This study provides a thorough theoretical investigation into how the donor impurity’s position, geometric deformation, and an external electric field collectively influence the nonlinear optical properties of CdS/ZnSe core/shell prolate quantum dots (CSPQDs) housed within a HfO2 dielectric matrix. We employed the finite element method in COMSOL Multiphysics to solve the Schrödinger equation, utilizing the effective mass approximation (EMA). Our model incorporated the confinement potential, impurity localization, Coulomb interaction, and self-polarization energy. Subsequently, the nonlinear optical rectification (NOR) and second harmonic generation (SHG) coefficients were determined via the compact density matrix (CDM) formalism. Our findings indicate that both impurity displacement and geometric anisotropy disrupt confinement symmetry, thereby boosting NOR and SHG coefficients and inducing a redshift in resonance peaks. Furthermore, applying an external electric field intensifies these effects, presenting an effective strategy for manipulating optical nonlinearity. The key novelty of this is the introduction of a three-dimensional asymmetric CdS/ZnSe CSPQDs system with an off-center impurity, enabling a simultaneously assessment of geometric anisotropy, electric field modulation and dielectric confinement on the nonlinear optical response. These results reveal new mechanisms for tuning nonlinear optical properties through structural and electrostatic modulation, enabling the development of tunable photonic devices, including optical modulators, frequency doublers and nanophotonic sensors.
Effective management of diabetes, a chronic metabolic disorder affecting more than 530 million adults worldwide, relies greatly on precise measurement of glucose levels. In this article, a novel surface plasmon resonance (SPR) biosensor is numerically modeled and optimized for the refractometric detection of glucose in urine. The proposed sensor design, based on the Kretschmann configuration, consists of a multilayered nanostructure comprising indium tin oxide (ITO), silver (Ag), black phosphorus (BP), and antimonene over a BK-7 glass substrate. The optical properties of the proposed sensor are numerically analyzed using the transfer matrix method (TMM) and COMSOL Multiphysics. The structural parameters are then systematically optimized to enhance the overall performance of the designed sensor. The findings show that the proposed sensor configuration exhibits a notable sensitivity of 410.25°/RIU and a linear glucose detection range, with a limit of detection (LOD) of 2.44E-06, confirming strong energy confinement at the sensing interface. Therefore, the proposed sensor is a promising candidate for glucose detection due to its excellent selectivity, stability, and high sensitivity. This work highlights the potential of an analytically modeled SPR sensor and lays the groundwork for future experimental validation and its integration into portable glucose monitor devices.
Surface plasmon resonance (SPR) biosensors are recognized as powerful analytical tools offering real-time, label-free, and highly sensitive detection of biomolecular interactions, making them particularly valuable for medical diagnostics such as malaria screening. SPR arises at the metal-dielectric interface when collective oscillations of free electrons generate confined surface excitations. In this study, we present a Kretschmann-configured SPR sensor optimized for near-infrared (NIR) operation, composed of an SF10 prism, a 70-nm silver film, and a periodic graphene, MoS₂ bilayer (with 7-nm MoS₂ thickness) designed to enhance plasmonic performance and suppress optical interference. The ABCD matrix formalism was employed to compute reflectivity and to monitor refractive index (RI) variations in red blood cells (RBCs). The RI of healthy RBCs was measured as 1.399 ± 0.006 (RIU), while malaria-infected cells exhibited reduced indices of 1.395 ± 0.005 (RIU) (ring stage), 1.383 ± 0.005 (RIU) (trophozoite), and 1.373 ± 0.006 (RIU) (schizont). The optimized structure achieved sensitivities of 198.08 (Deg/RIU) and 201.52 (Deg/RIU) for RI variations of 0.005 and 0.007, respectively. Corresponding reflectivity values were 0.9530 ± 0.006 (a.u.) for healthy cells and 0.9439, 0.8946, and 0.8193 (a.u.) for the infection stages. These results demonstrate the capability of the proposed sensor to discriminate malaria infection stages through precise refractive index analysis, highlighting its strong potential for rapid, label-free, and stage-specific malaria diagnosis.
This paper proposes a novel Au/WS2/bio-recognition element (BRE) fiber-optic surface plasmon resonance (SPR) biosensor, numerically demonstrated for the high-sensitivity refractometric discrimination and sequential profiling of human blood components such as plasma, hemoglobin (Hb), white blood cells (WBCs), and red blood cells (RBCs). The sensor architecture, comprising an etched multimode fiber core sequentially coated with gold (45 nm), a two-dimensional WS₂ layer (15 nm), and a BRE layer (10 nm, n = 1.45), exploits the superior light–matter interaction and evanescent field confinement of WS₂ to outperform conventional Au-only plasmonic interfaces. The optimized sensor achieves a maximum sensitivity of 35,500 nm/RIU and figure of merit of 406.50 RIU⁻¹ at RI = 1.40. The resonance wavelength shifting from 975 nm to 1995 nm across the RI range of 1.33–1.40. The highest pairwise sensitivity (25,550 nm/RIU) and finest resolution (3.91 × 10⁻⁶ RIU) occur for the Hb–RBCs pair, while the sensor resolves the narrow 0.01 RI gap between plasma and WBCs with a resolution of 1.05 × 10⁻⁵ RIU. A peak detection algorithm enables automated analyte identification, through an IoT-based ESP32 platform simulated in Wokwi with an average processing time of 184 µs. Comparative analysis confirms superior resolution over existing sensors, highlighting an economical, IoT-enabled diagnostic pipeline for point-of-care blood-component monitoring.
Terahertz (THz) on-chip plasmonics antennas are promising candidates for next-generation integrated communication and sensing systems; however, their practical implementation remains challenging due to substrate losses, impedance mismatch, and limited radiation performance at sub-millimeter wavelengths. In this work, a physics-informed machine learning approach is proposed for the optimization of broadband THz on-chip antennas based on a GaAs–TiN heterostructure platform. The proposed framework combines full-wave electromagnetic simulations with machine learning-assisted surrogate modeling to efficiently explore the complex relationship between antenna geometry, material characteristics, and radiation performance. The integration of high-permittivity GaAs with conductive TiN provides an effective platform for controlling electromagnetic confinement and improving energy coupling at THz frequencies. By employing the developed optimization strategy, the antenna parameters are optimized to achieve enhanced impedance matching, broadband operation, and improved directional radiation within a compact footprint. Numerical results demonstrate improved impedance matching and radiation performance, with a minimum reflection coefficient of − 56.53 dB at 193.44 THz, a − 10 dB bandwidth of 8.01 THz corresponding to a fractional bandwidth of 4.14
