Simultaneous sensing of strain and temperature based on the inline-MZI embedded point-shaped taper structure with low crosstalk.

An embedded spherical dot taper structure (EDT) based on the MZI principle is proposed in this paper, which is mainly fabricated by using two special arc discharges in the preparation process. The proposed structure involves two specialized arc discharge techniques. First, an oversaturated discharge fusion process creates a micro-arc spherical area on the fiber end face to form the first link type. Second, an unsaturated discharge-pulling taper fusion joint creates a local micro-extrusion operation on this micro-arc fiber end face to form the second link. The thermal stress from instantaneous discharge causes a reverse spherical expansion zone to form in the end face structure, similar to the micromachining of long-period fiber gratings that use local CO2 laser etching to create modulated zones. The study involves a mathematical and theoretical analysis of how geometric parameters in the spherical modulation zone impact the structure's characteristic spectrum. The research demonstrates the potential for this structure to function as a light-intensity modulated strain sensor device through both theoretical and experimental means. As per the experimental findings, the optimized structure displays a high level of strain sensing sensitivity at 0.03 dB/µÎµ and temperature sensing sensitivity of 73 pm/°C (20°C-75°C) and 169 pm/°C (75°C-120°C). Additionally, it possesses excellent cross-sensitivity at only ∼0.0015 µÎµ/°C. Therefore, this sensor presents a favorable option for strain and temperature synchronization sensing and monitoring components, and exhibits notable application prospects in precision engineering, which encompasses mechanical manufacturing, the power and electrical industry, healthcare domain, and certain specialized areas of small-scale precision engineering.

Dynamic distributed optical fiber sensing based on simultaneous measurement of Brillouin gain and loss spectra with frequency-agile technique.

To simplify the experimental equipment and improve the signal-to-noise ratio (SNR) of the traditional Brillouin optical time-domain analysis (BOTDA) system, we propose a scheme using the frequency-agile technique to measure Brillouin gain and loss spectra simultaneously. The pump wave is modulated into the double-sideband frequency-agile pump pulse train (DSFA-PPT), and the continuous probe wave is up-shifted by a fixed frequency value. With the frequency-scanning of DSFA-PPT, pump pulses at the -1st-order sideband and the +1st-order sideband interact with the continuous probe wave via stimulated Brillouin scattering, respectively. Therefore, the Brillouin loss and gain spectra are generated simultaneously in one frequency-agile cycle. Their difference relates to a synthetic Brillouin spectrum with a 3.65-dB SNR improvement for a 20-ns pump pulse. This work simplifies the experimental device, and no optical filter is needed. Static and dynamic measurements are performed in the experiment.

Machine Learning-Driven 3D Plasmonic Cavity-in-Cavity Surface-Enhanced Raman Scattering Platform with Triple Synergistic Enhancement Toward Label-Free Detection of Antibiotics in Milk.

The surface-enhanced Raman scattering (SERS) technique with ultrahigh sensitivity has gained attention to meet the increasing demands for food safety analysis. The integration of machine learning and SERS facilitates the practical applicability of sensing devices. In this study, a machine learning-driven 3D plasmonic cavity-in-cavity (CIC) SERS platform is proposed for sensitive and quantitative detection of antibiotics. The platform is prepared by transferring truncated concave nanocubes (NCs) to an obconical-shaped template surface. Owing to the triple synergistic enhancement effect, the highly ordered 3D CIC arrays improve the simulated electromagnetic field intensity and experimental SERS activity, demonstrating a 33.1-fold enhancement compared to a typical system consisting of Au NCs deposited on a flat substrate. The integration of machine learning and Raman spectroscopy eliminates subjective judgments on the concentration of detectors using a single feature peak and achieves accurate identification. The machine learning-driven CIC SERS platform is capable of detecting ampicillin traces in milk with a detection limit of 0.1 ppm, facilitating quantitative analysis of different concentrations of ampicillin. Therefore, the proposed platform has potential applications in food safety monitoring, health care, and environmental sampling.

Brillouin dynamic grating erasure technique for fast all-optical signal processing.

Brillouin dynamic grating (BDG) is an attractive storage unit for all-optical signal storage and processing. However, the processing speed of the traditional "write-read" scheme is severely limited by the inter-process interference (IPI) due to the residual BDG. Here, we propose an all-optical "write-read-erase" scheme to avoid the IPI effect, which can effectively eliminate the residual BDG through an erase pulse. In a numerical simulation, for multi-processes to store a 7 × 7-bits Simplex code, each time, the residual BDGs from the former process are erased for the proposed scheme, and the power fluctuation of the retrieved waveform is suppressed within ±10%. In a preliminary experiment, residual BDG erase efficiencies up to 88.5% can be achieved by introducing erase pulses to neglect the IPI effect on the retrieved waveform. Without the IPI effect, all-optical signal processing will availably be speeded up, especially for short on-chip integrated circuits.

Qualitative and quantitative detection and identification of two benzodiazepines based on SERS and convolutional neural network technology.

Drug abuse is a global social issue of concern. As the drug market expands, there is an urgent need for technological methods to rapidly detect drug abuse to meet the needs of different situations. Here, we present a strategy for the rapid identification of benzodiazepines (midazolam and diazepam) using surface-enhanced Raman scattering (SERS) combined with neural networks (CNN). The method uses a self-assembled silver nanoparticle paper-based SERS substrate for detection. Then, a SERS spectrum intelligent recognition model based on deep learning technology was constructed to realize the rapid and sensitive distinction between the two drugs. In this work, a total of 560 SERS spectra were collected, and the qualitative and quantitative identification of the two drugs in water and a beverage (Sprite) was realized by a trained convolutional neural network (CNN). The predicted concentrations for each scenario could reach 0.1-50 ppm (midazolam in water), 0.5-50 ppm (midazolam in water and diazepam in Sprite), and 5-150 ppm (diazepam in Sprite), with a strong coefficient of determination (R2) larger than 0.9662. The advantage of this method is that the neural network can extract data features from the entire SERS spectrum, which makes up for information loss when manually identifying the spectrum and selecting a limited number of characteristic peaks. This work clearly clarifies that the combination of SERS and deep learning technology has become an inevitable development trend, and also demonstrates the great potential of this strategy in the practical application of SERS.

Millimeter-level recognition capability of BOTDA based on a transient pump pulse and algorithm enhancement.

Brillouin optical time-domain analysis requires a pulsed pump to obtain a distributed Brillouin gain spectrum (BGS) containing environmental information, whose width corresponds to spatial resolution (SR). We propose a rising edge demodulation (RED) algorithm acting on Brillouin information generated by a transient pump pulse (

Highly monodisperse Au@Ag nanospheres: synthesis by controlled etching route and size-dependent SERS performance of their surperlattices.

For accurate experimental and theoretical research, the preparation of nanocrystrals with regular morphology is of significant importance. In this work, we investigated a facile and effective route for generating highly spherical Au@Ag nanospheres (NSs) with tuneable size and uniform morphology at room temperature. The aqueous synthesis mainly involved seed-mediated growth method together with oxidation etching employing sodium hypochlorite (NaClO) as etching agent. The termination of the etching reaction with NaClO as etching agent was simply related to the amount of NaClO and had no connection with time. Thus Au@Ag NSs with controllable diameters in range from 24 to 87 nm were prepared only by varying the amount of NaClO added into the solutions of Au@Ag nanocubes. Additionally, combined with interface self-assembly technique, Au@Ag NSs were assembled into densely arranged two-dimensional (2D) monolayer film. Moreover, the SERS performance of these monolayers were evaluated by calculating the analytical enhancement factor using crystal violet as probe molecule. The AEF increased obviously as the diameter of Au@Ag NSs went up, and the maximum AEF could reach to 0.94 × 107 at the laser excitation wavelength of 785 nm. Besides, the electromagnetic field distribution for Au@Ag NSs array were also confirmed by Mie theory and FDTD solutions software and the results revealed the similar trend with the experimental results. In general, this 2D assemblies in term of high quality Au@Ag NSs have broad prospects to act as promising candidates for SERS analytical sensor and other applications.

Facile Synthesis of Monodisperse Silver Nanospheres in Aqueous Solution via Seed-Mediated Growth Coupled with Oxidative Etching.

Because of their perfect geometrical symmetry, spherical metal nanoparticles have attracted much attention for various applications, including fundamental studies and construction of plasmonic devices. In this work, monodisperse silver nanospheres (Ag NSs) in aqueous solution were directly prepared by a continuous process of seed-mediated growth followed by oxidative etching. Silver nanocubes (Ag NCs) were synthesized by a seed-mediated growth method and subsequently were transformed to Ag NSs by simple injection of Cu2+ to the freshly prepared Ag NCs solution. Not requiring any centrifugation steps at both growth and etching stages makes this procedure convenient and efficient. The etching process and morphology evolution of silver nanostructure were monitored by UV-vis spectromater, SEM, and XRD. Monodisperse Ag NSs with broadly tunable diameters (from 37 to 68 nm) have been successfully prepared. The optical property of Ag NSs has been studied and the experimental results show fairly good consistency with simulation results. Furthermore, these Ag NSs prepared by our approach could be constructed into ordered superlattice by self-assembly technique based on their high monodispersity and sphericity.

Fluctuation initiation of Stokes signal and its effect on stimulated Brillouin scattering pulse compression.

In this paper, a theoretical model is developed to demonstrate that fluctuations in the Stokes signal and occurrence position contribute to the final compression ratio in stimulated Brillouin scattering (SBS). This theoretical analysis can be applied to the investigation of the temporal characteristics of SBS pulse compression. This model agrees well with the experimental results in a two-stage SBS compressor.

[Study on Detection of Prohibited Pigments in Drinks Using Paper-Based SERS Substrates].

In this paper, a novel paper-based Surface enhanced Raman spectroscopy (SERS) substrate with high sensitivity, good uniformity and popular price is developed via liquid/liquid interface self-assembly technique. Three pigment, rhodamine B, sunset yellow and chrysoidine were detected through paper-based SERS substrates using a portable Raman spectrometer. The structures of the three pigments were investigated and vibrational modes of characteristic peaks of three pigments were assigned. SERS spectra of rhodamine B, sunset yellow and chrysoidine in aqueous solution with different concentrations were detected respectively. Rhodamine B, sunset yellow and chrysoidine in drinks were also detected in drinks without any pretreatment. Within a certain range of concentrations, it meets certain function. For rhodamine B and sunset yellow, the relationship between concentration and Raman peak intensity is on an index curve, while for chrysoidine, the relationship is linear. In addition, high recoveries are achieved for detecting rhodamine B, sunset yellow and chrysoidine in drinks, which indicated our method is suited for semi-quantitative analysis for the concentration of rhodamine B, sunset yellow and chrysoidine in drinks. Surface-enhanced Raman spectroscopy provides an easy approach to fast and efficient detection for multiple pigments in drinks and can be used for quality control and market monitoring of drinks.

Fabrication of transparent SERS platform via interface self-assembly of gold nanorods and gel trapping technique for on-site real time detection.

A transparent SERS platform was fabricated via the gel-trapping method coupled with a liquid/liquid interface self-assembly technique. We employed gold nanorods as the building blocks for interface self-assembly because of their strong localized surface plasmons upon excitation by infrared radiation. Based on a "top cover" configuration, this transparent SERS platform endows high signal reproducibility for directly detecting liquid samples by confining the sample droplet into a regular shape. The Au NR PDMS platform was able to directly detect crystal violet in aqueous solutions down to 10 ppb level with high enhancement factor (0.87 × 10(5)) and signal uniformity (RSD = 3.9%). Furthermore, SERS-based anti-fungal agent detection on a fish scale was demonstrated by simply covering the fish scale with a tailored GNRs PDMS film. The experimental results clearly show that the Au NR PDMS SERS platform has great potential for on-site real time detection of contaminants in water as well as on curved surfaces.

[Detection of Cinnabars in Mongolian Medicines Using Raman Spectroscopy].

Cinnabar could soothe the nerves and the powder of cinnabar is always added in traditional Chinese medicine or mongolian medicines. The surface-enhanced Raman spectrum of cinnabar was identified using a portable Raman spectrometer and most structure vibration information was obtained. The results show that the Raman peaks of cinnabars were located at 253, 290, 343 cm(-1) and this three Raman characteristic peaks were selected for cinnabar identification. Meanwhile, the Raman spectra of several mongolian medicines were collected. The results showed that Raman signal of cinnabar could be observed in several mongolian medicines which contain cinnabar and Raman signal of cinnabar couldn't be detected in several mongolian medicines without cinnabar. In addition, the cinnabar in the oral ulcer powder was semi-quantitative analyzed and the limit of detection could reach to 10% of mass fraction. The relationship between the doped amount of cinnabar in the oral ulcer powder and the Raman intensity of characteristic peak was fitted and the correlation coefficient (r) was 0.995 9, which validated the accuracy of the result. This Raman analysis method for cinnabar detection is rapid, simple and accurate and it can be applied widely in mongolian medicines determination.

[Research on the surface-enhanced Raman spectrum of sodium sulfide nonahydrate and its application in monosodium glutamate detecting].

The surface-enhanced Raman spectrum of sodium sulfide was studied and the structure vibration information was obtained. The raman characteristic peak at 472 cm(-1) was selected to evaluate enhanced effects. Gold colloid was used as active substrate, The relationship between gold nanoparticle size and enhanced efficiency was analysised and the optimum size for SERS is 97 nm. The surface enhanced Raman scattering spectra of sodium sulfide at different concentrations were also presented. The results indicated that SERS spectra of sodium sulfide can be found even the concentration reach 10(-6) g x mL(-1). The performance of active substrate is related with the ratio of sample and gold colloid. In the real situation, 1 g monosodium glutamate was added to 10mL sodium sulfide solution with different concentration and then SERS spectra of these samples were collected respectively. The lever of qualitative detection can reach 10mg x kg(-1). And due to the simple procedure in sample preparation, this method is of great potential in on-line qualitative detection.

Automated identification of pesticide mixtures via machine learning analysis of TLC-SERS spectra.

Identification of components in pesticide mixtures has been a major challenge in spectral analysis. In this paper, we assembled monolayer Ag nanoparticles on Thin-layer chromatography (TLC) plates to prepare TLC-Ag substrates with mixture separation and surface-enhanced Raman scattering (SERS) detection. Spectral scans were performed along the longitudinal direction of the TLC-Ag substrate to generate SERS spectra of all target analytes on the TLC plate. Convolutional neural network classification and spectral angle similarity machine learning algorithms were used to identify pesticide information from the TLC-SERS spectra. It was shown that the proposed automated spectral analysis method successfully classified five categories, including four pesticides (thiram, triadimefon, benzimidazole, thiamethoxam) as well as a blank TLC-Ag data control. The location of each pesticide on the TLC plate was determined by the intersection of the information curves of the two algorithms with 100 % accuracy. Therefore, this method is expected to help regulators understand the residues of mixed pesticides in agricultural products and reduce the potential risk of agricultural products to human health and the environment.

A temperature compensated fiber probe for highly sensitive detection in virus gene biosensing.

This research presents an innovative reflective fiber optic probe structure, mutinously designed to detect H7N9 avian influenza virus gene precisely. This innovative structure skillfully combines multimode fiber (MMF) with a thin-diameter seven-core photonic crystal fiber (SCF-PCF), forming a semi-open Fabry-Pérot (FPI) cavity. This structure has demonstrated exceptional sensitivity in light intensity-refractive index (RI) response through rigorous theoretical and experimental validation. The development of a quasi-distributed parallel sensor array, which provides temperature compensation during measurements, has achieved a remarkable RI response sensitivity of up to 532.7 dB/RIU. The probe-type fiber optic sensitive unit, expertly functionalized with streptavidin, offers high specificity in detecting H7N9 avian influenza virus gene, with an impressively low detection limit of 10-2 pM. The development of this biosensor marks a significant development in biological detection, offering a practical engineering solution for achieving high sensitivity and specificity in light-intensity-modulated biosensing. Its potential for wide-ranging applications in various fields is now well-established.

Study on the classification and identification of various carbonate and sulfate mineral medicines based on Raman spectroscopy combined with PCA-SVM algorithm.

The efficacy of mineral medicines varies greatly between different origins. Therefore, investigating a method to quickly identify similar mineral medicines is meaningful. In this paper, a visual classification and identification model of Raman spectroscopy combined with principal component analysis (PCA) and support vector machine (SVM) algorithms was developed to rapidly classify and identify carbonate and sulfate mineral medicines. The results reveal that although the Raman spectra are too similar to distinguish by naked eye, the PCA-SVM algorithm can perform accurate classification and identification, and its accuracy, precision, recall and F1-score parameters all reach 100%. The proposed method is rapid, accurate, nondestructive, convenient, portable, and low cost, and has important application value for the classification, identification and quality supervision of various carbonate and sulfate mineral medicines.

Interfacial Self-Assembly of Surfactant-Free Au Nanoparticles as a Clean Surface-Enhanced Raman Scattering Substrate for Quantitative Detection of As5+ in Combination with Convolutional Neural Networks.

Surface-enhanced Raman scattering (SERS) is a highly sensitive tool in the field of environmental testing. However, the detection and accurate quantification of weakly adsorbed molecules (such as heavy metal ions) remain a challenge. Herein, we combine clean SERS substrates capable of capturing heavy metal ions with convolutional neural network (CNN) algorithm models for quantitative detection of heavy metal ions in solution. The SERS substrate consists of surfactant-free Au nanoparticles (NPs) and l-cysteine molecules. As plasmonic nanobuilt blocks, surfactant-free Au NPs without physical or chemical barriers are more accessible to target molecules. The amino and carboxyl groups in the l-cysteine molecule can chelate As5+ ions. The CNN algorithm model is applied to quantify and predict the concentration of As5+ ions in samples. The results demonstrated that this strategy allows for fast and accurate prediction of As5+ ion concentrations, and the determination coefficient between the predicted and actual values is as high as 0.991.

Detection of Soluble Mercury in Cinnabar Using a CV-Ag NPs SERS Probe.

In the current work a uniform morphological Ag nanoparticles (Ag NPs) were prepared with ascorbic acid as a reducing agent and citrate as a stabilizer. The surface of Ag NPs modified by crystal violet (CV) and potassium iodide (KI) was used as an aggregation agent to obtain CV modified Ag NPs (CV-Ag NPs) probes for detecting mercury ions. The mercury ions could be reduced to mercury molecules by citrate, and then deposited on the surface of Ag NPs, leading to the separation of CV molecules from the surface of Ag NPs. Therefore, the SERS signal intensity of CV decreased with the increase of the Hg2+ concentration and the concentration of Hg2+ was in the range of 1 × 10-11 to 1 × 10-5 M. Taking the change of the characteristic peak intensity of CV at 913 cm-1 as a reference, the SERS spectrum intensity of CV has a linear relationship with the Hg2+ concentration. The equation is y = -333.55x + 1343.05, where the linear correlation coefficient is R2 = 0.980, and the recovery rate is between 84.20 to 105.60%. Finally, the CV-Ag NPs probe was used to quickly detect soluble mercury in cinnabar. Compared with the conventional large-scale instrument detection method, this simple and fast method, can be applied for rapid detection of soluble mercury, and has a certain significance for concerning the research of mineral medicine processing mechanism.

Sensitive and reliable identification of fentanyl citrate in urine and serum using chloride ion-treated paper-based SERS substrate.

Recently, the phenomenon of fentanyls overdose leading to death is emerging in an endless stream. There is an urgent requirement to quickly identify fentanyl content in body fluids for medical and judicial purposes. With this in mind, we present a paper-based SERS substrate decorated with uniform gold nanospheres treated by chloride ion for the detection of fentanyl citrate in urine and serum. In particular, the paper-based SERS sensor was prepared by liquid/liquid self-assembly technique and chloride ion was introduced to clean and modify the substrate surface, which improved the sensitivity of the solid substrate with an enhancement factor (EF) as high as 1.64 × 105. Moreover, the uniformity of each paper-based substrate and the repeatability on different batches of substrate were excellent, and there was no obvious change in the intensity response of Raman spectra within a month. As a result, the quantitative analysis of fentanyl citrate in artificial urine and rat serum were performed based on the modified paper-based substrate with the limit of detection as low as 0.59 µg/mL and 2.78 µg/mL, respectively. Both the concentrations of the two biological samples with the Raman signal intensity were linearly plotted and the recovery of the spiked samples with different concentrations was collected to verify the accuracy of the quantitative curves. All the results suggest that this work makes SERS method available for the rapid identification and quantitative analysis of illicit drug in the real biological samples.

Rapid Detection of Dezocine in Biological Fluids Based on SERS Technology.

This paper describes a method based on surface enhanced Raman spectroscopy (SERS) technology for rapid detection of dezocine in urine and serum. Firstly, an Ag colloid substrate was prepared and characterized. Then the Raman characteristic peaks of dezocine were assigned from both theoretical and experimental aspects. Finally, the Raman peak at 661 cm-1 was selected as its characteristic peak to perform SERS detection on dezocine in urine and serum, and the detection limits of dezocine in urine and serum were determined. The relationships between the characteristic peak intensity and the concentration of dezocine in urine and serum were fitted and the recovery rates were calculated. This rapid, accurate and non-destructive method establishes a good foundation for rapid on-site detection of dezocine in biological samples.