Surface-enhanced Raman Scattering Research Articles

Three-Dimensional (3D) Surface-Enhanced Raman Spectroscopy (SERS) Substrates for Sensing Low-Concentration Molecules in Solution.

The use of surface-enhanced Raman spectroscopy (SERS) in liquid solutions has always been challenging due to signal fluctuations, inconsistent data, and difficulties in obtaining reliable results, especially at very low analyte concentrations. In our study, we introduce a new method using a three-dimensional (3D) SERS substrate made of silica microparticles (SMPs) with attached plasmonic nanoparticles (NPs). These SMPs were placed in low-concentration analyte solutions for SERS analysis. In the first approach to perform SERS in a 3D environment, glycerin was used to immobilize the particles, which enabled high-resolution SERS imaging. Additionally, we conducted time-dependent SERS measurements in an aqueous solution, where freely suspended SMPs passed through the laser focus. In both scenarios, EFs larger than 200 were achieved, which enabled the detection of low-abundance analytes. Our study demonstrates a reliable and reproducible method for performing SERS in liquid environments, offering significant advantages for the real-time analysis of dynamic processes, sensitive detection of low-concentration molecules, and potential applications in biomolecular interaction studies, environmental monitoring, and biomedical diagnostics.

Rapid and accurate bacteria identification through deep-learning-based two-dimensional Raman spectroscopy

Surface-enhanced Raman spectroscopy (SERS) offers a distinctive vibrational fingerprint of the molecules and has led to widespread applications in medical diagnosis, biochemistry, and virology. With the rapid development of artificial intelligence (AI) technology, AI-enabled Raman spectroscopic techniques, as a promising avenue for biosensing applications, have significantly boosted bacteria identification. By converting spectra into images, the dataset is enriched with more detailed information, allowing AI to identify bacterial isolates with enhanced precision. However, previous studies usually suffer from a trade-off between high-resolution spectrograms for high-accuracy identification and short training time for data processing. Here, we present an efficient bacteria identification strategy that combines deep learning models with a spectrogram encoding algorithm based on wavelet packet transform and Gramian angular field techniques. In contrast to the direct analysis of raw Raman spectra, our approach utilizes wavelet packet transform techniques to compress the spectra by a factor of 1/15, while concurrently maintaining state-of-the-art accuracy by amplifying the subtle differences via Gramian angular field techniques. The results demonstrate that our approach can achieve a 99.64 % and a 90.55 % identification accuracy for two types of bacterial isolates and thirty types of bacterial isolates, respectively, while a 90 % reduction in training time compared to the conventional methods. To verify the model's stability, Gaussian noises were superimposed on the testing dataset, showing a specific generalization ability and superior performance. This algorithm has the potential for integration into on-site testing protocols and is readily updatable with new bacterial isolates. This study provides profound insights and contributes to the current understanding of spectroscopy, paving the way for accurate and rapid bacteria identification in diverse applications of environment monitoring, food safety, microbiology, and public health.

Label-Free SERS Sensors for Real-Time Monitoring of Tyrosine Phosphorylation.

Dysregulation of receptor tyrosine kinases (RTKs) has been shown to correlate with cancer cell proliferation and drug resistance. Thus, monitoring the activity of RTKs at a chemical level could provide new biomedical insights and methods to assess the drug efficacy. However, direct monitoring of kinase activity is challenging and most commonly relies on in vitro techniques such as Western blotting and ELISAs. Herein, we report the development of a gold nanoparticle-based surface-enhanced Raman scattering (SERS) sensor, which allows the real-time monitoring of tyrosine phosphorylation of a reporter peptide (Axltide) by the Axl enzyme. We demonstrate that our sensor shows strong signal localization, and we are able to detect tyrosine phosphorylation of the reporter peptide through chemical phosphorylation and enzymatically with similar peak changes to those observed in the spontaneous Raman spectra. Through monitoring the SERS spectrum, we can observe changes in phosphorylation in real time.

Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG

This work reports an efficient method to detect SARS-CoV-2 antibodies in blood samples based on SERS combined with a machine learning tool. For this purpose, gold nanoparticles directly conjugated with spike protein were used in human blood samples to identify anti-SARS-CoV-2 antibodies. The comprehensive database utilized Raman spectra from all 594 blood serum samples. Machine learning investigations were carried out using the Scikit-Learn library and were implemented in Python, and the characteristics of Raman spectra of positive and negative SARS-CoV-2 samples were extracted using the Uniform Manifold Approximation and Projection (UMAP) technique. The machine learning models used were k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Decision Trees (DTs), logistic regression (LR), and Light Gradient Boosting Machine (LightGBM). The kNN model led to a sensitivity of 0.943, specificity of 0.9275, and accuracy of 0.9377. This study showed that combining Raman spectroscopy and a machine algorithm can be an effective diagnostic method. Furthermore, we highlighted the advantages and disadvantages of each algorithm, providing valuable information for future research.

SERS Sensor for Acetylcholine Detection Based on Covalent Organic Framework Hybridized Gold Nanoparticles As Nanozymes.

An innovative and potent nanozyme with reductase-like activity was developed by integrating the in situ synthesis of gold nanoparticles (Au NPs) onto the surface of a covalent organic framework (COF). Based on the reductase-like activity of the COF-hybridized Au NPs, this nanozyme could efficiently catalyze the reduction of 4-nitrophenol (4-NPH). Moreover, the prepared nanohybrid was utilized as an excellent surface-enhanced Raman scattering (SERS) substrate for highly sensitive SERS detection by combining the excellent adsorption properties of COFs and the large number of Raman hotspots between the high-density Au NPs. For the first time, 4-NPH was used as an SERS marker to detect the electron receptor acetylcholine (Ach), with high sensitivity; Ach acted as an inhibitor in this catalytic reaction. The linear range of Ach was 1.0 pM to 10 nM, the correlation coefficient was 0.993, the detection limit was approximately 0.3 pM with a signal-to-noise ratio (s/n) of 3, and the acceptable recovery in the serum was 97.2% to 104.5%. The results from this study show that the nanozyme-SERS system has great potential for chemical and bioassay applications.

Lateral Flow Immunoassay for the Rapid Detection of Thiamethoxam in Tea Based on a SERS Tag Constructed by Phenolic-Mediated Coating Engineering.

Sensitive and accurate detection of thiamethoxam in tea is significant to ensuring consumer health. In this study, surface-enhanced Raman scattering (SERS) tags were prepared by using a polyphenol-mediated coating engineering strategy. This approach involved the self-assembly of tannic acid (TA) and self-polymerization of benzene-1,4-dithiol (BDT) on the surface of gold nanoparticles, resulting in the formation of Au@pBDT-TA. The SERS tags possess high Raman signals and antibody adsorption properties, avoiding the complex decoration or label steps of SERS reporters. We discovered that Au@pBDT-TA, composed of gold nanoparticles at 40 nm and a pBDT-TA thickness of 10 nm, was optimal for the development of the SERS lateral flow immunoassay (LFIA). In comparison to the gold nanoparticle-based LFIA, the SERS-LFIA demonstrated 4-fold and 720-fold enhancements in visual and quantitative limits of detection in buffer solution. The SERS-LFIA demonstrated quantitative limits of detection of 0.06 and 0.1 ng/g for black and green tea, respectively, with a broader linear range spanning over 2 orders of magnitude and a short detection time of 20 min. The proposed SERS-LFIA not only offers a sensitive and reliable method for monitoring thiamethoxam in tea but also possesses a versatile potential that can be easily adapted for the trace detection of various other targets.

Experimental and Numerical Investigations of Surface-Enhanced Raman Scattering (SERS) Sensors for Trace Mancozeb Detection: A Step Toward the Prevention of Reproductive Disorders Caused by this Hazardous Substance

Experimental and Numerical Investigations of Surface-Enhanced Raman Scattering (SERS) Sensors for Trace Mancozeb Detection: A Step Toward the Prevention of Reproductive Disorders Caused by this Hazardous Substance

Unveiling multimodal hot carrier excitation in plasmonic bimetallic Au@Ag nanostars for photochemistry and SERS sensing

AbstractPlasmonic nanostructures stand at the forefront of nanophotonics research, particularly in sensing and energy conversion applications. Their unique ability to confine light energy at the nanoscale makes them indispensable for a wide array of technological advancements. The study of these structures often makes use of different materials and, even more extensively, explores new shapes and configurations to extend our common repertoire of useful nanophotonics tools. Exploring the creation of bimetallic plasmonic nanostructures combines these two dimensions determining the space of possible plasmonic resonators and opens the possibility of tailoring systems with behavior unavailable to single-metal plasmonic structures. In this paper, we delve into the exploration of bimetallic systems employing plasmonic nanostars. These structures have demonstrated remarkable capabilities for surface-enhanced Raman scattering (SERS) spectroscopy and photochemistry, due to the strong plasmonic response of their peaks, whose disposition following a spherical symmetry makes them largely polarization- and orientation-insensitive. Herein, we report the colloidal synthesis of two different water-stable Au@Ag nanostars, explore their performance as photocatalysts and SERS substrates, and provide an in-depth account of their non-trivial physical response.

Reusable SERS Platform of Femtosecond Laser Processed Substrate for Detection of Malachite Green.

This study presents the development of highly efficient Surface-Enhanced Raman Scattering (SERS) substrates through femtosecond (fs) laser processing of crystalline silicon (Si), resulting in mountain-like microstructures. These microstructures, when decorated with gold nanoparticles (Au NPs), exhibit remarkable SERS performance due to the creation of concentrated hotspots. The enhanced Raman signals originate from the excitation of localized surface plasmon resonance (LSPR) of the Au NPs and the multi-scale rough morphology of the Si substrates. Finite-element method simulations confirm the electromagnetic field enhancement in narrow gaps, supporting the experimental observations. The fabricated substrates show high uniformity, oxidation resistance, long-term stability, and exceptional reproducibility, making them ideal for molecular detection, especially in food safety applications. A remarkable enhancement factor (EF) of 1010 is attained in the detection of Malachite Green (MG), boasting a limit of detection (LoD) as low as 10-14 M. This underscores the immense potential of this technique for achieving highly sensitive and dependable SERS-based sensing capabilities.

Bifunctional MoO3-x/CuS Heterojunction Nanozyme-Driven "Turn-On" SERS Signal for the Sensitive Detection of Cerebral Infarction Biomarker S100B.

The use of nanozymes has become a promising auxiliary approach to "turn on" surface-enhanced Raman scattering (SERS) signals for the label-free detection of disease markers. Nevertheless, there are still major challenges to develop bifunctional nanomaterials with both excellent enzyme-like activity and high SERS performance. To this end, a novel Z-scheme MoO3-x/CuS heterojunction was first constructed as a powerful "two-in-one" substrate, which can not only catalyze leucomalachite green (LMG) to SERS-active malachite green (MG) but also serve as an efficient substrate to amplify the SERS signal of catalysate. Due to the strong interfacial coupling effect between the MoO3-x and CuS nanomaterial, which promoted the separation and transport of carriers in the heterojunction, the MoO3-x/CuS heterojunction showed higher peroxidase-like activity compared to individual components and the previously reported heterojunction nanozymes. Inspired by these results, a sandwich-type SERS immunoassay for the detection of the cerebral infarction biomarker S100 calcium-binding protein (S100B) was proposed based on the output signal of MG at 1620 cm-1. Furthermore, introducing the antifouling material chitosan on the surface of the MoO3-x/CuS heterojunction can effectively resist nonspecific protein adsorption and significantly improve the detection accuracy of the immunoassay. Therefore, the SERS immunoassay based on the MoO3-x/CuS heterojunction realized highly sensitive and selective detection of S100B in the concentration range of 0.001 to 100 ng/mL, with a low limit of detection of 0.47 pg/mL. The developed method has been successfully used for the accurate detection of S100B in clinical serum. The results showed that the level of S100B in the serum of cerebral infarction patients can be distinguished from those of healthy individuals and intracranial tumor patient controls. In addition, the acquired values of S100B in the serum of cerebral infarction patients based this strategy were well consistent with the results of electrochemiluminescence (ECL) detection with a relative error of less than ±7.3. It is expected that this work may open up a paradigm for improving detection sensitivity and accuracy for the early diagnosis and treatment monitoring of cerebral infarction in the clinic.

Ionophore-Based SERS Sensing for Electrolyte Cations.

We present here a surface-enhanced Raman spectroscopy (SERS)-based platform for selectively detecting electrolyte cations. This platform facilitates the reorientation of the chromoionophore I (CHI) molecule positioned on the surface of silver nanoparticles, regulated by the targeted electrolyte cation within the ionophore-based ion-selective sensing framework. When exposed to the target ions, leading to deprotonation, the aromatic plane of the CHI molecule shifts from an endwise to an edgewise configuration on the SERS substrate surface. This reorientation improves the coupling of the induced dipole within the molecule and the induced electromagnetic field normal to the surface, leading to a heightened and more discernible SERS signal. Additionally, NMR data revealed a protonation-dependent conformational change in the CHI molecules. Upon protonation, the side chain of the CHI molecule extends away from its aromatic ring, whereas upon deprotonation, the carbon chain folds back and closely approaches the edge of the aromatic plane, further confirming this conclusion. Using Ca2+ and Na+ as examples, this method shows a detection limit of 0.1 μM for Ca2+ and 1 μM for Na+ with high selectivity. The successful application of the versatile and rapidly advancing SERS technique for electrolyte cation detection shows a promising pathway for enhancing current ion detection capabilities. As a preliminary demonstration, the total Ca2+ concentration in undiluted human serum was successfully determined, with common matrix interference effectively circumvented.

Binding of Glutamic Acid to Silver and Gold Nanoparticles Investigated by Surface-Enhanced Raman Spectroscopy

Glutamate is the most important excitatory neurotransmitter, which is relevant for the study of several diseases such as amyotrophic lateral sclerosis and Alzheimer. It is the form L-glutamic acid (Glu) takes at physiologically relevant pHs. The surface-enhanced Raman spectra of Glu obtained at pH values ranging from 3.3 to 12 are collected in the presence of silver and gold colloids and on solid substrates. The observed bands are compared with the positions of calculated normal modes for free neutral glutamic acid, glutamic acid monohydrate, glutamic acid bound to gold and silver atoms, and sodium glutamate. Although gold atoms prefer to bind to the NH2 group as compared to carbonyl groups, silver atoms prefer binding to hydroxyl groups more than binding to the amino group. SERS spectra of glutamic acid solutions with a pH value of 12, in which both carboxylic groups are deprotonated, indicate a complexation of the glutamic acid dianion with the sodium cation, which was introduced into the solution to adjust the pH value. Further research towards an optimal substrate is needed.

Plasmonic chitosan cross-linked dialdehyde chitosan aerogels as 3D surface-enhanced Raman scattering substrates for sensitive and reliable detection

Surface Enhanced Raman Scattering (SERS) has the advantages of non-destructive detection and high sensitivity, however, low hotspots utilisation and inhomogeneous distribution limit its application. In response to the problem of underutilization of hotspots in most SERS substrates, a crosslinking of chitosan with dialdehyde chitosan aerogels loaded with Ag NPs (CDSA) as SERS substrates with chitosan (CS) as the main body and dialdehyde chitosan (DACS) as the reducing and cross-linking agent were prepared. Unlike other 3D structures such as hydrogels, CDSA can capture probe molecules in a much shorter period of time and enhance SERS signals by collapse. The SERS performance of CDSA was evaluated by rhodamine 6 G (R6G) and methylene blue (MB), indicating that the CDSA substrate has good stability (90 days), repeatability (RSD=4.15 %) and reproducibility (RSD=4.86 %). The enhancement factors (EFs) was 1.3×108 for R6G and 9.2×107 for MB. Both gas sample and liquid sample were detected to evaluate the performances of CDSA in practical application. 4-Aminothiophenol (4-ATP) was utilized to test the efficacy of CDSA on gas-phase to-be-tested substances and the limit of detection (LOD) was 0.59 mg/L. The tests performed on sildenafil (SD) in real samples showed recoveries ranging from 97.5 % to 103.4 % with a LOD of 0.055 mg/L. It was indicated that the CDSA substrate could serves as a satisfactory SERS sensor for sensitive and rapid detection for both gas phase and liquid samples.

Self-Supervised Learning for Generic Raman Spectrum Denoising.

Raman and surface-enhanced Raman scattering (SERS) spectroscopy are highly specific and sensitive optical modalities that have been extensively investigated in diverse applications. Noise reduction is demanding in the preprocessing procedure, especially for weak Raman/SERS spectra. Existing denoising methods require manual optimization of parameters, which is time-consuming and laborious and cannot always achieve satisfactory performance. Deep learning has been increasingly applied in Raman/SERS spectral denoising but usually requires massive training data, where the true labels may not exist. Aiming at these challenges, this work presents a generic Raman spectrum denoising algorithm with self-supervised learning for accurate, rapid, and robust noise reduction. A specialized network based on U-Net is established, which first extracts high-level features and then restores key peak profiles of the spectra. A subsampling strategy is proposed to refine the raw Raman spectrum and avoid the underlying biased interference. The effectiveness of the proposed approach has been validated by a broad range of spectral data, exhibiting its strong generalization ability. In the context of photosafe detection of deep-seated tumors, our method achieved signal-to-noise ratio enhancement by over 400%, which resulted in a significant increase in the limit of detection thickness from 10 to 18 cm. Our approach demonstrates superior denoising performance compared to the state-of-the-art denoising methods. The occlusion method further showed that the proposed algorithm automatically focuses on characterized peaks, enhancing the interpretability of our approach explicitly in Raman and SERS spectroscopy.

SERS Performance Factor: A Convenient Parameter for the Enhancement Evaluation of SERS Substrates.

Surface-enhanced Raman spectroscopy (SERS), with molecular fingerprint information and single-molecule sensitivity, has been widely used for qualitative and quantitative analysis in various fields. Plenty of nanostructured plasmonic materials have been fabricated to achieve high SERS activity. Currently, great difficulty lies in evaluating the SERS performance among substrates, making it difficult to standardize. Addressing this problem, this work proposed the SERS performance factor () as a practically operational parameter to evaluate the sensitivity of SERS substrates. Experimentally, SPF can be obtained by taking the ratio of the slopes (i.e., the sensitivity) for concentration-dependent SERS and normal Raman measurements in the linear range of the intensity response under identical experimental conditions. Theoretically, SPF quantitatively describes the overall contribution to the SERS performance, (i.e., the electromagnetic (EM) enhancement of the SERS substrate and the interfacial interaction between the probe and substrate). The use of SPF as the criterion for evaluating the SERS performance was validated on Au nanoparticles in colloidal and solid states, where the tendency of SPF is consistent with that of the sensitivity of the probe molecules. Derived from the typically used surface enhancement factor EF in which accurate parameters are hardly achievable and different from concentration-dependent analytical enhancement factor AEF, SPF distinguishes itself with a simpler calculation and thereby offers a convenient and reliable protocol for the evaluation of the performance of different SERS substrates, which is very important to the practical application of SERS.

Optical Biosensors for Detection of Cancer Biomarkers: Current and Future Perspectives.

Optical biosensors are emerging as a promising technique for the sensitive and accurate detection of cancer biomarkers, enabling significant advancements in the field of early diagnosis. This study elaborates on the latest developments in optical biosensors designed for detecting cancer biomarkers, highlighting their vital significance in early cancer diagnosis. When combined with targeted nanoparticles, the bio-fluids can help in the molecular stage diagnosis of cancer. This enhances the discrimination of disease from the normal subjects drastically. The optical sensor methods that are involved in the disease diagnosis and imaging of cancer taken for the present review are surface plasmon resonance, localized surface plasmon resonance, fluorescence resonance energy transfer, surface-enhanced Raman spectroscopy and colorimetric sensing. The article meticulously describes the specific biomarkers and analytes that optical biosensors target. Beyond elucidating the underlying principles and applications, this article furnishes an overview of recent breakthroughs and emerging trends in the field. This encompasses the evolution of innovative nanomaterials and nanostructures designed to augment sensitivity and the incorporation of microfluidics for facilitating point-of-care testing, thereby charting a course towards prospective advancements.

One-step detection of nanoplastics in aquatic environments using a portable SERS chessboard substrate

Nanoplastics present a significant hazard to both the environment and human health. However, the development of rapid and sensitive analysis techniques for nanoplastics is limited by their small size, lack of specificity, and low concentrations. In this study, a surface-enhanced Raman scattering (SERS) chessboard substrate was introduced as a multi-channel platform for the pre-concentration and detection of nanoplastics, achieved by polydomain aggregating silver nanoparticles (PASN) on a hydrophilic and a punched hydrophobic PVDF combined filter membrane. Through a straightforward suction filtration process, nanoplastics were captured by the PASN gap in a single step for subsequent SERS detection, while excess moisture was promptly eliminated from the filter membrane. The PASN-based SERS chessboard substrate, benefiting from the enhanced electromagnetic (EM) field, effectively discriminated polystyrene (PS) nanoplastics ranging in size from 30 nm to 1000 nm. Furthermore, this substrate demonstrated favorable repeatability (RSD of 8.6 %), high sensitivity with a detection limit of 0.001 mg/mL for 100 nm of PS nanoplastics, and broad linear detection ranges spanning from 0.001 to 0.5 mg/mL (R2 = 0.9916). Additionally, the SERS chessboard substrate enabled quantitative analysis of nanoplastics spiked in tap and lake water samples. Notably, the entire pre-concentration and detection procedure required only 3 μL of sample and could be completed within 1 min. With the accessibility of portable detection instruments and the ability to prepare substrates on demand, the PASN-based SERS chessboard substrate is anticipated to facilitate the establishment of a comprehensive global nanoplastics map.

Surface-enhanced Raman Scattering Biosensor by Combining Rare Earth Elements: Integration with Machine Learning Tools for Diosgenin and Tectorigenin Detection and Classification

Surface-enhanced Raman Scattering Biosensor by Combining Rare Earth Elements: Integration with Machine Learning Tools for Diosgenin and Tectorigenin Detection and Classification

Immobilization of Membrane-Associated Protein Complexes on SERS-Active Nanomaterials for Structural and Dynamic Characterization.

Exploring the structural basis of membrane proteins is significant for a deeper understanding of protein functions. In situ analysis of membrane proteins and their dynamics, however, still challenges conventional techniques. Here we report the first attempt to immobilize membrane protein complexes on surface-enhanced Raman scattering (SERS)-active supports, titanium dioxide-coated silver (Ag@TiO2) nanoparticles. Biocompatible immobilization of microsomal monooxygenase complexes is achieved through lipid fission and fusion. SERS activity of the Ag@TiO2 nanoparticles enables in situ monitoring of protein-protein electron transfer and enzyme catalysis in real time. Through SERS fingerprints of the monooxygenase redox centers, the correlations between these protein-ligand interactions and reactive oxygen species generation are revealed, providing novel insights into the molecular mechanisms underlying monooxygenase-mediated apoptotic regulation. This study offers a novel strategy to explore structure-function relationships of membrane protein complexes and has the potential to advance the development of novel reactive oxygen species-inducing drugs for cancer therapy.

In-situ imaging and time-resolved investigation of local pH in electrocatalytic CO2 reduction

Local pH near the catalyst surface exerts a substantial influence on both catalytic selectivity and activity in electrocatalytic CO2 reduction (CO2RR). While the investigation of local pH remains challenging in the aspect of in-situ imaging and monitoring. In this study, a novel lateral-type surface-enhanced Raman spectroscopy (L-SERS) system with a wide pH testing range from acidic to alkaline, is developed for the in-situ observation of local pH distributions with high spatial and temporal resolution during CO2RR. Excellent in-situ imaging of local pH near the catalyst surface is obtained, showing good consistency with simulation results. Besides, the gradual pH elevation and stabilization can be monitored via this time-resolved local pH investigation. This study presents a direct visualization of local pH distribution and variation at the Nernst layer during CO2RR, providing direct evidence in local pH for the feasibility of acidic electrolyte and offering theoretical guidance for optimizing CO2RR conditions.