Detecting trace amounts of explosives to ensure personal safety is important, and this is possible by using laser-based spectroscopy techniques. We performed surface-enhanced Raman scattering (SERS) using plasmonic nanogap substrates for the solution phase detection of some nitro-based compounds, taking advantage of the hot spot at the nanogap. An excitation wavelength of 785 nm with an incident power of as low as ≈0.1 mW was used to excite the nanogap substrates. Since both RDX and PETN cannot be dissolved in water, acetone was used as a solvent. TNT was dissolved in water as well as in hexane. The main SERS peaks of TNT, RDX, and PETN were clearly observed down to the order of picomolar concentration. The variations in SERS spectra observed from different explosives can be useful in distinguishing and identifying different nitro-based compounds. This result indicates that our nanogap substrates offer an effective approach for explosives identification.

Green tea is consumed worldwide for its health-promoting properties, but it remains vulnerable to microplastic contamination during packaging and processing. Microplastics such as polystyrene (PS) and polyethylene terephthalate (PET) pose potential risks to human health and food safety, underscoring the need for effective detection methods. In this study, Surface-Enhanced Raman Scattering (SERS) using gold nanoparticle substrates was applied to detect and classify PS and PET contamination in four green tea powder varieties across the 400–1650 cm⁻¹ spectral range. To classify the samples, we compared two chemometric techniques—Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM)—with a deep learning approach namely one-dimensional convolutional neural network (1D-CNN). Using optimized preprocessing, PLS-DA achieved perfect classification accuracy (100 %) for all tea varieties except Ryokucha. SVM also showed strong performance but with slightly reduced accuracies of 83.89 % for Matcha, 100 % for Jasmine, and 93.24 % for Sencha. Although the 1D-CNN model achieved higher validation accuracies—93.52 % (Matcha), 99.91 % (Jasmine), and 94.26 % (Sencha)—than SVM, its performance was still slightly lower compared to the PLS-DA models. Additionally, for unknown samples from distinct green tea varieties, the SERS–PLS-DA approach again delivered the highest validation accuracies of 100 % (Matcha), 99.80 % (Jasmine), and 96.46 % (Sencha), further demonstrating the strong potential of this technique. These findings confirm that SERS with gold nanoparticle substrates, especially when integrated with PLS-DA, provides a highly sensitive, non-destructive, and reliable platform for the rapid detection and classification of microplastic contamination in green tea.

Plasmonic molecules, which are geometrically well-defined plasmonic metal nanoparticle clusters, have attracted significant attention due to their enhancement of light–matter interactions owing to a stronger electric field enhancement than that by single particles. High-resolution lithography techniques provide precise positioning of plasmonic nanoparticles, but their fabrication costs are excessively high. In this study, we propose a lithography-free, self-assembly fabrication method, termed the dual-dewetting process, which allows the control of the size and density of gold nanoparticles. This process involves depositing a gold thin film on a substrate and inducing dewetting through thermal annealing, followed by a second deposition and annealing. The method achieves a uniform distribution of particle size and density, along with increased particle density, across a 6-inch wafer. The superiority of the method is confirmed by a 30-fold increase in the signal intensity of surface-enhanced Raman scattering following the additional dewetting with an 8 nm film, compared to single dewetting alone. Our findings indicate that the dual-dewetting method provides a simple and efficient approach to enable a variety of plasmonic applications through efficient plasmonic molecule large-area fabrication.

This study reported an application of Au nanogap substrates for surface-enhanced Raman scattering (SERS) measurements to quantitatively analyze melamine and its derivative products at trace levels in pet liquid food (milk) combined with a waveband selection approach, namely variable importance in projection (VIP). Six different concentrations of melamine, cyanuric acid, and melamine combined with cyanuric acid were created, and SERS spectra were acquired from 550 to 1620cm⁻¹. Detection was possible up to 200 pM for melamine-contaminated samples, and 400 pM concentration detection for other two groups. The VIP-PLSR models obtained correlation coefficient (R²) values of 0.997, 0.985, and 0.981, with root mean square error of prediction (RMSEP) values of 18.492 pM, 19.777 pM, and 15.124 pM for prediction datasets. Additionally, partial least square discriminant analysis (PLS-DA) was used to classify both pure and different concentrations of spiked samples. The results showed that the maximum classification accuracy for melamine was 100%, for cyanuric acid it was 96%, and for melamine coupled with cyanuric acid it was 95%. The results obtained clearly demonstrated that the Au nanogap substrate offers low-concentration, rapid, and efficient detection of hazardous additive chemicals in pet consuming liquid food.

Molybdenum sulfide–oxide (MoS₂, MS) emerges as the prime electrocatalyst candidate demonstrating hydrogen evolution reaction (HER) activity comparable to platinum (Pt). This study presents a facile electrochemical approach for fabricating a hybrid copper (Cu)/MoS₂ (CMS) nanostructure thin-film electrocatalyst directly onto nickel foam (NF) without a binder or template. The synthesized CMS nanostructures were characterized utilizing energy-dispersive X-ray spectroscopy (EDS), scanning electron microscopy (SEM), X-ray diffraction (XRD), and electrochemical methods. The XRD result revealed that the Cu metal coating on MS results in the creation of an extremely crystalline CMS nanostructure with a well-defined interface. The hybrid nanostructures demonstrated higher hydrogen production, attributed to the synergistic interplay of morphology and electron distribution at the interface. The nanostructures displayed a significantly low overpotential of −149 mV at 10 mA cm⁻² and a Tafel slope of 117 mV dec⁻¹, indicating enhanced catalytic activity compared to pristine MoS₂.This research underscores the significant enhancement of the HER performance and conductivity achieved by CMS, showcasing its potential applications in renewable energy.