Surface-enhanced Raman Scattering Research Articles

Theoretical Procedure for Precise Evaluation of Chemical Enhancement in Molecular Surface-Enhanced Raman Scattering.

The enhancement of the molecular Raman signal in plasmon-assisted surface-enhanced Raman scattering (SERS) results from electromagnetic and chemical mechanisms, the latter determined to a large extent by the chemical interaction between the molecules and the hosting plasmonic nanoparticles. A precise quantification of the chemical mechanism in SERS based on quantum chemistry calculations is often challenging due to the interplay between the chemical and electromagnetic effects. Based on an atomistic description of the SERS signal, which includes the effect of strong field inhomogeneities, we introduce a comprehensive approach to evaluate the chemical enhancement in SERS, which conveniently removes the electromagnetic contribution inherent to any quantum calculation of the Raman polarization. Our approach uses density functional theory (DFT) and time-dependent DFT to compute the total SERS signal, together with the electromagnetic and chemical enhancement factors. We apply this framework to study the chemical enhancement of biphenyl-4,4'-dithiol embedded between two gold clusters. Although we find that for small clusters the total SERS enhancement is mainly determined by the chemical mechanism, our procedure enables removal of the electromagnetic contribution and isolation of the contribution of the bare chemical effect. This approach can be applied to reproduce and understand Raman line activation and strength in practical and challenging SERS configurations such as in plasmonic nano- and pico-cavities.

Dispersion and self-assembly of 2,5-dimercapto-1,3,4-thiadiazole (DMTD) functionalized silver QDs in 6 CHBT liquid crystal

ABSTRACT Silver nanoparticles were synthesised using silver nitrate and tri-sodium citrate and were functionalized with 2,5-Dimercapto-1,3,4-thiadiazole (DMTD) linking molecules. Functionalized silver nanoparticles dispersed within 4-(trans-4-n hexylcyclohexyl) isothiocyanatobenzoate (6CHBT) liquid crystal (LC) matrices have attracted significant attention because of their potential for tailoring tuneable surface plasmonic, optical, and electronic properties by controlling the self-assembly of silver dots. The unique combination of functionalized nanoparticles and 6CHBT LCs offers a versatile platform for manipulating interactions at the nanoscale. The dispersion of functionalized Ag nanoparticles within the LCs appears to substantially modify the plasmonic and opto-electronic properties of the Ag QDs. The formation and morphology of the self-assembled structures of silver nanoparticles were confirmed by Optical and Scanning Electron Microscopy. Optical microscopy (OM) and Scanning Electron Microscopy (SEM) images of Ag-doped 6CHBT confirmed the formation of nano-micro spheres, as well as ring and rod-like structures in LC media. We have used High-Resolution X-ray diffraction (HR-XRD), Fourier-Transform Infrared Spectroscopy (FTIR), Surface-Enhanced Raman Spectroscopy (SERS), Optical Microscopy (OM), Scanning Electron Microscopy (SEM), Ultraviolet-visible-near-infrared Spectroscopy (UV-Vis-NIR), and Fluorescence (FL) Spectroscopy. We calculated the electronic properties such as the bandgap of the synthesised Ag QDs and functionalized Ag QDs using UV-Vis-NIR and Tauc’s plot curves. Surface-enhanced Raman spectroscopy, Optical microscopy, SEM, and UV-Vis-NIR spectral data were used to correlate the properties of the LC and QDs hybrid systems.

Plasmonic Nanocubes with a Controllable "Crescent Arc" Facet: Tunable Hotspot Engineering for Highly Reliable and Sensitive SERS Detection.

The fine control of the nanogap and morphology of metal nanoparticles (NPs) has always been an obstacle, hindering the development and application of surface-enhanced Raman scattering (SERS) quantitative detection. Here, Au/4-mercaptobenzoic acid@Ag@Au-Ag bimetal core-shell nanocubes (NCs) with a "crescent arc" facet (C-Au/4MBA@Ag NCs) as a highly reliable and sensitive surface-enhanced Raman scattering SERS substrate is proposed for the first time. The bifunctional internal standard (IS) molecules (4MBA) govern the morphology of metal shells to maintain cubic configuration and provide calibration for SERS signals' flotation. In parallel, the controllable curvature of the C-Au/4MBA@Ag NCs is directly modulated by adjusting the relative rates of the galvanic replacement and co-reduction reaction, which generates a controllable interparticle nanogap to offer large depositing spaces for analytes and improve authoritative SERS signals' enhancement. The proposed C-Au/4MBA@Ag NCs exhibit an enhancement factor of up to 4.8 × 1010 and contribute to the ultralow RSD (7.9%). These C-Au/4MBA@Ag NCs also enable the detection of hazardous pesticide residues such as methamidophos and thiram in herbal plants with a complex matrix, with an average detection accuracy of up to 96%. In summary, this study achieves a fine control strategy of the "crescent arc" surface for improving SERS performance and explores the practical application potential for accurate and sensitive Raman detection of hazardous substances.

Urine-based SERS and multivariate statistical analysis for identification of non-muscle-invasive bladder cancer and muscle-invasive bladder cancer.

Bladder cancer (BC) is an epidemiological urologic malignancy that continues to increase each year. Early diagnosis and prognosis monitoring is always significant in clinical practice, especially in distinguishing non-muscle-invasive bladder cancer (NMIBC) from muscle-invasive bladder cancer (MIBC), due to the various depths of tumor invasion related to different therapeutic schedules and recurrence rates. Common diagnostic approaches are too invasive or generally inefficient in accuracy and specificity. In this work, a totally non-invasive and cost-effective method is established by investigating urine samples using surface-enhanced Raman spectroscopy (SERS) and multivariate statistical analysis. The comparison of urine SERS spectra shows the intensities of characteristic peaks for DNA/RNA, hypoxanthine, albumin, D-( +)-galactosamine, fatty acids, and some amino acids are distinguishable in BC occurrence and invasion progression. A PLS-LDA-based two-step binary classification scheme is performed on urine SERS spectra and the diagnostic accuracies were 97.7% and 96.3% for healthy individuals versus BC patients and NMIBC versus MIBC patients, respectively. Moreover, the impact of urine SERS spectral lengths in reaching high-precision recognition of BC is investigated. The results show that the Raman peaks at 803, 893, 1139, 1375, and 1466cm-1 play an essential role in correctly categorizing healthy control, NMIBC, and MIBC patients, and SERS spectra ranges from 400 to 1600cm-1 are enough for this identification task. These findings provide a sensitive, label-free, rapid, and totally non-invasive way for assessment of invasion depth of BC to its early diagnosis and prognosis monitoring, as well as valuable insights for selecting reasonable spectral range to enhance the measurement efficiency especially in large-scale sample datasets.

Customization of Nanogap Arrays Using Stereolithography and Nanoskiving for Surface-Enhanced Raman Scattering

Customization of Nanogap Arrays Using Stereolithography and Nanoskiving for Surface-Enhanced Raman Scattering

Multiwavelength Surface-Enhanced Raman Scattering Fingerprints of Human Urine for Cancer Diagnosis.

Label-free surface-enhanced Raman spectroscopy (SERS) is capable of capturing rich compositional information from complex biosamples by providing vibrational spectra that are crucial for biosample identification. However, increasing complexity and subtle variations in biological media can diminish the discrimination accuracy of traditional SERS excited by a single laser wavelength. Herein, we introduce a multiwavelength SERS approach combined with machine learning (ML)-based classification to improve the discrimination accuracy of human urine specimens for bladder cancer (BCa) diagnosis. This strategy leverages the excitation-wavelength-dependent SERS spectral profiles of complex matrices, which are mainly attributed to wavelength-related vibrational changes in individual analytes and differences in the variation ratios of SERS intensity across different wavelengths among various analytes. By capturing SERS fingerprints under multiple excitation wavelengths, we can acquire more comprehensive and unique chemical information on complex samples. Further experimental examinations with clinical urine specimens, supported by ML algorithms, demonstrate the effectiveness of this multiwavelength strategy and improve the diagnostic accuracy of BCa and staging of its invasion with SERS spectra from increasing numbers of wavelengths. The multiwavelength SERS holds promise as a convenient, cost-effective, and broadly applicable technique for the precise identification of complex matrices and diagnosis of diseases based on body fluids.

Rapid detection of drugs in blood using “molecular hook” surface-enhanced Raman spectroscopy and artificial intelligence technology for clinical applications

Accurate detection of cardiovascular drugs in blood is complicated by interference from serum biomolecules. This study introduces a novel surface-enhanced Raman spectroscopy (SERS) platform incorporating “molecular hooks” to capture small drug molecules while excluding larger biomolecules selectively. The self-assembled nanoparticles with the A13 molecule enhance Raman signals by creating dense electromagnetic “hotspot” regions, achieving detection limits of 10 pg/mL for dobutamine hydrochloride and 10 ng/mL for milrinone-substantially below therapeutic thresholds. Artificial intelligence (AI) integration enables automated spectral analysis, allowing rapid and precise drug detection in clinical blood samples. This approach offers a transformative solution for real-time diagnostics, significantly advancing personalized treatment strategies in clinical settings.

Energy–dependent femtosecond LIPSS on germanium and application in explosives sensing

Abstract In this study, we fabricated laser-induced periodic surface structures (LIPSS) on a germanium surface through laser ablation in air using axicon and femtosecond (fs) pulses. This novel approach permitted the nanoscale material processing outcome refinement via an fs Bessel beam. Our investigations aimed at systematically understanding the formation of periodic structures under various experimental conditions, such as (i) different pulse energies ranging from 50 µJ to 1000 µJ at a constant scan speed and (ii) constant energy with different scan speeds (0.1–3 mm s−1). By adjusting the fluences and scan speeds, we were able to identify the parametric space and alter the periodicity of the low-spatial frequency LIPSS and high-spatial frequency LIPSS on germanium, which were analyzed using field emission scanning electron microscopy. An optimal LIPSS formation over a large area of germanium was achieved at an input energy of 250 µJ and a scan speed of 0.75 mm s−1. Additionally, we measured the contact angles of the Ge nanostructures (GeNSs) to demonstrate their hydrophobic nature and non-wetting properties, providing insights into the behavior of LIPSS. Subsequently, the GeNSs were coated with a ∼15 nm thick gold (Au) film using a thermal deposition method. Utilizing these, the surface-enhanced Raman spectroscopy (SERS) technique detected diverse analytes, such as tetryl (an explosive) at a concentration of 50 µM and thiram (a pesticide) at 500 nM. The SERS enhancement factors for tetryl and thiram molecules on GeNSs coated with a 15 nm-thick Au layer were determined to be 2.5 × 104 and 4.2 × 105, respectively.

Applying Machine Learning and SERS for Precise Typing of DNA Secondary Structures.

Surface-enhanced Raman spectroscopy (SERS) has been demonstrated as an effective method for elucidating secondary structural characteristics of DNA. However, the inherent complexity of the DNA conformation and the lack of SERS samples pose challenges for identifying numerous secondary structures. To address these issues, a synergistic method integrating machine learning with SERS was proposed so as to analyze the SERS spectra of 54 well-defined conformational oligonucleotides, namely, G-quadruplex (G4), i-motif (iM), double-strand (DS), and single-strand (SS) configurations. Principal component analysis (PCA) effectively segregated the oligonucleotides into three distinct conformational groups (G4s, iMs, and others). Furthermore, linear discriminant analysis (LDA), K-nearest neighbor (KNN), and support vector machine (SVM) approaches were utilized to improve the typing accuracy of 54 trained sequences. This enabled the correct classification of the structures of five untrained sequences, as well as the identification of the predominant conformations including G4, iM, and DS formed by two complementary G-rich and C-rich sequences in acidic and neutral pH conditions. The results of this study demonstrated the potential of the proposed methodology for rapid screening and prediction of secondary DNA conformations.

SERS-Driven Ceftriaxone Detection in Blood Plasma: A Protein Precipitation Approach

Accurate detection of antibiotics in biological samples is essential for clinical diagnoses and therapeutic drug monitoring. This research examines how proteins and other substances in blood plasma affect the detection of the antibiotic ceftriaxone using surface-enhanced Raman spectroscopy (SERS). We detected ceftriaxone spiked in blood plasma without sample preparation within the range of 1 mg/mL to 50 µg/mL. By employing a pretreatment approach involving methanol-based protein precipitation to eliminate interfering substances from a spiked blood plasma solution, we could detect ceftriaxone down to 20 µg/mL. The comparative analysis demonstrates that the protein precipitation step enhances the sensitivity of SERS-based detection of drugs in the matrix blood plasma. The insights derived from this study are highly beneficial and can prove advantageous in developing new antibiotic detection methods that are both sensitive and selective in complex biological matrices. These methods can have important implications for clinical treatments.

Development and Biomedical Application of Non-Noble Metal Nanomaterials in SERS.

Surface-enhanced Raman scattering (SERS) is vital in many fields because of its high sensitivity, fast response, and fingerprint effect. The surface-enhanced Raman mechanisms are generally electromagnetic enhancement (EM), which is mainly based on noble metals (Au, Ag, etc.), and chemical enhancement (CM). With more and more studies on CM mechanism in recent years, non-noble metal nanomaterial SERS substrates gradually became widely researched and applied due to their superior economy, stability, selectivity, and biocompatibility compared to noble metal. In addition, non-noble metal substrates also provide an ideal new platform for SERS technology to probe the mechanism of biomolecules. In this paper, we review the applications of non-noble metal nanomaterials in SERS detection for biomedical engineering in recent years. Firstly, we introduce the development of some more common non-noble metal SERS substrates and discuss their properties and enhancement mechanisms. Subsequently, we focus on the progress of the application of SERS detection of non-noble metal nanomaterials, such as analysis of biomarkers and the detection of some contaminants. Finally, we look forward to the future research process of non-noble metal substrate nanomaterials for biomedicine, which may draw more attention to the biosensor applications of non-noble metal nanomaterial-based SERS substrates.

Ultrasensitive SERS Detection of Five β-Blockers Achieved Using Chemometrics with a Two-Dimensional Substrate Formed by Large-Sized Ag@SiO2 Nanoparticles.

We report on a surface-enhanced Raman scattering (SERS) platform for the detection of five beta-blockers (β-blockers): atenolol, esmolol, labetalol, sotalol, and propranolol. Key to this platform was a two-dimensional substrate formed by self-assembling large Ag@SiO2 nanoparticles (Ag@SiO2 NPs) on a silicon wafer. The close arrangement of these large nanoparticles on the surface generated a strong and uniform electromagnetic field, which enhanced SERS signal intensity for the detection of small amounts of the target molecules. The intensities of characteristic peaks of the five β-blocker drugs increased linearly with the increase of their concentrations in the range of 10-5 to 10-8 mol/L. The detection limits were 10-10 mol/L for propranolol, 10-9 mol/L for atenolol, labetalol, and sotalol, and 10-8 mol/L for esmolol. Determination of these five β-blocker drugs added to human urine samples, using a portable Raman spectroscopy instrument, showed quantitative recovery (93-101%). Principal component analysis (PCA) and hierarchical cluster analysis (HCA) of SERS spectral data improved the differentiation among these five β-blockers. This study highlights the potential of the developed SERS platform for rapid, on-site detection of illicit drugs and for antidoping screening.

The Influence of Temperature on the Spatial Distribution of AuNPs on a Ceramic Substrate for Biosensing Applications

In this study, we investigated the spatial distribution and homogeneity of gold nanoparticles (AuNPs) on an alumina (Al2O3; AAO) substrate for potential application as surface-enhanced Raman scattering (SERS) sensors. The AuNPs were synthesized through thermal treatment at 450 °C at varying times (5, 15, 30, and 60 min), and their distribution was characterized using field-emission scanning electron microscopy (FE-SEM) and scanning transmission electron microscopy (STEM). The FE-SEM and STEM analyses revealed that the size and interparticle distance of the AuNPs were significantly influenced by the duration of thermal treatment, with shorter times promoting smaller and more closely spaced nanoparticles, and longer times resulting in larger and more dispersed particles. Raman spectroscopy, using Rhodamine 6G (R6G) as a probe molecule, was employed to evaluate the SERS enhancement provided by the AuNPs on the AAO substrate. Raman mapping (5 µm × 5 µm) was conducted on five sections of each sample, demonstrating improved homogeneity in the SERS effect across the substrate. The topological features of the AuNPs before and after R6G incubation were analyzed using atomic force microscopy (AFM), confirming the correlation between a decrease in surface roughness and an increase in R6G adsorption. The reproducibility of the SERS effect was quantified using the maximum intensity deviation (D), which was found to be below 20% for all samples, indicating good reproducibility. Among the tested conditions, the sample synthesized for 15 min exhibited the most favorable characteristics, with the smallest average nanoparticle size and interparticle distance, as well as the most consistent SERS enhancement. These findings suggest that AuNPs on AAO substrates, particularly those synthesized under the optimized condition of 15 min at 450 °C, are promising candidates for use in SERS-based sensors for detecting cancer biomarkers. This could be attributed to temperature propagation promoted at the time of synthesis. The results also provide insights into the influence of thermal treatment on the spatial distribution of AuNPs and their subsequent impact on SERS performance.

Two- and Three-Dimensional Ag-Au Nanoalloying: Heterogeneous Building Blocks Enhancing Near-Field Focusing.

In this study, we report a strategy for fabricating binary surface-enhanced Raman scattering (SERS) substrates composed of plasmonic Pt@Ag and Pt@Au truncated-octahedral (TOh) dual-rim nanoframes (DNFs) functioning as a "nanoalloy." Pt TOh frameworks act as a scaffold to develop nanoarchitectures with surface decoration using plasmonically active materials (i.e., Au or Ag), resulting in identical sizes and shapes for the two distinct plasmonic elements, facilitating the fabrication of a "nanoalloy" blend of two shape-complex building blocks. The structural complexity from the dual-rim on (111) facets, combined with the mirror charge effect (i.e., enhanced polarization between Ag and Au) at the interface of heterogeneous components, significantly amplifies SERS activity. We carefully investigated near-field focusing of binary SERS substrates through single-particle and bulk SERS measurements corroborated by finite element method (FEM) calculations. Crucially, we developed free-standing superpowders (SPs) in which each heterogeneous building block formed micron-sized supercrystals with adjustable component ratios. These plasmonic SPs were applied to contaminated areas for analyte detection, demonstrating their potential for practical SERS applications.

Amplified Sensitivity in SERS Detection of L1CAM With Silver Plasmonic Mesoporous Silica Capsules on an Imprinted Films

AbstractThis study presents a novel approach for dual detection, leveraging a combination of a Raman reporter‐bearing nanomaterial and molecular imprinting polymers (MIP). A core‐shell Au‐Ag nanoparticles (Au‐Ag NPs) encapsulated in mesoporous silica nanocapsules (Au‐Ag NCs) and a new MIP‐based material targeting L1CAM are used. The MIP prepared via surface imprinting on a carbon screen‐printed electrode (C‐SPE) used thionine (TH) as a monomer. The plasmonic Au‐AgNCs are further functionalized with the Raman reporter 4‐mercaptobenzoic acid (MBA) and anti‐L1CAM for selective detection by surface‐enhanced Raman scattering (SERS) spectroscopy. The biosensor's analytical performance is evaluated using both SERS and electrochemical impedance spectroscopy (EIS). EIS analysis reveals a linear response within the concentration range of 0.1 to 100 ng mL−1 in buffer and serum samples. SERS demonstrates a sensitivity ten times higher than EIS. Selectivity study demonstrates the biosensor's excellent specificity toward L1CAM, with minimal interference from other compounds such as creatinine, glucose, and carbohydrate antigen 19‐9 (CA 19‐9). The Raman signal from the reporter molecule correlates with increasing L1CAM concentrations, reinforcing the analytical findings obtained through electrochemical analysis. Thus, the combination of dual detection and recognition capabilities presents promising potential for detecting diverse biomarkers, especially in critical scenarios where reducing false‐positive or false‐negative errors is crucial.

Simultaneous Recognition and Detection of Adenosine Phosphates by Machine Learning Analysis for Surface-Enhanced Raman Scattering Spectral Data.

Adenosine phosphates (adenosine 5'-monophosphate (AMP), adenosine 5'-diphosphate (ADP), and adenosine 5'-triphosphate (ATP)) play important roles in energy storage and signal transduction in the human body. Thus, a measurement method that simultaneously recognizes and detects adenosine phosphates is necessary to gain insight into complex energy-relevant biological processes. Surface-enhanced Raman scattering (SERS) is a powerful technique for this purpose. However, the similarities in size, charge, and structure of adenosine phosphates (APs) make their simultaneous recognition and detection difficult. Although approaches that combine SERS and machine learning have been studied, they require massive quantities of training data. In this study, limited AP spectral data were obtained using fabricated gold nanostructures for SERS measurements. The training data were created by feature selection and data augmentation after preprocessing the small amount of acquired spectral data. The performances of several machine learning models trained on these generated training data were compared. Multilayer perceptron model successfully detected the presence of AMP, ADP, and ATP with an accuracy of 0.914. Consequently, this study establishes a new measurement system that enables the highly accurate recognition and detection of adenosine phosphates from limited SERS spectral data.

A facile approach for growing Ag nano-dendrites employing towards E. coli cells monitoring using SERS: a proof-of-concept study

Abstract Here, a highly efficient and inexpensive technique to fabricate silver dendritic nanostructures with unique numerous lateral branches has been demonstrated. The study utilized the metal-assisted growth (MAG) technique, which is a simple, one-step technique that allows exact control over the size and morphology of the generated dendrites. The prepared dendrites are characterized using various techniques and the fabrication mechanism is well explored. The final synthesized Ag dendrites are then utilized to detect in-house cultured E. coli. cells using surface-enhanced Raman scattering (SERS) activity. The proposed reason for their superior SERS performance is due to the contribution from the large amount of plasmon-active ‘hotspots’ present in dendritic structures at the sharp edges enhancing the nearby local electromagnetic fields. The proposed work suggests Ag dendritic nanostructures as a potential candidate for SERS-based applications.

Multiplexed SERS Detection of Serum Cardiac Markers Using Plasmonic Metasurfaces.

Surface-enhanced Raman spectroscopy (SERS) possesses exquisite molecular-specific properties with single-molecule sensitivity. Yet, translation of SERS into a quantitative analysis technique remains elusive owing to considerable fluctuation of the SERS intensity, which can be ascribed to the SERS uncertainty principle, a tradeoff between "reproducibility" and "enhancement". To provide a potential solution, herein, an integrated multiplexed SERS biosensing strategy is proposed, which features two distinct advantages. First, a subwavelength-structured plasmonic metasurface consisting of alternately stacked metal-dielectric pyramidal meta-atoms is fabricated and could provide simultaneously enhanced electric and magnetic fields to enable spatially extended and weakly wavelength-dependent SERS. Second, nanomechanical perturbations are harnessed to transduce signals in the form of SERS frequency shifts, which are not directly affected by the SERS uncertainty principle. By also employing 3D printing methods, a proof-of-concept study of multiplexed detection of a panel of serum cardiac biomarkers for acute myocardial infarction is provided. Success in the development of both the electric and magnetic fields-active plasmonic metasurfaces could transform future designs of SERS substrates with newly endowed functionalities, and frequency shift-based SERS multiplexing could open new opportunities to develop innovative quantitative optical techniques for applications in chemistry, biology, and medicine.

Metal–Organic Framework-Ag Nanocomposites as Photocatalysts and in Situ Surface-Enhanced Raman Spectroscopy Sensors to Study the Mechanisms of CO2 Reduction Reactions

Metal–Organic Framework-Ag Nanocomposites as Photocatalysts and in Situ Surface-Enhanced Raman Spectroscopy Sensors to Study the Mechanisms of CO₂ Reduction Reactions

Introduction and Development of Surface-Enhanced Raman Scattering (SERS) Substrates: A Review.

Since its discovery, the phenomenon of Surface Enhanced Raman Scattering (SERS) has gradually become an important tool for analyzing the composition and structure of substances. As a trace technique that can efficiently and nondestructively detect single molecules, the application of SERS has expanded from environmental and materials science to biomedical fields. In the past decade or so, the explosive development of nanotechnology and nanomaterials has further boosted the research of SERS technology, as nanomaterial-based SERS substrates have shown good signal enhancement properties. So far, it is widely recognized that the morphology, size, composition, and stacking mode of nanomaterials have a very great influence on the strength of the substrate SERS effect. Herein, an overview of methods for the preparation of surface-enhanced Raman scattering (SERS) substrates is provided. Specifically, this review describes a variety of common SERS substrate preparation methods and explores the potential and promise of these methods for applications in chemical analysis and biomedical fields. By detailing the influence of different nanomaterials (e.g., metallic nanoparticles, nanowires, and nanostars) and their structural features on the SERS effect, this article aims to provide a comprehensive understanding of SERS substrate preparation techniques.