Flexible SERS sensor using AuNTs-assembled PDMS film coupled chemometric algorithms for rapid detection of chloramphenicol in food

Generalized ratiometric surface-enhanced Raman scattering biosensor for okadaic acid in food based on Au-triggered signal amplification

BACKGROUND

Reliability and robustness have been recognized as key challenges for Surface-enhanced Raman scattering (SERS) analytical techniques. Quantifying the concentration of an analyte using a single characteristic peak from SERS has been a controversial topic because the Raman signal is susceptible to highly concentrated electromagnetic hotspots, inhomogeneity of SERS substrate, or non-standardization of measurement conditions. Ratiometric SERS strategies have been demonstrated as a promising solution to effectively balance and compensate for signal fluctuations caused by matrix heterogeneity. However, it is not easy to construct ratiometric SERS sensors with monitoring the ratio of two different signal intensities for target analysis.

RESULTS

An attempt has been made to develop a novel ratiometric biosensor that can be applied to detect okadaic acid (OA). Aptamer-anchored magnetic particles were first combined with gold-tagged short complementary DNA (Au-cDNA) to create heterogeneous nanostructures. When the target was present, the Au-cDNA was dissociated from nanostructures, and 4-nitrothiophenol (4-NTP) was initiated to reduce to 4-aminothiophenol (4-ATP) in the presence of hydrogen sources. The SERS ratio change of 4-NTP and 4-ATP was finally detected by AuNPs-coated film. OA was successfully quantified, and the detection limit was as low as 2.4524 ng/mL. The constructed biosensor had good stability and reproducibility with a relative standard deviation of less than 4.47%. The proposed method used gold nanoparticles as an intermediate to achieve catalytic signal amplification and subsequently increased the sensitivity of the biosensor.

SIGNIFICANCE AND NOVELTY

Catalytic reaction-based ratiometric SERS biosensors combine the multiple advantages of catalytic signal amplification and signal self-calibration and provide new insights into the development of stable, reproducible, and reliable SERS detection techniques. This ratiometric SERS technique offered a universal method that is anticipated to be applicable for the detection of other targets by substituting the aptamer.

Quantification of antibiotics in food by octahedral gold-silver nanocages-based SERS sensor coupling multivariate calibration

The abuse of antibiotics has caused gradually increases drug-resistant bacterial strains that pose health risks. Herein, a sensitive SERS sensor coupled multivariate calibration was proposed for quantification of antibiotics in milk. Initially, octahedral gold-silver nanocages (Au@Ag MCs) were synthesized by Cu2O template etching method as SERS substrates, which enhanced the plasmonic effect through sharp edges and hollow nanostructures. Afterwards, five chemometric algorithms, like partial least square (PLS), uninformative variable elimination-PLS (UVE-PLS), competitive adaptive reweighted sampling-PLS (CARS-PLS), random frog-PLS (RF-PLS), and convolutional neural network (CNN) were applied for TTC and CAP. RF-PLS performed optimally for TTC and CAP (Rc = 0.9686, Rp = 0.9648, RPD = 3.79 for TTC and Rc = 0.9893, Rp = 0.9878, RPD = 5.88 for CAP). Furthermore, the detection limit of 0.0001 µg/mL for both TTC and CAP was obtained. Finally, satisfactory (p > 0.05) results were obtained with the standard HPLC method. Therefore, SERS combined RF-PLS could be applied for fast, nondestructive sensing of TTC and CAP in milk.

A simple and efficient alginate hydrogel combined with surface-enhanced Raman spectroscopy for quantitative analysis of sodium nitrite in meat products

Sodium nitrite is a commonly used preservative and color protectant in the food industry. Conventional analytical methods are highly susceptible to food matrix interference, time-consuming and costly. In this study, the ion cross-linking method was employed to prepare alginate hydrogel substrates, and phenosafranin was chosen as a single-molecule probe to analyze sodium nitrite. Our investigation centered on elucidating the effects of alginate and cross-linking ion concentrations on Raman signal characteristics. The optimal Raman response was observed in the precursor solution with 1% sodium alginate and 0.1 mol L-1 cross-linking ions. The relative standard deviations (RSDs) of the feature peaks from the three substrate batches ranged from 1.22% to 16.30%, attesting the robustness and consistency of the substrates. The signal reduction of the substrates after a four-week storage period remained below 10%, indicating that the substrates had good reproducibility and stability. The limits of detection (LODs) for sodium nitrite in extracts from cured meat, luncheon meat, and sliced ham were determined to range from 3.75 mg kg-1 to 8.11 mg kg-1, with low interference from the food matrix. The support vector machine algorithm was utilized to train and predict the data, which proved to be more accurate (98.6%-99.8% recovery) than the traditional linear regression model (81.9%-112.7% recovery) in predicting the spiked samples. The application of hydrogel-based surface-enhanced Raman spectroscopy (SERS) substrates for nitrite detection in food, combined with machine learning for regression prediction in data processing, collectively augmented the potential of SERS technology in the field of food analysis.

Design of MXene-Based Multiporous Nanosheet Stacking Structures Integrating Multiple Synergistic SERS Enhancements for Ultrasensitive Detection of Chloramphenicol

Motivated by the desire for more sensitivity and stable surface-enhanced Raman scattering (SERS) substrates to trace detect chloramphenicol due to its high toxicity and ubiquity, MXene has attracted increasing attention and is encountering the high-priority task of further observably improving detection sensitivity. Herein, a universal SERS optimization strategy that incorporates NH4VO3 to induce few-layer MXenes assembling into multiporous nanosheet stacking structures was innovatively proposed. The synthesized Nb2C-based multiporous nanosheet stacking structure can achieve a low limit of detection of 10-10 M and a high enhancement factor of 2.6 × 109 for MeB molecules, whose detection sensitivity is improved by 3 orders of magnitude relative to few-layer Nb2C MXenes. Such remarkably enhanced SERS sensitivity mainly originates from the multiple synergistic contributions of the developed physical adsorption, the chemical enhancement, and the conspicuously improved electromagnetic enhancement arising from the intersecting MXenes. Furthermore, the improved SERS sensitivity endows Nb2C-based multiporous structures with the capability to achieve ultrasensitive detection of chloramphenicol with a wide linear range from 100 μg/mL to 1 ng/mL. We believe it is of great significance in conspicuously developing the SERS sensitivity of other MXenes with surficial negative charges and has a great promising perspective for the trace detection of other antibiotics in microsystems.

Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis

Breast cancer is a prevalent form of cancer worldwide, and the current standard screening method, mammography, often requires invasive biopsy procedures for further assessment. Recent research has explored microRNAs (miRNAs) in circulating blood as potential biomarkers for early breast cancer diagnosis. In this study, we employed a multi-modal spectroscopy approach, combining attenuated total reflection Fourier transform infrared (ATR-FTIR) and surface-enhanced Raman scattering (SERS) to comprehensively characterize the full-spectrum fingerprints of RNA biomarkers in the blood serum of breast cancer patients. The sensitivity of conventional FTIR and Raman spectroscopy was enhanced by ATR-FTIR and SERS through the utilization of a diamond ATR crystal and silver-coated silicon nanopillars, respectively. Moreover, a wider measurement wavelength range was achieved with the multi-modal approach than with a single spectroscopic method alone. We have shown the results on 91 clinical samples, which comprised 44 malignant and 47 benign cases. Principal component analysis (PCA) was performed on the ATR-FTIR, SERS, and multi-modal data. From the peak analysis, we gained insights into biomolecular absorption and scattering-related features, which aid in the differentiation of malignant and benign samples. Applying 32 machine learning algorithms to the PCA results, we identified key molecular fingerprints and demonstrated that the multi-modal approach outperforms individual techniques, achieving higher average validation accuracy (95.1%), blind test accuracy (91.6%), specificity (94.7%), sensitivity (95.5%), and F-score (94.8%). The support vector machine (SVM) model showed the best area under the curve (AUC) characterization value of 0.9979, indicating excellent performance. These findings highlight the potential of the multi-modal spectroscopy approach as an accurate, reliable, and rapid method for distinguishing between malignant and benign breast tumors in women. Such a label-free approach holds promise for improving early breast cancer diagnosis and patient outcomes.

Effect of Aspect Ratio of a Gold-Nanorod-Modified Screen-Printed Carbon Electrode for Carbaryl Detection in Three Different Samples of Vegetables

In this study, three different sizes of gold nanorods (AuNRs) were synthesized using the seed-growth method by adding various volumes of AgNO3 as 400, 600, and 800 μL into the growth solution of gold nanoparticles. Three different sizes of AuNRs were then characterized using UV-vis spectroscopy, high-resolution transmission electron microscopy (HRTEM), selected area electron diffraction (SAED) patterns, and atomic force microscopy (AFM) to investigate the surface morphology, topography, and aspect ratios of each synthesized AuNR. The aspect ratios from the histogram of size distributions of three AuNRs as 2.21, 2.53, and 2.85 can be calculated corresponding to the addition of AgNO3 volumes of 400, 600, and 800 μL. Moreover, each AuNR in three different aspect ratios was drop-cast onto the surface of a commercial screen-printed carbon electrode (SPCE) to obtain three different SPCE-modified AuNRs (SPCE-A400, SPCE-A600, and SPCE-A800, respectively). All SPCE-modified AuNRs were then evaluated for their electrochemical behavior using cyclic voltammetry and electrochemical impedance spectroscopy (EIS) techniques and the highest electrochemical performance was shown as the order of magnitude of SPCE-A400 > SPCE-A600/SPCE-A800. The reason for the highest electrocatalytic activity of SPCE-A400 might be due to the smallest particle size and uniform distribution of AuNRs ∼ 2.2, which enhanced the charge transfer, thus providing the highest electroactive surface area (0.6685 cm2) compared to other electrodes. These results also confirm that the sensing mechanism for all SPCE-modified AuNRs is controlled by diffusion phenomena. In addition, the optimum pH was obtained as 4 for carbaryl detection for all SPCE-modified AuNRs with the highest current shown by SPCE-A400. Furthermore, SPCE-A400 has the highest fundamental parameters (surface coverage, catalytic rate constant, electron transfer rate constant, and adsorption capacity) for carbaryl detection, which were investigated using cyclic voltammetry and chronoamperometric techniques. The electroanalytical performances of all SPCE-modified AuNRs for carbaryl detection were also investigated with SPCE-A400 displaying the best performance among other electrodes in terms of its linearity (0.2-100 μM), limit of detection (LOD) ∼ 0.07 μM, and limit of quantification (LOQ) ∼ 0.2 μM. All SPCE-modified AuNRs were also subsequently evaluated for their stability, reproducibility, and selectivity in the presence of interfering species such as NaNO2, NH4NO3, Zn(CH3CO2)2, FeSO4, diazinon, and glucose and show reliable results as depicted from %RSD values less than 3%. At last, all SPCE-modified AuNRs have been employed for carbaryl detection using a standard addition technique in three different samples of vegetables (cabbage, cucumber, and Chinese cabbage) with its results (%recovery ≈ 100%) within the acceptable analytical range. In conclusion, this work demonstrates the great potential of a disposable device based on an AuNR-modified SPCE for rapid detection and high sensitivity in monitoring the concentration of carbaryl as a residual pesticide in vegetable samples.

High-Performance Au@Ag Nanorods Substrate for SERS Detection of Malachite Green in Aquatic Products

In order to improve the detection performance of surface-enhanced Raman scattering (SERS), a low-cost Au@Ag nanorods (Au@Ag NRs) substrate with a good SERS enhancement effect was developed and applied to the detection of malachite green (MG) in aquaculture water and crayfish. By comparing the SERS signal enhancement effect of five kinds of Au@Ag NRs substrates with different silver layer thickness on 4-mercaptobenzoic acid (4-MBA) solution, it was found that the substrate prepared with 100 µL AgNO3 had the smallest aspect ratio (3.27) and the thickest Ag layer (4.1 nm). However, it showed a good signal enhancement effect, and achieved a detection of 4-MBA as low as 1 × 10-11 M, which was 8.7 times higher than that of the AuNRs substrate. In addition, the Au@Ag NRs substrate developed in this study was used for SRES detection of MG in crayfish; its detection limit was 1.58 × 10-9 M. The developed Au@Ag NRs sensor had the advantages of stable SERS signal, uniform size and low cost, which provided a new tool for SERS signal enhancement and highly sensitive SERS detection method development.