Large-Area Monolayer Films of Hexagonal Close-Packed Au@Ag Nanoparticles as Substrates for SERS-Based Quantitative Determination

Macroscopic Superlattice Membranes Self-Assembled from Gold Nanobipyramids with Precisely Tunable Tip Arrangements for SERS

A persistent challenge in utilizing Au nanocrystals for surface-enhanced Raman spectroscopy (SERS) lies in achieving controllable superstructures that maximize SERS performance. Here, a novel strategy is proposed to enhance the SERS performance by precisely adjusting the tip arrangements of Au nanobipyramids (BPs) in two-dimensional (2D) superlattices (SLs). This is achieved through ligand-exchange of Au BPs, followed by liquid-air interfacial assembly, resulting in large-area, transferrable SL membranes. The key to controlling the arrangement of Au BPs in the SLs is the regulation of the amount of free ligands added during self-assembly, which allows for the precise formation of various configurations such as tilted SLs, tip-on-tip SLs, and tip-to-tip SLs. Among these configurations, tip-on-tip SLs exhibit the highest enhancement factor for SERS, reaching an impressive value of 1.95×108, with uniform and consistent SERS signals across a large area. The experimental findings are further corroborated by simulations using the finite element method. This study establishes an efficient method for engineering the microstructure of 2D SLs composed of Au BPs, highlighting the importance of fine-tuning the tip arrangements of Au BPs to regulate SERS performance.

Ag Nanoparticles@Au Nanograting Array as a 3D Flexible and Effective Surface-Enhanced Raman Scattering Substrate

Surface-enhanced Raman scattering (SERS) is a powerful analytical technique for chemical identification, but it remains a great challenge to realize the large-scale and well-controlled fabrication of sensitive and repeatable SERS substrates. Here, we report a facile strategy to fabricate centimeter-sized periodic Au nanograting (Au-NG) decorated with well-arranged Ag nanoparticles (Ag-NPs) (denoted as Ag-NPs@Au-NG) as a three-dimensional (3D) flexible hybrid SERS substrate with high sensitivity and good reproducibility. The Au-NG patterns with periodic ridges and grooves are fabricated through nanoimprint lithography by employing a low-cost digital versatile disc (DVD) as a master mold, and the Ag-NPs are assembled by a well-controlled interface self-assembly method without any coupling agents. Multiple coupling electromagnetic field effects are created at the nanogaps between the Ag-NPs and Au-NG patterns, leading to high-density and uniform hot spots throughout the substrate. As a result, the Ag-NPs@Au-NG arrays demonstrate an ultrahigh SERS sensitivity as low as 10-13 M for rhodamine 6G with a high average enhancement factor (EF) of 1.85 × 108 and good signal reproducibility. For practical applications, toxic organic pollutants including crystal violet, thiram, and melamine have been successfully detected with high sensitivity at a low detection limit, showing a good perspective in the rapid detection of toxic organic pollutants.

"On-site" analysis of pesticide residues in complex sample matrix by plasmonic SERS nanostructure hybridized hydrogel

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) has been extensively used in biomedical and food safety detection due to its advantages of label-free, in situ and fingerprint spectrum. However, it is challenging to develop an excellent SERS substrate that possesses all three of these characteristics including sensitivity, repeatability and stability.

RESULTS

In this work, a specific sodium alginate hydrogel flexible SERS substrate encapsulated gold-silver core-shell nanoparticles (Au@Ag NPs) was developed to address the aforementioned issue. The Au@Ag NPs with SERS "hot spot" structure were evenly dispersed in the hydrogel, which achieved the direct and high efficiency detection of the pesticide residues from complex sample matrix. Taking thiram as objective, this SERS substrates exhibit high sensitivity (detection limit of approximately 1 × 10-10 mol/L), excellent stability (maintain above 78.35 % of SERS activity after 7 weeks) and outstanding repeatability (RSD in one substrate as low as 3.56 %). Furthermore, the flexible hydrogel SERS substrates can be used to analyze a variety of small molecules in real samples (juices, vegetables and fruits), without the need for a laborious pretreatment process.

SIGNIFICANCE

In light of the aforementioned benefits, the functional flexible hydrogel SERS substrates present a reliable platform for the accurate and on-site measurement of chemical contaminants from complex samples.

Lab-on-Fiber Sensors with Ag/Au Nanocap Arrays Based on the Two Deposits of Polystyrene Nanospheres

Surface-enhanced Raman spectroscopy (SERS) can boost the pristine Raman signal significantly which could be exploited for producing innovative sensing devices with advanced properties. However, the inherent complexity of SERS systems restricts their further applications in rapid detection, especially in situ detection in narrow areas. Here, we construct an efficient and flexible SERS-based Lab-on-Fiber (LOF) sensor by integrating Ag/Au nanocap arrays obtained by Ag/Au coating polystyrene nanospheres on the optical fiber face. We obtain rich "hot spots" at the nanogaps between neighboring nanocaps, and further achieve SERS performance with the assistance of laser-induced thermophoresis on the metal film that can achieve efficiency aggregation of detected molecules. We achieve a high Raman enhancement with a low detection limitation of 10-7 mol/L for the most efficient samples based on the above sensor. This sensor also exhibits good repeatability and stability under multiple detections, revealing the potential application for in situ detection based on the reflexivity of the optical fiber.

Interfacial Self-Assembly of Surfactant-Free Au Nanoparticles as a Clean Surface-Enhanced Raman Scattering Substrate for Quantitative Detection of As5+ in Combination with Convolutional Neural Networks

Surface-enhanced Raman scattering (SERS) is a highly sensitive tool in the field of environmental testing. However, the detection and accurate quantification of weakly adsorbed molecules (such as heavy metal ions) remain a challenge. Herein, we combine clean SERS substrates capable of capturing heavy metal ions with convolutional neural network (CNN) algorithm models for quantitative detection of heavy metal ions in solution. The SERS substrate consists of surfactant-free Au nanoparticles (NPs) and l-cysteine molecules. As plasmonic nanobuilt blocks, surfactant-free Au NPs without physical or chemical barriers are more accessible to target molecules. The amino and carboxyl groups in the l-cysteine molecule can chelate As5+ ions. The CNN algorithm model is applied to quantify and predict the concentration of As5+ ions in samples. The results demonstrated that this strategy allows for fast and accurate prediction of As5+ ion concentrations, and the determination coefficient between the predicted and actual values is as high as 0.991.

Silver-based SERS substrates fabricated using a 3D printed microfluidic device

The detection of harmful chemicals in the environment and for food safety is a crucial requirement. While traditional techniques such as GC-MS and HPLC provide high sensitivity, they are expensive, time-consuming, and require skilled labor. Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical tool for detecting ultralow concentrations of chemical compounds and biomolecules. We present a reproducible method for producing Ag nanoparticles that can be used to create highly sensitive SERS substrates. A microfluidic device was employed to confine the precursor reagents within the droplets, resulting in Ag nanoparticles of uniform shape and size. The study investigates the effects of various synthesis conditions on the size distribution, dispersity, and localized surface plasmon resonance wavelength of the Ag nanoparticles. To create the SERS substrate, the as-synthesized Ag nanoparticles were assembled into a monolayer on a liquid/air interface and deposited onto a porous silicon array prepared through a metal-assisted chemical etching approach. By using the developed microfluidic device, enhancement factors of the Raman signal for rhodamine B (at 10-9 M) and melamine (at 10-7 M) of 8.59 × 106 and 8.21 × 103, respectively, were obtained. The detection limits for rhodamine B and melamine were estimated to be 1.94 × 10-10 M and 2.8 × 10-8 M with relative standard deviation values of 3.4% and 4.6%, respectively. The developed SERS substrate exhibits exceptional analytical performance and has the potential to be a valuable analytical tool for monitoring environmental contaminants.

A Novel SPR Immunosensor Based on Dual Signal Amplification Strategy for Detection of SARS-CoV-2 Nucleocapsid Protein

Since the global outbreak of coronavirus disease 2019 (COVID-19), it has spread rapidly around the world. The nucleocapsid (N) protein is one of the most abundant SARS-CoV-2 proteins. Therefore, a sensitive and effective detection method for SARS-CoV-2 N protein is the focus of research. Here, we developed a surface plasmon resonance (SPR) biosensor based on the dual signal-amplification strategy of Au@Ag@Au nanoparticles (NPs) and graphene oxide (GO). Additionally, a sandwich immunoassay was utilized to sensitively and efficiently detect SARS-CoV-2 N protein. On the one hand, Au@Ag@Au NPs have a high refractive index and the capability to electromagnetically couple with the plasma waves propagating on the surface of gold film, which are harnessed for amplifying the SPR response signal. On the other hand, GO, which has the large specific surface area and the abundant oxygen-containing functional groups, could provide unique light absorption bands that can enhance plasmonic coupling to further amplify the SPR response signal. The proposed biosensor could efficiently detect SARS-CoV-2 N protein for 15 min and the detection limit for SARS-CoV-2 N protein was 0.083 ng/mL, with a linear range of 0.1 ng/mL~1000 ng/mL. This novel method can meet the analytical requirements of artificial saliva simulated samples, and the developed biosensor had a good anti-interference capability.

Machine Learning-Driven 3D Plasmonic Cavity-in-Cavity Surface-Enhanced Raman Scattering Platform with Triple Synergistic Enhancement Toward Label-Free Detection of Antibiotics in Milk

The surface-enhanced Raman scattering (SERS) technique with ultrahigh sensitivity has gained attention to meet the increasing demands for food safety analysis. The integration of machine learning and SERS facilitates the practical applicability of sensing devices. In this study, a machine learning-driven 3D plasmonic cavity-in-cavity (CIC) SERS platform is proposed for sensitive and quantitative detection of antibiotics. The platform is prepared by transferring truncated concave nanocubes (NCs) to an obconical-shaped template surface. Owing to the triple synergistic enhancement effect, the highly ordered 3D CIC arrays improve the simulated electromagnetic field intensity and experimental SERS activity, demonstrating a 33.1-fold enhancement compared to a typical system consisting of Au NCs deposited on a flat substrate. The integration of machine learning and Raman spectroscopy eliminates subjective judgments on the concentration of detectors using a single feature peak and achieves accurate identification. The machine learning-driven CIC SERS platform is capable of detecting ampicillin traces in milk with a detection limit of 0.1 ppm, facilitating quantitative analysis of different concentrations of ampicillin. Therefore, the proposed platform has potential applications in food safety monitoring, health care, and environmental sampling.