration of surface-enhanced Raman spectroscopy (SERS) as a promising method for preeclampsia liquid biopsies, while our novel fabrication process can provide a versatile and scalable substrate for many other SERS applications.

Liquid Surface-Enhanced Raman Spectroscopy (SERS) Sensor-Based Au-Ag Colloidal Nanoparticles for Easy and Rapid Detection of Deltamethrin Pesticide in Brewed Tea

Deltamethrin pesticides can cause inflammation, nephrotoxicity and hepatotoxicity as well as affect the activity of antioxidant enzymes in tissues. As a result of this concern, there is a rising focus on the development of fast and reliable pesticide residue testing to minimise potential risks to humans. The goal of this study is to use Au-Ag colloid nanoparticles as liquid surface-enhanced Raman spectroscopy (SERS) to improve the Raman signal in the detection of deltamethrin pesticide in a brewed tea. The liquid SERS system is fascinating to study due to its ease of use and its unlikeliness to cause several phenomena, such as photo-bleaching, combustion, sublimation and even photo-catalysis, which can interfere with the Raman signal, as shown in the SERS substrate. Our liquid SERS system is simpler than previous liquid SERS systems that have been reported. We performed the detection of pesticide analyte directly on brewed tea, without diluting it with ethanol or centrifuging it. Femtosecond laser-induced photo-reduction was employed to synthesise the liquid SERS of Au, Au-Ag, and Ag colloidal nanoparticles. The SERS was utilised to detect deltamethrin pesticide in brewed tea. The result showed that liquid SERS-based Ag NPs significantly enhance the Raman signal of pesticides compared with liquid SERS-based Au NPs and Au-Ag Nanoalloys. The maximum residue limits (MRLs) in tea in Indonesia are set at 10 ppm. Therefore, this method was also utilised to detect and improve, to 0.01 ppm, the deltamethrin pesticide Limit of Detection (LOD).

Dual-enhancement and dual-tag design for SERS-based sandwich immunoassays: evaluation of a metal–metal effect in 3D architecture

The design of a sandwich-type SERS immunoassay (surface-enhanced Raman spectroscopy) is demonstrated operating in dual surface enhancement and dual-tag paradigm. The capture and detection antibodies are linked to two SERS-active substrates and form together the three-dimensional (3D) structure after specific binding to interleukin 6. A variety of metal combinations is tested (Au–Ag, Au–Au, and Ag–Ag), but an enhanced electromagnetic field is generated only due to coupling of Ag and Au nanoparticles with an Au hexagonal nanoarray. The amplified in that way Raman signals improve the limit of detection over 3 times in comparison to the assay with only one SERS-active substrate. It is also shown that the proper readout of the true-positive signal can be achieved in assays with two Raman tags, and this approach also improves LOD. For the optimal combination of the metal–metal junction and Raman tags, a linear relationship between the Raman signal and the concentration of IL-6 is obtained in the range 0–1000 pg⋅mL−1with LOD of 25.2 pg mL−1and RSD < 10%. The presented proof-of-concept of the SERS immunoassay with the dual-enhancement and dual-tag opens additional opportunities for engineering reliable SERS biosensing. Graphical abstract