This study investigates the optical response of silica-core gold-shell ( SiO 2 @ Au ) core–shell nanoparticles (CSNPs) using a classical electromagnetic approach based on the quasi-static approximation combined with the Lorentz–Drude model for the frequency-dependent dielectric response of gold. The influence of geometrical parameters, including core radius and shell thickness, together with the surrounding dielectric environment, on localized surface plasmon resonance (LSPR), optical cross-sections, and electromagnetic field enhancement is systematically analyzed. The results demonstrate that the plasmonic response is strongly controlled by the metallic shell volume fraction and dielectric screening effects. Increasing the gold-shell contribution modifies the induced dipole response and alters the balance between absorption and scattering processes, while increasing the host dielectric constant produces a systematic redshift of the resonance energy. Furthermore, optimized geometrical configurations provide strong near-field localization due to enhanced electromagnetic confinement. These results provide physical insight into geometry- and environment-dependent control of SiO 2 @ Au plasmonic nanoparticles for potential applications in optical sensing, photothermal conversion, and nanophotonic devices.
The growing concern over antibiotic residues in food products has accelerated the development of sensitive and reliable analytical technologies capable of supporting food safety surveillance. Among emerging nanomaterial-based approaches, CdTe quantum dots have attracted considerable attention due to their tunable optical properties, high fluorescence efficiency, and compatibility with ratiometric sensing strategies. This review provides a comprehensive analysis of the scientific foundations and technological evolution of ratiometric CdTe quantum-dot platforms for food antibiotic monitoring. For the first time, the field is examined through an integrated framework that connects the intrinsic photophysical characteristics of CdTe quantum dots with the mechanisms governing ratiometric signal generation and their translation into practical sensing architectures. Particular emphasis is placed on the relationships between electronic structure, excited-state behavior, optical tunability, self-referenced signal transduction, and analytical performance. Recent advances in hybrid nanocomposites, doped quantum dots, dual-emission probes, electrochemiluminescent systems, and multimodal sensing platforms are systematically evaluated in the context of antibiotic detection in complex food matrices. Emerging trends toward multiplex analysis, information-rich sensing, intelligent surveillance, and data-driven interpretation are further discussed. Finally, major challenges related to standardization, real-world applicability, sustainability, and regulatory acceptance are highlighted, together with future directions that may shape next-generation food antibiotic monitoring technologies.
This study investigates the optical response of silica-core gold-shell (SiO2@Au) core–shell nanoparticles (CSNPs) using a classical electromagnetic approach based on the quasi-static approximation combined with the Lorentz–Drude model for the frequency-dependent dielectric response of gold. The influence of geometrical parameters, including core radius and shell thickness, together with the surrounding dielectric environment, on localized surface plasmon resonance (LSPR), optical cross-sections, and electromagnetic field enhancement is systematically analyzed. The results demonstrate that the plasmonic response is strongly controlled by the metallic shell volume fraction and dielectric screening effects. Increasing the gold-shell contribution modifies the induced dipole response and alters the balance between absorption and scattering processes, while increasing the host dielectric constant produces a systematic redshift of the resonance energy. Furthermore, optimized geometrical configurations provide strong near-field localization due to enhanced electromagnetic confinement. These results provide physical insight into geometry- and environment-dependent control of SiO2@Au plasmonic nanoparticles for potential applications in optical sensing, photothermal conversion, and nanophotonic devices.
We investigate a non-volatile tuning architecture for silicon microring resonators based on proximal, rather than directly clad, Ge2Sb2Te5 (GST) phase-change disks separated from the ring by a nanoscale gap. The exact novelty of the approach is the use of phase-dependent, edge-localized hybridization with nearby GST nanodisks to perturb the microring resonance through evanescent coupling, while preserving a physically separated geometry that provides an independent overlap-control parameter. Finite-difference time-domain simulations show that GST radius, disk–ring gap, and phase state jointly control the resonance wavelength, extinction, and linewidth. In particular, larger crystalline disks produce the strongest spectral separation, and a three-disk configuration produces four representative discrete optical states under the assumption of ideal, independent phase assignment, while retaining zero static power to hold a programmed state. The loaded quality factors extracted from the radius sweep are approximately 8.3×102–1.01×103, while the gap sweep reaches approximately 1.47×103 as the disk is evanescently decoupled from the ring. These values confirm that the observed features are resonances of the coupled ring–disk system. Because an otherwise identical directly clad control ring was not simulated, these results establish a tunable loss–shift trade-off within the proximal geometry but do not establish lower loss than direct cladding. Electrothermal switching, programming energy, and inter-disk thermal crosstalk are outside the present optical model. The most practical use case of this architecture is post-fabrication trimming and reconfiguration of microring filters that have been detuned by process variation.
Ethanol is blended in biodiesel and diesel due to decreasing emissions and increasing performance characteristics of diesel engines. To overcome the drawbacks of laboratory methods such as difficulty and complexity, time duration, and cost, the goal of the present research was to develop a modern system to estimate ethanol percentage in biodiesel-ethanol-diesel fuel blends. Hyperspectral imaging was applied to record the spectral reflection of different fuel blends and they were analyzed using image processing technique. The blends included 0, 1, 2, 3, and 5% ethanol. After image preprocessing, wavelength selection, and feature extraction, artificial neural network and support vector machine methods were used for classification of the samples. Also, efficient features were selected and then classified. Artificial neural network method had higher correct classification rate. The results showed that feature selection step cause to increase classification accuracy.
