Search Results (1,882)

In this study, Fe₂O₃–TiO₂ nanocomposites with different TiO₂ contents (1–50%) were synthesized via a solvothermal method using pre-formed α-Fe₂O₃ nanoparticles as cores. We systematically evaluated the influence of TiO₂ loading on the nanocomposites’ structural, morphological, optical, and photocatalytic properties. X-ray diffraction revealed the coexistence of hematite and anatase phases, with an increase in TiO₂ content inducing reduced crystallite size, enhanced dislocation density, and microstrain, indicating interfacial lattice distortion. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) showed a uniform elemental distribution at low TiO₂ contents, evolving into irregular agglomerates at higher loadings. Fourier-transform infrared (FTIR) spectra indicated the suppression of Fe–O vibrations and the appearance of hydroxyl-related bands with TiO₂ enrichment. Diffuse reflectance spectroscopy (DRS) analysis confirmed the simultaneous presence of hematite (~2.0 eV) and anatase (3.2–3.35 eV) absorption edges, with a slight blue shift in the TiO₂ band gap at higher concentrations. Photocatalytic activity, assessed using methylene blue degradation under xenon lamp irradiation, demonstrated a strong dependence on the TiO₂ fraction. The composite containing 33% TiO₂ achieved the best performance, with 98% dye removal and a pseudo-first-order rate constant of 0.045 min⁻¹, outperforming both pure hematite and commercial P25 TiO₂. These results highlight that intermediate TiO₂ content (~33%) provides an optimal balance between structural integrity and photocatalytic efficiency, making Fe₂O₃–TiO₂ heterostructures promising candidates for water purification under simulated solar irradiation. Full article

This paper addresses the long-standing question of understanding the origin and evolution of low-frequency unsteadiness interactions associated with shock waves impinging on a turbulent boundary layer in transonic flow (Mach: $1.1$ to $1.3$). To that end, high-speed experiments in a blowdown open-channel wind tunnel have been performed across a convergent–divergent nozzle for different expansion ratios (PR = 1.44, 1.6, and 1.81). Quantitative evaluation of the underlying spectral energy content has been obtained by processing time-resolved pressure transducer data and Schlieren images using the following spectral analysis methods: Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT), as well as coherence and time-lag evaluations. The images demonstrated the presence of increased normal shock-wave impact for PR = 1.44, whereas the latter were linked with increased oblique $\lambda$-foot impact. Hence, significant disparities associated with the overall stability, location, and amplitude of the shock waves, as well as quantitative assertions related to spectral energy segregation, have been inferred. A subsequent detailed spectral analysis revealed the presence of multiple discrete frequency peaks (magnitude and frequency of the peaks increasing with PR), with the lower peaks linked with large-scale shock-wave interactions and higher peaks associated with shear-layer instabilities and turbulence. Wavelet transform using the Morlet function illustrates the presence of varying intermittency, modulation in the temporal and frequency scales for different spectral events, and a pseudo-periodic spectral energy pulsation alternating between two frequency-specific events. Spectral analysis of the pixel densities related to different regions, called spatial FFT, highlights the increased influence of the feedback mechanism and coupled turbulence interactions for higher PR. Collation of the subsequent coherence analysis with the previous results underscores that lower PR is linked with shock-separation dynamics being tightly coupled, whereas at higher PR values, global instabilities, vortex shedding, and high-frequency shear-layer effects govern the overall interactions, redistributing the spectral energy across a wider spectral range. Complementing these experiments, time-resolved numerical simulations based on a transient 3D RANS framework were performed. The simulations successfully reproduced the main features of the shock motion, including the downstream migration of the mean position, the reduction in oscillation amplitude with increasing PR, and the division of the spectra into distinct frequency regions. This confirms that the adopted 3D RANS approach provides a suitable predictive framework for capturing the essential unsteady dynamics of shock–boundary layer interactions across both temporal and spatial scales. This novel combination of synchronized Schlieren imaging with pressure transducer data, followed by application of advanced spectral analysis techniques, FFT, CWT, spatial FFT, coherence analysis, and numerical evaluations, linked image-derived propagation and coherence results directly to wall pressure dynamics, providing critical insights into how PR variation governs the spectral energy content and shock-wave oscillation behavior for nozzles. Thus, for low PR flows dominated by normal shock structure, global instability of the separation zone governs the overall oscillations, whereas higher PR, linked with dominant $\lambda$-foot structure, demonstrates increased feedback from the shear-layer oscillations, separation region breathing, as well as global instabilities. It is envisaged that epistemic understanding related to the spectral dynamics of low-frequency oscillations at different PR values derived from this study could be useful for future nozzle design modifications aimed at achieving optimal nozzle performance. The study could further assist the implementation of appropriate flow control strategies to alleviate these instabilities and improve thrust performance. Full article

Interpreting multi-component ¹H NMR spectra is difficult due to peak overlap, concentration variability, and low-abundance signals. We cast mixture identification as a single-pass multi-label task. A compact CNN–Transformer (“Hybrid”) model was trained end-to-end on domain-informed and realistically simulated spectra derived from a 13-component flavor library; the model requires no real mixtures for training. On 16 real formulations, the Hybrid attains micro-F1 = 0.990 and exact-match (subset) accuracy = 0.875, outperforming CNN-only and Transformer-only ablations, while remaining efficient (~0.47 M parameters; ~0.68 ms on GPU, V100). The approach supports abstention and shows robustness to simulated outsiders. Although the evaluation set was small, and the macro-ECE (per-class, 15 bins) was inflated by sparse classes (≈0.70), the micro-averaged Brier is low (0.0179), and temperature scaling had negligible effect (T ≈ 1.0), indicating the good overall probability quality. The pipeline is readily extensible to larger libraries and adjacent applications in food authenticity and targeted metabolomics. Classical chemometric baselines trained on simulation failed to transfer to real measurements (subset accuracy 0.00), while the Hybrid model maintained strong performance. Full article

Hyperspectral underwater target detection holds great potential for marine exploration and environmental monitoring. A key challenge lies in accurately estimating water inherent optical properties (IOPs) from hyperspectral imagery. To address these limitations, we propose a novel water IOP estimation network to support the interpretation of bathymetric models. We propose the IOPs physical model that focuses on the description of the water IOPs, describing how the concentrations of chlorophyll, colored dissolved organic matter, and detrital material influence the absorption and backscattering coefficients. Building on this foundation, we proposed an innovative IOP estimation network integrating a physical model and deep learning (IOPE-IPD). This approach enables precise and physically interpretable estimation of the IOPs. Specially, the IOPE-IPD network takes water spectra as input. The encoder extracts spectral features, while dual parallel decoders simultaneously estimate four key parameters. Based on these outputs, the absorption and backscattering coefficients of the water body are computed using the IOPs physical model. Subsequently, the bathymetric model is employed to reconstruct the water spectrum. Under the constraint of a consistency loss, the retrieved spectrum is encouraged to closely match the input spectrum. To ensure the IOPE-IPD’s applicability across various scenarios, multiple actual and Jerlov-simulated aquatic environments were used. Comprehensive experimental results demonstrate the robustness and effectiveness of our proposed IOPE-IPD over the compared method. Full article

Magnetic nanoparticles (MNPs) perturb magnetic field homogeneity, influencing transverse relaxation and the full width at half maximum (FWHM) of nuclear magnetic resonance (NMR) spectra. In Nuclear Magnetic Resonance (NMR), this appears as decay of the free induction decay (FID) signal, whose relaxation rate determines spectral FWHM. In D₂O containing MNPs, both nanoparticles and solvent molecules undergo Brownian motion and diffusion. Under a vertical main field ($B_0$), MNPs respond to their magnetization behavior, evolving toward a dynamic steady state in which the time-averaged distribution of local field fluctuations remains stable. The resulting spatial magnetic field can thus characterize field homogeneity. Within this framework, Monte Carlo simulations of spatial field distributions approximate the dynamic environment experienced by nuclear spins. NMR experiments confirm that increasing MNP concentration and particle size significantly broadens FWHM, while stronger $B_0$ enhances sensitivity to MNP-induced inhomogeneities. Full article

In this study, the electrospinning technique was employed to encapsulate mountain germander (MG) polyphenolic extract into pullulan/zein (PUL:ZE) delivery systems stabilized with sunflower lecithin. The rheological and physical properties of the pullulan (PUL), PUL:ZE, and zein (ZE) polymer solutions were evaluated to assess their electrospinnability potential. Fabricated nanofibers were then characterized for their morphology, physicochemical, and thermal properties, as well as encapsulation efficiency and simulated in vitro digestion. The elastic component of the polymer solution, quantified by the Deborah number, showed a strong correlation with nanofiber diameter (r = 0.75). FT-IR spectra confirmed the role of sunflower lecithin as a mediator in the formation of hydrogen and hydrophobic interactions among PUL, ZE, and polyphenols. The circular dichroism spectra confirmed the influence of the MG extract on the change in the secondary conformation of the protein structure. The PUL:ZE delivery matrix proved to be suitable for the retention of phenylethanoid glycosides (encapsulation efficiency > 73%). The formulation 50PUL:50ZE was found to have the highest potential for prolonged release of polyphenols under gastrointestinal in vitro conditions. These findings propose a water-based electrospinning approach for designing polyphenolic delivery systems stabilized with lecithin for potential applications in active food packaging or nutraceutical products. Full article

Agroforestry systems offer clear environmental and agronomic advantages, but their effect on plant–biotic stressor interactions remains poorly understood. Specifically, the shade from companion trees can create microclimates favorable to fungal diseases on herbaceous crops. This potential drawback may offset other benefits, highlighting the urgent need for advanced plant health monitoring in these systems. This study assessed the potential of hyperspectral reflectance to detect the single and combined effects of simulated tree shading and infection by the fungal pathogen Alternaria alternata on grain sorghum (Sorghum bicolor L. Moench) under rainfed field conditions. Sorghum was grown either under full light or 50% shading conditions. Half of the plots were artificially inoculated with an A. alternata spore suspension (2 × 10⁸ CFU mL⁻¹), while the others served as controls. Leaf and ground-canopy measurements were acquired with a full range spectroradiometer (VNIR-SWIR, 400–2,400 nm) and UAV imagery covered the VIS-NIR range (400–1,000 nm) before the onset of visible symptoms. Permutational multivariate analysis of variance of leaf and ground-canopy data revealed significant effects of shading (Sh), infection (Aa), and their interaction (p < 0.05), allowing early detection of infection two days before symptom appearance, while UAV data showed only singular significant effects. Partial least squares discriminant analysis accuracy reached 78% at the leaf level, 90% at the ground-canopy level, and 74% (Sh) and 75% (Aa) at the UAV scale. Furthermore, vegetation spectral indices derived from the spectra confirmed greater physiological stress in shaded and infected plants, consistent with disease incidence assessments. Our results establish scale-specific hyperspectral reflectance spectroscopy as a powerful, non-destructive technique for early plant health surveillance in agroforestry. This advanced optical sensing capability is poised to illuminate complex stressor interactions, marking a significant step forward for precision agroforestry management. Full article

The mass profile of the central galaxy of the Antlia cluster, NGC 3268, is studied through a spherical Jeans analysis, combined with a Bayesian approach. The prior distributions are derived from dark matter simulations. The observational dataset consists of Gemini/GMOS multi-object spectra observed from several programmes, supplemented with the kinematics of a small sample of globular clusters from the literature. An NFW mass profile and several options of constant anisotropy are considered. The analysis indicates a moderately massive halo, with a virial mass of (1.4 – 4.3)$\times {10}^{13}{M}_{\odot }$, depending on the assumed anisotropy. A comparison with the kinematics of the galaxy population from the Antlia cluster suggests that a fraction of galaxies is not yet virialised and may currently be infalling into the cluster. Full article

The metal–dielectric hybrid tetramer metasurface has received a lot of attention in the field of optical sensing owing to the excellent refractive index sensing performance. However, achieving simultaneous high-quality Q-factor, polarization insensitivity, multi-band tunability across visible to near-infrared spectra, and ultra-narrow linewidth is an urgent problem to be solved. To overcome this challenge, we proposed a metal–dielectric yurt tetramer metasurface. The finite-difference time-domain method was used to simulate the sensing properties. We explored the physical mechanism of different resonance modes, optimized the structure parameters of the metasurface, and investigated the influence of incident light and environmental parameters on the sensing properties. The results show that the proposed structure not only possesses a high Q-factor but also exhibits excellent wavelength tunability in the visible to near-infrared band and has polarization insensitivity. By skillfully introducing the structural size perturbation, the surface plasmon resonance mode and two Fano resonance modes are successfully excited at the wavelengths of 737.43 nm, 808.99 nm, and 939.50 nm. The light–matter interaction at the Fano resonance frequencies is highly enhanced so that a maximum refractive index sensitivity, figures of merit (FOM), and Q-factor of 500.94 nm/RIU, 491.12 RIU⁻¹, and 793.13 are obtained. The narrowest full width at half maximum (FWHM) is 1.02 nm, respectively. This work provides a theoretical basis for the realization of a high-performance metasurface refractive index sensor. Full article

Tall-pier, long-span continuous rigid-frame bridges are prone to wind-induced vibration due to their large spans and pier heights; during cantilever erection, the maximum double-cantilever stage has reduced stiffness and buffeting becomes more evident. Accordingly, a time-domain framework driven by three-component aerodynamic coefficients and their angle-of-attack derivatives is adopted. Code-based target spectra are used to synthesize multi-point fluctuating wind time histories via harmonic superposition, followed by statistical and spectral consistency checks. Buffeting forces are then computed under the quasi-steady assumption, mapped to finite-element nodes, and integrated in time to obtain global responses (displacement and acceleration). In parallel, static six-component wind tunnel tests provide mean force and moment coefficients and their derivatives for comparison. The results indicate that the three-component time-domain approach captures the buffeting features dominated by vertical and torsional responses. When pronounced along-span sectional variation and high angle-of-attack sensitivity are present, errors associated with the strip assumption increase, whereas the force–moment coupling revealed by the six-component data helps explain discrepancies between simulation and tests. These response patterns and error characteristics delineate the applicability and limits of the three-component time-domain evaluation for variable-depth continuous rigid-frame bridges, offering a reference for wind resistance assessment and construction-stage checking of similar bridges. Full article

Reliable DC cable insulation is crucial for photovoltaic (PV) systems and high-voltage DC (HVDC) networks. However, conventional materials such as cross-linked polyethylene (XLPE) and polyvinyl chloride (PVC) face challenges under prolonged DC stress—notably space charge buildup, dielectric losses, and thermal aging. Cross-linked polyolefin (XLPO) has emerged as a halogen-free, thermally stable alternative, but its comparative DC performance remains underreported. Methods: We evaluated the insulations of virgin XLPO, XLPE, and PVC PV cables under ±1 kV DC using time-domain indices (IR, DAR, PI, Loss Index), supported by MATLAB and FTIR. Multi-layer cable geometries were modeled in MATLAB to simulate radial electric field distribution, and Fourier-transform infrared (FTIR) spectroscopy was employed to reveal polymer chemistry and functional groups. Results: XLPO exhibited an IR on the order of 10⁸–10⁹ Ω, and XLPE (IR ~ 10⁸ Ω) and PVC (IR ~ 10⁷ Ω, LI ≥ 1) at 60 s, with favorable polarization indices under both polarities. Notably, they showed high insulation resistance and low-to-moderate loss indices (≈1.3–1.5) under both polarities, indicating controlled relaxation with limited conduction contribution. XLPE showed good initial insulation resistance but revealed polarity-dependent relaxation and higher loss (especially under positive bias) due to trap-forming cross-linking byproducts. PVC had the lowest resistance (GΩ-range) and near-unit DAR/PI, dominated by leakage conduction and dielectric losses. Simulations confirmed a uniform electric field in XLPO insulation with no polarity asymmetry, while FTIR spectra linked XLPO’s low polarity and PVC’s chlorine content to their electrical behavior. Conclusions: XLPO outperforms XLPE and PVC in resisting DC leakage, charge trapping, and thermal stress, underscoring its suitability for long-term PV and HVDC applications. This study provides a comprehensive structure–property understanding to guide the selection of advanced, polarity-resilient cable insulation materials. Full article

This study introduces an innovative microfluidic-based approach for extracting drugs from oral fluids using self-assembled tripeptide hydrogels as sorbents. Peptide microfiber derived from the heterochiral tripeptide ^DLeu-^LPhe-^LPhe was formed in situ within the 14 mm-long microchannel of a two-inlet microfluidic device. The methodology enables the laminar flow-driven mixing of buffer solutions, inducing hydrogel formation at their interface. The resulting fiber exhibited a well-defined morphology and β-sheet structure, confirmed by Raman spectroscopy and Thioflavin T fluorescence. The peptide fibers co-assembled successfully with 5-fluorouracil (5-FU) and naproxen (39.8 ± 1.4 nmol of 5-FU and 27.4 ± 6.6 nmol of naproxen per 112 nmol of peptide used to prepare the fiber), resulting in a molar ratio drug/peptide ratio of approximately 1:3 and 1:4, respectively, demonstrating versatility in drug entrapment. The use of the gel fiber as a sorbent phase was first assessed in buffer, and subsequently, the optimized method was applied to saliva. Adsorption studies under stopped-flow conditions showed a significant drug adsorption capability from buffered solutions by the pre-formed hydrogel (32.8 ± 0.9% of 5-FU and 36.4 ± 3.3% of naproxen per fiber preformed with 112 nmol of peptide), demonstrating their suitability as sorbent material. The extension of the methodology to simulated saliva samples allowed extraction of 36% of 5-FU by the fiber, as determined by ¹⁹F NMR spectroscopy on microcoils, which enabled us to work with the small volume of fluid extracted from the microfluidic device and provided clean spectra and quantitative results. These findings highlight the potential of this tripeptide hydrogel as a sorbent material for therapeutic drug monitoring and toxicological analysis via a simple, non-invasive and rapid approach for drug detection in oral fluids. Full article

The evolution of space technology in recent years, fueled by advancements in computing such as Artificial Intelligence (AI) and machine learning (ML), has profoundly transformed our capacity to explore the cosmos. Missions like the James Webb Space Telescope (JWST) have made information about distant objects more easily accessible, resulting in extensive amounts of valuable data. As part of this work-in-progress study, we are working to create an atmospheric absorption spectrum prediction model for exoplanets. The eventual model will be based on both collected observational spectra and synthetic spectral data generated by the ROCKE-3D general circulation model (GCM) developed by the climate modeling program at NASA’s Goddard Institute for Space Studies (GISS). In this initial study, spline curves are used to describe the bin heights of simulated atmospheric absorption spectra as a function of one of the values of the planetary parameters. Bayesian Adaptive Exploration is then employed to identify areas of the planetary parameter space for which more data are needed to improve the model. The resulting system will be used as a forward model so that planetary parameters can be inferred given a planet’s atmospheric absorption spectrum. This work is expected to contribute to a better understanding of exoplanetary properties and general exoplanet climates and habitability. Full article

Neuromorphic circuits emulate the brain’s massively parallel, energy-efficient, and robust information processing by reproducing the behavior of neurons and synapses in dense networks. Memristive technologies have emerged as key enablers of such systems, offering compact and low-power implementations. In particular, locally active memristors (LAMs), with their ability to amplify small perturbations within a locally active domain to generate action potential-like responses, provide powerful building blocks for neuromorphic circuits and offer new perspectives on the mechanisms underlying neuronal firing dynamics. This paper introduces a novel second-order locally active memristor (LAM) governed by two coupled state variables, enabling richer nonlinear dynamics compared to conventional first-order devices. Even when the capacitances controlling the states are equal, the device retains two independent memory states, which broaden the design space for hysteresis tuning and allow flexible modulation of the current–voltage response. The second-order LAM is then integrated into a FitzHugh–Nagumo neuron circuit. The proposed circuit exhibits oscillatory firing behavior under specific parameter regimes and is further investigated under both DC and AC external stimulation. A comprehensive analysis of its equilibrium points is provided, followed by bifurcation diagrams and Lyapunov exponent spectra for key system parameters, revealing distinct regions of periodic, chaotic, and quasi-periodic dynamics. Representative time-domain patterns corresponding to these regimes are also presented, highlighting the circuit’s ability to reproduce a rich variety of neuronal firing behaviors. Finally, two unknown system parameters are estimated using the Aquila Optimization algorithm, with a cost function based on the system’s return map. Simulation results confirm the algorithm’s efficiency in parameter estimation. Full article

Mid-infrared molecular sensing faces challenges in simultaneously achieving high-resolution qualitative identification and quantitative analysis of multiple biomolecules. To address this, we present a tunable mid-infrared sensing platform, integrating the simulation of a single-layer graphene square-aperture array sensor with a machine learning algorithm called principal component analysis for advanced spectral processing. The graphene square-aperture structure excites dynamically tunable localized surface plasmon resonances by modulating the graphene’s Fermi level, enabling precise alignment with the vibrational fingerprints of target molecules. This plasmon–molecule coupling amplifies absorption signals and serves as discernible “molecular barcodes” for precise identification without change in the structural parameters. We demonstrate the platform’s capability to detect and differentiate carbazole-based biphenyl molecules and protein molecules, even in complex mixtures, by systematically tuning the Fermi level to match their unique vibrational bands. More importantly, for mixtures with unknown total amounts and different concentration ratios, the principal component analysis algorithm effectively processes complex transmission spectra and presents the relevant information in a simpler form. This integration of tunable graphene plasmons with machine learning algorithms establishes a label-free, multiplexed mid-infrared sensing strategy with broad applicability in biomedical diagnostics, environmental monitoring, and chemical analysis. Full article