A highly sensitive SERS sensor based on PVDF/Au nanofibers for trace analysis of nitrite ions

Nitrite ions present a significant risk to both environmental and human health, necessitating precise and sensitive detection methods. Herein, we fabricated a highly sensitive SERS sensor based on PVDF/Au nanofibers for nitrite ion detection. The synthesis of PVDF nanofibers involved the utilization of electrospinning apparatus, while the uniformity and high density of SERS activity "hot spots" were achieved by directly coating plasma gold nanoparticles onto the PVDF surface adopting thermal evaporation. The efficient charge transfer of the interface dipole layer directly generated on the surface of PVDF nanofibers was achieved through thermal evaporation. The enhanced Raman responses were due to the combined effects of local surface plasmon resonance of Au nanoparticles and photoelectric and piezoelectric properties of PVDF. It is noteworthy that the prepared SERS substrate exhibited high sensitivity towards rhodamine 6G, boasting an enhancement factor of 9.4 × 107 and a detection limit spanning from 10-6-10-11 M. Furthermore, the PVDF/Au membrane functionalized with p-aminothiophenol (PATP) effectively captured NO2- ions at concentrations as low as 10-8 M and successfully detected NO2- in river water samples. Additionally, the SERS substrate has good repeatability and stability, and can be applied to trace detection in food safety and medical diagnosis.

Ag nanoisland functionalized hollow carbon nanofibers as a non-invasive, label-free SERS salivary biosensor platform for salivary nitrite detection for pre-diagnosis of oral cancer

A highly selective, label-free, surface-enhanced Raman spectroscopy (SERS) based sensor platform employing hollow carbon nanofibers functionalized with silver nanoparticles (Ag@HCNFs) has been developed to monitor anomalous concentrations of potential biomarkers, such as salivary nitrite facilitating pre-diagnosis of oral cancer. Co-axial electrospinning was used for the fabrication of the nanofibrous Ag@HCNFs followed by thermal treatment of PAN/PVP core-shell nanofibers and chemical reduction of silver nanoislands. The developed plasmonic Ag@HCNFs was structurally and morphologically characterized using X-Ray diffraction, Raman spectroscopy, scanning electron microscopy, and transmission electron microscopy, which clearly demonstrated the successful anchoring of silver nanoparticles on hollow carbon nanofibers. The properties of Ag@HCNFs showed significant SERS enhancement of the order of 107 with a detection limit of 10-11 M with R6G, demonstrating its efficacy to investigate real-time salivary samples, particularly towards the detection of salivary nitrite within the clinically relevant range (50 μM-300 μM) towards the pre-diagnosis of oral cancer. The proposed SERS-based salivary platform has the potential to be used as a low-cost, non-invasive pre-diagnostic tool for early diagnosis and mass screening of oral cancer.

Internal standard optimization advances sensitivity and robustness of ratiometric detection method

We design a p-aminothiophenol (pATP) modified Au/ITO chip to determine nitrite ions in lake water by a ratiometric surface-enhanced Raman scattering (SERS) method based on nitrite ions triggering the transformation of pATP to p,p'-dimercaptoazobenzene (DMAB). Intriguingly, by using the SERS peak (at 1008 cm-1) from benzoic ring deforming as an internal standard instead of the traditional peak at 1080 cm-1, the detection sensitivity of the method was improved 10 times.

Robust and sensitive determination of nitrites in pickled food by surface-enhanced Raman spectroscopy

Nitrites are ubiquitous in food and pose a serious threat to human health. Therefore, the rapid and accurate determination of nitrite ion concentration in food is a prerequisite for eliminating the damage of nitrites. In this study, a robust, rapid, and sensitive method is proposed for nitrite detection in pickled food, in which Au@Ag nanoparticles are used as a reliable surface-enhanced Raman spectroscopy (SERS) substrate taking advantage of the high enhancement effect of silver and the good stability of gold. Nitrites were anchored to the surface of the SERS substrate by bridging with 4-aminophenylthiophenol (PATP). With Raman scattering cross-section amplification and internal calibration by PATP, a satisfactory linear relationship (R2 = 0.987) was established for nitrite detection in the concentration range of 5.00-100.00 μM, and the limit of detection (LOD) was 0.17 μM. This SERS-based method demonstrated high selectivity, good precision (RSD < 7.00 %), and satisfying recovery rates (101.42-107.35 %) in real samples, thus improving the determination method for nitrites. Therefore, this method has application potential in food safety and supervision.

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.

Capillary-force-assisted self-assembly of gold nanoparticles into highly ordered plasmonic thin films for ultrasensitive SERS

In this study, a capillary device based on the surface plasmon-enhanced Raman scattering effect was prepared by a simple and easy method. First, the capillary was treated with APTES solution. Due to the electrostatic effect, gold nanoparticles could be easily and tightly assembled in the capillary inner wall. On this basis, the effects of changing the concentration of APTES, the concentration of colloids and the soaking time of the capillary in the colloids on the assembly of gold nanoparticles on the inner wall of the capillary were studied, and the SERS enhancement effect under different conditions was analyzed, and the optimal solution was successfully found. At the same time, the reason why the capillary substrate shows better SERS performance than the traditional planar substrate is deeply discussed. Since the nanoparticles can be attached to the upper and lower surfaces of the inner wall of the capillary, the utilization rate of nanoparticles and laser is improved, thereby achieving higher enhancement. For the detection of the probe molecule rhodamine 6G, it was proved that the substrate has good uniformity and the lowest detection limit can reach 10^-10 M. Finally, the real-life pesticide thiram and the food additive aspartame were tested, and the detection limits could reach 10^-6 M and 0.25 g L^-1. It is confirmed that the prepared capillary shows excellent SERS performance and can be used for rapid detection in various fields.

An efficient and robust procedure to calculate absorption spectra of aqueous charged species applied to NO2. Phys Chem Chem Phys 2021;23:14857-14872. [PMID: 34223573 DOI: 10.1039/d1cp00652e]

Accurate calculation of absorption spectra of aqueous NO2- requires rigorously sampling the quantum potential energy surfaces for microsolvation of NO2- with at least five explicit water molecules and embedding the resulting clusters in a continuum solvent accounting for the statistical weighted contributions of individual isomers. This method, which we address as ASCEC + PCM, introduces several desired features when compared against MD simulations derived QM/MM spectra: comparatively fewer explicit solvent molecules to be treated with expensive QM methods, the identification of equilibrium structures in the quantum PES to be used in further vibrational spectroscopy, and the unequivocal identification of cluster orbitals undergoing electronic transitions and charge transfer that originate the spectral bands.

In situ SERS detection of dissolved nitrate on hydrated gold substrates

The accurate and fast measurement of nitrate in seawater is important for monitoring and controlling water quality to prevent ecologic and economic disasters. In this work we show that the in situ detection of nitrate in aqueous solution is feasible at nanomolar concentrations through surface enhanced Raman spectroscopy (SERS) using native nanostructured gold substrates without surface functionalization. Spectra were analyzed as collected or after standard normal variate (SNV) normalization, which was shown through Principal Component Analysis (PCA) to reduce spectral variations between sample sets and improve Langmuir adsorption model fits. An additional normalization approach based on the substrate silicon template showed that silicon provided an internal standard that accounted for the spectral variance without the need for SNV normalization. Nitrate adsorption was well-described by the Langmuir adsorption model, consistent with an adsorbed monolayer, and a limit of detection of 64 nM nitrate was obtained in ultrapure water, representing environmentally relevant concentrations. Free energy calculations based on the Langmuir adsorption constants, approximating equilibrium adsorption constants, and calculated self-energy arising from image charge, accounting for electrostatic interactions with a polarizable nanostructured substrate, suggest that nitrate adsorption was partially driven by an entropy gain presumably due to dehydration of the gold substrate and/or nitrate ion. This work is being extended to determine if similar statistical and normalization methods can be applied to nitrate detection in complex natural waters where non-target ions and molecules are expected to interfere.

Point-of-Care Detection of Salivary Nitrite Based on the Surface Plasmon-Assisted Catalytic Coupling Reaction of Aromatic Amines

Rapid quantification of nitrite (NO₂^-) in food, drink and body fluids is of significant importance for both food safety and point-of-care (POA) applications. However, conventional nitrite analytical methods are complicated, constrained to sample content, and time-consuming. Inspired by a nitrite-triggered surface plasmon-assisted catalysis (SPAC) reaction, a rapid point-of-care detection salivary nitrate was developed in this work. NO₂^- ions can trigger the rapid conversion of p-aminothiophenol (PATP) to p,p'-dimercaptozaobenzene (DMAB) on gold nanoparticles (GNPs) under light illumination, and the emerged new bands at ca. 1140, 1390, 1432 cm^-1 originating from DMAB can be used to the quantification of nitrite. Meanwhile, to make the method entirely suitable for on-site fast screen or point-of-care application, the technique is needed to be further optimized. The calibration graph for nitrates was linear in the range of 1-100 µM with a correlation coefficient of 0.9579. The limit of detection was 1 µM. The facile method could lead to a further understanding of the progression and treatment of periodontitis and to guide professionals in planning on-site campaigns to effectively control periodontal diseases.

Fluorescent and colorimetric dual-readout sensor based on Griess assay for nitrite detection

In this work, we presented a sensitive and selective colorimetric and fluorescent dual-readout sensor based on Griess assay for nitrite (NO₂^-) detection under acidic condition. The sensor system was constituted of acid-resistant carbon quantum dots (CQDs) and 3-aminophenol (3-Aph) with acidic condition regulated using HCl. During the sensing procedure, reaction of 3-Aph and NO₂^- under acidic condition can yield a yellow-colored azoic compound (AZO), which gives the colorimetric readout; meanwhile, the fluorescence of CQDs (fluorescence reporter) quenched due to the strong absorption of AZO, leading to fluorescent readout. Wherein, CQDs were synthesized via hydrothermal method through using polyacrylamide as precursor and characterized by AFM, XRD and XPS. Under the optimized condition, the sensor exhibit broad linear relationships towards NO₂^- in the range of 10 to 100 nM and 2.5 to 100 μM, with practical detection limits of 10 nM and 2.5 μM for the fluorescent and colorimetric readout, respectively. And the sensor displayed excellent capability of selectivity according to interferences study. Furthermore, testing of sprouts, bacon and ham sausage real samples demonstrated good recoveries and reproducibility of the sensor system. All these results suggested the presented colorimetric and fluorescent dual-readout sensor can be a promising candidate for the NO₂^- detection in real applications.

A colorimetric fluorescent probe for rapid and specific detection of nitrite

The method of fluorescent probes has been an important technique for detection of nitrite (NO₂ ^- ). As an important inorganic salt, excessive nitrite would threaten humans and the environment. In this paper, a colorimetric fluorescent probe P-N (1,2-diaminoanthraquinone) with rapid response and high selectivity, which could detect NO₂ ^- by visual colour changes and fluorescence spectroscopy is presented. The probe P-N solution (pH 1) changed from pink to colourless with the addition of NO₂ ^- and fluorescence intensity at 639 nm clearly decreased. Good linear exists between fluorescence intensities and NO₂ ^- concentrations for the range 0-16 μM, and the detection limit was 54 nM (based on a 3σ/slope). Moreover, probe P-N could also detect NO₂ ^- in real water samples, and results were all satisfactory. Probe P-N shows great practical application value for detecting NO₂ ^- in the environment.

Developing cysteamine-modified SERS substrate for detection of acidic pigment with weak surface affinity

In this paper, we developed cysteamine-modified surface-enhanced Raman scattering (SERS) substrate for detecting detect trace amount of acidic pigment that shows weak affinity with gold nanoparticles (Au NPs). To realize sensitive and reproducible detection of pigment with weak affinity, the SERS substrate was prepared by attaching cysteamine (CA) to the Au NPs, the acidic pigment molecule could rapidly reached to the surface of Au NPs because of the formation of multi‑hydrogen-bond and electrostatic interaction between the pigment and CA molecule. The proposed method allowed us to detect five kinds of acidic pigment with a limit of 1.0 ppm, which is below the strictest safety limit. Compared with the previous methods, the advantages of the present substrate were its simple substrate preparation, high reproducibility and good universality. Furthermore, the reliable and enough accurate results had been obtained by using of the proposed substrates in the assay of trace pigment in real samples.