Search Results (3,605)

The development of efficient, single-phase-excitable white-light phosphors remains a critical challenge for solid-state lighting applications. In this work, white-light-emitting CaWO₄:Eu³⁺/g-C₃N₄ composites were successfully developed by integrating red-emitting CaWO₄:7%Eu³⁺ with blue-emitting graphitic carbon nitride (g-C₃N₄). Under 365 nm near-UV excitation, the composite exhibits dual-band emission originating from the ⁵D₀ → ⁷F₂ transition of Eu³⁺ (~616 nm) and the intrinsic band-edge luminescence of g-C₃N₄ (~460 nm). The optimal white-light performance is achieved at a g-C₃N₄ content of 0.5 wt%, yielding CIE chromaticity coordinates of (0.294, 0.324) and a correlated color temperature (CCT) of 7673 K. This sample demonstrates a photoluminescence quantum yield (PLQY) of 3.25%. Moreover, the CaWO₄:Eu³⁺/g-C₃N₄ composite shows enhanced thermal stability, retaining 78% of its initial emission intensity at 175 °C, with an activation energy of 0.41 eV—significantly higher than that of the pristine CaWO₄:Eu³⁺ (0.22 eV). These results indicate that the CaWO₄:Eu³⁺/g-C₃N₄ heterostructured phosphor is a promising candidate for single-phase-excitable white-light applications. Full article

4-Dimethylamino-2′-hydroxy chalcone (DHC) 1 is an important natural compound that is nearly non-fluorescent in solution but highly fluorescent in its crystalline state. At room temperature, the weak fluorescence from the DHC solution is exclusively from its keto tautomer, without notable contribution from its enol tautomer. By using low-temperature fluorescence, the study found that the enol emission could be detected upon cooling with liquid N₂ in a protic solvent (e.g., EtOH). This led to observation of the fluorescence vibronic structure of enol tautomer, in addition to its enol emission λ_em ≈ 473 nm that is well separated from its keto tautomer emission (λ_em ≈ 600 nm). By freezing DHC in a solvent matrix, the study revealed the fluorescent characteristics of a single molecule in a rigid environment. Further comparison of DHC in a solvent matrix and crystalline state disclosed that the emission of crystalline DHC was primarily from the keto tautomer, along with some minor contribution from the enol tautomer, despite the tight packing environment in the crystalline state. Full article

Electric vehicles (EVs) have fundamentally changed the noise, vibration, and harshness (NVH) landscape of automotive powertrains. In the absence of masking internal-combustion-engine noise, harmonic components such as gear whine, electric-motor orders, and inverter-related tones become more perceptible and more critical to vehicle refinement. This review synthesizes the current state of the art in harmonic NVH engineering for electric drivetrains, focusing on the interactions between gear geometry, manufacturing variability, electromechanical coupling, structural transfer, and human sound perception. Classical mechanisms of gear-mesh excitation are revisited together with emerging EV-specific challenges, including long-wavelength flank deviations, ghost orders, lightweight housing dynamics, and psychoacoustic sound-quality requirements. The review further examines recent progress in predictive and data-driven approaches, including machine-learning-based gear-noise modeling, digital-twin concepts, and virtual NVH assessment workflows. Overall, the literature shows that harmonic NVH engineering in EVs is evolving from a conventional gear-noise problem into a multidisciplinary system-level task integrating gear dynamics, manufacturing science, structural acoustics, electric-drive control, psychoacoustics, and data-driven optimization. This review provides a structured synthesis of these developments and identifies key research gaps and future directions for the next generation of refined electric drivetrains. Full article

Wood coatings play a critical role in protecting wood substrates from environmental degradation, particularly ultraviolet (UV)-induced photodegradation. This review comprehensively examines the mechanisms of wood coating photodegradation, the factors influencing their durability, and current anti-aging strategies. Photodegradation arises from polymer chain scission, chemical structure reorganization, and photo-oxidation of lignin and cellulose, leading to coating chalking, cracking, gloss loss, and color changes, ultimately compromising wood mechanical properties and service life. Key anti-aging strategies include UV absorbers, which convert harmful UV radiation into heat; hindered amine light stabilizers (HALSs) that capture free radicals and quench excited-state molecules; barrier and shielding materials that form dense physical or nanostructured networks to block UV penetration and enhance mechanical and water resistance; and antioxidants that neutralize free radicals or decompose peroxides at the molecular level. Each approach can be employed individually or synergistically to enhance coating durability. Challenges remain in achieving long-term outdoor stability, balancing transparency and UV shielding, optimizing nanoparticle dispersion, and maintaining the activity of natural antioxidants. Future research should focus on multifunctional composite coatings integrating bio-based materials and nanotechnology, smart responsive systems, adaptive protection mechanisms, and standardized long-term evaluation protocols. These advancements will facilitate the development of high-performance, sustainable wood coatings and promote the value-added utilization of wood resources. Full article

To investigate the battery impedance variation after the occurrence of nail penetration, this paper adopts Dynamic Electrochemical Impedance Spectroscopy (DEIS) for real-time monitoring of the impedance changes of lithium-ion batteries during the nail penetration process. A piecewise multi-frequency superimposed sinusoidal excitation is designed, which not only complies with the stability principle of battery testing but also ensures the signal-to-noise ratio of the excitation signal. By injecting the designed excitation signal into the operating battery and combining it with the rapid DEIS generation technology, the acquisition of DEIS data within the target frequency band in a short time is realized. Based on the obtained DEIS data, a fractional-order model is established and fitted for analysis before and after nail penetration. The results show that the steel nail introduces inductive reactance and impedance to the battery. Due to the parallel connection between the steel nail and the internal resistance of the battery, the overall impedance decreases, exhibiting a short-circuit state, and both the real and imaginary parts of the impedance experience an abrupt change at the moment of nail penetration. Considering the characteristic of abrupt impedance change of the battery after nail penetration, a battery nail penetration detection method based on DEIS is proposed. Considering the abrupt change characteristics of battery impedance after nail penetration, this paper proposes a battery nail penetration detection method based on DEIS. This method can effectively solve the problem of low sensitivity of traditional voltage monitoring methods in detecting nail penetration during battery operation. It has higher sensitivity and faster response speed compared with traditional methods, enabling online monitoring of battery states. Additionally, this paper also explores its potential application in real-world vehicles. Full article

Low-frequency seismic disturbances significantly limit the performance of precision engineering systems and active vibration isolation platforms. Model predictive control (MPC) is widely applied in such systems due to its ability to handle multi-variable dynamics and constraints. However, its performance strongly depends on model accuracy. To address this issue, this paper proposes a multilayer perceptron-enhanced model predictive control (MLP-MPC) framework for active vibration isolation. In the proposed approach, a multilayer perceptron (MLP) is trained offline to learn the mapping between the current system state and the free-response term in the MPC prediction equation. During online implementation, the trained MLP replaces the model-based free-response calculation while preserving the original quadratic programming structure of conventional MPC. The proposed method is evaluated on a single-degree-of-freedom active vibration isolation system under low-frequency sinusoidal excitation and measured seismic disturbances. The simulation results show that MLP-MPC achieves reduced running RMS tracking error and lower moving-window RMS error compared with conventional MPC and Proportional–Integral–Derivative (PID) control. The results suggest that integrating data-driven free-response estimation into predictive control provides a practical approach to enhancing the performance of low-frequency vibration suppression while maintaining computational feasibility. Full article

Carbon quantum dots (CQDs) are inherently photochemically active nanomaterials, exhibiting excitation-dependent emission, proton-responsive surface states, and modifiable redox properties, enabling various sensing applications across fluorescence, electrochemistry, and electrochemiluminescence (ECL) modalities. This comprehensive review elucidates their methodologies, including PET-driven “turn-off/on” fluorescence, ratiometric pH sensing, electrocatalytic currents, and co-reactant-amplified ECL, achieving low detection limits for metal ions, biomolecules, and environmental analytes. Surface-mediated responsiveness is essential to CQD performance, offering exceptional sensitivity while also conferring inherent cross-reactivity. Meta-analysis was conducted using data extracted from previously published studies on CQDs for the detection property, in which the failure ratio was computed as the number of unsuccessful detections divided by the total number of tests reported in each study. Additionally, critical examination reveals inconsistencies in the limit of detection (LOD) metrics and mechanistic uncertainties, as well as strategies for enhancing selectivity through rational doping and molecular recognition hybrids. Full article

The photophysical processes and photochemical reactions of 1,4-di(azastyryl)benzene (1) derivative {[(E,E)-1](ClO₄)₂} were investigated by absorption, luminescence, and laser kinetic spectroscopy in the water solution. The observed photo processes include dimerization, E-Z isomerization, and intersystem crossing to the triplet state, as well as the complexation [(E,E)-1](ClO₄)₂ with cucurbit[7]uril (CB[7]). The [(E,E)-1](ClO₄)₂ dye dimerization was shown to be energetically more favorable in the excited state than in the ground state. The reversible photoinduced migration of the dye dication in the CB[7] cavity takes place as a result of partial exit of the [(E,E)-1]²⁺ from the cavity and its subsequent conversion to the (E,Z)-isomer in the excited state, which undergoes conversion to the initial complex of {[(E,E)-1]@CB[7]}²⁺ after returning to the ground state. This photoprocess is of interest in relation to the scientific problem of designing photocontrolled supramolecular machines. Full article

The eddy current braking system (ECBS) is a crucial non-contact technology for high-speed railway. Conventional DC-excited systems face significant challenges such as excessive rail heating and high-capacity power supply requirements. This paper proposes a novel ECBS with AC excitation and auxiliary capacitor to achieve integrated energy recovery and power supply optimization. To evaluate its performance, a rigorous analytical framework is developed. First, a 2D subdomain model is established by incorporating the longitudinal end effect to solve the magnetic field distribution. Subsequently, an equivalent circuit is derived from the subdomain results to investigate steady-state braking characteristics and power flow. Analysis results demonstrate that the proposed system not only generates controllable braking force but also converts a portion of kinetic energy into storable electrical energy, effectively mitigating secondary rail heating. Most significantly, the implementation of an optimal auxiliary capacitor (134 μF) is found to reduce the required inverter capacity compared to inverter-only conditions. These findings provide a theoretical foundation and a practical design tool for developing high-performance, energy-efficient braking systems in high-speed transportation. Full article

Resonant transfer and excitation (RTE) is a correlated two-electron ion–atom collision process mediated by the two-center electron–electron interaction: a projectile electron is excited while a target electron is captured, forming doubly excited states. These states decay via X-ray (RTEX) or Auger (RTEA) emission. For sufficiently fast collisions with light targets, RTE becomes analogous to dielectronic capture (DC)—a key plasma process—and is successfully described by the impulse approximation (IA). Early (1983–1992) RTEX and more stringent, state-selective RTEA measurements provided essential indirect DC cross-section information before direct electron–ion measurements became available. A 1992 review by the first author,focusing on zero-degree Auger projectile spectroscopy (ZAPS) of state-selective KLL D states, validated the IA for low-$Z_p$ ($Z_p\le 9$) projectile ions, yet a puzzling systematic discrepancy remained: IA RTEA cross-sections were consistently larger than experimental, with the disagreement increasing as $Z_p$ decreased. The present article reviews RTEA progress since 1992, including new refinements to IA calculations, an exact analytic IA formulation, and instrumental ZAPS improvements. A methodical analysis demonstrates impressive agreement across measurements spanning both pre- and post-1992 eras, including new experimental results, effectively eliminating previous systematic discrepancies. IA validity is confirmed down to boron ions, with He${}^{+}$ and certain Li-like ions remaining the only notable exceptions. Recently, a rigorous quantum mechanical ion–atom collision treatment has emerged: nonperturbative close-coupling calculations of transfer excitation for He-like carbon ions colliding with He confirm the dominance of RTE via two-center electron–electron interactions at large impact parameters, yielding RTEA results in excellent agreement with experiments. Full article

The discovery of dynamical quantum phase transitions (DQPTs) has fundamentally challenged the traditional view that phase transitions only occur in thermal equilibrium. Experimental platforms and 1D numerical methods, like matrix product states (MPS), have made great progress. However, exploring true 2D DQPTs remains difficult due to finite-size limitations and the geometric biases of quasi-1D cylinder mappings. Here, we bypass these limitations by deploying a tree tensor network (TTN) approach. This allows us to directly compute the quench dynamics of the transverse-field Ising model (TFIM) on an open 2D square lattice. Because the TTN architecture naturally mirrors 2D lattice connectivity, we can extract the global Loschmidt echo. Our simulations reveal that while deep quenches yield standard DQPTs, quenching within the ferromagnetic phase produces an anomalous dynamical response. In this regime, the rate function exhibits sharp non-analytic peaks even as the macroscopic order parameter maintains its initial sign. This decoupled behavior strongly indicates that local spin excitations drive 2D DQPTs, rather than the macroscopic domain-wall motions seen in 1D chains. These results provide a quantitative numerical baseline for understanding non-equilibrium quantum matter in higher dimensions. Full article

High-frequency transformer (HFT) noise is a pivotal indicator of equipment performance. To conduct a comprehensive evaluation, this study systematically performed testing and evaluation on the noise generated by a 70 kW HFT under no-load conditions. Acoustic data were collected using acoustic sensors and a head-and-torso simulator, followed by an analysis of noise characteristics focusing on the impacts of voltage levels and operating frequencies. A multi-dimensional evaluation of HFT noise was carried out using sound quality parameters to unravel its intrinsic attributes under electrical parameter excitation. The key findings are as follows: HFT noise exhibits steady-state time-domain behavior and distinct tonal frequency-domain features; the dominant frequency is twice the operating frequency, with prominent harmonics. The noise intensity increases with the voltage levels (~47.0 dB (A) at 200 V to ~72.0 dB (A) at 750 V at 5 kHz) but decreases with the operating frequencies (~82.0 dB (A) at 4 kHz to ~47.0 dB (A) at 10 kHz at 750 V). This study establishes correlations between the electrical parameters and sound quality metrics; the loudness, sharpness, tone-to-noise ratio and prominence ratio are sensitive to the electrical parameters of HFT. Single-frequency noise from HFT exhibits remarkable perceptual salience, exacerbating the perceived annoyance. Thus, HFT design should prioritize reducing single-frequency noise to alleviate such issues. Full article

Partially liquid-filled rotor systems subjected to lateral excitation exhibit pronounced fluid–structure interaction, leading to complex and highly sensitive vibration responses. To enable efficient probabilistic prediction under parametric uncertainty, this study develops a deterministic–data-driven framework for a rigid hollow rotor partially filled with liquid. Based on small-perturbation flow theory, the liquid-induced feedback forces are analytically derived and incorporated into the coupled rotor–liquid dynamic equations, yielding a closed-form steady-state solution. The results reveal that lateral excitation in one direction induces coupled vibration in the orthogonal direction, resulting in an elliptical whirl trajectory of the rotor center. The vibration characteristics depend jointly on excitation frequency and rotor angular velocity, and for a given angular velocity, two critical excitation frequencies are identified at which the response amplitude increases sharply. Surrogate models based on a backpropagation neural network (BPNN) and a support vector machine (SVM) are constructed and validated, with the BPNN demonstrating superior predictive accuracy. Uncertainty analysis further shows that the maximum vibration amplitude exhibits asymmetric, non-Gaussian distributions even under normally distributed inputs, and excessive amplification may occur beyond certain uncertainty levels. The proposed framework provides a robust tool for probabilistic vibration assessment and uncertainty-informed design of partially liquid-filled rotor systems. Full article

We present an updated overview of the symmetry-preserving contact interaction model in hadronic physics, which was developed a little over a decade ago to describe the mass spectrum and internal structure of mesons and diquarks composed of light and heavy quarks. Over the years, the contact interaction model has evolved into a framework capable of treating both ground and excited states, providing a simple yet consistent approach to nonperturbative QCD. In this review, we examine the mass spectrum and elastic form factors of forty mesons with different spins and parities, together with their corresponding diquark partners. Importantly, we update the comparison of contact interaction predictions using recent results from the literature, offering a fresh perspective on the model’s performance, strengths, and limitations. The analysis presented here refines previous conclusions and supports the contact interaction model as a practical tool for hadron structure studies, with potential applications to baryons and multiquark states. We also present comparisons with other theoretical models and approaches, including lattice quantum chromodynamics, and comment on future prospects in view of ongoing and planned experimental programs regarding hadron structure. In particular, forthcoming measurements at FAIR together with future studies at Jefferson Lab and the Electron Ion Collider are expected to provide key insights into hadron structure, with FAIR offering indirect constraints via hadron spectroscopy, hadronic interactions, and in-medium properties; high-precision data on meson structure and form factors from Jefferson Lab and the Electron Ion Collider will provide valuable benchmarks with which to confront predictions based on the contact interaction model. Full article

Unmanned aerial vehicles (UAVs) operating in safety-critical missions require effective anomaly detection methods to identify propulsion-system faults before they cause catastrophic failures. However, current vibration-based diagnostic models typically rely on datasets representing only discrete, isolated fault states, and do not capture the continuous structural degradation that occurs during real flight operations. To address this gap, this study proposes a severity-ordered vibration data augmentation framework for anomaly detection in rotary-wing UAV propulsion systems. Controlled experiments were conducted under healthy, tape-induced imbalance, scratch, and cut propeller conditions using stepped throttle excitation from 10% to 100% in 10% increments, with 40 s per level. A severity-ordered arrangement strategy based on throttle level and a robust peak-to-peak severity metric generated approximately 7.5 h of augmented vibration data per axis, representing a continuous degradation trajectory. Three-axis continuous wavelet transform (CWT) scalograms of size $48\times 96\times 3$ were used to train an unsupervised anomaly detection framework. Comparative experiments with Isolation Forest, One-Class SVM, and LSTM–AE demonstrated that the proposed Convolutional Neural Network (CNN)–Bidirectional Gated Recurrent Unit (BiGRU)–State-Space Model (SSM)–Autoencoder (AE) architecture achieved the best performance, reaching 0.9959 precision, 0.4428 recall, 0.6131 F1-score, and 0.9284 Area Under the Receiver Operating Characteristic Curve (AUROC). The ablation study further showed that incorporating temporal modeling and state-space dynamics improves detection robustness compared with CNN–AE and CNN–BiGRU–AE baselines. These results show that combining severity-ordered augmentation with deep temporal learning improves progressive propulsion anomaly detection in UAV vibration monitoring. This work introduces a methodology that connects rotor dynamics principles with deep learning, providing a continuous degradation manifold that improves early-stage detection and condition monitoring of UAV propulsion systems. Full article