A Widely Applicable Silver Sol for TLC Detection with Rich and Stable SERS Features

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.

Thin layer chromatography coupled with surface enhanced Raman scattering for rapid separation and on-site detection of multi-components

The separation and detection of multi-component mixtures has always been a challenging task. Traditional detection methods often suffer from complex operation, high cost, and low sensitivity. Surface enhanced Raman scattering (SERS) technique is a high sensitivity, powerful and rapid detection tool, which can realize the specific detection of single substance components, but it must solve the problem that multi-component mixtures cannot be accurately determined. Thin layer chromatography (TLC) technology, as a high-throughput separation technology, uses chromatographic plate as the stationary phase, and could select different developing phases for separation experiments. The advantages of TLC technology in short distance and rapid separation are widely used in protein, dye and biomedical fields. However, TLC technology has limitations in detection ability and difficulty in obtaining ideal signal intensity. The combination of TLC technology and SERS technology made the operation procedure simple and the sample size small, which can achieve rapid on-site separation and quantitative detection of mixtures. Due to the rapid development of TLC-SERS technology, it has been widely used in the investigation of various complex systems. This paper reviews the application of TLC-SERS technology in food science, environmental pollution and biomedicine.

Investigating Perampanel Antiepileptic Drug by DFT Calculations and SERS with Custom Spinning Cell

SERS, a clinical practice where medical doctors can monitor the drug concentration in biological fluids, has been proposed as a viable approach to therapeutic drug monitoring (TDM) of the antiepileptic drug Perampanel. The adoption of an acidic environment during the SERS experiments was found to be effective in enhancing the spectroscopic signal. In this work, we combine SERS experiments, conducted with a custom spinning cell in controlled acidic conditions, with DFT calculations aimed at investigating the possible protonated forms of Perampanel. The DFT-simulated Raman spectra of protonated Perampanel accounts for most of the observed SERS signals, thus explaining the effective role of protonation of the analyte. Our results suggest protonation as a viable approach to fostering SERS of alkaline drugs.

Functional cotton fabric-based TLC-SERS matrix for rapid and sensitive detection of mixed dyes

A facile cotton fabric with a built-in TLC-SERS structure was fabricated to demonstrate an integrated TLC separation and SERS identification of mixed dyes. The soft and flexible SERS fabric was firstly fabricated using a simple method in which gold nanoparticles were in-situ synthesized on cotton fabrics by heating. β-CD was then grafted onto cotton fabric through crosslinking with citric acid in presence of sodium hypophosphite monohydrate via esterification reaction. The adsorption and TLC development performance of β-CD grafted fabrics were comprehensively investigated with two organic dyes, one anionic dye and one nonionic dye. Besides, the recyclable adsorption and separation performance were tested to evaluate its sustainable application prospects. It displayed less adsorption capacity loss and reusable separation performance after several cycles than the pristine cotton fabrics. Finally, two sets of mixed dyes were successfully separated on the TLC fabrics and then identified via on-site SERS according to their different migration distance. The developed TLC-SERS fabric shows the advantage of quick, easy to handle, low-cost, sensitive, and could be exploited in on-site study of synthetic dyes in art objects, textile and packaging products or forensic applications.

On-site detection of pyrene from mixture with ppb level sensitivity by plasmonic TLC-DSERS

Polycyclic aromatic hydrocarbons are a kind of persistent organic pollutants, which bring harmful effects to the ecological environment and human health. Therefore, it is critical to identify PAHs. In this study, we developed a highly efficient device for on-site identification of pyrene in edible oil using high performance plasmonic thin layer chromatography (PTLC) and dynamic surface-enhanced Raman spectroscopy (DSERS). PAHs in the mixture sample were efficiently separated on the PTLC plate and visualized under UV light, in which the plasmonic feature of the stationary phase could enhance the fluorescence of PAH. Then DSERS measurement was developed on a portable Raman spectrometer. The smaller size of Au NPs in the stationary phase could provide a lower theoretical plate height and provide higher separation efficiency. The sensitivity of the PTLC-DSERS method is down to 0.1 ppb, that nearly 4 orders of magnitude higher than current TLC-SERS method. The results indicate that this PTLC-DSERS method has the potential for on-site and sensitive identification of pyrene in the mixture.

Multiplex Sensing of Complex Mixtures by Machine Vision Analysis of TLC-SERS Images

Thin layer chromatography in tandem with surface-enhanced Raman scattering (TLC-SERS) has demonstrated tremendous potentials as a new analytical chemistry tool to detect a wide range of substances from real-world samples. However, it still faces significant challenges of multiplex sensing from complex mixtures due to the imperfect separation by TLC and the resulting interference of SERS detection. In this article, we propose a multiplex sensing method of complex mixtures by machine vision analysis of the scanning image of the TLC-SERS results. Briefly, various pure substances in solution and the complex mixture solution are separated by TLC followed by one-dimensional SERS scanning of the entire TLC plate, which generates TLC-SERS images of all target substances along the chromatography path. After that, a machine vision method is employed to extract the template images from the TLC-SERS images of pure substance solutions. Finally, we apply a feature point matching strategy based on the Winner-take-all principle, which matches the template image of each pure substance with the mixture image to confirm the existence and derive the position of each target substance in the TLC plate, respectively. Our experimental results based on the mixture solution of five different substances show that the proposed machine vision analysis is highly selective, sensitive and does not require artificial analysis of the SERS spectra. Therefore, we envision that the proposed machine vision analysis of the TLC-SERS imaging is an objective, accurate, and efficient method for multiplex sensing of trace level of target substances from complex mixtures.

Surface-enhanced Raman spectroscopy for bioanalysis and diagnosis

In recent years, bioanalytical surface-enhanced Raman spectroscopy (SERS) has blossomed into a fast-growing research area. Owing to its high sensitivity and outstanding multiplexing ability, SERS is an effective analytical technique that has excellent potential in bioanalysis and diagnosis, as demonstrated by its increasing applications in vivo. SERS allows the rapid detection of molecular species based on direct and indirect strategies. Because it benefits from the tunable surface properties of nanostructures, it finds a broad range of applications with clinical relevance, such as biological sensing, drug delivery and live cell imaging assays. Of particular interest are early-stage-cancer detection and the fast detection of pathogens. Here, we present a comprehensive survey of SERS-based assays, from basic considerations to bioanalytical applications. Our main focus is on SERS-based pathogen detection methods as point-of-care solutions for early bacterial infection detection and chronic disease diagnosis. Additionally, various promising in vivo applications of SERS are surveyed. Furthermore, we provide a brief outlook of recent endeavours and we discuss future prospects and limitations for SERS, as a reliable approach for rapid and sensitive bioanalysis and diagnosis.