Finite-difference Time-domain Analysis Research Articles

Catalytic Dual-Mode Immunotherapy: Anisotropic AuPt Heterostructure Decorated with Starry Pt Nanoclusters for Robust Cancer Photometalloimmunotherapy.

To overcome current limitations in photoimmunotherapy, such as insufficient tumor antigen generation and a subdued immune response, a novel photo-/metallo dual-mode immunotherapeutic agent (PMIA) is introduced for potent near-infrared (NIR) light-triggered cancer therapy. PMIA features a dumbbell-like AuPt heterostructure decorated with starry Pt nanoclusters, meticulously engineered for enhancing plasmonic catalysis through multi-dimensional regulation of Pt growth on Au nanorods. Under NIR laser exposure, end-tipped Pt nanoclusters induce efficient electron-hole spatial separation along the longitudinal axis, resulting in radial and axial electron distribution polarization, conferring unique anisotropic properties to PMIA. Additionally, starry Pt nanoclusters on the sides of Au nanorods augment the local electron enrichment field. Validated through finite-difference time-domain analysis and Raman scattering, this configuration fosters local electron enrichment, facilitating robust reactive oxygen species generation for potent photoimmunotherapy. Moreover, Pt nanoclusters facilitate Pt2+ ion release, instigating intranuclear DNA damage and inducing synergistic immunogenic cell death (ICD) for metalloimmunotherapy. Consequently, PMIA elicits abundant danger-associated molecular patterns, promotes T cell infiltration, and triggers systemic immune responses, effectively treating primary and distant tumors, inhibiting metastasis in vivo. This study unveils a pioneering dual-mode ICD amplification strategy driven by NIR light, synergistically integrating photoimmunotherapy and metalloimmunotherapy, culminating in potent cancer photometalloimmunotherapy.

Detection of surface enhanced Raman scattering active hotspot using near field scanning optical microscopy

Hotspots are high-intensity electromagnetic zones that form, for example, at the interstitials of plasmonic nanoaggregates, resulting in a considerable rise in the enhancement factor. However, it is inevitable to achieve specific nanometric geometry as well as a suitable technique to capture the details of hotspots. We report near-field surface-enhanced Raman scattering (SERS) spectroscopy of a well-defined gold nanoaggregate of a few nanoparticles adsorbed with a small number of target analytes. A spectrally and spatially resolved SERS measurement setup using an aperture near-field scanning optical microscope (a-NSOM) facilitated the direct observation of localized electromagnetic (EM) fields at the interstitials through SERS. Correlated optical image and corresponding nanometric geometry were captured through the home-built a-NSOM setup. Near-field SERS spectra were recorded at different sites of interest. It was evident that the interstitial positioned at the center of the tetramer provided the most intense Raman scattering, implying the possibility of a SERS-active hotspot therein. SERS bands of the spectrum of the Raman-active dye Rhodamine 6G recorded at the same hotspot coincided well with those reported so far. It was noteworthy that most of the SERS bands in such scenery got enhanced. Such direct observation with high spatial resolution is indispensable to understanding the origin of localized EM fields at “hotspots” and the EM enhancement factor in the SERS process. A finite-difference time-domain (FDTD) analysis was carried out to validate the results.

FDTD Analysis of Electric Field and Step Voltage in a Clay‐Wall House Struck by Lightning

The density of lightning flashes is quite high in Rwanda and its neighboring countries in Africa. In rural areas of these countries, there are still many clay‐wall houses. Sometimes people in clay‐wall houses are fatally injured by direct lightning strikes to the houses. In this paper, the electric field in a clay‐wall house and the step voltage on the floor, associated with a direct lightning strike to the house, are analyzed using the finite‐difference time‐domain (FDTD) method. Also, the electric field and the step voltage in the same clay‐wall house but with a horizontal air termination conductor connected to buried vertical bar earth conductors or a square ring earth conductor via bare or insulated down conductors are analyzed using the FDTD method. It has been shown that installing these protection systems reduces the inside electric field and the step voltage on the floor particularly if insulated down conductors are employed. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Hollow Microcavity Electrode for Enhancing Light Extraction.

Luminous efficiency is a pivotal factor for assessing the performance of optoelectronic devices, wherein light loss caused by diverse factors is harvested and converted into the radiative mode. In this study, we demonstrate a nanoscale vacuum photonic crystal layer (nVPCL) for light extraction enhancement. A corrugated semi-transparent electrode incorporating a periodic hollow-structure array was designed through a simulation that utilizes finite-difference time-domain computational analysis. The corrugated profile, stemming from the periodic hollow structure, was fabricated using laser interference lithography, which allows the precise engineering of various geometrical parameters by controlling the process conditions. The semi-transparent electrode consisted of a 15 nm thick Ag film, which acted as the exit mirror and induced microcavity resonance. When applied to a conventional green organic light-emitting diode (OLED) structure, the optimized nVPCL-integrated device demonstrated a 21.5% enhancement in external quantum efficiency compared to the reference device. Further, the full width at half maximum exhibited a 27.5% reduction compared to that of the reference device, demonstrating improved color purity. This study presents a novel approach by applying a hybrid thin film electrode design to optoelectronic devices to enhance optical efficiency and color purity.

A High-Efficiency Wideband Grating Coupler Based on Si3N4 and a Silicon-on-Insulator Heterogeneous Integration Platform.

To fully utilize the advantages of Si3N4 and Silicon-On-Insulator to achieve a high-efficiency wideband grating coupler, we propose and numerically demonstrate a grating coupler based on Si3N4 and a Silicon-On-Insulator heterogeneous integration platform. A two-dimensional model of the coupler was established and a comprehensive finite difference time domain analysis was conducted. Focusing on coupling efficiency as a primary metric, we examined the impact of factors such as grating period, filling factor, etching depth, and the thicknesses of the SiO2 upper cladding, Si3N4, silicon waveguide, and SiO2 buried oxide layers. The calculations yielded an optimized grating coupler with a coupling efficiency of 81.8% (-0.87 dB) at 1550 nm and a 1-dB bandwidth of 540 nm. The grating can be obtained through a single etching step with a low fabrication complexity. Furthermore, the fabrication tolerances of the grating period and etching depth were studied systematically, and the results indicated a high fabrication tolerance. These findings can offer theoretical and parameter guidance for the design and optimization of high-efficiency and broad-bandwidth grating couplers.

Advances in machine learning optimization for classical and quantum photonics

The development and optimization of photonic devices and various other nanostructure electromagnetic devices present a computationally intensive task. Much optimization relies on finite-difference time-domain or finite element analysis simulations, which can become very computationally demanding for finely detailed structures and dramatically reduce the available optimization space. In recent years, various inverse design machine learning (ML) techniques have been successfully applied to realize previously unexplored optimization spaces for photonic and quantum photonic devices. In this review, recent results using conventional optimization methods, such as the adjoint method and particle swarm, are examined along with ML optimization using convolutional neural networks, Bayesian optimizations with deep learning, and reinforcement learning in the context of new applications to photonics and quantum photonics.

Mask correction method for surface plasmon lithography.

Surface plasmon lithography (SPL) has emerged as an innovative approach to nano-fabrication, offering an alternative to traditional patterning methods. To enhance its pattern fidelity in manufacturing, it is essential to incorporate mask correction to reduce critical dimension (CD) errors between the intended target features and the photoresist image. Traditionally, the aerial image of SPL has been modeled and simulated using methods such as finite difference time domain (FDTD) or rigorous coupled wave analysis (RCWA). These models have allowed us to obtain aerial images of the mask patterns. However, relying solely on the aerial image proves insufficient for meeting the rigorous manufacturing standards for mask correction. In our research, we propose a comprehensive model that combines the optical model, employing the FDTD method, and the resist model, tailored to the specific surface plasmon lithography process. Test patterns were meticulously designed with a target CD of 130nm, and the model was applied to simulate these test patterns, producing the after-development image (ADI) under predefined process conditions. Following a thorough analysis and data processing of the test patterns and ADI data, we established rule tables for the correction of both 1D line patterns and line end patterns. The simulation results unequivocally demonstrate the improved CD error performance achieved by the post-corrected patterns.

One-step fabrication of flexible polyamide@Ag-dodecanethiol membranes for highly sensitive SERS detection of thiram

The surface-enhanced Raman scattering (SERS) is an effective spectral technology based on Raman scattering, but in practice, the commonly used SERS substrates suffer from low sensitivity and poor stability. In order to overcome these limitations, the SERS substrates were prepared from hydrophobic modification of dodecanethiol (C12) coupled with a flexible substrate, which was then used for pesticides detection in water. A flexible PA@Ag-C12 substrate with surface functionalization has been obtained. This work aims to investigate the self-assembly of Ag NPs modified with C12 onto polyamide (PA) membranes. Initially, transmission electron microscopy and scanning electron microscopy were used to analyze the substrate’s morphology. Then with the help of an energy-dispersive spectrometer, sulfur content of C12-modified Ag NPs was analyzed. In order to determine the hydrophobicity of the modified Ag NPs, the contact angle was used. The results indicate that the gap between Ag NPs on PA membrane can be effectively controlled in order to prevent Ag NPs from aggregating. Furthermore, the finite-difference time-domain analysis indicated that the PA@Ag-C12 substrate exhibited a stronger electromagnetic enhancement effect than the PA@Ag substrate. By reducing NPs gaps on the PA membrane, the number of ‘hot spots’ increased, and the SERS performance of the substrate was improved as a result. According to the results of this study, this method can greatly reduce the manufacturing costs and time costs of the SERS substrate while maintaining the original uniformity. The SERS performance of PA@Ag-C12 was found to be three orders of magnitude better than that of PA@Ag direct self-assembled substrate, and the detection limit for Rhodamine 6G (R6G) was approximately 8.47 × 10−14 M. On the basis of the PA@Ag-C12 substrate, thiram is detectable at a detection limit of 5.88 × 10−11 M with a high degree of sensitivity and repeatability.

A novel core-shell heterostructure of zinc oxide/metal-organic frameworks anchored silver nanoparticles for enhanced SERS and photocatalytic performance

Here, a novel well-ordered ZnO rod-flower/zeolitic imidazolate framework-M (ZIF-M, using 5-mercapto-1-methyltetrazole as ligand) core-shell heterostructure (Al/ZnO/ZIF-M, AZZ*) is induced by aluminum (Al) sheet. Subsequently, Ag nanoparticles (AgNPs) are tightly attached to the ZIF-M layer in AZZ* through the strong Ag-N and Ag-S bonds, and a portable bifunctional substrate of Al/ZnO/ZIF-M/Ag (AZZ*Ag) is constructed. The finite difference time domain (FDTD) analysis shows that the AZZ*Ag substrate generates the stronger electromagnetic field intensity than the AZAg. Also, there is a strong synergistic effect among Al, ZnO, ZIF-M and Ag which can effectively facilitate the electron transfer. Importantly, the AZZ*Ag substrate exhibits outstanding SERS response with high sensitivity and selectivity for crystal violet (CV) molecules. The low detection limit (LOD) of CV molecule is 2.81 × 10−11 mol·L−1 with high analysis enhancement factor (AEF) of 1.93 × 106. In addition, AZZ*Ag substrate shows good photocatalytic performance for CV molecules with high degradation efficiency of 97.35% within 105 min, which is 4.72 times that of AZAg. Importantly, the SERS and photocatalytic performance of AZZ*Ag are 4.4 times and 3.09 times those of the Al/ZnO/ZIF-8/Ag (AZZ#Ag, using 2-methylimidazole as ligand), respectively. Moreover, the AZZ*Ag substrate displays excellent stability, good reproducibility, recyclability and uniformity. Thus, the designed portable bifunctional composite exhibits great potential for SERS detection and photocatalytic degradation of other pollutants in environmental wastewater.

Unified GSTC-FDTD Algorithm for the Efficient Electromagnetic Analysis of 2D Dispersive Materials

The finite-difference time-domain (FDTD) method has been widely used for the electromagnetic wave analysis of complex media. Conventional FDTD analyses of very thin two-dimensional (2D) dispersive materials require overwhelming computing resources because they should use very refined FDTD spatial grids. In this work, we propose a computationally efficient and unified FDTD formulation for 2D dispersive materials based on a combination of the generalized sheet transition condition (GSTC) and the modified Lorentz dispersion model. The proposed FDTD formulation can lead to a significant improvement in computational efficiency compared to the conventional FDTD method, while maintaining high accuracy. Numerical examples validate the improved computational efficiency of the proposed FDTD formulation.

Molecularly imprinted polymers-isolated AuNP-enhanced CdTe QD fluorescence sensor for selective and sensitive oxytetracycline detection in real water samples

A molecularly imprinted polymers (MIPs)-isolated AuNP-enhanced fluorescence sensor, AuNP@MIPs-CdTe QDs, was developed for highly sensitive and selective detection of oxytetracycline (OTC) in aqueous medium. The developed sensor combined the advantages of strong fluorescence signal of metal-enhanced fluorescence (MEF), high selectivity of MIPs, and stability of CdTe QDs. The MIPs shell with specific recognition served as an isolation layer to adjust the distance between AuNP and CdTe QDs to optimize the MEF system. The sensor demonstrated the detection limit as low as 5.22 nM (2.40 μg/L) for a concentration range of 0.1–3.0 μM OTC and good recovery rates of 96.0–103.0% in real water samples. In addition, high specificity recognition for OTC over its analogs was achieved with an imprinting factor of 6.10. Molecular dynamics (MD) simulation was utilized to simulate the polymerization process of MIPs and revealed H-bond formation as the mainly binding sites of APTES and OTC, and finite-difference time-domain (FDTD) analysis was employed to obtain the distribution of electromagnetic field (EM) for AuNP@MIPs-CdTe QDs. The experimental results combined with theoretical analyses not only provided a novel MIP-isolated MEF sensor with excellent detection performance for OTC but also established a theoretical basis for the development of a new generation of sensors.

Plasmonic enhanced ultra-thin solar cell: A combined approach using fractal and nano-antenna structure to maximize absorption

In this study, a combined structure is proposed to develop ultra-thin silicon solar cells. This integrated structure consists of silver fractal-like nano-particles and leaky wave nanoantennas. The nano-cuboid pattern embedded inside anti-reflective coating benefits from different optical modes, such as surface plasmons and cavity modes, which trap more light and amplify the electric field in the upper region of the absorber layer. On the bottom side, the hybrid plasmonic mode in the structure of the optical nanoantenna makes it possible to focus and direct the incoming light on the bottom of the absorber layer and increase the optical pathlengths in the ultra-thin film solar cell. The nanoantenna behavior and three-dimensional finite-difference time-domain analysis show that photon absorptions improve significantly at long wavelength lightwaves through this proposed combined structure. The short circuit current enhancement of the solar cell under 1 sun standard illumination is obtained by a factor of 1.94 and 1.80 for TM and TE polarization of incident light, respectively. Due to the acceptable results for different incident angles and polarizations, ultra-thin thickness, and nano-cuboids synthesis feasibility, our structure has the potential to be applied in the design of miniaturized photovoltaic devices.

Electronically defective tellurium-doped TiO2 catalysts for enhanced photoelectrochemical water splitting

Density functional theory (DFT) was employed to explore the effect of Tellurium substitutional (TeTi) doping in titanium dioxide (TiO2). The incorporation of Te was found to be favorable under O-rich conditions. Te doping resulted in band gap narrowing with the formation of mid-gap states, maintaining high dielectric constant, and reducing the birefringence of TiO2 in the UV region. The combination of TeTi with native oxygen vacancies (Ov) and hydrogen interstitials (Hi) was also studied. Band-gap narrowing was found to be prevalent in all structures, revealing their potential as good catalysts for photoelectrochemical water-splitting. The finite difference time domain (FDTD) analysis of 150-nm particle showed increased absorption cross-section over a wide band (up to ∼700 nm), better than that of Silicon at most wavelengths, and surpassing the performance of Hi without TeTi. Finally, the catalytic activity of the materials was investigated by studying the water-splitting intermediate reactions. In case of oxygen evolution reaction (OER), the TeTi + Ov material showed the best catalytic activity followed by the TeTi material. Whereas for the hydrogen evolution reaction (HER), the TeTi + Hi material was the best performing followed by the TeTi material, where the ΔGH was found to be −0.04 eV, close to the ideal 0 value and also has the closest d band center position to fermi level.

Strongly enhanced sensitivities of CMOS compatible plasmonic titanium nitride nanohole arrays for refractive index sensing under oblique incidence.

Titanium nitride (TiN) is a complementary metal-oxide-semiconductor (CMOS) compatible material with large potential for the fabrication of plasmonic structures suited for device integration. However, the comparatively large optical losses can be detrimental for application. This work reports a CMOS compatible TiN nanohole array (NHA) on top of a multilayer stack for potential use in integrated refractive index sensing with high sensitivities at wavelengths between 800 and 1500 nm. The stack, consisting of the TiN NHA on a silicon dioxide (SiO2) layer with Si as substrate (TiN NHA/SiO2/Si), is prepared using an industrial CMOS compatible process. The TiN NHA/SiO2/Si shows Fano resonances in reflectance spectra under oblique excitation, which are well reproduced by simulation using both finite difference time domain (FDTD) and rigorous coupled-wave analysis (RCWA) methods. The sensitivities derived from spectroscopic characterizations increase with the increasing incident angle and match well with the simulated sensitivities. Our systematic simulation-based investigation of the sensitivity of the TiN NHA/SiO2/Si stack under varied conditions reveals that very large sensitivities up to 2305 nm per refractive index unit (nm RIU-1) are predicted when the refractive index of superstrate is similar to that of the SiO2 layer. We analyze in detail how the interplay between plasmonic and photonic resonances such as surface plasmon polaritons (SPPs), localized surface plasmon resonances (LSPRs), Rayleigh Anomalies (RAs), and photonic microcavity modes (Fabry-Pérot resonances) contributes to this result. This work not only reveals the tunability of TiN nanostructures for plasmonic applications but also paves the way to explore efficient devices for sensing in broad conditions.

Graded-Index Active Layer for Efficiency Enhancement in Polymer Solar Cell

In this paper, narrow-bandgap polymer acceptors combining a benzotriazole (BTz)-core fused-ring segment, named the PZT series, were used with a high-absorption-efficiency polymer (PBDB) compound with branched 2-butyl octyl, linear n-octyl, and methyl to be utilized as a graded-index (GI) active layer of the polymer solar cells (PSCs) to increase the photocurrent and enhance solar efficiency compared to the existing PBDB-T:PZT and PBDB-T:PZT-γ. In addition, a two-dimensional photonic crystal (2D-PhC) structure was utilized as a light-trapping anti-reflection coating (ARC) thin film based on indium tin oxide (ITO) to reduce incident light reflection and enhance its absorption. The dimensions of the cell layers were optimized to achieve the maximum power-conversion efficiency (PCE). Furthermore, the design and simulations were conducted from a 300 nm to 1200 nm wavelength range using a finite difference time-domain (FDTD) analysis. One of the most important results expected from the study was the design of a nano solar cell at (64 µm)2 with a PCE of 25.1%, a short-circuit current density (JSC) of 27.74 mA/cm2, and an open-circuit voltage (VOC) of 0.986 V.

Penrose tiling-inspired graphene-covered multiband terahertz metamaterial absorbers.

In this work, we propose two different graphene-covered nanostructured metamaterial absorbers inspired by Penrose tiling. These absorbers allow spectrally tunable absorption within the terahertz spectrum corresponding to 0.2-20 THz. We have conducted finite-difference time-domain analyses to determine the tunability of these metamaterial absorbers. The proposed structures, Penrose models 1 and 2, perform differently from each other due to their design characteristics. Penrose model 2 reaches a perfect absorption at 8.58 THz. In addition, the relative absorption bandwidth calculated at full-wave at half-maximum in Penrose model 2 varies between 5.2% and 9.4%, which characterizes the metamaterial absorber as a wideband absorber. Also, we can observe that as we increase the Fermi level of graphene from 0.1 to 1 eV, the absorption bandwidth and relative absorption bandwidth both increase. Our findings show the high tunability of both models through varying graphene's Fermi level, the graphene's thickness, the substrate's refractive index, and the proposed structures' polarization. We can further observe multiple tunable absorption profiles that may find applications in designer infrared absorbers, optoelectronic devices, and THz sensors.

Electromagnetic Wave Absorption in the Human Head: A Virtual Sensor Based on a Deep-Learning Model.

Determining the amount of electromagnetic wave energy absorbed by the human body is an important issue in the analysis of wireless systems. Typically, numerical methods based on Maxwell's equations and numerical models of the body are used for this purpose. This approach is time-consuming, especially in the case of high frequencies, for which a fine discretization of the model should be used. In this paper, the surrogate model of electromagnetic wave absorption in human body, utilizing Deep-Learning, is proposed. In particular, a family of data from finite-difference time-domain analyses makes it possible to train a Convolutional Neural Network (CNN), in view of recovering the average and maximum power density in the cross-section region of the human head at the frequency of 3.5 GHz. The developed method allows for quick determination of the average and maximum power density for the area of the entire head and eyeball areas. The results obtained in this way are similar to those obtained by the method based on Maxwell's equations.

Collective lattice and plasmonic resonances in the enhancement of circular dichroism in disk–rod metasurface

Compact planar photonic elements serving for efficient control over the polarization of light are of paramount importance in photonics. Here, we propose a design of a chiral periodic metasurface based on plasmonic nanodisks and nanorods arranged asymmetrically in a unit cell. Using the finite-difference time-domain analysis, we show that the collective lattice resonance harnessed by the diffraction coupling of the plasmonic unit cells is the heart of the revealed resonant 38% circular dichroism effect. The circular dichroism enhancement of the considered structure is improved using the deep-learning-assisted optimization of the metasurface design.

Extremely long nanojet formation from a ballpoint photonic pen

Certain microelements can facilitate light manipulation in the form of an intense electromagnetic beam known as a “photonic nanojet” (PNJ), which is an emerging candidate in the field of spectroscopy, nanolithography, and bio/chemical sensing. However, inherent limitations of the PNJs such as short jet length, focal point, and working length minimize their application areas. Herein, by considering readily available materials and technologies, we report a simple and physically realizable “photonic pen” (PP)-like microstructure to yield extremely long PNJs under plane-wave illumination. Essentially, by semi-immersing a sphere of certain diameter (acting as pen tip) in a barrel material with the width equal to and height 5 times the sphere diameter, a “ballpoint pen”-like microstructure is formed where illuminated light acts as the ink. Finite-difference time-domain analysis shows the use of low refractive index (RI) spherical tips ( n = 1.48 ) with 10 µm diameter, and a high RI SU-8 barrel ( n = 1.62 ) creating a net index difference of Δ n = 0.14 to effectively form PNJs with extremely long jet length, working distance, and full width at half-maximum of over 330 λ (121.6 µm), 319.45 λ (116.6 µm), and 4.22 λ (1.54 µm) respectively, and a figure of merit of ∼ 155 in water environment. S i O 2 / SU- 8 PPs show over ∼ 3 times longer jet length than previous studies. We show that such SU- 8 / SiO 2 PPs with extremely long PNJs can act as epidermal photonic imagers to scan biological tissues such as trilayer human skin to detect melanoma (skin cancer), both in contact and in noncontact mode with efficiency enhancement reaching ∼ 45 % compared to plain microspheres without barrels.

Predicting strongly localized resonant modes of light in disordered arrays of dielectric scatterers: a machine learning approach.

In this work, we predict the most strongly confined resonant mode of light in strongly disordered systems of dielectric scatterers employing the data-driven approach of machine learning. For training, validation, and test purposes of the proposed regression architecture-based deep neural network (DNN), a dataset containing resonant characteristics of light in 8,400 random arrays of dielectric scatterers is generated employing finite difference time domain (FDTD) analysis technique. To enhance the convergence and accuracy of the overall model, an auto-encoder is utilized as the weight initializer of the regression model, which contains three convolutional layers and three fully connected layers. Given the refractive index profile of the disordered system, the trained model can instantaneously predict the Anderson localized resonant wavelength of light with a minimum error of 0.0037%. A correlation coefficient of 0.95 or higher is obtained between the FDTD simulation results and DNN predictions. Such a high level of accuracy is maintained in inhomogeneous disordered media containing Gaussian distribution of diameter of the scattering particles. Moreover, the prediction scheme is found to be robust against any combination of diameters and fill factors of the disordered medium. The proposed model thereby leverages the benefits of machine learning for predicting the complex behavior of light in strongly disordered systems.