Search Results (299)

In the present work, the turbulent wake of a circular cylinder in a confined flow environment at a blockage ratio of 14% is experimentally investigated in a wind tunnel consisting of a parallel test section followed by a constant-area distorting duct, under subcritical Re inlet conditions. The initial stage of wake development, extending from the bluff body to the end of the parallel section, is analyzed, with the use of hot-wire anemometry and laser-sheet visualization. The near field reveals partial similarity to unbounded wakes, with the principal difference being a modification of the Kármán vortex street topology, attributed to altered vortex dynamics under confinement. Further downstream, the mean and fluctuating velocity distributions of the confined wake gradually evolve toward channel-flow characteristics. To elucidate this transition, wake measurements are systematically compared with channel flow data obtained in the same configuration under identical inlet conditions and with reference channel-flow datasets from the literature. Experimental results show that a vortex-transportation mechanism exists due to confinement effect, resulting in the progressive crossing and realignment of counter-rotating vortices toward the tunnel centerline. Although wake flow characteristics are preserved, suppression of classical periodic shedding is clearly depicted. Furthermore, it is shown that the confined near-wake spectral peak persists up to x₁/d~60 as in the free case and then vanishes as the spectra broadens. Coincidentally, the confined wake exhibits a narrower halfwidth than its free wake counterpart, while a centerline shift of the shed vortices is observed. Farfield wake-flow maintains strong anisotropy, while a weaker downstream growth of the streamwise integral scale is observed when compared to channel flow. Together, these findings explain how confinement reforms the nearfield topology and reorganizes momentum transport as the flow evolves to channel-like flow. Full article

Piezoelectric materials are fundamental elements in modern science and technology due to their unique ability to convert mechanical and electrical energy bidirectionally. They are widely employed in sensors, actuators, and energy-harvesting systems. In this work, we investigate the behavior of commercial lead zirconate titanate (PZT) sensors under frequency-mode excitation using a combined approach of impedance spectroscopy and optical interferometry. The impedance spectra reveal distinct resonance–antiresonance features that strongly depend on geometry, while interferometric measurements capture dynamic strain fields through fringe displacement analysis. The strongest deformation occurs near the first kilohertz resonance, directly correlated with the impedance phase, enabling the extraction of an effective piezoelectric constant (~40 pC/N). Moving beyond the linear regime, laser-induced excitation demonstrates optically driven activation of piezoelectric modes, with a frequency-dependent response and nonlinear scaling with optical power, characteristic of coupled pyroelectric–piezoelectric effects. These findings introduce a frequency-mode approach that combines impedance spectroscopy and optical interferometry to simultaneously probe electrical and mechanical responses in a single setup, enabling non-contact, frequency-selective sensing without surface modification or complex optical alignment. Although focused on macroscale ceramic PZTs, the non-contact measurement and activation strategies presented here offer scalable tools for informing the design and analysis of piezoelectric behavior in micro- and nanoscale systems. Such frequency-resolved, optical-access approaches are particularly valuable in the development of next-generation nanosensors, MEMS/NEMS devices, and optoelectronic interfaces where direct electrical probing is challenging or invasive. Full article

This study aims to evaluate the effects of repeated mechanical recycling on the properties of a novel aliphatic polyketone composite reinforced with 15 wt% and 30 wt% glass fibers (PK15GF and PK30GF), providing insights into its potential for sustainable engineering applications. The investigation focuses on three main aspects: changes in melt flow index (MFI) and viscosity, the influence of glass fiber content on thermal and mechanical stability, and the retention of structural integrity and crystallinity under multiple processing cycles. Composites, commercially available since 2019, were subjected to single- and five-cycle recycling with 100% reprocessed content. Comprehensive characterization—including tensile testing, Differential Scanning Calorimetry (DSC), Dynamic Mechanical Analysis (DMA), Fourier Transform Infrared Spectroscopy (FT-IR), Melt-Flow Index (MFI), Differential Thermal Analysis (DTA), and mechanical tensile testing—revealed filler-dependent alterations in morphology, thermal stability, and crystallinity. MFI decreased from 100.56 to 42.63 g/10 min for PK15GF, indicating pronounced chain scission, recombination, and crosslinking, whereas PK30GF decreased only from 89.00 to 59.76 g/10 min. FT-IR spectra confirmed greater crosslinking in PK15GF, while DSC and DMA demonstrated smaller T_g and ΔHm variations in PK30GF (T_g +0.45 °C, ΔHm −13.93 J·g⁻¹) versus PK15GF (T_g +1.13 °C, ΔHm −69.24 J·g⁻¹). These findings reveal that higher glass fiber content mitigates degradation, preserves structural integrity, and maintains thermal and viscoelastic stability, establishing clear correlations between filler content, mechanical performance, and recyclability. Overall, this work provides mechanistic insights into degradation pathways and demonstrates the potential of glass fiber-reinforced aliphatic polyketones for sustainable, high-performance engineering and automotive applications. 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

IRS 16CC and IRS 33N are among more than 100 young, massive stars identified within 0.5 pc from the Galactic central supermassive black hole Sgr A*, where conventional star formation processes are expected to be strongly suppressed. A subset of these stars, including IRS 16CC, has been confirmed to reside in a clockwise rotating stellar disk, and is thought to have formed in a massive, gaseous disk around Sgr A*. In contrast, other young massive stars, such as IRS 33N, exhibit dynamical behaviors that deviate significantly from those of the disk population, and their formation mechanism is still uncertain. To investigate their formation mechanism, we carried out near-infrared, high-resolution spectroscopic observations of IRS 16CC and IRS 33N using the Infrared Camera and Spectrograph on the Subaru telescope, equipped with an adaptive optics system. We compared the profiles of He I absorption lines with synthetic spectra generated from model atmospheres, and then compared derived stellar parameters with stellar evolutionary tracks to estimate their ages and initial masses. Our analysis yields their effective temperatures of ∼23,000 K, surface gravities of ∼2.8, and initial masses of $37\pm 6M_{\odot }$ and ${27}_{-3}^{+4}{M}_{\odot }$, consistent with spectral types of B0.5–1.5 supergiants. The ages of IRS 16CC and IRS 33N are estimated to be $4.4\pm 0.7$ Myr and $5.3_{-0.7}^{+1.1}$ Myr, respectively. These results suggest that, despite their different dynamical properties, the two stars are likely to share a common origin. Full article

The vibration properties of materials play a role in their conduction of electric charges. Ionic conductors such as electrodes and solid electrolytes are also relevant in this respect. The vibration properties are typically assessed with infrared and Raman spectroscopy, and inelastic neutron scattering, which all allow for the derivation of the phonon density of states (PDOS) in part of a full portion of the Brioullin zone. Nuclear resonant vibration spectroscopy (NRVS) is a novel method that produces the element-specific PDOS from Mössbauer-active isotopes in a compound. We employed NRVS operando on a pouch cell battery containing a Li⁵⁷FePO₄ electrode, and thus could derive the PDOS of the ⁵⁷Fe in the electrode during charging and discharging. The spectra reveal reversible vibrational changes associated with the two-phase conversion between LiFePO₄ and FePO₄, as well as signatures of metastable intermediate states. We demonstrate how the NRVS data can be used to tune the atomistic simulations to accurately reconstruct the full vibration structures of the battery materials in operando conditions. Unlike optical techniques, NRVS provides bulk-sensitive, element-specific access to the full phonon spectrum under realistic operando conditions. These results establish NRVS as a powerful method to probe lattice dynamics in working batteries and to advance the understanding of ion transport and phase transformation mechanisms in electrode materials. Full article

Hydrogels with protein–polysaccharide combinations are widely used in the field of tissue engineering, as they can mimic the in vivo environments of native tissues, specifically the extracellular matrix (ECM). However, achieving stability and mechanical properties comparable to those of tissues by employing natural polymers remains a challenge due to their weak structural characteristics. In this work, we optimized the fabrication strategy of a hydrogel composite, comprising gelatin and sodium alginate (Gel-SA), by varying reaction parameters. Magnetite (Fe₃O₄) nanoparticles were incorporated to enhance the mechanical stability and structural integrity of the scaffold. The changes in hydrogel stiffness and viscoelastic properties due to variations in polymer mixing ratio, crosslinking time, and heating cycle, both before and after nanoparticle incorporation, were compared. FTIR spectra of crosslinked hydrogels confirmed physical interactions of Gel-SA, metal coordination bonds of alginate with Ca²⁺, and magnetite nanoparticles. Tensile and rheology tests confirmed that even at low magnetite concentration, the Gel-SA-Fe₃O₄ hydrogel exhibits mechanical properties comparable to soft tissues. This work has demonstrated enhanced resilience of magnetite-incorporated Gel-SA hydrogels during the heating cycle, compared to Gel-SA gel, as thermal stability is a significant concern for hydrogels containing gelatin. The interactions of thermoreversible gelatin, anionic alginate, and nanoparticles result in dynamic hydrogels, facilitating their use as viscoelastic acellular matrices. Full article

We report high-resolution, cavity-enhanced direct frequency comb Fourier transform spectroscopy of cold acetylene (C₂H₂) molecules in a planar supersonic jet expansion. The experiment is based on a near-infrared frequency comb with a 300 MHz effective repetition rate, matched to a high-finesse enhancement cavity traversing the jet. The rotational and translational cooling of acetylene was achieved via expansion in argon carrier gas through a slit nozzle. By interleaving successive mode-resolved spectra measured at different comb repetition rates, we retrieved full absorption line profiles. Spectroscopic analysis reveals sharp, Doppler-limited transitions corresponding to a jet core rotational temperature below 7 K. Frequency comb and cavity stabilization were achieved through active Pound–Drever–Hall locking and mechanical vibration damping, enabling a spectral precision better than 2 MHz, limited by the vibrations induced by the pumping system. The demonstrated sensitivity reaches a minimum detectable absorption of 7.8 × 10⁻⁷ cm⁻¹ over an 18 m effective path length in the jet core. This work illustrates the potential of cavity-enhanced direct frequency comb spectroscopy for precise spectroscopic characterization of cold supersonic expansions, with implications for studies in molecular dynamics, reaction kinetics, and laboratory astrophysics. Full article

Polymer-based hybrid composites have emerged as promising platforms for multifunctional energy applications, combining structural versatility with tunable dielectric behavior. In this study, synthesized [Fe(bpy)₃]SO₄; (tris(2,2′-bipyridine)iron(II) sulfate) coordination compound was incorporated into a green epoxy resin matrix to fabricate nanocomposites aimed at enhancing dielectric permittivity (ε′), piezoelectric coefficient (d₃₃, pC/N), energy-storage efficiency (n_rel, %), and mechanical strength (σ, MPa). The integration of the Fe(II) complex via Scanning Electron Microscopy (SEM) confirmed a homogeneous dispersion within the matrix. Broadband Dielectric Spectroscopy (BDS) revealed the presence of three relaxation processes in the spectra of the tested systems, demonstrating enhanced dielectric permittivity with increasing Fe(II) content. Under progressively shorter relaxation times (τ, s), key processes such as interfacial polarization, the polymer matrix’s transition from a glassy to a rubbery state, and the dynamic reorganization of polar side groups along the polymer backbone are activated. The ability to store and retrieve electric energy was confirmed by varying filler content under direct current (dc) conditions. The nanocomposite with 10 phr (mass parts/100 mass parts of resin) filler achieved a piezoelectric coefficient of d₃₃ = 5.1 pC/N, an energy-storage efficiency of n_rel = 44%, and a tensile strength of σ = 55.5 MPa, all of which surpass values reported for conventional epoxy-based composites. These results confirm the ability of the system to store and retrieve electric energy under direct current (dc) fields, while maintaining mechanical robustness and thermal stability due to synergistic interactions between the epoxy matrix and the Fe(II) complex. The multifunctional behavior of the composites underscores their potential as advanced materials for integrated dielectric, piezoelectric, and energy storage and harvesting applications. Full article

This study conducted field experiments in 2024 in Meidaizhao Town, Tumed Right Banner, Baotou City, Inner Mongolia Autonomous Region, adopting a plant-level sampling design with 10 maize plots selected as sampling areas (20 plants per plot). At four critical growth stages—jointing, heading, filling, and maturity—multimodal data, including that covering leaf spectra, root-zone soil spectra, and leaf chlorophyll and nitrogen content, were synchronously collected from each plant. In response to the prevalent limitations of the existing yield prediction methods, such as insufficient accuracy and limited generalization ability due to reliance on single-modal data, this study takes the acquired multimodal maize data as the research object and innovatively proposes a multimodal fusion prediction network. First, to handle the heterogeneous nature of multimodal data, a parallel feature extraction architecture is designed, utilizing independent feature extraction branches—leaf spectral branch, soil spectral branch, and biochemical parameter branch—to preserve the distinct characteristics of each modality. Subsequently, a dual-path feature fusion method, enhanced by a cross-attention mechanism, is introduced to enable dynamic interaction and adaptive weight allocation between cross-modal features, specifically between leaf spectra–soil spectra and leaf spectra–biochemical parameters, thereby significantly improving maize yield prediction accuracy. The experimental results demonstrate that the proposed model outperforms single-modal approaches by effectively leveraging complementary information from multimodal data, achieving an R² of 0.951, an RMSE of 8.68, an RPD of 4.50, and an MAE of 5.28. Furthermore, the study reveals that deep fusion between soil spectra, leaf biochemical parameters, and leaf spectral data substantially enhances prediction accuracy. This work not only validates the effectiveness of multimodal data fusion in maize yield prediction but also provides valuable insights for accurate and non-destructive yield prediction. Full article

Lead-free perovskite variants have emerged as promising candidates due to their self-trapped exciton emission. However, in ASnX₃ systems, facile oxidation of Sn(II) to Sn(IV) yields A₂SnCl₆ vacancy-ordered derivatives. Paradoxically, despite possessing a direct bandgap, these variants exhibit diminished photoluminescence (PL). Doping engineering thus becomes essential for precise optical tailoring of A₂SnX₆ materials. Herein, through integrated first-principles calculations and spectroscopic analysis, we elucidate the luminescence mechanism in Te⁴⁺-doped (NH₄)₂SnCl₆ lead-free perovskites. Density functional theory, X-ray diffraction (XRD) patterns and X-ray photoelectron spectroscopy (XPS) confirm Te⁴⁺ substitution at Sn sites via favorable chemical potentials. Spectral interrogations, including absorption and emission profiles, reveal that the intense emission originates from the triplet STE recombination (³P₁ → ¹S₀) of Te centers. Temperature-dependent PL spectra further demonstrate strong electron–phonon coupling that induces symmetry-breaking distortions to stabilize STEs. Complementary electronic band structure and molecular orbital calculations unveil the underlying photophysical pathway. Leveraging these distinct thermal responses of PL intensity and peak position, 0.5%Te:(NH₄)₂SnCl₆ emerges as a highly promising candidate for non-contact, dual-parameter optical thermometry over an ultra-broad range (100–400 K). This work provides fundamental insights into the exciton dynamics and thermal engineering of optical properties in this material system, establishing its significant potential for advanced temperature-sensing applications. Full article

Citrus diseases severely impact fruit yield and quality. To facilitate in-field, non-destructive, and rapid detection of citrus leaf physiological and pathological conditions, this study proposes a classification method for citrus leaf physiological and pathological statuses that integrates visible/near-infrared multispectral technology with deep learning. First, a handheld spectrometer was employed to acquire spectral images of five sample categories—Healthy, Huanglongbing, Yellow Vein Disease, Magnesium Deficiency and Manganese Deficiency. Mean spectral data were extracted from regions of interest within the 350–2500 nm wavelength range, and various preprocessing techniques were evaluated. The Standard Normal Variate (SNV) transformation, which demonstrated optimal performance, was selected for data preprocessing. Next, we innovatively introduced an adaptive spectral positional encoding mechanism into the Transformer framework. A lightweight, learnable network dynamically optimizes positional biases, yielding the ASTransformer (Adaptive Spectral Transformer) model, which more effectively captures complex dependencies among spectral features and identifies critical wavelength bands, thereby significantly enhancing the model’s adaptive representation of discriminative bands. Finally, the preprocessed spectra were fed into three deep learning architectures (1D-CNN, 1D-ResNet, and ASTransformer) for comparative evaluation. The results indicate that ASTransformer achieves the best classification performance: an overall accuracy of 97.7%, underscoring its excellent global classification capability; a Macro Average of 97.5%, reflecting balanced performance across categories; a Weighted Average of 97.8%, indicating superior performance in classes with larger sample sizes; an average precision of 97.5%, demonstrating high predictive accuracy; an average recall of 97.7%, showing effective detection of most affected samples; and an average F1-score of 97.6%, confirming a well-balanced trade-off between precision and recall. Furthermore, interpretability analysis via Integrated Gradients quantitatively assesses the contribution of each wavelength to the classification decisions. These findings validate the feasibility of combining a handheld spectrometer with the ASTransformer model for effective citrus leaf physiological and pathological detection, enabling efficient classification and feature visualization, and offer a valuable reference for disease detection of physiological and pathological conditions in other fruit crops. Full article

To address the challenges of narrow peak characteristics and low signal-to-noise ratio (SNR) detection in Laser-Induced Breakdown Spectroscopy (LIBS), in this paper, we combine the Sparse Bayesian Learning–Baseline Correction (SBL-BC) algorithm with residual skewness monitoring to propose a spectral estimation method tailored for LIBS. In LIBS spectra, discrete peaks are susceptible to baseline fluctuations and noise, while the Gaussian dictionary modeling and fixed convergence criterion of the existing SBL-BC lead to the inaccurate characterization of narrow peaks and high computational complexity. To overcome these limitations, we introduce a residual skewness dynamic tracking mechanism to mitigate residual negative skewness accumulation caused by positivity constraints under high noise levels, preventing traditional convergence criterion failure. Simultaneously, by eliminating the dictionary matrix and directly modeling the spectral peak vector, we transform matrix operations into vector computations, better aligning with LIBS’s narrow peak features and high-channel-count processing requirements. Through simulated and real spectral experiments, the results demonstrate that this method outperforms the SBL-BC algorithm in terms of spectral peak fitting accuracy, computational speed, and convergence performance across various SNRs. It effectively separates spectral peaks, baseline, and noise, providing a reliable approach for both quantitative and qualitative analysis of LIBS spectra. Full article

This paper presents a comprehensive dynamical analysis of a nonlinear oscillator subjected to both deterministic and stochastic excitations. Utilizing a diverse suite of analytical tools—including phase portraits, Poincaré sections, Lyapunov exponents, recurrence plots, Fokker–Planck equations, and sensitivity diagnostics—we investigate the transitions between quasi-periodicity, chaos, and stochastic disorder. The study reveals that quasi-periodic attractors exhibit robust topological structure under moderate noise but progressively disintegrate as stochastic intensity increases, leading to high-dimensional chaotic-like behavior. Recurrence quantification and Lyapunov spectra validate the transition from coherent dynamics to noise-dominated regimes. Poincaré maps and sensitivity analysis expose multistability and intricate basin geometries, while the Fokker–Planck formalism uncovers non-equilibrium steady states characterized by circulating probability currents. Together, these results provide a unified framework for understanding the geometry, statistics, and stability of noisy nonlinear systems. The findings have broad implications for systems ranging from mechanical oscillators to biological rhythms and offer a roadmap for future investigations into fractional dynamics, topological analysis, and data-driven modeling. Full article

Three fluorophenyl-substituted cyclometalated Ir(III) complexes (Ir1–Ir3) have been synthesized by changing the position of the fluorine atom. All complexes exhibit distinct aggregation-induced phosphorescence emission (AIPE) characteristics in CH₃CN/H₂O and demonstrate satisfactory detection performance for 2,4,6-trinitrophenols (TNPs) with limits of detection of 124 nM, 101 nM, and 127 nM, respectively. In addition, Ir1–Ir3 possess excellent selectivity and anti-interference capability for TNP detection, showing outstanding performance even in different common water samples. The ultraviolet–visible absorption spectra and luminescence lifetimes of the complexes show that their quenching processes include both a static process and dynamic process, and the detection mechanism may be assigned to a combination of photo-induced electron transfer and an inner-filter effect. Full article