Search Results (437)

The integration density of photonic integrated circuits is fundamentally limited by evanescent field overlap and subsequent inter-channel crosstalk. Layered transition metal dichalcogenides (TMDCs) bypass these confinement constraints through intrinsic optical birefringence and high refractive indices. Here, we report the near-infrared optical constants and waveguide dispersion of molybdenum diselenide (MoSe₂). Ellipsometry performed on centimeter-scale crystals yields an in-plane refractive index of 4.1–4.7 over 1000–2000 nm, with an extinction coefficient close to the sensitivity limit of the fit away from strong excitonic resonances. To validate the anisotropic dielectric tensor at the device scale, scattering-type scanning near-field optical microscopy (s-SNOM) was utilized to map the propagation of transverse-magnetic modes in 235 nm thick exfoliated flakes. Spatial Fourier analysis of the edge-scattered near-field interference yields effective mode indices that precisely match the modeled dispersion. Using the verified dielectric tensor, finite-element simulations demonstrate that single-mode MoSe₂ waveguides optically outperform equivalent tungsten disulfide (WS₂) benchmarks. The enhanced evanescent field suppression in the claddings of MoSe₂ waveguide increases the coupling length by a factor of 3.5, reducing the required routing pitch and enabling a 12.5% direct increase in on-chip integration density. The results identify MoSe₂ as a high-index anisotropic platform for compact waveguiding in the near-infrared. Full article

Soluble solids content (SSC) is one of the most important indicators of sweetness, ripeness, and market quality in sweet cherries. However, conventional SSC determination is destructive, labor-intensive, and unsuitable for rapid or large-scale quality assessment. Therefore, there is a need for fast, non-destructive, and data-driven sensing approaches that can estimate internal fruit quality without damaging the sample. This study aimed to develop a non-destructive approach for SSC prediction in sweet cherries by combining open-ended coaxial probe dielectric spectroscopy with deep learning models. An open-ended coaxial probe measurement system was designed and developed to determine the dielectric properties of sweet cherries and was coupled with an Agilent E4991A impedance analyzer operating over a frequency range of 5–3005 MHz. A total of 10,080 dielectric measurements and 2100 reference SSC measurements were collected over 26 experimental days. The dielectric constant (ε′), loss factor (ε″), and loss tangent (tan δ) were extracted and used to construct separate ε′, ε″, tan δ, and integrated combined datasets. Six deep learning architectures, namely convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU), CNN-LSTM, and convolutional long short-term memory (ConvLSTM), were trained and optimized using Bayesian optimization and early stopping. CNN achieved the best performance on the tan δ dataset (test R² = 0.9099, RMSE = 0.8354 °Brix, MAE = 0.6599 °Brix), whereas GRU yielded the highest accuracy on the integrated combined dataset (test R² = 0.8622, RMSE = 1.0331 °Brix, MAE = 0.7958 °Brix). ConvLSTM provided the most consistent performance across all four datasets (test R² = 0.8081–0.8651), demonstrating strong predictive capability and practical computational efficiency. These findings confirm the potential of reduced-range dielectric spectroscopy combined with deep learning for rapid, non-destructive SSC assessment in sweet cherries. Full article

There are several methods which are utilized for flooding oil process monitoring, such as the seismic methods, and the electromagnetic methods. As the gas flooding oil process is complicated, conventional methods are not capable of monitoring the gas flooding oil process accurately. This study utilizes the Ground Penetrating Radar (GPR) method to monitor the CO₂ flooding oil and water flooding oil processes, as the difference in dielectric constants and conductivity of CO₂, oil and water is utilized to infer distributions of CO₂, oil and water. Moreover, as GPR data processing is time-consuming, it is impossible to process the GPR data in real-time by a conventional method, such as the full waveform inversion method. This study utilizes U-Net neural networks to invert for the subsurface dielectric constants and conductivity distributions of CO₂, oil and water in real-time. A deep learning inversion network based on the U-Net architecture is trained to extract multi-scale features through an encoder–decoder structure, achieving an end-to-end mapping from GPR echo signals to subsurface electrical parameters. The study utilizes the gprMax forward tool to simulate the dynamic response changes in rock-electrical parameters during flooding and constructs a high-resolution training dataset of 100,000 samples. Each sample contains the relationships between a subsurface electrical parameter model and its corresponding multi-transmitter, multi-receiver GPR responses. This method was first tested by the synthetic data of oil–water flooding and oil–water–gas flooding, and then it was tested by observed data from physical core experiments. Numerical and physical core experimental results show that the method accurately inverts the electrical parameter distributions of oil, water, and gas in the sandstone model, successfully capturing the position and morphology changes in the displacement front. The average relative error of dielectric constant inversion is controlled within 8% with the error mainly from the low dielectric constant regions and the relative error of conductivity is smaller than 10%, with the error mainly concentrated in high-conductivity water regions for conductivity inversion results. The results reveal the feasibility and superiority of the neural network-based deep learning method in GPR electromagnetic inversion, providing a new method for real-time flooding monitoring and intelligent reservoir development during oil and gas flooding. Moreover, the proposed approach offers a fast inversion solution and is less affected by the initial model and noise. Full article

To develop an improved crystallization process for meropenem trihydrate, an important carbapenem antibiotic, the pH-dependent solubility of meropenem trihydrate in different solvent systems was investigated using the laser monitoring dynamic method. The solubility data displayed a characteristic U-shaped profile. The solvent composition could also affect the solubility data and the isoelectric point of meropenem. The experimental solubility data were well correlated with the Tsuji model. Hirshfeld surface (HS) analysis highlighted the significance of O···H/H···O interactions in the crystal structure. Molecular electrostatic potential surfaces (MEPs) revealed complementary positive and negative regions on the meropenem molecule, enabling solute–solute aggregation through electrostatic interactions. Furthermore, molecular dynamics (MD) simulations provided deeper insights into the influence of ethanol and acetone on the solvation layer of meropenem. Additionally, the effects of pH value and solvent composition on the crystallization process of meropenem trihydrate were discussed. The crystal habit is affected by variations in solvent composition and pH. Finally, the combined effects of solvent polarity, dielectric constant, and solvation on both solubility and the crystallization process were comprehensively summarized. Full article

We develop a theoretical model for the flexoelectric instability in bent-core nematic liquid crystals, focusing on the coupling between elastic distortions and an external electric field through flexoelectric polarization. The analysis is carried out in the nematic phase close to the twist-bend transition, where both the flexoelectric coefficients and the effective bend elastic constant exhibit strong temperature dependence. Within a Landau–de Gennes framework, we derive analytical expressions for the threshold electric field and the corresponding wave vector of the emerging periodic modulation by minimizing the total free energy and assuming $K_1=K_2$. Numerical simulations illustrate the temperature dependence of the threshold parameters and the role of dielectric anisotropy and elastic constants. The results indicate that the flexoelectric instability may occur only within a finite temperature interval above the transition into the twist-bend phase and that both the threshold electric field and the periodic structure’s wave vector decrease as the temperature decreases. Full article

Thermally activated delayed fluorescence (TADF) emitters doped in host–guest systems are widely utilized for organic light-emitting diodes (OLEDs), where key rate constants and the fluorescence quantum yield (Φ_F) are strongly influenced by the surrounding environment. However, the multifaceted interactions, i.e., dipole–dipole interaction and conformational restraint between the emitter and environment have been rarely investigated systematically, where excited state charge transfer (CT) and structural relaxation (SR) of emitters should be considered equally. In this study, four representative CT–TADF emitters were selected as model systems and studied in PS/PMMA:TADF:CA host–guest doped films with varied dielectric constants and matrix rigidity. Within D–A and D–A–D configurations, donor substitution from PXZ to DMAC varied CT characteristics, whereas TRZ-based D–A and DPS-based D–A–D emitters provided a relative difference in SR owing to their different rigidity. The total reorganization energy (λ_Total) was introduced as a quantitative measure of these multifaceted interactions and correlated with the rate constants. The results indicate that the dielectric dependence of the nonradiative decay rate (k_nr^S) for D–A–D molecules cannot be explained by the simplified energy gap law, where the vibronic effect plays the role of a game changer. This work provides a quantitative framework and highlights vibrational frequency as a key design parameter for optimizing Φ_F in host–guest doped OLED devices. Full article

This study addresses the impact of temperature gradient-induced non-uniform aging on the accuracy of space charge measurements in cross-linked polyethylene (XLPE) insulation for high-voltage direct-current cables. Existing pulse-echo acoustic (PEA) recovery algorithms neglect the evolution of material acoustic and dielectric properties during aging. To overcome this limitation, the systematic degradation of sound velocity, attenuation dispersion, and dielectric constant subjected to temperature gradient aging was experimentally investigated. Specimens were aged at temperatures ranging from 40 to 100 °C for durations up to 49 days. Then, quantitative models describing the dependence of acoustic and dielectric properties on aging severity were established. A space charge signal correction algorithm was then developed, incorporating nonlinear adjustments for sound velocity, attenuation, and permittivity according to the through-thickness aging profile. The algorithm’s accuracy was validated by comparing recovered charge waveforms and electric field distributions under 5 kV/mm for samples aged under different temperature gradients. The application of the method under high-voltage DC conditions revealed that aging induces non-monotonic changes in sound velocity, increased attenuation coefficients, and elevated low-frequency dielectric constants. Temperature gradient aging promotes heteropolar charge accumulation. This work provides a theoretical and methodological basis for improving the accuracy of the insulation condition assessment in long-term service HVDC cables. Full article

This paper presents a non-imaging landmine detection system that integrates a highly sensitive multiple-input multiple-output (MIMO) microwave sensor with a machine learning (ML) classifier for automated classification. The proposed sensor consists of two circular patch elements fed with two ports designed in a unique configuration, comprising a dual loop with a cross dipole, for enhancing sensitivity to changes in the environmental electrical properties (dielectric constant and electrical conductivity) induced by buried metallic objects. It operates in dual bands of 1.58 GHz and 1.75 GHz, within the operating frequency range of 1.3 to 2 GHz. The system’s performance was assessed using full-wave simulations and experimental measurements, involving a sand-filled foam container with a metal surrogate landmine placed at different depths. The sensor’s performance was evaluated by monitoring changes in the magnitude and phase of the reflection coefficient ($S_{11}$) and the transmission coefficient ($S_{21}$). The acquired scattering parameters data were processed using a Support Vector Machine (SVM) algorithm for automated classification. Results demonstrate the sensor’s high capability in detecting metallic targets at various depths and standoff distances. Compared to conventional imaging technologies, this system offers significant advantages in cost, simplicity, and ease of data processing. The SVM models trained on measurement data with proper feature selection showed a high level of agreement with their counterparts trained on simulation data. Stratified k-fold cross-validation was used to improve the reliability of accuracy metrics, with results showing 85% or higher mean accuracy in all classification scenarios. Full article

Oxide perovskites with simultaneously large band gaps and high-static dielectric constants are of considerable interest for advanced microelectronics, dielectric devices, and energy storage applications, yet their discovery remains challenging because electronic insulation, lattice polarizability, and thermodynamic accessibility are strongly coupled and often mutually competitive. Here, we develop a physics-guided multitask learning framework for the joint prediction of the band gap and static dielectric response in chemically constrained single-perovskite oxide ABO₃ compounds. To ensure data fidelity and physical comparability, the learning space is strictly restricted to simple oxide ABO₃ perovskites from the Materials Project, while mixed-fidelity band gaps, heterogeneous dielectric definitions, and chemically inconsistent samples are excluded. The model integrates role-aware A-/B-site descriptors, perovskite-specific geometric and structural features, multitask prediction of Eg, εtotal, εelectronic, and εionic, explicit physical consistency constraints, auxiliary candidate classification, ranking learning, and reliability-aware screening with uncertainty and out-of-distribution control. Under B-site-grouped cross-validation, the framework achieves 97.4% accuracy, Recall of 96.5%, and an F1 score of 96.1%, while maintaining robust transferability on the independent JARVIS validation set. The results show that high-gap/high-k candidates occupy a chemically non-random subspace governed by B-site-centered electronic–lattice coupling, and that physically consistent multitask learning substantially improves both predictive coherence and candidate enrichment. More broadly, this study establishes a data-consistent, physics-constrained, and transferable paradigm for the intelligent discovery of functional oxide dielectrics. Full article

Under the deformation potential model, the superconducting phenomenon in ABC-stacked multilayer graphene under a vertical electric field is investigated using linear combination operators and unitary transformation methods. Through the deformation potential model applied to a linear continuous medium, the effect of the external electric field is converted into the deformation potential energy of the crystal. Deformation potential phonons (LA phonons) act as propagators, generating electron–electron interactions. As the electric field increases, the ratio of the electric displacement vector to the dielectric function ($D/\epsilon$) rises, leading to an increase in the electron ground-state energy, the opening of the band gap, and an enhancement of the attractive electron–electron interaction. With further increases in the external electric field, the deformation potential constant of the crystal ($D_l$) increases. When the phonon vibration frequency ($\omega$) is around 8.5 THz, and the conditions are satisfied—where the wave vectors of different LA phonons are equal in magnitude and opposite in direction, and the electron spins are opposite—the attractive electron–electron interaction reaches its maximum ($H_{eff}$), resulting in the emergence of superconductivity. Our study also provides a new perspective for understanding the unique quantum properties—such as strong correlations, superconductivity, and ferromagnetism—in different stacking configurations like AB, ABC, and ABCA. Full article

Terahertz time-domain spectroscopy (THz-TDS) has emerged as a powerful tool for probing hydrated materials and biological tissues, where water dynamics dominate the dielectric response. This study focuses on improving the methodology of THz-TDS by replacing conventional cuvettes, which introduce unwanted absorption, reflections, and liquid bubbles that must be accounted for during measurement interpretation, with nitrocellulose membranes of various pore sizes. The membranes were hydrated with deionized water and sealed with food-grade cling film, and their transmission properties were measured using THz-TDS. To interpret the measurements, transfer matrix method simulations were performed using the optical constants of water reported by some experimentalists, allowing verification of our data. The findings for deionized water highlight the reliability of the methodology. Our results demonstrate that nitrocellulose membranes provide stable and reproducible transmission measurements in good agreement with theoretical reference models, supported by weight retention studies and reproducibility tests conducted in spatial, temporal, and random measurement conditions. These improvements contribute to the development of more robust THz-TDS approaches for hydrated biological materials and suggest future applications in non-invasive tissue hydration monitoring and biomedical diagnostics. Full article

Pulse repetition frequency (PRF) controls pulse off-time and, therefore, the extent of thermal accumulation, melt expulsion, and dielectric recovery in finishing electrical discharge machining (EDM). This study clarifies how PRF modifies crater evolution and surface integrity in finishing EDM of 4Cr13 martensitic stainless steel, a corrosion-resistant mold steel used in precision dies and molds. A 2D axisymmetric electro-thermo-fluid model was established in COMSOL, where Gaussian current density, heat-flux, and plasma pressure were periodically imposed at PRFs of 25–100 kHz, while pulse-on time (6 μs) and peak current (8 A) were kept constant. The simulations tracked the transient pressure, heat-flux, velocity, and temperature fields over a common elapsed time of 25 μs. Finishing experiments were then carried out on flat 4Cr13 coupons at 50, 75, and 100 kHz using a copper electrode and deionized water, followed by characterization by laser confocal microscopy, SEM/EDS, and X-ray diffraction using the cosα method. Increasing PRF localized the coupled pressure-heat-flow fields near the crater rim, but shortened off-time and intensified inter-pulse heat accumulation. Accordingly, the surface roughness decreased from Ra = 1.18 μm at 50 kHz to 0.63 μm at 75 kHz, and then slightly increased to 0.71 μm at 100 kHz because of crater overlap, re-melting, and incomplete gap recovery. SEM observations confirmed large irregular craters with cracks at 50 kHz, more uniform fine craters at 75 kHz, and overlapping re-solidified traces at 100 kHz. The residual stress remained compressive for all tested conditions (−341 to −409 MPa). Overall, 75 kHz offers the best compromise between crater uniformity, roughness, and compressive stress for finishing EDM of 4Cr13 steel. Full article

In the current study, the dielectric behavior of ternary mixtures composed of glycerol and water with various salt additives is investigated over a frequency range that extends from 0.5 to 20 GHz and at temperatures between 5 and 55 °C. The investigated mixtures consisted of glycerol and water with glycerol volume ratios of 20%, 40%, and 60%. To explore the salt addition’s effect on the dielectric properties, different moderate ionic strengths of glycerol–water mixtures were prepared with NaCl concentrations of 0.10, 0.20, and 0.30 M for the same glycerol volume ratios. The ion-specific effects on the dielectric properties were investigated for prepared mixtures with a 0.10 M concentration of Na₂SO₃, NaNO₃, and KCl for the 20% glycerol ratio to explore ions with different charge density and hydration tendencies. Using dielectric spectroscopy, the frequency dependence of the real (${\epsilon }^{\prime }$) and imaginary (${\epsilon }^{″}$) dielectric constants was measured, and the associated dielectric parameters were extracted using the Cole–Cole model. This study shows that increasing the salt concentration results in a slight decrease in ${\epsilon }^{\prime }$ while ${\epsilon }^{″}$ increases dramatically, especially at lower frequencies, due to enhanced DC conductivity. An isopermittivity behavior is observed in ${\epsilon }^{\prime }$ as the temperature changes across all mixtures, and it is found to be insensitive to the addition of salt, indicating that it is mainly dictated by the glycerol–water dipolar relaxation network. Among the tested mixtures is the 20% glycerol mixture with 0.10 M KCl, which exhibits the highest ${\epsilon }^{″}$ value in the low-frequency range, attributed to its relatively high DC conductivity. Additionally, the dielectric properties of mixtures with higher glycerol ratios are found to be less sensitive to the addition of salt due to their high viscosity and the higher structured solvent network, which collectively limit ionic mobility and suppress changes in dielectric response. Full article

Alumina is a commonly used ceramic material known for high permittivity, low dielectric loss, good thermal conductivity, and low cost. In the development of electronic devices, the size effect of powdery materials is crucial, particularly in applications involving composite materials. This study introduces the field-enhancement method (FEM) to measure the resonant frequency ($f_0$) and the quality factor (Q) of alumina powders packed in a Teflon container and placed on top of the central rod in the proposed cavity. The measured resonant condition ($f_0$ and Q) is mapped to a contour plot and simulated using a high-frequency structure simulator (HFSS). The contour mapping technique allows the researchers to obtain the effective complex permittivity of alumina–air composites. The complex permittivity of the alumina powder is retrieved using a hybrid model and the effective medium theories (EMTs), respectively. The Landau–Lifshitz–Looyenga (LLL) model is compared with the results using the hybrid model for its applicability. The dielectric constant and the loss tangent of the alumina powder are found to increase as the powder size reduces. A power relation is found to fit the obtained permittivity, covering sizes ranging from nanometers to micrometers, and a surface-charge scaling argument is proposed to explain the observed trend. This finding opens a new avenue for manipulation of permittivity in composite materials and has potential applications in stealth/absorber technology and as a self-limiter for grain growth during sintering. Full article

Conducting polymer–metal nanocomposites are widely investigated as tunable photonic and optoelectronic media; however, reported property trends often remain empirical because electronic disorder at the absorption edge, refractive-index dispersion, free carrier dielectric response, and third-order nonlinearity are rarely quantified within a single, composition-controlled film series. This limitation is particularly relevant for electrochemically grown PANI coatings on transparent conductive substrates, where nanoparticle incorporation can simultaneously enhance polarization while introducing aggregation-driven heterogeneity. Here, Ag/PANI nanocomposite thin films were fabricated directly on indium tin oxide (ITO) by potentiostatic electrodeposition from an aniline/camphorsulfonic acid electrolyte containing controlled Ag nanoparticle loadings (5–15 wt.%). This study addresses the research gap by integrating complementary optical-disorder and dispersion formalisms with dielectric and nonlinear analyses to establish a composition structure optics map for device-relevant films. Ag incorporation narrows the indirect optical gap from 1.98 eV (PANI) to 1.81 eV (5 wt.%), 1.38 eV (10 wt.%), and 1.19 eV (15 wt.%), while markedly broadening the Urbach tail (0.377 eV → 1.28–1.64 eV at 5–10 wt.%). Wemple–DiDomenico modeling and Drude-type dielectric dispersion reveal strongly non-monotonic evolution of oscillator energetics and the carrier response, culminating in large bound-electron dielectric constants (ε_∞ up to 469.8) and plasma frequencies (ω_p up to 248 × 10¹² Hz) at 15 wt.% Ag. Third-order nonlinearity is substantially enhanced but composition-sensitive: χ³ increases from 6.73 × 10⁻⁹ esu (PANI) to ~7.6 × 10⁻⁸ esu at 5 and 15 wt.%, whereas the Kerr coefficient peaks at 25.91 × 10⁻⁷ esu for 5 wt.% and is suppressed at intermediate/high loading. These results demonstrate that the optimal nonlinear performance is governed by a disorder–dispersion balance and microstructure-dependent local-field effects rather than the Ag fraction alone. Full article