Wafer-scale Silver Nanodendrites with Homogeneous Distribution of Gold Nanoparticles for Biomolecules Detection

Recent developments and applications of surface enhanced Raman scattering spectroscopy in safety detection of fruits and vegetables

This article reviewed the latest research progress of Surface-enhanced Raman Spectroscopy (SERS) in the security detection of fruits and vegetables in recent years, especially in three aspects: pesticide residues, microbial toxin contamination and harmful microorganism infection. The binding mechanism and application potential of SERS detection materials (including universal type and special type) and carrier materials (namely rigid and flexible materials) were discussed. Finally, the application prospect of SERS in fruit and vegetable safety detection was explored, and the problems to be solved and development trends were put forward. The poor stability and reproducibility of SERS substrates make it difficult for practical applications. It is necessary to continuously optimize SERS substrates and develop small and portable Raman spectroscopy analyzers. In the future, SERS technology is expected to play an important role in human health, food safety and economy.

Silver dendrite metasurface SERS substrates prepared by photoreduction method for perfluorooctanoic acid detection

Perfluorooctanoic acid (PFOA), a novel organic pollutant, has been shown to be toxic, persistent, bioaccumulative, long-range transportable, and globally prevalent. This article is based on surface enhanced Raman scattering (SERS) spectroscopy analysis technology. The monolayer of SiO2 was prepared by chemical reduction etching self-assembly method and silver dendrites were grown on it, thus forming the SERS substrate with silver dendrite Metasurface structure with Raman detection enhancement factor up to 2.32 × 105. The prepared silver dendrite Metasurface SERS substrate was applied to the qualitative and quantitative detection of PFOA, with a quantitative detection limit of 15.89 ppb. The results of this paper provide a new, simple, and quick method for the detection of PFOA in the environment.

A novel colorimetric assay for sensitive detection of kanamycin based on the aptamer-regulated peroxidase-mimicking activity of Co3O4 nanoparticles

Kanamycin is used widely in livestock farming due to its antimicrobial properties and low cost, but has led to antibiotic residues in food, which can damage human health. Therefore, there is an urgent need for convenient technology that can be used to detect kanamycin rapidly. We found that Co₃O₄ nanoparticles (NPs) possessed peroxidase-like activity that catalyzed the oxidation of 3,3',5,5'-tetramethylbenzidine to change color. Interestingly, a target-specific aptamer could regulate the catalytic activity of Co₃O₄ NPs and inhibit this effect through aptamer-target binding. On the basis of a colorimetric assay combined with an aptamer-regulatory mechanism, the linear range for quantitative detection of kanamycin was 0.1-30 μM, the minimum limit of detection was 44.2 nM, and the total time needed for detection was 55 min. Moreover, this "aptasensor" displayed excellent selectivity and could be applied to detect KAN in milk samples. Our sensor might have promising applications for kanamycin detection in animal husbandry and agricultural products.

Protocol for designing AuNP-capped Ag dendrites as surface-enhanced Raman scattering sensors for trace molecular detection

Surface-enhanced Raman spectroscopy (SERS) is a label-free, non-destructive technique for rapid identification of molecules with the interest of public safety and forensics. In the current work, we present a detailed protocol for designing a SERS-active substrate comprising Au-nanoparticles-decorated Ag nano-dendrites for the trace detection of explosives, biomolecules, dye, and pesticides. We elaborate the procedure for studying near-field enhancements in plasmonic structures. This protocol also addresses some of the challenges faced in SERS experiments and the potential solutions to overcome them. For complete details on the use and execution of this protocol, please refer to Vendamani et al. (2022).1.

Deep learning approach to overcome signal fluctuations in SERS for efficient On-Site trace explosives detection

Surface-enhanced Raman spectroscopy (SERS) is an improved Raman spectroscopy technique to identify the analyte under study uniquely. At the laboratory scale, SERS has realised a huge potential to detect trace analytes with promising applications across multiple disciplines. However, onsite detection with SERS is still limited, given the unwanted glitches of signal reliability and blinking. SERS has inherent signal fluctuations due to multiple factors such as analyte adsorption, inhomogeneous distribution of hotspots, molecule orientation etc. making it a stochastic process. Given these signal fluctuations, validating a signal as a representation of the analyte often relies on an expert's knowledge. Here we present a neural network-aided SERS model (NNAS) without expert interference to efficiently identify reliable SERS spectra of trace explosives (tetryl and picric acid) and a dye molecule (crystal violet). The model uses the signal-to-noise ratio approach to label the spectra as representative (RS) and non-representative (NRS), eliminating the reliability of the expert. Further, experimental conditions were systematically varied to simulate general variations in SERS instrumentation, and a deep-learning model was trained. The model has been validated with a validation set followed by out-of-sample testing with an accuracy of 98% for all the analytes. We believe this model can efficiently bridge the gap between laboratory and on-site detection using SERS.

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.