Surface-enhanced Raman Scattering Spectra Research Articles

Beyond single-molecule chemistry for electrified interfaces using molecule polaritons

Abstract This review introduces the preparation, observation, and characterization of systems of molecule polaritons. We summarize recent progress by our group on the control of molecular properties in the spaces of various optical cavities, such as plasmonic metal nanostructures and Fabry–Perot mirrors. After stating our motivations, we introduce topics on single-molecule observations based on surface-enhanced Raman scattering (SERS) spectroscopy. Our original findings are not limited to the ultrasensitive detection of molecules/materials, but include unique resonant enhancements of SERS through exotic electronic excitation processes at electrified interfaces. Comprehensive analyses of SERS spectra provide novel routes to electronic excitation using plasmonic metal nanostructures. This behavior leads to the distinct photoenergy conversion based on excited electrons and holes with anomalous electrochemical potentials. Such unique resonant electronic excitation emerges as a large optical force that can be used to manipulate small single molecules on surfaces in solution, even at room temperature. We also introduce systems for examining unique interactions between molecules and the optical modes of cavities, from single molecules to molecular ensembles. Plasmonic surface lattices and Fabry–Perot mirrors allow the formation of electronic and vibrational strong coupling states, respectively, showing unique properties of molecule polaritons, even under dark conditions, i.e. without photoillumination.

Two-Dimensional SERS Sensor Array for Identifying and Visualizing the Gas Spatial Distributions of Two Distinct Odor Sources

The spatial distribution of gas emitted from an odor source provides valuable information regarding the composition, size, and localization of the odor source. Surface-enhanced Raman scattering (SERS) gas sensors exhibit ultra-high sensitivity, molecular specificity, rapid response, and large-area detection. In this paper, a SERS gas sensor array was developed for visualizing the spatial distribution of gas evaporated from benzaldehyde and 4-ethylbenzaldehyde odor sources. The SERS spectra of the gas were collected by scanning the sensor array using an automatic detection system. The non-negative matrix factorization algorithm was employed to extract feature and concentration information at each spot on the sensor array. A heatmap image was generated for visualizing the gas spatial distribution using concentration information. Gaussian fitting was applied to process the image for localizing the odor source. The size of the odor source was estimated using the processed image. Moreover, the spectra of benzaldehyde, 4-ethylbenzaldehyde, and their gas mixture were simultaneously detected using one SERS sensor array. The feature information was recognized using a convolutional neural network with an accuracy of 98.21%. As a result, the benzaldehyde and 4-ethylbenzaldehyde odor sources were identified and visualized. Our research findings have various potential applications, including odor source localization, environmental monitoring, and healthcare.

Investigation of selective SERS enhancement mechanism of Au nanospheres and Au nanorods based on 2T2D-SERS correlation spectroscopy

The selective enhancement mechanism in surface-enhanced Raman scattering (SERS) is demonstrated. Two different types of single nanoparticles (Au nanosphere and Au nanorod) were used to investigate the role of the localized surface plasmon resonance (LSPR) in SERS spectra by using the two-trace two-dimensional (2T2D) correlation spectroscopy. The SERS intensities of three probe molecules, 4-mercaptobenzoic acid (4-MBA), 4-aminothiophenol (4-ATP), and 4-bromobenzenethiol (4-BBT), respectively, were enhanced but slightly different when adsorbed on Au nanospheres and Au nanorods. 2T2D correlation SERS spectra clearly showed that even with the same shape of Au nanoparticles, the main factors influencing the SERS enhancement can vary depending on the specific type of SERS tags used. Such subtle difference could not be clearly identified by the conventional spectral analysis. This result sheds light on potential applications of 2T2D correlation spectroscopy. For 4-MBA molecules, the a1 and b2 modes are mainly affected by the Au nanospheres and Au nanorods. For 4-ATP molecules, the a1 and b2 modes related to C–S stretching combined with C–C stretching band are mainly affected by Au nanorods and Au nanospheres. For 4-BBT molecules, the a1 and b2 modes of C–C (aromatic ring) stretching band are mainly affected by Au nanorods and Au nanospheres. This study offers valuable insights into the relationship between nanoparticle shape and SERS enhancement.

Label-Free Surface-Enhanced Raman Scattering Detection of Fire Blight Pathogen Using a Pathogen-Specific Bacteriophage.

Fire blight is one of the most devastating plant diseases, causing severe social and economic problems. Herein, we report a novel method based on label-free surface-enhanced Raman scattering (SERS) combined with an Erwinia amylovora-specific bacteriophage that allows detecting efficiently fire blight bacteria E. amylovora for the first time. To achieve the highest SERS signals for E. amylovora, we synthesized and compared plasmonic nanoparticles (PNPs) with different sizes, i.e., bimetallic gold core-silver shell nanoparticles (Au@AgNPs) and monometallic gold nanoparticles (AuNPs) and utilized the coffee-ring effect for the self-assembly of PNPs and enrichment of fire blight bacteria. Furthermore, we investigated the changes in the SERS spectra of E. amylovora after incubation with an E. amylovora-specific bacteriophage, and we found considerable differences in the SERS signals as a function of the bacteriophage incubation time. The results indicate that our bacteriophage-based label-free SERS analysis can specifically detect E. amylovora without the need for peak assignment on the SERS spectra but simply by monitoring the changes in the SERS signals over time. Therefore, our facile method holds great potential for the label-free detection of pathogenic bacteria and the investigation of viral-bacterial interactions.

Synergistic enhancement of ultrahigh SERS activity via Cu2O@Ag Core-Shell structure for accurate label-free identification of breast tumor subtypes

Conventional methodology for accurate identification of breast tumor subtypes is complex and time-consuming. Label-free SERS (surface-enhanced Raman scattering) enables rapid detection of biomolecules and provides intrinsic fingerprint information of analytes. Exploring reliable SERS bioprobes with high sensitivity and repeatability is essential for efficient typing of cancer cells. In this study, a novel core-shell noble metal-semiconductor Cu2O@Ag heterostructure with uniform morphology and outstanding SERS activity for label-free detection has been proposed. The Cu2O@Ag SERS substrates exhibit exceptional sensitivity to detect 4 nitrophenthiol (4NTP) at extreme low concentration (10−15 M), and the SERS spectra demonstrate excellent selectivity and repeatability (RSD ≈ 8.84%). The enhancement mechanism is attributed to the synergistic enhancement of photoinduced charge transfer and boosted surface plasmon resonance effect, confirmed by reduced fluorescence lifetime of methylene blue on SERS substrates and enhanced electromagnetic field intensity via finite-difference time-domain computational simulation. The ultrahigh sensitivity and good signal stability endow Cu2O@Ag SERS bioprobe potential for accurate detection of four breast cancer cells subtypes. The rich fingerprint information from SERS spectra is processed using linear differentiation analysis, revealing a high accuracy of 93.3% for subtypes classification. These results demonstrate an excellent SERS bioprobe with high sensitivity and repeatability for precision diagnosis has been developed.

Early colorectal cancer detection: a serum analysis platform combining SERS and machine learning.

Colorectal cancer (CRC) is one of the deadliest malignancies globally, with high incidence and mortality rates. Early detection is crucial for improving treatment success rates and patient survival. However, due to the difficulty in detecting early symptoms, many cases are diagnosed at advanced stages, necessitating more sensitive and accurate detection methods. This study proposes a novel approach combining the Principal Component Analysis (PCA)-Dynamic Weighted Nearest Neighbor (DWNN) model with Surface-Enhanced Raman Scattering (SERS) technology to detect the serum of CRC mice at different stages. Establishing the CRC mice model, serum samples were collected for further analysis. An Au Nanocluster (AuNC) substrate was synthesized to ensure optimal SERS enhancement. The PCA-DWNN recognition model was constructed to classify the SERS spectra of CRC at different stages. The synthesized AuNC substrate has high sensitivity, good reproducibility, uniformity, and stability, making it a high-performance nanomaterial. The PCA-DWNN model has significant advantages in identifying high-dimensional and complex SERS spectra, offering excellent classification accuracy and robustness, with an accuracy rate of 97.5%. By analyzing the PCA loading plot, it was observed that as CRC progressed, the content and structure of proteins, lipids, amino acids, and carbohydrates in the serum changed, reflected in different characteristic peaks in the SERS spectra. This study suggests that SERS combined with PCA-DWNN has potential in the early detection of CRC, possibly providing a novel approach for clinical diagnostics.

Digital surface enhanced Raman spectroscopy for quantifiable single molecule detection in flow.

Surface enhanced Raman scattering (SERS) provides a label free method of analyzing molecules from diverse and complex signals, potentially with single molecule sensitivity. The chemical specificity inherent in the SERS spectrum can identify molecules; however signal variability arising from the diversity of plasmonic environments can limit quantification, particularly at low concentrations. Here we show that digitizing, or counting SERS events, can decrease the limit of detection in flowing solutions enabling quantification of single molecules. By using multivariate curve resolution and establishing a score threshold, each individual spectrum can be classified as containing an event or not. This binary "yes/no" can then be quantified, and a linear region can be established. This method was shown to lower the limit of detection to the lowest physical limit, and lowered the limit of detection by an order of magnitude from the traditional, intensity based LOD calculations.

Trace level detection of melamine and cyanuric acid extracted from pet liquid food (milk) using a SERS Au nanogap substrate

This study reported an application of Au nanogap substrates for surface-enhanced Raman scattering (SERS) measurements to quantitatively analyze melamine and its derivative products at trace levels in pet liquid food (milk) combined with a waveband selection approach, namely variable importance in projection (VIP). Six different concentrations of melamine, cyanuric acid, and melamine combined with cyanuric acid were created, and SERS spectra were acquired from 550 to 1620cm−1. Detection was possible up to 200 pM for melamine-contaminated samples, and 400 pM concentration detection for other two groups. The VIP-PLSR models obtained correlation coefficient (R2) values of 0.997, 0.985, and 0.981, with root mean square error of prediction (RMSEP) values of 18.492 pM, 19.777 pM, and 15.124 pM for prediction datasets. Additionally, partial least square discriminant analysis (PLS-DA) was used to classify both pure and different concentrations of spiked samples. The results showed that the maximum classification accuracy for melamine was 100%, for cyanuric acid it was 96%, and for melamine coupled with cyanuric acid it was 95%. The results obtained clearly demonstrated that the Au nanogap substrate offers low-concentration, rapid, and efficient detection of hazardous additive chemicals in pet consuming liquid food.

Three-dimensional hotspot structures constructed from nanoporous gold with a V-cavity and gold nanoparticles for surface-enhanced Raman scattering.

The intensity and sensitivity of surface-enhanced Raman scattering (SERS) spectra are highly dependent on the consistency and homogeneity of the nanomaterials. In this study, we developed a large-area three-dimensional (3D) hotspot substrate with good homogeneity and reproducibility in SERS signals. The substrate is based on the synergistic structures of nanoporous gold (NPG) and gold nanoparticles (AuNPs). NPG was combined with a periodic V-shaped nanocavity array to create nanoporous gold with a V-cavity (NPGVC) array featuring uniform hotspots. A nanoporous gold V-shaped resonant cavity (NPGVRC) structure was developed by incorporating AuNPs into the NPGVC array. The coupling action between the AuNPs and NPGVC resulted in a SERS-enhanced electromagnetic field with 3D hotspot distribution. The strategic incorporation of NPG and V-cavity array significantly expanded the surface area available for analyte adsorption and interaction with AuNPs. Using rhodamine 6G (R6G) and malachite green (MG) as probe molecules, the SERS performance was investigated, and the NPGVRC substrate not only showed excellent enhancement with the limit of detection as low as 10-11 M, but also presented good homogeneity. NPGVRC was then used for biological detection of the influenza A virus, where we acquired and examined the characteristic SERS spectra of two spike proteins. It is demonstrated that there is significant potential for our proposed SERS platform to be used in biosensors.

Noble metal-free SERS: mechanisms and applications.

Surface-enhanced Raman scattering (SERS) is a very important tool in vibrational spectroscopy. The coupling of nanomaterials induces local surface plasmon resonance (LSPR), which contributes greatly to SERS. Due to its remarkable sensitivity in trace detection, SERS has gained prominence in the fields of catalysis, biosensors, drug tracking, and optoelectronic devices. SERS activity is believed to be closely related to the LSPR and charge transfer (CT) of the material. Noble metal nanostructures have been commonly used as SERS-active substrates due to their strong local electric fields and relatively mature preparation, application, and enhancement mechanisms. In recent years, SERS research based on semiconductor materials has attracted significant attention because semiconductor materials have advantages such as repeatable preparation, simple pretreatment, stable SERS spectra and superior biocompatibility, stability, and reproducibility. Semiconductor-based SERS has the potential to enrich SERS theory and applications. Thus, the development of semiconductor materials will introduce a new epoch for SERS-based research. In this review, we outline the two main kinds of semiconductor SERS-active substrates: inorganic and organic semiconductor SERS-active substrates. We also provide an overview of the SERS mechanism for different kinds of materials and SERS-based applications.

Surface-enhanced Raman scattering enhancement using a hybrid gold nanoparticles@carbon nanodot substrate for herbicide detection.

The widespread distribution of herbicides in the environment poses a significant risk to human health and wildlife. Surface-enhanced Raman scattering (SERS) has emerged as a powerful technique for detecting and analyzing herbicides. However, developing a low-cost, highly sensitive, reproducible, stable, and Raman-active nanostructured substrate for herbicide detection remains a particular challenge. In this research, a nanohybrid substrate consisting of gold nanoparticles@carbon nanodots (AuNPs@CNDs) was synthesized by reducing HAuCl4 in the presence of CNDs at 100 °C. The optical, chemical, and physical properties of CNDs, AuNPs, and the hybrid AuNPs@CND substrates were thoroughly investigated using various techniques including UV-vis spectrometry, Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and CytoViva darkfield and hyperspectral imaging. The SERS effect of the substrates was evaluated using rhodamine 6G (Rh6G), a Raman-active probe, and two groups of herbicides containing mesotrione or S-metolachlor. The results demonstrated a significant signal amplification in the SERS spectra of Rh6G and herbicide molecule detection using the AuNPs@CND substrate compared to bare CNDs and AuNPs alone. This suggests that the nanohybrid AuNPs@CND SERS substrate holds promise for the detection of herbicides and other organic compounds in environmental applications.

A machine learning-driven SERS platform for precise detection and analysis of vascular calcification.

Vascular calcification (VC) significantly increases the incidence and mortality rates of cardiovascular diseases, severely threatening public health as a global issue. Currently, there are no effective methods to prevent and treat vascular calcification. This study proposes a machine learning-assisted surface-enhanced Raman scattering (SERS) technique for label-free, highly sensitive analysis of VC rat serum. We prepared gold nanobipyramid (GNBP) substrates using seed-mediated and liquid-liquid interface self-assembly methods and measured the SERS spectra of the serum. The collected spectral data were processed using a Principal Component Analysis (PCA)-Linear Discriminant Analysis (LDA) model to achieve effective sample differentiation. In this analysis model, GNBP substrates enabled rapid, sensitive, and label-free serum spectral detection, achieving classification accuracy, sensitivity, and specificity of 96.0%, and an AUC value of 0.98, significantly outperforming currently used machine learning methods. By analyzing the PCA loading plots, key spectral features that distinguished VC were successfully captured. This study demonstrates that combining SERS technology with machine learning provides a new method and foundation for real-time diagnosis and identification of VC, showcasing the significant advantages of GNBP substrates in high-sensitivity and high-specificity detection, potentially improving the early diagnosis and treatment of VC significantly.

Stable SERS Detection of Lactobacillus fermentum Using Optical Tweezers in a Microfluidic Environment.

Rapid identification of fermented lactic acid bacteria has long been a challenge in the brewing industry. This study combined label-free surface-enhanced Raman scattering (SERS) and optical tweezer technology to construct a test platform within a microfluidic environment. Six kinds of lactic acid bacteria common in industry were tested to prove the stability of the SERS spectra. The results demonstrated that the utilization of optical tweezers to securely hold the bacteria significantly enhanced the stability of the SERS spectra. Furthermore, SVM and XGBoost machine learning algorithms were utilized to analyze the obtained Raman spectra for identification, and the identification accuracies exceeded 95% for all tested lactic acid bacteria. The findings of this study highlight the crucial role of optical tweezers in improving the stability of SERS spectra by capturing bacteria in a microfluidic environment, prove that this technology could be used in the rapid identification of lactic acid bacteria, and show great significance in expanding the applicability of the SERS technique for other bacterial testing purposes.

Nasopharyngeal carcinoma cell screening based on the electroporation-SERS spectroscopy

Nasopharyngeal carcinoma (NPC) is a malignant tumor in head and neck. Early diagnosis can effectively improve the survival rate of patients. Nasopharyngeal exfoliative cytology, as a convenient and noninvasive auxiliary diagnostic method, is suitable for the population screening of NPC, but its diagnostic sensitivity is low. In this study, an electroporation-based SERS technique was proposed to detect and screen the clinical nasopharyngeal exfoliated cell samples. Firstly, nasopharyngeal swabs was used to collected the nasopharyngeal exfoliated cell samples from NPC patients (n = 54) and healthy volunteers (n = 60). Then, gold nanoparticles, as the Raman scattering enhancing substrates, were rapidly introduced into cells by electroporation technique for surface-enhanced Raman scattering (SERS) detection. Finally, SERS spectra combined with principal component analysis (PCA) and linear discriminant analysis (LDA) were employed to diagnose and distinguish NPC cell samples. Raman peak assignments combined with spectral differences reflected the biochemical changes associated with NPC, including nucleic acid, amino acid and carbohydrates. Based on the PCA-LDA approach, the sensitivity, specificity and accuracy of 98.15 %, 96.67 % and 97.37 %, respectively, were achieved for screening NPC. This study offers valuable assistance for noninvasive NPC auxiliary diagnosis, and has grate potential in expanding the application of the SERS technique in clinical cell sample testing.

On the Measurements of the Surface-Enhanced Raman Scattering Spectrum: Effective Enhancement Factor, Optical Configuration, Spectral Distortion, and Baseline Variation

In this paper, a comprehensive theoretical framework for understanding surface-enhanced Raman scattering (SERS) measurements in both solution and thin-film setups, focusing on electromagnetic enhancement principles, was presented. Two prevalent types of SERS substrates found in the literature were investigated: plasmonic colloidal particles, including spherical and spheroid nanoparticles, nanoparticle diameters, and thin-film-based SERS substrates, like ultra-thin substrates, bundled nanorods, plasmonic thin films, and porous thin films. The investigation explored the impact of analyte adsorption, orientation, and the polarization of the excitation laser on effective SERS enhancement factors. Notably, it considered the impact of analyte size on the SERS spectrum by examining scenarios where the analyte was significantly smaller or larger than the hot spot dimensions. The analysis also incorporated optical attenuations arising from the optical properties of the analyte and the SERS substrates. The findings provide possible explanations for many observations made in SERS measurements, such as variations in relative peak intensities during SERS assessments, reductions in SERS intensity at high analyte concentrations, and the occurrence of significant baseline fluctuations. This study offers valuable guidance for optimizing SERS substrate design, enhancing SERS measurements, and improving the quantification of SERS detection.

Surface-Enhanced Raman Scattering-Based Surface Chemotaxonomy: Combining Bacteria Extracellular Matrices and Machine Learning for Rapid and Universal Species Identification.

Rapid, universal, and accurate identification of bacteria in their natural states is necessary for on-site environmental monitoring and fundamental microbial research. Surface-enhanced Raman scattering (SERS) spectroscopy emerges as an attractive tool due to its molecule-specific spectral fingerprinting and multiplexing capabilities, as well as portability and speed of readout. Here, we develop a SERS-based surface chemotaxonomy that uses bacterial extracellular matrices (ECMs) as proxy biosignatures to hierarchically classify bacteria based on their shared surface biochemical characteristics to eventually identify six distinct bacterial species at >98% classification accuracy. Corroborating with in silico simulations, we establish a three-way inter-relation between the bacteria identity, their ECM surface characteristics, and their SERS spectral fingerprints. The SERS spectra effectively capture multitiered surface biochemical insights including ensemble surface characteristics, e.g., charge and biochemical profiles, and molecular-level information, e.g., types and numbers of functional groups. Our surface chemotaxonomy thus offers an orthogonal taxonomic definition to traditional classification methods and is achieved without gene amplification, biochemical testing, or specific biomarker recognition, which holds great promise for point-of-need applications and microbial research.

Ultraviolet Exposure Improves SERS Activity of Graphene-Coated Ag/ZrO2 Substrates

This study reveals a significant improvement in surface-enhanced Raman scattering (SERS) activity of Ag/ZrO2 substrates covered with a few-layer graphene preliminary exposed to ultraviolet (UV) light. The SERS-active substrates are formed by the “silver mirror” deposition of Ag nanoparticles on annealed zirconia blocks. The film composed of ~3 graphene layers is grown on copper foil by a chemical vapor deposition and then wet-transferred to the SERS-active substrates. The graphene-free Ag/ZrO2 samples are found to provide an enhancement of the Raman scattering from rhodamine 6G (R6G) at a micromolar concentration, which is associated with combined effects from the surface plasmon resonance in the Ag nanoparticles and a charge transfer facilitated by zirconium dioxide. It is revealed that the SERS signal from the analyte molecules can be suppressed by a UV exposure of the Ag/ZrO2 samples due to photocatalytic activity of the wide band gap semiconductor. However, if the samples are covered with a few-layer graphene (Gr/Ag/ZrO2) it prevents the dye molecule decomposition upon the UV treatment and improves SERS activity of the substrates. The 365 nm treatment leads to a 40% increase in the 10–6 M R6G SERS spectrum intensity, while the 254 nm irradiation causes it to rise by 47%, which is explained by different responses from the surface and bulk zirconia crystals to the short and long UV wavelengths. This enhancement is attributed to the distinct responses of surface and in-depth zirconia crystals to varied UV wavelengths and underscores the pivotal role of graphene as a protective and enhancing layer.

Quantitation of surface-enhanced Raman spectroscopy based on deep learning networks

Surface-enhanced Raman scattering (SERS) is a highly sensitive detection method that is widely applied in numerous fields. However, the distribution of SERS "hotspots" and their sensitive response at the nanoscale render the reproducibility and quantitative analysis of SERS spectra difficult. In this study, an analytical method based on deep learning was applied for the quantitative detection of SERS spectra. Using Ag/TiO2 composite nanofilms as SERS substrates, the SERS spectra of Rhodamine 6G (R6G) at concentrations of 10−3, 10−4, 10−5, and 10−6 mol/L were employed as the datasets for quantitative analysis. Using the normalized SERS spectral dataset, the deep learning network autonomously searched for features related to quantitative detection under complex conditions with less dependence on Raman peak intensities and without additional preprocessing, which afforded deep-learning-based SERS quantitative detection with excellent reproducibility and feasibility. SERS spectra of stable physical condition were extracted for statistical analysis, and the trained neural network model adequately predicted the trend of variations in the concentration. Using R6G as the probe molecule, a superior recognition result with an accuracy of 98.1 % for the concentrations of 10−3, 10−4, 10−5, and 10−6 mol/L was obtained using a convolutional neural network on the test set. Therefore, this method provides a feasible new strategy to overcome the quantitative detection limitations of current SERS analysis methods.

Silver nanosheets self-assembled on polystyrene microspheres to form “hot spots” with different nanogap distances for high sensitive SERS detection

Herein, we reported a facile method to control the nanogap distance of silver (Ag) nanosheets to obtain high sensitive plasmonic Surface-enhanced Raman scattering (SERS) substrates. The sulfonated polystyrene (SPS) microspheres with different diameters were first fabricated using micro-emulsion synthesis, and then the SPS microspheres were coated with Ag nanosheets through chemical synthesis with citric acid/ascorbic acid to form Ag nanosheets@SPS (Ag@SPS) substrates with different nanogap distances. The results showed that the nanogap distance of Ag nanosheets self-assembled on SPS microspheres reduced from 80-150 nm to 28–68 nm when the diameter of SPS microspheres increased from 0.9 to 3.5 μm, and the enhancement factor (EF) increased from 105 to 107, the limit of detection of rhodamine 6G (R6G) for the Ag@SPS microspheres reduced from 10−10 to 10−13 mol/L. It confirmed that the Ag nanosheets coated on the surface of SPS microspheres could achieve ultra trace detection of analyte. Furthermore, the low concentration detection limit for melamine with the Ag@SPS microspheres substrate was about 10−8 mg/L, which is lower than the standard legislated by the European Union and the Food & Drug Administration. In addition, the SERS spectrum of 3-mercaptopropionic acid (3-MPA) could be also detected when its concentration was 10−8 mol/L. The prepared substrate offered a promising opportunity for SERS practical applications.

Label-free SERS for rapid identification of interleukin 6 based on intrinsic SERS fingerprint of antibody‑gold nanoparticles conjugate

A label-free surface-enhanced Raman scattering (SERS) was designed for sensitive detection of interleukin-6 (IL-6). The sensing element composed of anti-IL-6 antibodies adsorbed on the surface of spherical gold nanoparticles (AuNPs) as SERS-active surface. The principle of detection was probing antibody conformational changes using its intrinsic SERS fingerprint after binding to IL-6. Comparison of SERS spectra of antibody before and after binding to IL-6 showed that secondary structure of antibody does not change upon binding to IL-6. Vibrational information from disulfide bonds ν(SS) in antibody structure indicated some changes of geometry around SS bridges as a consequence of the immunocomplex formation. Transmission electron microscopy (TEM) and UV–Vis spectroscopy were used to confirm AuNPs conjugation with antibody as well as IL-6 binding to antibody on the surface of AuNPs. The SERS-based immunoassay showed a wide linear range (2.0–1000 pg mL−1) and a high sensitivity with a limit of detection (LOD) as low as 0.91 pg mL−1 (0.04 pM) without using any extrinsic Raman label. UV–Vis spectroscopy was employed as a conventional method for IL-6 detection based on observation of any change in the position of localized surface plasmon resonance (LSPR) band of AuNPs-antibody conjugates with LOD of 10 ng mL−1.