Label-free SERS detection of proteins based on machine learning classification of chemo-structural determinants

Construction of the Au Nanoparticle/Graphene Oxide/Au Nanotube (AuNP/GO/AuNT) Sandwich Membrane for Surface-Enhanced Raman Scattering Sensing

Au nanotube-based composite membrane served as surface-enhanced Raman scattering (SERS) substrate with an ultralarge aspect ratio possesses an excellent flexibility and widely tunable surface plasmon resonance, and by introducing graphene oxide (GO) as a spacer layer, the SERS enhancement of the composite membrane is obviously better than those from the individual blocks of the Au nanotubes (AuNTS) membrane and the Au nanoparticle/graphene oxide (AuNP/GO) membrane. Such a "sandwich" (AuNP/GO/AuNT) structured membrane has a high SERS sensitivity and a wide tunability by controlling the size of Au nanoparticles and the thickness of graphene oxide, and the detection limits of the AuNP/GO/AuNT substrate for R6G and NBA are as low as 10-12 and 10-7 M, respectively; the large enhancement is attributed to the adsorption and chemical mechanism of graphene oxide and the physical mechanism of the Au nanoparticles and nanotubes (the electromagnetic field coupling between them).

Quasi-spherical silver nanoparticles for human prolactin detection by surface-enhanced Raman spectroscopy

Prolactin is a polypeptide hormone made of 199 amino acids; 50% of the amino acid chain forms helices, and the rest forms loops. This hormone is typically related to initiating and maintaining lactation, although it is also elevated in various pathological conditions. Serum prolactin levels of 2 to 18 ng ml-1 in men, up to 30 ng ml-1 in women, and 10 to 210 ng ml-1 in pregnant women are considered normal. Immunoassay techniques used for detection are susceptible to error in different clinical conditions. Surface-enhanced Raman spectroscopy (SERS) is a technique that allows for obtaining the protein spectrum in a simple, fast, and reproducible manner. Nonetheless, proper characterization of human prolactin's Raman/SERS spectrum at different concentrations has so far not been deeply discussed. This study aims to characterize the Raman spectrum of human prolactin at physiological concentrations using silver nanoparticles (AgNPs) as the SERS substrate. The Raman spectrum of prolactin at 20 ng ul-1 was acquired. Quasi-spherical AgNPs were obtained using chemical synthesis. For SERS characterization, decreasing dilutions of the protein were made by adding deionized water and then a 1 : 1 volume of the AgNPs colloid. For each mixture, the Raman spectrum was determined. The spectrum of prolactin by SERS was obtained with a concentration of up to 0.1 ng ml-1. It showed characteristic bands corresponding to the side chains of aromatic amino acids in the protein's primary structure and the alpha helices of the secondary structure of prolactin. In conclusion, using quasi-spherical silver nanoparticles as the SERS substrate, the Raman spectrum of human prolactin at physiological concentration was determined.

Protein classification by autofluorescence spectral shape analysis using machine learning

Depending on the relative numbers and spatial arrangement of Tryptophan (Trp; W) and Tyrosine (Tyr; Y) residues, different proteins produce distinct autofluorescence (AF) spectral shapes when excited at ∼280 nm. Yet, considering the vast number and heterogeneous forms in nature, visual analysis and precise identification of proteins based on their AF spectra is challenging and further compounded in cases when different proteins produce substantially similar AF spectral shapes. There is, thus, a serious need to develop a methodology to address this problem. The current study proposes a practical technology to quickly identify proteins using machine learning (ML) algorithms based on their AF spectra. Specifically, AF spectra of fifteen different standard proteins of varying origin with distinct structural and Trp/Tyr compositions were recorded; based on the spectral features selected by the Minimum-Redundancy-Maximum-Relevance (mRMR) algorithm, a multiclass Support Vector Machine (SVM) learning model with Radial Basis Function (RBF), Polynomial, and Linear kernels classified the proteins with high accuracy of 99.06%, 99.03%, and 98.29% respectively. Since protein identification is the key to understand biological functions and disease diagnosis, the proposed methodology could offer a viable alternative to and improve the existing protein identification techniques.

Molecular beacon decorated silver nanowires for quantitative miRNA detection by a SERS approach

Advantages of biosensors based on surface enhanced Raman scattering (SERS) rely on improved sensitivity and specificity, and suited reproducibility in detecting a target molecule that is localized in close proximity to a SERS-active surface. Herein, a comprehensive study on the realization of a SERS biosensor designed for detecting miRNA-183, a miRNA biomarker that is specific for chronic obstructive pulmonary disease (COPD), is presented. The used strategy exploits a signal-off mechanism by means of a labelled molecular beacon (MB) as the oligonucleotide biorecognition element immobilized on a 2D SERS substrate, based on spot-on silver nanowires (AgNWs) and a multi-well low volume cell. The MB was properly designed by following a dedicated protocol to recognize the chosen miRNA. A limit of detection down to femtomolar concentration (3 × 10-16 M) was achieved and the specificity of the biosensor was proved. Furthermore, the possibility to regenerate the sensing system through a simple procedure is shown: with regeneration by using HCl 1 mM, two detection cycles were performed with a good recovery of the initial MB signal (83%) and a reproducible signal after hybridization.

Label-Free SERS and MD Analysis of Biomarkers for Rapid Point-of-Care Sensors Detecting Head and Neck Cancer and Infections

For the progress of point-of-care medicine, where individual health status can be easily and quickly monitored using a handheld sensor, saliva serves as one of the best-suited body fluids thanks to its availability and abundance of physiological indicators. Salivary biomarkers, combined with rapid and highly sensitive detection tools, may pave the way to new real-time health monitoring and personalized preventative therapy branches using saliva as a target matrix. Saliva is increasing in importance in liquid biopsy, a non-invasive approach that helps physicians diagnose and characterize specific diseases in patients. Here, we propose a proof-of-concept study combining the unique specificity in biomolecular recognition provided by surface-enhanced Raman spectroscopy (SERS) in combination with molecular dynamics (MD) simulations, which give leave to explore the biomolecular absorption mechanism on nanoparticle surfaces, in order to verify the traceability of two validated salivary indicators, i.e., interleukin-8 (IL-8) and lysozyme (LYZ), implicated in oropharyngeal squamous cell carcinoma (OSCC) and oral infection. This strategy simultaneously assures the detection and interpretation of protein biomarkers in saliva, ultimately opening a new route for the evolution of fast and accurate point-of-care SERS-based sensors of interest in precision medicine diagnostics.

Using Machine Learning and Silver Nanoparticle-Based Surface-Enhanced Raman Spectroscopy for Classification of Cardiovascular Disease Biomarkers

Characterizing complex biofluids using surface-enhanced Raman spectroscopy (SERS) coupled with machine learning (ML) has been proposed as a powerful tool for point-of-care detection of clinical disease. ML is well-suited to categorizing otherwise uninterpretable, patient-derived SERS spectra that contain a multitude of low concentration, disease-specific molecular biomarkers among a dense spectral background of biological molecules. However, ML can generate false, non-generalizable models when data sets used for model training are inadequate. It is thus critical to determine how different SERS experimental methodologies and workflow parameters can potentially impact ML disease classification of clinical samples. In this study, a label-free, broadband, Ag nanoparticle-based SERS platform was coupled with ML to assess simulated clinical samples for cardiovascular disease (CVD), containing randomized combinations of five key CVD biomarkers at clinically relevant concentrations in serum. Raman spectra obtained at 532, 633, and 785 nm from up to 300 unique samples were classified into physiological and pathological categories using two standard ML models. Label-free SERS and ML could correctly classify randomized CVD samples with high accuracies of up to 90.0% at 532 nm using as few as 200 training samples. Spectra obtained at 532 nm produced the highest accuracies with no significant increase achieved using multiwavelength SERS. Sample preparation and measurement methodologies (e.g., different SERS substrate lots, sample volumes, sample sizes, and known variations in randomization and experimental handling) were shown to strongly influence the ML classification and could artificially increase classification accuracies by as much as 27%. This detailed investigation into the proper application of ML techniques for CVD classification can lead to improved data set acquisition required for the SERS community, such that ML on labeled and robust SERS data sets can be practically applied for future point-of-care testing in patients.

Impact of seed amplification assay and surface-enhanced Raman spectroscopy combined approach on the clinical diagnosis of Alzheimer's disease

BACKGROUND

The current diagnosis of Alzheimer's disease (AD) is based on a series of analyses which involve clinical, instrumental and laboratory findings. However, signs, symptoms and biomarker alterations observed in AD might overlap with other dementias, resulting in misdiagnosis.

METHODS

Here we describe a new diagnostic approach for AD which takes advantage of the boosted sensitivity in biomolecular detection, as allowed by seed amplification assay (SAA), combined with the unique specificity in biomolecular recognition, as provided by surface-enhanced Raman spectroscopy (SERS).

RESULTS

The SAA-SERS approach supported by machine learning data analysis allowed efficient identification of pathological Aβ oligomers in the cerebrospinal fluid of patients with a clinical diagnosis of AD or mild cognitive impairment due to AD.

CONCLUSIONS

Such analytical approach can be used to recognize disease features, thus allowing early stratification and selection of patients, which is fundamental in clinical treatments and pharmacological trials.

Extracting higher-conductivity designs for solid polymer electrolytes by quantum-inspired annealing

Data-driven optimal structure exploration has become a hot topic in materials for energy-related devices. However, this method is still challenging due to the insufficient prediction accuracy of material properties and large exploration space for candidate structures. We propose a data trend analysis system for materials using quantum-inspired annealing. Structure-property relationships are learned by a hybrid decision tree and quadratic regression algorithm. Then, ideal solutions to maximize the property are explored by a Fujitsu Digital Annealer, which is unique hardware that can quickly extract promising solutions from the ample search space. The system's validity is investigated with an experimental study examining solid polymer electrolytes as potential components for solid-state lithium-ion batteries. A new trithiocarbonate polymer electrolyte offers a conductivity of 10^-6 S cm^-1 at room temperature, even though it is in a glassy state. Molecular design through data science will enable accelerated exploration of functional materials for energy-related devices.

Deep learning approach to overcome signal fluctuations in SERS for efficient On-Site trace explosives detection

Surface-enhanced Raman spectroscopy (SERS) is an improved Raman spectroscopy technique to identify the analyte under study uniquely. At the laboratory scale, SERS has realised a huge potential to detect trace analytes with promising applications across multiple disciplines. However, onsite detection with SERS is still limited, given the unwanted glitches of signal reliability and blinking. SERS has inherent signal fluctuations due to multiple factors such as analyte adsorption, inhomogeneous distribution of hotspots, molecule orientation etc. making it a stochastic process. Given these signal fluctuations, validating a signal as a representation of the analyte often relies on an expert's knowledge. Here we present a neural network-aided SERS model (NNAS) without expert interference to efficiently identify reliable SERS spectra of trace explosives (tetryl and picric acid) and a dye molecule (crystal violet). The model uses the signal-to-noise ratio approach to label the spectra as representative (RS) and non-representative (NRS), eliminating the reliability of the expert. Further, experimental conditions were systematically varied to simulate general variations in SERS instrumentation, and a deep-learning model was trained. The model has been validated with a validation set followed by out-of-sample testing with an accuracy of 98% for all the analytes. We believe this model can efficiently bridge the gap between laboratory and on-site detection using SERS.

Au/SiNCA-based SERS analysis coupled with machine learning for the early-stage diagnosis of cisplatin-induced liver injury

Cisplatin has been widely applied in the clinical treatment of various cancers, whereas liver injury induced by its hepatotoxicity is still a severe issue. Reliable identification of early-stage cisplatin-induced liver injury (CILI) can improve clinical care and help to streamline drug development. Traditional methods, however, cannot achieve enough information at the subcellular level due to the requirement of the labeling process and low sensitivity. To overcome these, we designed an Au-coated Si nanocone array (Au/SiNCA) to fabricate the microporous chip as the surface-enhanced Raman scattering (SERS) analysis platform for the early diagnosis of CILI. A CILI rat model was established, and the exosome spectra were obtained. The principal component analysis (PCA)-representation coefficient-based k-nearest centroid neighbor (RCKNCN) classification algorithm was proposed as the multivariate analysis method to build the diagnosis and staging model. The PCA-RCKNCN model has been validated to achieve a satisfactory result, with accuracy and AUC of over 97.5%, and sensitivity and specificity of over 95%, indicating that SERS combined with the PCA-RCKNCN analysis platform can be a promising tool for clinical applications.

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

Versatile Applications of Silver Nanowire-Based Electrodes and Their Impacts

Indium tin oxide (ITO) is currently the most widely used material for transparent electrodes; however, it has several drawbacks, including high cost, brittleness, and environmental concerns. Silver nanowires (AgNWs) are promising alternatives to ITO as materials for transparent electrodes owing to their high electrical conductivity, transparency in the visible range of wavelengths, and flexibility. AgNWs are effective for various electronic device applications, such as touch panels, biosensors, and solar cells. However, the high synthesis cost of AgNWs and their poor stability to external chemical and mechanical damages are significant challenges that need to be addressed. In this review paper, we discuss the current state of research on AgNW transparent electrodes, including their synthesis, properties, and potential applications.

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening.

SERS-Based Biosensors Combined with Machine Learning for Medical Application

Surface-enhanced Raman spectroscopy (SERS) has shown strength in non-invasive, rapid, trace analysis and has been used in many fields in medicine. Machine learning (ML) is an algorithm that can imitate human learning styles and structure existing content with the knowledge to effectively improve learning efficiency. Integrating SERS and ML can have a promising future in the medical field. In this review, we summarize the applications of SERS combined with ML in recent years, such as the recognition of biological molecules, rapid diagnosis of diseases, developing of new immunoassay techniques, and enhancing SERS capabilities in semi-quantitative measurements. Ultimately, the possible opportunities and challenges of combining SERS with ML are addressed.

A Review of Magnetic Nanoparticle-Based Surface-Enhanced Raman Scattering Substrates for Bioanalysis: Morphology, Function and Detection Application

Surface-enhanced Raman scattering (SERS) is a kind of popular non-destructive and water-free interference analytical technology with fast response, excellent sensitivity and specificity to trace biotargets in biological samples. Recently, many researches have focused on the preparation of various magnetic nanoparticle-based SERS substrates for developing efficient bioanalytical methods, which greatly improved the selectivity and accuracy of the proposed SERS bioassays. There has been a rapid increase in the number of reports about magnetic SERS substrates in the past decade, and the number of related papers and citations have exceeded 500 and 2000, respectively. Moreover, most of the papers published since 2009 have been dedicated to analytical applications. In the paper, the recent advances in magnetic nanoparticle-based SERS substrates for bioanalysis were reviewed in detail based on their various morphologies, such as magnetic core-shell nanoparticles, magnetic core-satellite nanoparticles and non-spherical magnetic nanoparticles and their different functions, such as separation and enrichment, recognition and SERS tags. Moreover, the typical application progress on magnetic nanoparticle-based SERS substrates for bioanalysis of amino acids and protein, DNA and RNA sequences, cancer cells and related tumor biomarkers, etc., was summarized and introduced. Finally, the future trends and prospective for SERS bioanalysis by magnetic nanoparticle-based substrates were proposed based on the systematical study of typical and latest references. It is expected that this review would provide useful information and clues for the researchers with interest in SERS bioanalysis.

Influence of TiO2 and ZnO Nanoparticles on α-Synuclein and β-Amyloid Aggregation and Formation of Protein Fibrils

The most common neurological disorders, i.e., Parkinson's disease (PD) and Alzheimer's disease (AD), are characterized by degeneration of cognitive functions due to the loss of neurons in the central nervous system. The aggregation of amyloid proteins is an important pathological feature of neurological disorders.The aggregation process involves a series of complex structural transitions from monomeric to the formation of fibrils. Despite its potential importance in understanding the pathobiology of PD and AD diseases, the details of the aggregation process are still unclear. Nanoparticles (NPs) absorbed by the human circulatory system can interact with amyloid proteins in the human brain and cause PD. In this work, we report the study of the interaction between TiO₂ nanoparticles (TiO₂-NPs) and ZnO nanoparticles (ZnO-NPs) on the aggregation kinetics of β-amyloid fragment 1-40 (βA) and α-synuclein protein using surface-enhanced Raman spectroscopy (SERS) and tip-enhanced Raman spectroscopy (TERS). The characterizations of ZnO-NPs and TiO₂-NPs were evaluated by X-ray diffraction (XRD) spectrum, atomic force microscopy (AFM), and UV-Vis spectroscopy. The interaction of nanoparticles with amyloid proteins was investigated by SERS. Our study showed that exposure of amyloid protein molecules to TiO₂-NPs and ZnO-NPs after incubation at 37 °C caused morphological changes and stimulated aggregation and fibrillation. In addition, significant differences in the intensity and location of active Raman frequencies in the amide I domain were found. The principal component analysis (PCA) results show that the effect of NPs after incubation at 4 °C does not cause changes in βA structure.

Human Angiotensin I-Converting Enzyme Produced by Different Cells: Classification of the SERS Spectra with Linear Discriminant Analysis

Angiotensin I-converting enzyme (ACE) is a peptidase widely presented in human tissues and biological fluids. ACE is a glycoprotein containing 17 potential N-glycosylation sites which can be glycosylated in different ways due to post-translational modification of the protein in different cells. For the first time, surface-enhanced Raman scattering (SERS) spectra of human ACE from lungs, mainly produced by endothelial cells, ACE from heart, produced by endothelial heart cells and miofibroblasts, and ACE from seminal fluid, produced by epithelial cells, have been compared with full assignment. The ability to separate ACEs’ SERS spectra was demonstrated using the linear discriminant analysis (LDA) method with high accuracy. The intervals in the spectra with maximum contributions of the spectral features were determined and their contribution to the spectrum of each separate ACE was evaluated. Near 25 spectral features forming three intervals were enough for successful separation of the spectra of different ACEs. However, more spectral information could be obtained from analysis of 50 spectral features. Band assignment showed that several features did not correlate with band assignments to amino acids or peptides, which indicated the carbohydrate contribution to the final spectra. Analysis of SERS spectra could be beneficial for the detection of tissue-specific ACEs.

Fraud detection in crude palm oil using SERS combined with chemometrics

Edible crude palm oil (CPO) is a vital oil utilized in various industries, including food, pharmaceuticals, and domestic cooking. Unfortunately, reports of CPO adulteration with harmful Sudan dyes have surfaced over the years. Surface-enhanced Raman spectroscopy (SERS) and chemometrics were employed to detect Sudan dyes adulteration in CPO within 900 - 1800 cm^{- 1} Raman peak. The concentration of Sudan dyes detected in CPO samples ranged between 0.005 and 4 ppm. The principal component analysis (PCA) model detected Sudan II and Sudan IV in CPO with 99.88 and 99.90% accuracy. Linear discriminant analysis (LDA) and K-Nearest Neighbors (KNN) also recorded high detection rates of Sudan II and IV dyes in CPO. Sudan II and IV dyes could be detected at 0.0028 ppm and 0.0019 ppm by this sensor. The performance of the Au@Ag SERS sensor was comparable to that of HPLC. This study proved SERS and chemometrics can be used to authenticate edible CPO.

Surface enhanced Raman scattering for probing cellular biochemistry

Surface enhanced Raman scattering (SERS) from biomolecules in living cells enables the sensitive, but also very selective, probing of their biochemical composition. This minireview discusses the developments of SERS probing in cells over the past years from the proof-of-principle to observe a biochemical status to the characterization of molecule-nanostructure and molecule-molecule interactions and cellular processes that involve a wide variety of biomolecules and cellular compartments. Progress in applying SERS as a bioanalytical tool in living cells, to gain a better understanding of cellular physiology and to harness the selectivity of SERS, has been achieved by a combination of live cell SERS with several different approaches. They range from organelle targeting, spectroscopy of relevant molecular models, and the optimization of plasmonic nanostructures to the application of machine learning and help us to unify the information from defined biomolecules and from the cell as an extremely complex system.

Large-area and low-cost SERS substrates based on a gold-coated nanostructured surface fabricated on a wafer-scale

This paper demonstrates a method to fabricate plasmonic nanostructures over a large area that can be implemented as SERS substrates. The proposed method comprises batch processes such as spin coating, reactive ion etching, and thin metal deposition. These processes can be performed on large wafers, resulting in large numbers of SERS substrates in a single run. The effects of different process parameters were studied to optimize the performance of the SERS substrates. The study of sensitivity on the optimized SERS substrates was conducted using the SERS-active molecule pMBA. The SERS substrates thus fabricated were able to detect molecule concentrations as low as 100 nM. The SERS substrates were also evaluated for uniformity across the sample and for sample-to-sample reproducibility. Finally, the SERS substrates were applied to demonstrate label-free detection of organophosphorous pesticides – paraoxon ethyl and paraoxon methyl.

A simple and novel fabrication process for fabricating a uniform and reproducible SERS substrate over a large area.

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

Future precision medicine will be undoubtedly sustained by the detection of validated biomarkers that enable a precise classification of patients based on their predicted disease risk, prognosis, and response to a specific treatment. Up to now, genomics, transcriptomics, and immunohistochemistry have been the main clinically amenable tools at hand for identifying key diagnostic, prognostic, and predictive biomarkers. However, other molecular strategies, including metabolomics, are still in their infancy and require the development of new biomarker detection technologies, toward routine implementation into clinical diagnosis. In this context, surface-enhanced Raman scattering (SERS) spectroscopy has been recognized as a promising technology for clinical monitoring thanks to its high sensitivity and label-free operation, which should help accelerate the discovery of biomarkers and their corresponding screening in a simpler, faster, and less-expensive manner. Many studies have demonstrated the excellent performance of SERS in biomedical applications. However, such studies have also revealed several variables that should be considered for accurate SERS monitoring, in particular, when the signal is collected from biological sources (tissues, cells or biofluids). This Perspective is aimed at piecing together the puzzle of SERS in biomarker monitoring, with a view on future challenges and implications. We address the most relevant requirements of plasmonic substrates for biomedical applications, as well as the implementation of tools from artificial intelligence or biotechnology to guide the development of highly versatile sensors.

Ion-exchanged glass microrods as hybrid SERS/fluorescence substrates for molecular beacon-based DNA detection

Ion-exchange in molten nitrate salts containing metal ions (i.e. silver, copper, etc.) represents a well-established technique able to modify the chemical-physical properties of glass materials. It is widely used not only in the field of integrated optics (IO) but also, more recently, in plasmonics due to the possibility to induce the formation of metal nanoparticles in the glass matrix by an ad hoc thermal post-process. In this work, the application of this technology for the realisation of low-cost and stable surface-enhanced Raman scattering (SERS) active substrates, based on soda-lime glass microrods, is reported. The microrods, with a radius of a few tens of microns, were obtained by cutting the end of an ion-exchanged soda-lime fibre for a length less than 1 cm. As ion source, silver nitrate was selected due to the outstanding SERS properties of silver. The ion-exchange and thermal annealing post-process parameters were tuned to expose the embedded silver nanoparticles on the surface of the glass microrods, avoiding the use of any further chemical etching step. In order to test the combined SERS/fluorescence response of these substrates, labelled molecular beacons (MBs) were immobilised on their surface for deoxyribonucleic acid (DNA) detection. Our experiments confirm that target DNA is attached on the silver nanoparticles and its presence is revealed by both SERS and fluorescence measurements. These results pave the way towards the development of low-cost and stable hybrid fibres, in which SERS and fluorescence interrogation techniques are combined in the same optical device.

Cost Effective Silver Nanowire-Decorated Graphene Paper for Drop-On SERS Biodetection

The use of SERS for real-world bioanalytical applications represents a concrete opportunity, which, however, is being largely delayed by the inadequacy of existing substrates used to collect SERS spectra. In particular, the main bottleneck is their poor usability, as in the case of unsupported noble metal colloidal nanoparticles or because of the need for complex or highly specialized fabrication procedures, especially in view of a large-scale commercial diffusion. In this work, we introduce a graphene paper-supported plasmonic substrate for biodetection as obtained by a simple and rapid aerosol deposition patterning of silver nanowires. This substrate is compatible with the analysis of small (2 μL) analyte drops, providing stable SERS signals at sub-millimolar concentration and a detection limit down to the nanogram level in the case of hemoglobin. The presence of a graphene underlayer assures an even surface distribution of SERS hotspots with improved stability of the SERS signal, the collection of well-resolved and intense SERS spectra, and an ultra-flat and photostable SERS background in comparison with other popular disposable supports.