Construction of dense film inside capillary wall and SERS application research

Fabrication of plasmonic Au-Ag alloy nanostars for ultrasensitive SERS detection

In this paper, Au-Ag alloy nanostar (Au-Ag NSt) substrates were prepared by liquid-liquid three-phase self-assembly method, and the influence of preparation conditions on their Raman activity of the substrate was explored. By adjusting the gold-silver ratios of alloy seeds and the concentration of AgNO3 in the growth solution, it was found that the LSPR peak can be adjusted. Under the conditions of Au1-Ag3 nanoparticles as seeds and 200 μL AgNO3 solution (2 mM) as epitaxial growth solution, the prepared Au1-Ag3 NSt substrate exhibits excellent SERS activity. The substrate can detect R6G and CV probe molecules with detection limits as low as 10-11 M and 10-10 M, respectively. Experiments have confirmed that the substrate has the characteristics of high sensitivity, good uniformity and strong stability. In addition, the substrate can detect APM molecules far below the safety standard, which further proves that the structure has excellent practical application potential as a SERS substrate.

Introduction and Development of Surface-Enhanced Raman Scattering (SERS) Substrates: A Review

Since its discovery, the phenomenon of Surface Enhanced Raman Scattering (SERS) has gradually become an important tool for analyzing the composition and structure of substances. As a trace technique that can efficiently and nondestructively detect single molecules, the application of SERS has expanded from environmental and materials science to biomedical fields. In the past decade or so, the explosive development of nanotechnology and nanomaterials has further boosted the research of SERS technology, as nanomaterial-based SERS substrates have shown good signal enhancement properties. So far, it is widely recognized that the morphology, size, composition, and stacking mode of nanomaterials have a very great influence on the strength of the substrate SERS effect. Herein, an overview of methods for the preparation of surface-enhanced Raman scattering (SERS) substrates is provided. Specifically, this review describes a variety of common SERS substrate preparation methods and explores the potential and promise of these methods for applications in chemical analysis and biomedical fields. By detailing the influence of different nanomaterials (e.g., metallic nanoparticles, nanowires, and nanostars) and their structural features on the SERS effect, this article aims to provide a comprehensive understanding of SERS substrate preparation techniques.

Fabrication of SERS composite substrates using Ag nanotriangles-modified SiO2 photonic crystal and the application of malachite green detection

A novel surface-enhanced Raman scattering (SERS) composite substrates on the basis of Ag triangular nanoplates(Ag TNPs)-modified SiO2 photonic crystals (PC) is fabricated and applied to the SERS detection of malachite green (MG). It consists of uniformly arranged Ag TNP@SiO2, a new PC. Notably, Ag TNP are uniformly aligned on the SiO2 surface, forming a three-dimensional high-density hotspot nanostructure. With the tip "hot spots" of Ag TNPs, Bragg diffraction of SiO2 and coupling enhancement between Ag TNPs and SiO2, the SERS enhancement of this composite substrates was multiplied. The effect on the SERS of Ag TNP@SiO2 composite substrate was systematically optimized by tuning Ag TNP size, size of SiO2 microspheres, coverage of Ag TNPs on SiO2 and fabrication method of Ag TNPs and PC. Moreover, the uniform of SERS composite substrates and Raman signal was dramatically increased by the method of vertical deposition. Eventually, the SERS composite substrates were employed in MG detection. Its broad detection range of 1 pM-1 μM and low limit of detection (LOD) of 0.49 pM indicated acceptable sensitivity and repeatability. This work illustrates the promising applicability in food safety analysis based on SERS composite substrates composed by Ag TNP@SiO2 with numerous SERS enhancements and excellent stability.

Pseudo-Siamese network combined with label-free Raman spectroscopy for the quantification of mixed trace amounts of antibiotics in human milk: A feasibility study

The utilization of antibiotics is prevalent among lactating mothers. Hence, the rapid determination of trace amounts of antibiotics in human milk is crucial for ensuring the healthy development of infants. In this study, we constructed a human milk system containing residual doxycycline (DXC) and/or tetracycline (TC). Machine learning models and clustering algorithms were applied to classify and predict deficient concentrations of single and mixed antibiotics via label-free SERS spectra. The experimental results demonstrate that the CNN model has a recognition accuracy of 98.85% across optimal hyperparameter combinations. Furthermore, we employed Independent Component Analysis (ICA) and the pseudo-Siamese Convolutional Neural Network (pSCNN) to quantify the ratios of individual antibiotics in mixed human milk samples. Integrating the SERS technique with machine learning algorithms shows significant potential for rapid discrimination and precise quantification of single and mixed antibiotics at deficient concentrations in human milk.

Cellulose Nanofiber Films with Gold Nanoparticles Electrostatically Adsorbed for Facile Surface-Enhanced Raman Scattering Detection

Cellulose nanofiber (CNF) holds great promise in applications such as surface-enhanced Raman scattering (SERS), catalysis, esthesia, and detection. This study aimed to build novel CNF-based SERS substrates through a facile synthetic method. Citrate-reduced gold nanoparticles (AuNPs) were adsorbed on the cationized CNF surface due to electrostatic interactions, and uniform AuNPs@(2,3-epoxypropyl trimethylammonium chloride)EPTMAC@CNF flexible SERS substrates were prepared by a simple vacuum-assisted filtration method. The probe molecule methylene blue was chosen to assess the performance of the CNF-based SERS substrate with a sensitivity up to 10-9 M, superior signal reproducibility (relative standard deviation (RSD) = 4.67%), and storage stability (more than 30 days). Tensile strength tests indicated that the CNF-based films had good mechanical properties. In addition, CNF-based substrates can easily capture and visually identify microplastics in water. These results demonstrate the potential application of the flexible, self-assembled AuNPs@EPTMAC@CNF flexible SERS substrate for prompt and sensitive detection of trace substances.