Preparation of SERS active filter paper for filtration and detection of pesticides residue from complex sample

Surface control in the adsorption of tebuthiuron on modified silver surfaces tracked by surface-enhanced Raman scattering spectroscopy

The vibrational assignment of the Raman and surface-enhanced Raman scattering (SERS) spectra of the herbicide tebuthiuron (TBH) was accomplished, which allowed unprecedented propositions for adsorption geometries on the surface of silver nanoparticles (AgNP). Ascribed SERS features allowed suggesting that the adsorption occurred through nitrogen atoms of thiadiazole group, since intense band shift assigned to ring mode was marking of the coordination with the metallic surface. AgNP were treated with different surface modifiers that leaded to substantial changes in TBH adsorption geometries. Spectral changes, as the enhancement of out-of-plane ring modes, were indicative of the presence of tilted thiadiazole geometries in relation to the silver surface. Density Functional Theory (DFT) calculations from TBH molecules, in isolation and in interaction with ten-atom cluster of silver leaded to obtain theoretical spectra that gave support to interpret experimental Raman and SERS spectra.

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.

Quantitative monitoring ofloxacin in beef by TLC-SERS combined with machine learning analysis

Ofloxacin is one kind of quinolone antibiotic drugs, the abuse of ofloxacin in livestock and aquaculture may bring bacterial resistance and healthy problem of people. The illegally feeding cattle with ofloxacin will help it keep health, but the sedimentation of ofloxacin could bring problem in food safety. The accurate, simple and instant monitoring ofloxacin from beef by portable sensor was of vital issue in food quality. A simple and reliable method was proposed for instant and quantitative detecting ofloxacin in beef, in which the thin-layer chromatography (TLC) -surface-enhanced Raman scattering (SERS) spectroscopy was in tandem with machine learning analysis base one principal component analysis-back propagation neural network (PCA-BPNN). The TLC plate was composed with diatomite, that was function as the stationary phase to separate ofloxacin from beef. The real beef juice was directly casted onto the diatomite plate for separating and detecting. The directly monitor ofloxacin from beef was achieved and the sensitivity down to 0.01 ppm. The PCA-BPNN was used as reliable model for quantitative predict the concentration of ofloxacin, that shown superior accuracy compared with the traditional model. The results verify that the diatomite plate TLC-SERS combined with machine-learning analysis is an effective, simple and accurate technique for detecting and quantifying antibiotic drug in meat stuff to improve the food safety.

Paper-based colorimetric sensor using Photoshop and a smartphone app for the quantitative detection of carbofuran

We developed a smartphone-assisted microchemistry analyzer for the quantitative detection of carbofuran using a paper-based colorimetric sensor, Photoshop software, and a smartphone app. The changes in color of the carbofuran enzymatic reaction in the paper-based sensor were captured and analyzed using a smartphone-controlled analyzer with an LED light source and a smartphone camera. The high accuracy of this method was demonstrated for the determination of carbofuran with a linear response in the range 0.05-1.0 ppm and limits of detection (LOD) of 0.02 and 0.018 ppm using Photoshop and smartphone app colorimetric analysis, respectively. These two methods not only show the high sensitivity and highly quantitative relationships between the concentrations of commercial carbofuran and characteristic color values of the blue channel in smartphone images but were also applied to infusions of tea. Moreover, the smartphone app is able to GPS tag the location of the test and transmit the results to a website that displays quantitative results from carbofuran samples on a map.

Paper-based substrates for surface-enhanced Raman spectroscopy sensing

Surface-enhanced Raman scattering (SERS) has been recognized as one of the most sensitive analytical methods by adsorbing the target of interest onto a plasmonic surface. Growing attention has been directed towards the fabrication of various substrates to broaden SERS applications. Among these, flexible SERS substrates, particularly paper-based ones, have gained popularity due to their easy-to-use features by full contact with the sample surface. Herein, we reviewed the latest advancements in flexible SERS substrates, with a focus on paper-based substrates. Firstly, it begins by introducing various methods for preparing paper-based substrates and highlights their advantages through several illustrative examples. Subsequently, we demonstrated the booming applications of these paper-based SERS substrates in abiotic and biological matrix detection, with particular emphasis on their potential application in clinical diagnosis. Finally, the prospects and challenges of paper-based SERS substrates in broader applications are discussed.

Bioinspired Multifunctional Silver Nanoparticles for Optical Sensing Applications: A Sustainable Approach

Silver nanoparticles developed via biosynthesis are the most fascinating nanosized particles and encompassed with excellent physicochemical properties. The bioinspired nanoparticles with different shapes and sizes have attracted huge attention due to their stability, low cost, environmental friendliness, and use of less hazardous chemicals. This is an ideal method for synthesizing a range of nanosized metal particles from plants and biomolecules. Optical biosensors are progressively being fabricated for the attainment of sustainability by using opportunities offered by nanotechnology. This review focuses mainly on tuning the optical properties of the metal nanoparticles for optical sensing to explore the importance and applications of bioinspired silver nanoparticles. Further, this review deliberates the role of bioinspired silver nanoparticles (Ag NPs) in biomedical, agricultural, environmental, and energy applications. Profound insight into the antimicrobial properties of these nanoparticles is also appreciated. Tailor-made bioinspired nanoparticles with effectuating characteristics can unsurprisingly target tumor cells and distribute enwrapped payloads intensively. Existing challenges and prospects of bioinspired Ag NPs are also summarized. This review is expected to deliver perceptions about the progress of the next generation of bioinspired Ag NPs and their outstanding performances in various fields by promoting sustainable practices for fabricating optical sensing devices.

A review on current progress of Raman-based techniques in food safety: From normal Raman spectroscopy to SESORS

Frequently occurrence of food safety incidents has induced global concern over food safety. To ensure food quality and safety, an increasing number of rapid and sensitive analytical methods have been developed for analysis of all kinds of food composition and contaminants. As one of the high-profile analytical techniques, Raman spectroscopy has been widely applied in food analysis with simple, rapid, sensitive, and nondestructive detection performance. Research on Raman techniques is a direction of great interest to many fields, especially in food safety. Hence, it is crucial to gain insight into recent advances on the use of Raman-based techniques in food safety applications. In this review, we introduce Raman techniques from normal Raman spectroscopy to developed ones (e.g., surface enhanced Raman scattering (SERS), spatially offset Raman spectroscopy (SORS), surface-enhanced spatially offset Raman spectroscopy (SESORS)), in view of their history and development, principles, design, and applications. In addition, future challenges and trends of these techniques are discussed regarding to food safety.