Portable Raman Device Research Articles

Multiplex Detection of Foodborne Pathogens using 3D Nanostructure Swab and Deep Learning-Based Classification of Raman Spectra.

Proactive management of foodborne illness requires routine surveillance of foodborne pathogens, which requires developing simple, rapid, and sensitive detection methods. Here, a strategy is presented that enables the detection of multiple foodborne bacteria using a 3D nanostructure swab and deep learning-based Raman signal classification. The nanostructure swab efficiently captures foodborne pathogens, and the portable Raman instrument directly collects the Raman signals of captured bacteria. a deep learning algorithm has been demonstrated, 1D convolutional neural network with binary labeling, achieves superior performance in classifying individual bacterial species. This methodology has been extended to mixed bacterial populations, maintaining accuracy close to 100%. In addition, the gradient-weighted class activation mapping method is used to provide an investigation of the Raman bands for foodborne pathogens. For practical application, blind tests are conducted on contaminated kitchen utensils and foods. The proposed technique is validated by the successful detection of bacterial species from the contaminated surfaces. The use of a 3D nanostructure swab, portable Raman device, and deep learning-based classification provides a powerful tool for rapid identification (≈5 min) of foodborne bacterial species. The detection strategy shows significant potential for reliable food safety monitoring, making a meaningful contribution to public health and the food industry.

Characterization and classification of oleogels and edible oil using vibrational spectroscopy in tandem with one-class and multiclass chemometric methods

Oleogel represents a promising healthier alternative to act as a substitute for conventional fat in various food products. Oil selection is a crucial factor in determining the technological properties and applications of oleogels due to their distinct fatty acid composition, molecular weight, and thermal properties, as well as the presence of antioxidants and oxidative stability. Hence, the relevance of monitoring oleogel properties by non-destructive, eco-friendly, portable, fast, and effective techniques is a relevant task and constitutes an advance in the evaluation of oleogels quality. Thus, the present study aims to classify oleogels rapidly and reliably, without the use of chemicals, comparing two handheld near infrared (NIR) spectrometers and one portable Raman device. Furthermore, two different multivariate methods are compared for oleogel classification according to oil type. Three types of oleogels were prepared, containing 95 % oil (sunflower, soy, olive) and 5 % beeswax as a structuring agent, melted at 90 °C. Polarized light microscopy (PLM) images were acquired, and fatty acid composition, peroxide index and free fatty acid content were determined using official methods. A total of 240 oleogel and 92 oil spectra were obtained for each instrument. After spectra pretreatment, Principal Component Analysis (PCA) was performed, and two classification methods were investigated. The Data Driven - Soft Independent Modelling of Class Analogy (DD-SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA) models demonstrated 95 % to 100 % of accuracy for the external test set. In conclusion, the use of vibrational spectroscopy using handheld and portable instruments in tandem with chemometrics showed to be an efficient alternative for classifying oils and oleogels and could be extended to other food samples. Although the classification of vegetable oils by NIR is widely used and known, this work proposes the classification of different types of oil in oleogel matrices, which has not yet been explored in the literature.

Learning algorithms for identification of whisky using portable Raman spectroscopy

Reliable identification of high-value products such as whisky is vital due to rising issues of brand substitution and quality control in the industry. We have developed a novel framework that can perform whisky analysis directly from raw spectral data with no human intervention by integrating machine learning models with a portable Raman device. We demonstrate that machine learning models can achieve over 99% accuracy in brand or product identification across twenty-eight commercial samples. To demonstrate the flexibility of this approach, we utilized the same algorithms to quantify ethanol concentrations, as well as measuring methanol levels in spiked whisky samples. To demonstrate the potential use of these algorithms in a real-world environment we tested our algorithms on spectral measurements performed through the original whisky bottle. Through the bottle measurements are facilitated by a beam geometry hitherto not applied to whisky brand identification in conjunction with machine learning. Removing the need for decanting greatly enhances the practicality and commercial potential of this technique, enabling its use in detecting counterfeit or adulterated spirits and other high-value liquids. The techniques established in this paper aim to function as a rapid and non-destructive initial screening mechanism for detecting falsified and tampered spirits, complementing more comprehensive and stringent analytical methods.

Core-Satellite Nanoassemblies as SPR/SERS Dual-Mode Plasmonic Sensors for Sensitively Detecting Ractopamine in Complex Media.

Highly sensitive and reliable detection of β-adrenergic agonists is especially necessary due to the illegal abuse of growth-promoting feed additives. Here, we develop a novel surface plasmon resonance/surface-enhanced Raman scattering (SPR/SERS) dual-mode plasmonic sensor based on core-satellite nanoassemblies for the highly sensitive and reliable detection of ractopamine (RAC). The addition of RAC results in the decomposition of core-satellite nanoassemblies and consequently changes the Rayleigh scattering color of dark-field microscopy (DFM) images and the Raman scattering intensity of SERS spectra. The excellent sensitivity, specificity, and uniformity of this strategy were confirmed by detecting RAC in various complex media in the farm-to-table chain, and the limit of detection (LOD) was 0.03 ng/mL in an aqueous solution. In particular, the convenient access to livestock sewage not only ensures animal welfare but also provides great convenience for the market regulation of β-agonists. The success of our on-site strategy only with a portable Raman device promises great application prospects for β-agonist detection.

Engineering Branched Au@Ag Nanostar Plasmonic Array for Coupling Electromagnetic Enhancement and SERS Trace Detection of Polystyrene in Aquatic Environments

Micro/nanoplastics are widespread in the environment and may cause severe damage to creatures and human beings. Micro/nanoplastic pollution has become a global focus issue; hence, the rapid and accurate detection of micro/nanoplastics is an essential step to ensure health. Herein, we report a surface-enhanced Raman scattering (SERS) technique to sensitively and quantitatively identify micro/nanoplastics in environmental water samples. A three-dimensional hierarchical Au@Ag nanostar (NSs) was synthesized and employed as an efficient SERS substrate. The “lightning rod effect” generated by tip branches of the nanostars and the coupling effect of the neighboring branches of the nanostar array enabled the ultra-trace detection of crystal violet (CV) down to 10−9 M, even with a portable Raman device. Moreover, the hydrophobic property of the SERS substrate endowed it with a desirable enrichment effect, which meant an increase in the concentration or quantity of the micro/nanoplastic particles. And thereafter, the SERS sensor achieved a highly sensitive detection of polystyrene (PS) particle standard solution at a low concentration of 25 μg/mL or 2.5 μg/mL. Importantly, the detected concentration and the SERS intensity followed a nearly linear relationship, indicating the capability of quantitative analysis of micro/nanoplastics. In addition, the SERS sensor was successfully extended to detect PS particles in environmental water samples, including tap water, sea water, and soil water, and the detection concentration was determined to be 25 μg/mL, 2.5 μg/mL, and 25 μg/mL, respectively. The present Au@AgNSs array substrate with a two-order magnitude signal amplification further exhibited significant advantages in the label-free analysis of micro/nanoplastics in real water samples.

Biomimetic Ag/ZnO@PDMS Hybrid Nanorod Array-Mediated Photo-induced Enhanced Raman Spectroscopy Sensor for Quantitative and Visualized Analysis of Microplastics.

Microplastics are persistent pollutants that accumulate in the environment and can cause serious toxicity to mammals. At present, few technologies are able to quantitatively detect chemicals and provide morphological information simultaneously. Herein, we developed a dragonfly-wing-mimicking ZnO nanorod array decorated with AgNPs on polydimethylsiloxane (PDMS) as a surface-enhanced Raman spectroscopy (SERS) and photo-induced enhanced Raman spectroscopy (PIERS) substrate for trace analysis of microplastics. The Ag/ZnO@PDMS hybrid nanorod array endows the sensor with high sensitivity and signal repeatability (RSD ∼ 5.89%), ensuring the reliable quantitative analysis of microplastics. Importantly, when the noble metal-semiconductor substrate was pre-radiated with ultraviolet light, a surprising PIERS was attained, achieving an additional enhancement of 11.3-fold higher than the normal SERS signal. By combining the PIERS technology with the "coffee ring effect", the sensor successfully discerned microplastics of polyethylene (PE) and polystyrene (PS) at a trace level of 25 μg/mL even with a portable Raman device. It was capable of identifying PS microspheres in contaminated tap water, lake water, river water, and seawater with detection limits of 25, 28, 35, and 60 μg/mL, respectively. The recovery rates of PS microspheres in four water environments ranged from 94.8 to 102.4%, with the RSD ranging from 2.40 to 6.81%. Moreover, quantitative and visualized detection of microplastics was readily realized by our sensor. This portable PIERS sensor represents a significant step toward the generalizability and practicality of quantitative and visual sensing technology.

Non-amplification on-spot identifying the sex of dioecious kiwi plants by a portable Raman device.

The kiwi plant is dioecious, and its sex is generally identified from flower morphology at blossoming, which takes several years. It is quite necessary but challenging to on-spot identify the plant sex in juvenile stage. Here the target DNA was obtained by screening the Friendly boy (FrBy) gene which is sex-related for different kiwi plant species. Its complementary sequence was divided into two parts as primer DNA and further attached to different gold nanoparticles (GNPs). The connection between target DNA and primer DNA will promote the formation of plasmonic dimers. Dark field microscopy (DFM) can distinguish particles in different aggregation states. Various conditions were optimized based on the standard of increasing the proportion of dimers while reducing that of large aggregates. Furthermore, two Raman reporters (RR) are separately labeled on the nanoprobes, and the plasmonic dimers lead to a tremendous Raman enhancement of two reporters located at the dimer nanogap. Double-blind tests proved the feasibility of this method on the actual samples of kiwi plant leaves. Our SERS method is sensitive, specific, and reliable for rapid sex identification analysis at the kiwi seeding stage, with great promise for decision-making in field management.

Lotus leaf mastoid inspired Ag micro/nanoarrays on PDMS film as flexible SERS sensor for in-situ analysis of pesticide residues on nonplanar surfaces

Conventional surface-enhanced Raman scattering (SERS) molecular detection are based on hard and brittle substrate, which are not suitable for in-situ detection of analytes adsorbed on nonplanar surfaces. Here, we report a simple biomimetic synthesis method to fabricate lotus leaf mastoid structured AgNPs micro/nanoarrays as reliable SERS substrate. By ideal replicating mastoid structure of lotus leaf into a flexible and transparent PDMS film, followed by depositing plasmonic AgNPs, a powerful chemical sensor with high sensitivity and multiplex detecting capability is demonstrated. The employ of periodic mastoid structure array endows the sensor with high signal repeatability (RSD ∼ 8.6 %), solving the general repeatability problem of SERS substrates. In addition, the detailed designed flexible and transparent PDMS substrate is capable of identifying trace analytes on curved surfaces with excellent durability. In the proof-of-concept experiment, a limit of detection (LOD) of (10−5 M to 10−7 M) was achieved on a portable Raman device for three common pesticides residues (thiram, fonofos and triadophos) on dendrobium leaves and stem according to the molecular fingerprint, indicating its excellent in-situ detection capability. Further, the multiplex detection ability of the Ag/PDMS film is also demonstrated by analyzing the mixture of four typical analytes. Benefiting from its high signal uniformity, this flexible Ag/PDMS substrate also showed good quantitative detection capabilities.

Development of a portable Raman device with artificial intelligence method for the detection and staging of endometrial cancer

AbstractThe success of a Raman spectroscopy device in cancer detection lies in its ability to acquire high‐quality Raman signals from samples and to employ efficient classification algorithms in analysing spectral data. Portable Raman systems enabled with artificial intelligence tools are well adaptable to clinical settings and for accuracy for community‐level rapid screening. Here, we developed a robotic Raman device with a high‐efficiency Raman probe, validating it against endometrial cancers detecting high‐grade, low‐grade cancers and normal classes. Algorithms like principal component analysis‐discriminant analysis, and support vector machine were compared against the deep learning methodology; convolutional neural network (CNN) with and without data augmentation. Eventually, the system could classify high‐grade, low‐grade and normal tissues with an F1‐score of 91%, 94% and 97%, respectively. CNN with data augmentation proved to be the most dependable classifier that works well even in the presence of high background noise. Thus, we demonstrate a unique portable Raman device with AI tools for high‐sensitivity Raman analysis of endometrial cancer.

Simultaneous Multiplexed Quantification of Banned Sudan Dyes Using Surface Enhanced Raman Scattering and Chemometrics.

Azo compounds such as the Sudan dyes I–IV are frequently used illegally as colorants and added to a wide range of foods. These compounds have been linked to a number of food safety hazards. Several methods have been proposed to detect food contamination by azo compounds and most of these are laboratory based; however, the development of reliable and portable methods for the detection and quantification of food contaminated by these chemicals in low concentration is still needed due to their potentially carcinogenic properties. In this study, we investigated the ability of surface enhanced Raman scattering (SERS) combined with chemometrics to quantify Sudan I–IV dyes. SERS spectra were acquired using a portable Raman device and gold nanoparticles were employed as the SERS substrate. As these dyes are hydrophobic, they were first dissolved in water: acetonitrile (1:10, v/v) as single Sudan dyes (I–IV) at varying concentrations. SERS was performed at 785 nm and the spectra were analyzed by using partial least squares regression (PLS-R) with double cross-validations. The coefficient of determination (Q2) were 0.9286, 0.9206, 0.8676 and 0.9705 for Sudan I to IV, respectively; the corresponding limits of detection (LOD) for these dyes were estimated to be 6.27 × 10−6, 5.35 × 10−5, 9.40 × 10−6 and 1.84 × 10−6 M. Next, quadruplex mixtures were made containing all four Sudan dyes. As the number of possible combinations needed to cover the full concentration range at 5% intervals would have meant collecting SERS spectra from 194,481 samples (214 combinations) we used a sustainable solution based on Latin hypercubic sampling and reduced the number of mixtures to be analyzed to just 90. After collecting SERS spectra from these mixture PLS-R models with bootstrapping validations were employed. The results were slightly worse in which the Q2 for Sudan I to IV were 0.8593, 0.7255, 0.5207 and 0.5940 when PLS1 models (i.e., one model for one dye) was employed and they changed to 0.8329, 0.7288, 0.5032 and 0.5459 when PLS2 models were employed (i.e., four dyes were modelled simultaneously). These results showed the potential of SERS to be used as a high-throughput, low-cost, and reliable methods for detecting and quantifying multiple Sudan dyes in low concentration from illegally adulterated samples.

Bimetallic Gold Nanostars Having High Aspect Ratio Spikes for Sensitive Surface-Enhanced Raman Scattering Sensing.

There has been increasing interest in evolution of plasmonic nanoplatforms based on noble metal nanoparticles to achieve ultrasensitive detection of trace analyte molecules through solution-based surface-enhanced Raman spectroscopy (SERS). This work presents a surfactant-free synthesis method of bimetallic gold nanostars coated with silver (BGNS-Ag) having sharp, high aspect-ratio spikes for achieving ultrahigh detection sensitivity and high reproducibility. Specifically, the unique BGNS-Ag platform combines both the strong SERS enhancement effects of gold nanostar sharp spikes and the high scattering feature of the silver–gold bimetallic structure. To achieve SERS reproducibility, this solution-based SERS measurement requires minimal sample preparation without addition of any external reagents, which can cause irregular aggregation of nanoparticles and reduce the reproducibility of SERS measurements. Moreover, we have streamlined our SERS sensing procedure by using standard well-plates and a portable Raman device for SERS measurements, which could be utilized for rapid on-site detection. This solution-based SERS performance was studied using methylene blue (MB) as a model analyte system. The detection limit of MB was as low as 42 pM, indicating high sensitivity of detection using BGNS-Ag. To illustrate the usefulness for environmental sensing, we showed that the SERS sensor can detect a pesticide, thiram, at a concentration as low as 0.8 nM. This study demonstrated that the BGNS-Ag system could serve as an effective and versatile plasmonic-active platform for reproducible, fast, and in-field detection of small organic analytes at trace levels.

Coupling enhanced SERS substrates and 1D dilated convolutional neural network: A new model to improve trace detection and identification

In this paper, a Raman spectral detection and identification method combined with a portable Raman device is proposed to realize the real-time detection of trace substances. By employing a composite Surface-Enhanced Raman Scattering (SERS) substrate coupled with surface plasmon polaritons (SPP) and localized surface plasmons (LSP), the Raman detection performance for trace can be improved significantly. The one-dimensional dilated convolutional neural network (1D CNN) model proposed in this paper combined with a spatial attention mechanism can accurately identify Raman spectra. Together, a combination of the two provides great advantages, especially for the identification of weak signals with indistinguishable characteristic peaks. At the same time, for the public Raman dataset Rruff, the model has higher accuracy and generalization ability than traditional Raman spectral recognition algorithms. The evidence presented in this paper reveals that the LSP–SPP​ coupled SERS substrate combined with a 1D CNN-based spectral identification algorithm can pave the way for the development of an accurate and portable Raman detection of trace.

Porous Copper Foam‐Based Plasmonic Nanocrystals Modified 3D Semiconductor Nanoflowers for Multifold, Recyclable, and Portable Detection of Environmental Contaminant

AbstractA multifunctional solid‐state surface‐enhanced Raman scattering (SERS) nanoarray exhibiting superior detection sensitivity with high stability and reproducibility has been a hotspot since it provides multiple analyses and on‐site detection platform. Herein, porous Cu foam‐based plasmonic nanocrystals modified 3D mixed phase TiO2 nanoflowers heterojunction for the effective photodegradation under visible‐light exposures is fabricated. Firstly, TiO2 nanoflowers composed of many upright one‐dimensional TiO2 nanopillars possess the merits of a large specific surface area and optical absorption. These nanopillars not only restrain the recombination of electron‐hole pairs by shortening the diffusion length of minority carrier to the TiO2 surface, but also are favorable for the carrier transport in the one‐dimensional nanostructure. Secondly, the introduction of plasmonic nanocrystals with localized surface plasmon resonance effect will boost the overall photoabsorption of TiO2 nanopillars and strengthen the electromagnetic field around the Ag nanocrystals, thus promoting the SERS effect. Under visible‐light excitation, the hot electrons generated by the nonradiative transition of Ag nanocrystals (≈50 nm) transfer to TiO2 to participate in the photocatalytic reaction, which improves the degradation efficiency of pollutants. The ultrasensitive and recyclable substrates displayed by this platform may lay a foundation for rapid detection analysis on environment toxicity when coupled with a portable Raman device.

Bolaform Surfactant‐Induced Au Nanoparticle Assemblies for Reliable Solution‐Based Surface‐Enhanced Raman Scattering Detection

AbstractSolution‐based surface‐enhanced Raman scattering (SERS) detection typically involves the aggregation of citrate‐stabilized Au nanoparticles into colloidal assemblies. Although this sensing methodology offers excellent prospects for sensitivity, portability, and speed, it is still challenging to control the assembly process by a salting‐out effect, which affects the reproducibility of the assemblies and, therefore, the reliability of the analysis. This work presents an alternative approach that uses a bolaform surfactant, B20, to induce the plasmonic assembly. The decrease of the surface charge and the bridging effect, both promoted by the adsorption of B20, are hypothesized as the key points governing the assembly. Furthermore, molecular dynamic simulations supported the bridging effect of the B20 by showing the preferential bridging of surfactant monomers between two adjacent Au(111) slabs. The colloidal assemblies showed excellent SERS capabilities towards the rapid, on‐site detection and quantification of beta‐blockers and analgesic drugs in the nanomolar regime, with a portable Raman device. Interestingly, the application of state‐of‐the‐art convolutional neural networks, such as ResNet, allows a 100% accuracy in classifying the concentration of different binary mixtures. Finally, the colloidal approach was successfully implemented in a millifluidic chip allowing the automation of the whole process, as well as improving the performance of the sensor in terms of speed, reliability, and reusability without affecting its sensitivity.

Development and evaluation of a gold nanourchin (GNU)-based sandwich architecture for SERS immunosensing in liquid

Nanosensors represent a class of emerging promising nanotools that can be used for the rapid, sensitive and specific detection of relevant molecules such as biomarkers of cancer or other diseases. The sensing platforms that rely on the exceptional physical properties of colloidal gold nanoparticles have gained a special attraction and various architectural designs were proposed with the aim of rapid and real-time detection, identification and monitoring of the capturing events. Moreover, biomarker sensing in liquid samples allows a more facile implementation of the nanosensors by circumventing the need for invasive practices such as biopsies, in favor of non-invasive investigations with potential for use as point-of-care assays. Herein, we propose a sandwich-type surface enhanced Raman scattering (SERS) immuno-nanosensor which is aimed for detecting and quantifying Carcinoembryonic antigen-related cell adhesion molecule 5 (CEA-CAM5), a protein involved in intercellular adhesion and signaling pathways that acts as a tumor marker in several types of cancer. For constructing the proposed system, colloidal gold nano spheres (GNS) and gold nano-urchins (GNU) were chemically synthesized, labeled with SERS active molecules, conjugated with polymers, functionalized with antibodies as capturing substrates and tested in two different sensing configurations: pairs of GNUs-GNUs and GNUs-GNSs. When the target antigen is present in the analyte solution, nanoparticle bridging occurs and a subsequent amplification of the characteristic Raman signal of the label molecule appears due to the formation of hot-spots in interparticle gaps. The capability of observing small analyte concentrations in liquid samples with an easy-to-handle portable Raman device makes the proposed system feasible for rapid, non-invasive and cost-effective clinical or laboratory use.

Surface-enhanced Raman scattering-based immunoassay for severe acute respiratory syndrome coronavirus 2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected humans worldwide for over a year now. Although various tests have been developed for the detection of SARS-CoV-2, advanced sensing methods are required for the diagnosis, screening, and surveillance of coronavirus disease 2019 (COVID-19). Here, we report a surface-enhanced Raman scattering (SERS)-based immunoassay involving an antibody pair, SERS-active hollow Au nanoparticles (NPs), and magnetic beads for the detection of SARS-CoV-2. The selected antibody pair against the SARS-CoV-2 antigen, along with the magnetic beads, facilitates the accurate direct detection of the virus. The hollow Au NPs exhibit strong, reproducible SERS signals, allowing sensitive quantitative detection of SARS-CoV-2. This assay had detection limits of 2.56 fg/mL for the SARS-CoV-2 antigen and 3.4 plaque-forming units/mL for the SARS-CoV-2 lysates. Furthermore, it facilitated the identification of SARS-CoV-2 in human nasopharyngeal aspirates and diagnosis of COVID-19 within 30 min using a portable Raman device. Thus, this assay can be potentially used for the diagnosis and prevention of COVID-19.

ZIF-8 derived TiO2/ZnO heterostructure decorated with AgNPs as SERS sensor for sensitive identification of trace pesticides

Pesticides are still abused and overused in agricultural production. Food safety monitoring in terms of reliable detection and quantification of pesticide residues is an ever increasing need. Surface-Enhanced Raman Scattering (SERS)-based chemical sensors and adsorption materials crafted for specific pesticide should provide high sensitivity and selectivity. Herein, zeolitic imidazolate framework (ZIF-8) derived TiO2/ZnO three-dimensional (3D) heterostructures were constructed on FTO substrate and were utilized to assemble silver nanoparticles (AgNPs) to create electromagnetic hot spots. The formation of TiO2/ZnO heterostructure greatly improve the SERS performance of the substrate. The calculated enhancement factor (EF) of the TiO2/ZnO/Ag is 39-fold higher than the TiO2/Ag substrates. Furthermore, the substrate shows high signal sensitivity and reproducibility for rapid detection of multiple pesticides. Even with a portable Raman device, the substrate was found to be highly efficient for model molecules of crystal violet (CV) and 4-mercaptobenzoic acid (4-MBA), and the limit of detection (LOD) for both of 10−9 M was demonstrated. Importantly, real life detection of multiple pesticides residues can be achieved with the TiO2/ZnO/Ag sensor, which was demonstrated by the detection of three common pesticides, thiram, acephate and phoxim at trace concentrations.

Quantitation of carotenoids and fatty acids from Atlantic salmon using a portable Raman device.

Raman spectroscopy can be a useful tool for performing non-destructive subsurface measurements, allowing for investigations on intact foods that would otherwise require food products to be prepared for analysis, thus creating food waste, and often requiring the addition of chemicals. To overcome this, we have developed a method to measure the concentration of pigment (astaxanthin) and fatty acids in whole, unfilleted Atlantic salmon through the skin using defocused Raman spectroscopy. Subsurface measurements were performed across different regions of Atlantic salmon. PLS regression of the measured spectra gave a moderate correlation between Raman bands and astaxanthin concentration (R2 = 0.5797) and good correlations for fatty acid concentrations (total, saturated, monounsaturated, polyunsaturated, trans; R2 = 0.7457 to 0.8249). This work shows that subsurface Raman measurements can provide useful quantitative information regarding important quality parameters (pigment and fatty acid concentrations) that can aid with production in aquaculture.

Tunable nanopillar array on a quartz-fiber tip for surface enhanced Raman scattering (SERS) detection

Abstract In this work, a simple two-step method to create tunable self-assembled three-dimensional nanostructure array-like nanoantennas directly on the tip of an optical quartz glass fiber is described. The structures are prepared by using dry etching of the fiber tip. For the etching process, gold nanoparticles fabricated by thermal dewetting technique were used as template. The structures are applied as sensors for label-free analysis of organic substances in ppb range, such as drug residues in liquid solutions. The measurements are carried out by a portable Raman device with an exchangeable sensor head utilizing the SERS-effect. This method allows in situ applications. In order to characterize the SERS cells, para-thiocresol and diclofenac sodium are used as model substances. For optimization of the substrate performance, different thicknesses of the dewetting-layer (6, 9, 12 nm), different etching times for formation of the pillars (6, 8, 10 and 12 min), and different thicknesses of SERS-metallization (25, 50, 75 nm) of gold and silver are compared. In order to show the applicability of the structure on the tip of a fiber, measurements from the upper side and from the underside of the substrate on quartz plates are compared. Reproducible SERS enhancement factors up to 10 7 {10^{7}} were achieved.

A capillary-based SERS sensor for ultrasensitive and selective detection of Hg2+ by amalgamation with Au@4-MBA@Ag core-shell nanoparticles.

A capillary-based SERS sensor was fabricated for ultrasensitive and selective detection of Hg2+ in water. Au@Ag core-shell NPs embedded with 4-mercaptobenzoic acid (4-MBA) (Au@4-MBA@Ag) were prepared by a seed growth method and fixed on the inner wall of the glass capillary to obtain the sensor. Owing to the amalgamation between Ag and Hg, the capillary-based SERS sensor can specifically recognize the reduced Hg2+ without any recognition element, and the resulted Ag/Hg amalgam can weaken the SERS activity of Ag shell; thus, the SERS intensity of the embedded 4-MBA at 1075cm-1 gradually decreased with the increase of Hg2+ concentration. Under the optimum condition, the fabricated sensor can sensitively determine Hg2+ in water with a limit of detection (LOD) as low as 0.03nM. The capillary-based SERS sensor offers the advantages of simple preparation, superior stability, and high selectivity, which is promising for rapid and on-site detection of Hg2+ in water combined with a portable Raman device.