Search Results (506)

Tunability of the localized surface plasmon resonance (LSPR) peak position of gold and silver nanoparticle arrays embedded in a liquid crystal cell is investigated in this paper. The extinction spectra are computed using the Finite-Difference Time Domain (FDTD) simulation algorithms. Results show that the LSPR properties exhibit significant dependence on nanoparticle size and shape, array periodicity, and liquid crystal layer thickness. Notably, the LSPR wavelength saturates when the liquid crystal thickness exceeds a critical value. Furthermore, controlled rotation of the liquid crystal optical axis within distinct planes (xoy and xoz) reveals systematic variations in LSPR characteristics. Finally, we identify the key factors governing the LSPR spectral sensitivity of these noble metal nano-arrays. Full article

The design of photocatalysts capable of generating localized surface plasmon resonance (LSPR) effects represents a promising strategy for enhancing photocatalytic activity. However, the mechanistic role of plasmonic nanoparticles-induced interfacial electric fields in driving photocatalytic processes remains poorly understood. To produce a Schottky junction, varying amounts of Au nanoparticles widely utilized to broaden the light absorption were loaded onto ultrathin carbon nitride sheets (Au/UCN). The Au/UCN-20 Schottky junction exhibits exceptional photocatalytic activity, achieving a hydrogen evolution rate (14.2 mmol·g⁻¹ over a 4 h period) while maintaining robust stability through five consecutive photocatalytic cycles. The LSPR activity of Au nanoparticles are responsible for the broadened light absorption spectrum of Au/UCN nanocomposites. The interfacial electric field generated at the Au /UCN heterojunction is proposed to enhance charge-transfer efficiency through Schottky barrier penetration of photocarriers, mediated by electric field-driven carrier migration, according to surface potential and finite-difference time-domain (FDTD). These findings uncover a previously obscured photocatalytic mechanism driven by LSPR-induced interfacial electric fields, pioneering a quantum-dot-directed strategy to precisely engineer charge dynamics in advanced photocatalysts via targeted manipulation of nanoscale electric field effects. Full article

Fast, spark-free detection of hydrogen leaks is indispensable for large-scale hydrogen deployment, yet electronic sensors remain power-intensive and prone to cross-talk. Optical schemes based on surface plasmons enable remote read-out, but single-metal devices offer either weak H2 affinity or poor plasmonic quality. Here we employ full-wave finite-difference time-domain (FDTD) simulations to map the hydrogen response of nanohole arrays (NAs) that can be mass-produced by colloidal lithography. Square lattices of 200 nm holes etched into 100 nm films of Pd, Mg, Ti, V, or Zr expose an intrinsic trade-off: Pd maintains sharp extraordinary optical transmission modes but shifts by only 28 nm upon hydriding, whereas Mg undergoes a large dielectric transition that extinguishes its resonance. Vertical pairing of a hydride-forming layer with a noble metal plasmonic cap overcomes this limitation. A Mg/Pd bilayer preserves all modes and red-shifts by 94 nm, while the predicted optimum Ag (60 nm)/Mg (40 nm) stack delivers a 163 nm shift with an 83 nm linewidth, yielding a figure of merit of 1.96—surpassing the best plasmonic hydrogen sensors reported to date. Continuous-film geometry suppresses mechanical degradation, and the design rules—noble-metal plasmon generator, buried hydride layer, and thickness tuning—are general. This study charts a scalable route to remote, sub-ppm, optical hydrogen sensors compatible with a carbon-neutral energy infrastructure. Full article

Permittivity measurements of concrete materials benefit from the application of high-frequency electromagnetic waves (HF-EMWs), but they still face the problem of being aleatory and exhibit epistemic uncertainty, originating from multi-phase heterogeneous materials and the limited knowledge of HF-EMW propagation. This limitation restricts the precision of non-destructive testing. This study proposes an evidential regression deep network for conducting permittivity measurements with uncertainty quantification. This method first proposes a finite-difference time-domain (FDTD) model with multi-phase heterogeneous concrete materials to simulate HF-EMW propagation in a concrete sample or structure, obtaining the HF-EMW echo that contains aleatory uncertainties owing to the limited knowledge of wave propagation. A U-net-based model is then proposed to denoise an HF-EMW, where the difference between a couple of observed and denoised HF-EMWs characterizes aleatory uncertainty owing to measurement noise. Finally, a Dempster–Shafer theory-based (DST-based) evidential regression network is proposed to compute permittivity, incorporating the quantification of two types of uncertainty using a Gaussian random fuzzy number (GRFN): a type of fuzzy set that has the characteristics of a Gaussian fuzzy number and a Gaussian random variable. An experiment with 1500 samples indicates that the proposed method measures permittivity with a mean square error of 7.50% and a permittivity uncertainty value of 74.70% in four types of concrete materials. Additionally, the proposed method can quantify the uncertainty in permittivity measurements using a GRFN-based belief measurement interval. Full article

This study proposes the use of a low-symmetry hexagonal photonic crystal (LSHPC) incorporating Kerr-type nonlinearity to enhance self-collimation. The equifrequency contours (EFCs) of a C₂-symmetric LSHPC composed of nonlinear LiNbO₃ rods are analyzed as a function of the nonlinear refractive index. The self-collimation characteristics, transmission spectrum, group velocity dispersion (GVD), and third-order dispersion (TOD) are investigated using the Plane Wave Expansion (PWE) and Finite Difference Time Domain (FDTD) methods. The results demonstrate that increasing the nonlinear index leads to a significant flattening of the EFCs, which enhances self-collimation performance. Furthermore, symmetry-lowering perturbations improve beam confinement and enable all-angle self-collimation. These findings highlight the potential of Kerr-type nonlinear photonic crystals for integrated photonic circuits requiring precise control over light propagation. Full article

Analog computing has re-emerged as a powerful tool for solving complex problems in various domains due to its energy efficiency and inherent parallelism. This paper summarizes recent advancements in analog computing, exploring discrete time and continuous time methods for solving combinatorial optimization problems, solving partial differential equations and systems of linear equations, accelerating machine learning (ML) inference, multi-beam beamforming, signal processing, quantum simulation, and statistical inference. We highlight CMOS implementations that leverage switched-capacitor, switched-current, and radio-frequency circuits, as well as non-CMOS implementations that leverage non-volatile memory, wave physics, and stochastic processes. These advancements demonstrate high-speed, energy-efficient computations for computational electromagnetics, finite-difference time-domain (FDTD) solvers, artificial intelligence (AI) inference engines, wireless systems, and related applications. Theoretical foundations, experimental validations, and potential future applications in high-performance computing and signal processing are also discussed. Full article

To improve the efficiency and stability of the system, this paper proposes a monolithic integrated optical path design for a cavity optomechanical accelerometer based on a 250 nm top silicon thickness silicon-on-insulator (SOI) wafer instead of readout through U-shape fiber coupling. Finite Element Analysis (FEA) and Finite-Difference Time-Domain (FDTD) methods are employed to systematically investigate the performance of key optical structures, including the resonant modes and bandgap characteristics of photonic crystal (PhC) microcavities, transmission loss of strip waveguides, coupling efficiency of tapered-lensed fiber-to-waveguide end-faces, coupling characteristics between strip waveguides and PhC waveguides, and the coupling mechanism between PhC waveguides and microcavities. Simulation results demonstrate that the designed PhC microcavity achieves a quality factor (Q-factor) of 2.26 × 10⁵ at a 1550 nm wavelength while the optimized strip waveguide exhibits a low loss of merely 0.2 dB over a 5000 μm transmission length. The strip waveguide to PhC waveguide coupling achieves 92% transmittance at the resonant frequency, corresponding to a loss below 0.4 dB. The optimized edge coupling structure exhibits a transmittance of 75.8% (loss < 1.2 dB), with a 30 μm coupling length scheme (60% transmittance, ~2.2 dB loss) ultimately selected based on process feasibility trade-offs. The total optical path system loss (input to output) is 5.4 dB. The paper confirms that the PhC waveguide–microcavity evanescent coupling method can effectively excite the target cavity mode, ensuring optomechanical coupling efficiency for the accelerometer. This research provides theoretical foundations and design guidelines for the fabrication of high-precision monolithic integrated cavity optomechanical accelerometers. Full article

The increasing demand for noninvasive inspection (NII) of complex civil infrastructures requires overcoming the limitations of traditional ground-penetrating radar (GPR) systems in addressing diverse and large-scale applications. The solution proposed in this study focuses on an initial design that integrates a low-SWaP (Size, Weight, and Power) ultra-wideband (UWB) impulse radar with realistic electromagnetic modeling for deployment on unmanned aerial vehicles (UAVs). The system incorporates ultra-realistic antenna and propagation models, utilizing Finite Difference Time Domain (FDTD) solvers and multilayered media, to replicate realistic airborne sensing geometries. Verification and calibration are performed by comparing simulation outputs with laboratory measurements using varied material samples and target models. Custom signal processing algorithms are developed to extract meaningful features from complex electromagnetic environments and support anomaly detection. Additionally, machine learning (ML) techniques are trained on synthetic data to automate the identification of structural characteristics. The results demonstrate accurate agreement between simulations and measurements, as well as the potential for deploying this design in flight tests within realistic environments featuring complex electromagnetic interference. Full article

Graphene-based micro-ring modulators are promising candidates for next-generation optical interconnects, offering compact footprints, broadband operation, and CMOS compatibility. However, most demonstrations to date have relied on conventional straight bus coupling geometries, which limit design flexibility and require extremely small coupling gaps to reach critical coupling. This work presents a comprehensive comparative analysis of straight, bent, and racetrack bus geometries in graphene-on-silicon nitride (Si₃N₄) micro-ring modulators operating near 1.31 µm. Based on finite-difference time-domain simulation results, a proposed racetrack-based modulator structure demonstrates that extending the coupling region enables critical coupling at larger gaps—up to 300 nm—while preserving high modulation efficiency. With only 6–12% graphene coverage, this geometry achieves extinction ratios of up to 28 dB and supports electrical bandwidths approaching 90 GHz. Findings from this work highlight a new co-design framework for coupling geometry and graphene coverage, offering a pathway to high-speed and high-modulation-depth graphene photonic modulators suitable for scalable integration in next-generation photonic interconnects devices. Full article

We propose a high-speed all-optical logic gate design based on silicon racetrack and ring resonators patterned on a silica substrate. The architecture features racetrack resonators at both the input and output, with a central ring resonator enabling the required phase-sensitive interference for logic processing. Logic operations are achieved through the interplay of constructive and destructive interference induced by phase-shifted input beams. Using the finite-difference time-domain (FDTD) method in Lumerical software, we simulate and demonstrate seven fundamental Boolean logic functions, namely XOR, AND, OR, NOT, NOR, NAND, and XNOR, at an operating wavelength of 1.33 µm. The system supports a data rate of 47.94 Gb/s, suitable for ultrafast optical computing. The performance is quantitatively evaluated using the contrast ratio (CR) as the reference metric, with more than acceptable values of 13.09 dB (XOR), 13.84 dB (AND), 13.14 dB (OR), 13.80 dB (NOT), 14.53 dB (NOR), 13.80 dB (NAND), and 14.67 dB (XNOR), confirming strong logic level discrimination. Comparative analysis with existing optical gate designs underscores the advantages of our compact silicon-on-silica structure in terms of speed, CR performance, and integration potential. This study validates the effectiveness of racetrack–ring configurations for next-generation all-optical logic circuits. Full article

An optical power splitter (OPS) with arbitrary splitting ratios has attracted significant research interest for its broad applications in photonic integrated circuits. A series of OPSs with arbitrary splitting ratios based on silicon nitride (Si₃N₄) platforms are presented. The devices are designed with ultra-compact dimensions using three-dimensional finite-difference time-domain (3D FDTD) analysis and an inverse design algorithm. Within a 50 nm bandwidth (1525 nm to 1575 nm), we demonstrated a 1 × 2 OPS with splitting ratios of 1:1, 1:1.5, and 1:2; a 1 × 3 OPS with ratios of 1:2:1 and 2:1:2; and a 1 × 4 OPS with ratios of 1:1:1:1 and 2:1:2:1. The target splitting ratios are achieved by optimizing pixel distributions in the coupling region. The dimensions of the designed devices are 1.96 × 1.96 µm², 2.8 × 2.8 µm², and 2.8 × 4.2 µm², respectively. The designed devices achieve transmission efficiencies exceeding 90% and exhibit excellent power splitting ratios (PSRs). Full article

To meet the requirements for radar echo acquisition and feature extraction from stratified media and rough-surfaced targets, a vehicle was geometrically modelled in CAD. Monte Carlo techniques were applied to generate the rough interfaces at air–snow and snow–soil boundaries and over the vehicle surface. Soil complex permittivity was characterized with a four-component mixture model, while snow permittivity was described using a mixed-media dielectric model. The composite electromagnetic scattering from a rough-surfaced vehicle on snow-covered soil was then analyzed with the finite-difference time-domain (FDTD) method. Parametric studies examined how incident angle and frequency, vehicle orientation, vehicle surface root mean square (RMS) height, snow liquid water content and depth, and soil moisture influence the composite scattering coefficient. Results indicate that the coefficient oscillates with scattering angle, producing specular reflection lobes; it increases monotonically with larger incident angles, higher frequencies, greater vehicle RMS roughness, and higher snow liquid water content. By contrast, its dependence on snow thickness, vehicle orientation, and soil moisture is complex and shows no clear trend. Full article

This study presents an in-depth dosimetry analysis of energy assimilation from EM waves and increase in the temperature during mobile phone usage within an elevator cabin. The cellphone operates at two different frequencies (1000 MHz and 1800 MHz) and is simulated at three different talk positions vertical, tilt, and cheek. Realistic numerical models of a woman in the third trimester of pregnancy and a girl at the age of 5 years are employed. The analysis highlights the necessity of a comprehensive approach to fully grasp the complexities of EM exposure. Full article

Electromagnetic wave (EMW) absorption materials are crucial for a wide range of applications, yet most existing materials suffer from complex fabrication and narrow absorption bands, particularly under harsh environmental conditions. In this study, we introduce a broadband metamaterial absorber based on Ti₃C₂O₂ MXene, a novel two-dimensional material that uniquely combines high electrical and metallic conductivity with hydrophilicity, biocompatibility, and an extensive surface area. Through advanced finite-difference time-domain (FDTD) simulations, the proposed absorber achieves over 95% absorption from 0.3 µm to 18 µm. Additionally, other MXene variants, including Ti₃C₂F₂ and Ti₃C₂(OH)₂, demonstrate robust absorption above 85%. This absorber not only outperforms previously reported structures in terms of efficiency and spectral coverage but also opens avenues for integration into applications such as infrared sensing, energy harvesting, wearable electronics, and Internet of Things (IoT) systems. Full article

The quantitative characterization of near-surface heterogeneity using ground-penetrating radar (GPR) is an important but challenging task. The estimation of subsurface geostatistical parameters from surface-based common-offset GPR reflection data has so far relied upon a Monte-Carlo-type inversion approach. This allows for a comprehensive exploration of the parameter space and provides some measure of uncertainty with regard to the inferred results. However, the associated computational costs are inherently high. To alleviate this problem, we present an alternative deep-learning-based technique, that, once trained in a supervised context, allows us to perform the same task in a highly efficient manner. The proposed approach uses a convolutional neural network (CNN), which is trained on a vast database of autocorrelations obtained from synthetic GPR images for a comprehensive range of stochastic subsurface models. An important aspect of the training process is that the synthetic GPR data are generated using a computationally efficient approximate solution of the underlying physical problem. This strategy effectively addresses the notorious challenge of insufficient training data, which frequently impedes the application of deep-learning-based methods in applied geophysics. Tests on a wide range of realistic synthetic GPR data generated using a finite-difference time-domain (FDTD) solution of Maxwell’s equations, as well as a comparison with the results of the traditional Monte Carlo approach on a pertinent field dataset, confirm the viability of the proposed method, even in the presence of significant levels of data noise. Our results also demonstrate that typical mismatches between the dominant frequencies of the analyzed and training data can be readily alleviated through simple spectral shifting. Full article