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.

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.

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.

Detection of Chlortetracycline Hydrochloride in Milk with a Solid SERS Substrate Based on Self-assembled Gold Nanobipyramids.

This paper described how a high-yield, monodisperse Au nanobipyramids (Au NBs) sol was prepared by a seed-mediated method, and gold nanoparticles were assembled on the surface of a silicon wafer by self-assembly technology to obtain a solid SERS substrate. Scanning electron microscopy (SEM) showed that the average length of Au NBs was 34.31 nm, and the analysis enhancement factor (AEF) was approximately 7.3 × 105 with rhodamine 6G (R6G) used as a probe. SERS detection of chlortetracycline hydrochloride (CCH) in milk was performed utilizing the prepared Au NBs substrate, and the limit of detection was 0.01 mg/mL. In the range of 0.01 - 1 mg/mL, the mass concentration of CCH and the SERS signal intensity satisfied the linear relationship y = 258.467x + 150.501; the value of the correlation coefficient was 0.9785. In addition, the recovery of spiked samples fluctuated between 96.80 to 111.38%. These results proved that the method is simple and fast, and it is promising to be applied to the field detection of antibiotics in milk.

A Rapid Detection Method for On-site Screening of Estazolam in Beverages with Au@Ag Core-shell Nanoparticles Paper-based SERS Substrate.

Estazolam (EST) is a common sedative-hypnotic drug with a risk of abuse. Therefore, rapid on-site detection of EST is necessary to control the abuse of EST. In this paper, a fast, simple, and sensitive method is demonstrated for the detection of EST in both water and beverages, using surface-enhanced Raman spectroscopy (SERS) techniques. Au@Ag core-shell nanoparticles (NPs) assembled on the filter paper as a SERS substrate exhibit good applicability and practicality. At the same time, density functional theory (DFT) is used to assign the vibration mode of the EST molecules, which can be used as a guide for subsequent experiments. The lowest detectable concentration of EST in aqueous solution can be as low as 5 mg/L, and signal uniformity is excellent (RSD687 = 5.56%, RSD1000 = 4.35%). In addition, EST components artificially added to orange juice and pomegranate juice can be effectively detected by simple pretreatment with a minimum detection concentration as low as 10 mg/L. Therefore, this study found that the use of Au@Ag core-shell nanoparticles paper-based SERS substrate provides a quick and easy method for the detection of illegally added drugs in beverages.

Detection of Scopolamine Hydrobromide via Surface-enhanced Raman Spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) was used to measure scopolamine hydrobromide. First, the Raman characteristic peaks of scopolamine hydrobromide were assigned, and the characteristic peaks were determined. The optimal aggregation agent was potassium iodide based on a comparative experimental study. Finally, the SERS spectrum of scopolamine hydrobromide was detected in aqueous solution, and the semi-quantitative analysis and the recovery rate were determined via a linear fitting. The detection limit of scopolamine hydrobromide in aqueous solution was 0.5 µg/mL. From 0 - 10 µg/mL, the curve of the intensity of the Raman characteristic peak of scopolamine hydrobromide at 1002 cm-1 is y = 4017.76 + 642.47x. The correlation coefficient was R2 = 0.983, the recovery was 98.5 - 109.7%, and the relative standard deviation (RSD) was about 5.5%. This method is fast, accurate, non-destructive and simple for the detection of scopolamine hydrobromide.