Search Results (223)

The efficient removal of tenacious oxide scales from hot-rolled steel surfaces represents a persistent challenge in advanced manufacturing, as traditional manual grinding methods exhibit poor efficiency and environmental compatibility. This investigation develops an innovative methodology, i.e., a “coarse-to-fine” hierarchical cleaning paradigm consisting of dual-stepwise laser cleaning with variable parameters that successfully addresses the restrictive process window inherent to conventional single-parameter techniques. Through a strategically designed sequential treatment protocol—employing initial low-frequency (20 kHz), high-energy-density (200 mm/s) laser irradiation for primary oxide ablation, succeeded by high-frequency (60 kHz), low-energy-density (4000 mm/s) processing for residual scale elimination—we demonstrate an optimal synergy between ablative and thermomechanical vibration mechanisms. Rigorous multi-modal characterization incorporating SEM-EDS microscopy, oxygen content quantification, and metallographic analysis confirms exceptional performance metrics, including 98.7% oxide removal efficiency and 43.2% reduction in substrate surface roughness relative to standard methods. The developed protocol achieves a 2.8-fold expansion of the operational parameter space while establishing a novel “coarse-to-fine” hierarchical cleaning paradigm. These findings offer fundamental insights into laser–matter interactions while delivering a transferable technological framework for high-value manufacturing sectors, particularly in automotive and aerospace component production. Full article

Localized optical absorption by nanoscale inclusions can profoundly alter energy deposition in optical traps, giving rise to nonlinear and long-timescale dynamics. Recent experiments have reported the formation of expanding optically darkened regions and episodic plasma-like emission during pulsed near-infrared optical trapping of magnetic beads interacting with biological cells. Here, we develop a reduced-order mechanistic model to investigate whether absorber-driven optical–thermal feedback associated with Fe₃O₄ inclusions is sufficient to reproduce the observed pre-plasma darkening dynamics. The model is constructed progressively from first-principles electromagnetic absorption and pulse-scale thermal diffusion to nonlinear feedback mediated by an evolving optically modified region. Single-pulse and multi-pulse simulations demonstrate that isolated iron-oxide absorbers cool too rapidly to sustain long-timescale thermal accumulation through linear heating alone. However, incorporation of a bubble-mediated optical feedback channel produces bounded growth, partial optical darkening, and slow relaxation dynamics consistent with experimentally observed minute-scale evolution. Electromagnetic absorption was computed using full core–shell Mie theory, yielding absorption cross-sections sufficient to support strong localized optical attenuation under experimentally relevant trapping conditions. The resulting reduced-order feedback framework reproduces stable growth–relaxation cycles, finite transmission plateaus, and self-limited optical darkening without requiring runaway heating or catastrophic cavitation. To evaluate the model quantitatively, simulated transmission dynamics were compared against experimentally measured normalized transmission traces digitized from previously reported optical trapping experiments. The fitted model reproduced the observed finite transmission plateau and slow post-activation relaxation with good agreement ($R^2\approx 0.86$, RMSE $\approx 1.3\times {10}^{-2}$). These results support the interpretation that experimentally observed optical darkening arises from a feedback-regulated optical–thermal process involving slowly evolving structural modification of the trapping region rather than cumulative thermal storage within isolated absorbers. The present framework provides a quantitatively constrained reduced-order description of feedback-mediated optical darkening under pulsed optical trapping conditions and establishes iron-oxide absorption as a physically plausible ignition mechanism for dark-state formation in the pre-plasma regime. Full article

Femtosecond laser surface texturing offers a promising route to tailor the functionality of bio-based wood coatings, yet the interplay between coating composition and laser processing remains poorly understood. In this study, bio-based epoxy coatings with eventual micronized wax additives were textured using a femtosecond laser to investigate the effects of laser processing parameters on pattern formation and resulting hydrophobicity. The epoxy coatings containing PE, PE/PTFE, HDPE, and rice bran waxes at 1, 5, and 7 wt.-% were characterized in terms of morphology, roughness, wettability, and chemical stability, followed by systematic variation of pulse repetition rate and laser power. The results reveal that the ablation threshold strongly depends on intrinsic coating properties. Ablation resistance increases with surface roughness and wax melting enthalpy, reflecting the role of phase transition energy in laser–matter interaction. The wax-filled coatings exhibit a transition from melting-dominated behavior at low energy input to ablation-dominated behavior at a higher energy. Laser texturing enhances hydrophobicity in parallel with theoretical values calculated from the Cassie–Baxter wetting model, with the highest hydrophobicity achieved for coatings combining intrinsic hydrophobicity and stable pattern formation. Chemical analysis confirms limited degradation of the epoxy matrix without significant carbonization, while wax additives provide partial thermal shielding. Overall, this work demonstrates clear options for tailoring surface morphology and wettability of hydrophobic polymer coatings through controlled femtosecond laser processing. Full article

The ablation behavior of 2.5D C/SiC composites under continuous-wave laser irradiation involves not only surface material removal but also internal structural degradation. In this study, laser ablation tests were conducted at power densities of 400, 800, and 1600 W/cm², and the ablated specimens were analyzed by macroscopic observation, infrared thermography, X-ray micro-computed tomography (micro-CT), cross-sectional scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDS), depth measurement, and homogeneous thermal-field simulation. The results show that the surface morphology evolved from a transition-zone-dominated response to a typical zoned morphology consisting of a central ablation zone, transition zone, and edge zone as the power density and irradiation time increased. Under the present temperature measurement conditions, the surface transition zone corresponded to an apparent temperature window of approximately 2300–2700 K. Cross-sectional characterization further revealed a distinguishable internal reaction front beneath the external ablation surface, above which microstructural damage and Si depletion were observed. Depth measurements showed that the external ablation depth underestimated the actual degradation depth along the thickness direction. The calibrated homogeneous thermal-field model indicated that the internal front position corresponded to a relatively stable temperature range, suggesting that its formation was mainly governed by local thermal history and matrix-related reactions. The proposed internal reaction front provides a supplementary parameter for evaluating laser-induced subsurface degradation in 2.5D C/SiC composites. Full article

Aiming at the inherent bottlenecks of traditional smoke detection technologies in high and large-volume building scenarios, this paper conducts research on an early fire smoke detection method for high and large-volume spaces based on Light Detection and Ranging (LiDAR). A special experimental platform was independently designed to obtain the physical characteristics of smoke particles from standard smoldering fires. Combined with the optical scattering and reflection interaction mechanism between laser and particulate matter, the theoretical feasibility of LiDAR for smoke detection was systematically verified. Smoke irradiation experiments were carried out in the full detection distance, and the LiDAR point cloud characterization characteristics of smoldering smoke were clarified. A special smoke detection algorithm based on point cloud features was designed, a LiDAR smoke detection system was built, and multi-condition comparative experiments with traditional photoelectric smoke detection methods were carried out in a full-scale laboratory. The experimental results show that the LiDAR-based smoke detection method proposed in this paper has significant advantages over traditional detection methods in terms of alarm response speed, detection coverage, and height adaptability. This research provides a brand-new technical path and reference for the theoretical research and engineering application of early fire warning technology for high and large-volume buildings. Full article

Metallic nanoantennas are promising structures for enhancing energy transfer in high-intensity laser–matter interactions, especially in nanoplasmonic-assisted fusion. Under ultrashort laser pulses, they generate strong localized fields, modify ionization dynamics, and significantly affect charge acceleration in dense media. In this work, we present a comprehensive particle-in-cell (PIC) study of gold nanoantennas of various geometries—dipoles, planar crosses, three-dimensional crosses, and Yagi-inspired planar structures—irradiated by near-infrared femtosecond pulses at intensities at a range of ~4 × 10¹⁷–4 × 10¹⁸ W/cm². The antenna structures are embedded in a dense hydrogen-rich medium, allowing us to follow electron emission, gold ionization, and proton acceleration self-consistently. Crossed and Yagi-type geometries exhibit more robust resonant behavior than dipoles, with higher field localization and greatly reduced sensitivity to incident polarization. The proton energies increase to ~200 keV at 4 × 10¹⁷ W/cm², and saturate around ~300 keV at a higher intensity >~4 × 10¹⁸ W/cm², dependent on the geometry. This happens largely due to a rapid loss of conduction electrons from the gold structures. Our results highlight Yagi-based and cross-based nanoantennas as promising resonant dopes for laser-driven energy coupling and point toward optimized multi-arm architectures for future nanofusion-target engineering applications. Full article

Accurate measurements of light–matter interactions at subwavelength scales are critical for advancing nanophotonic and quantum optical technologies. In this paper, we present the far-field terahertz (THz) spectroscopy of a single planar meta-atom of subwavelength dimensions embedded within a square or circular aperture on a thin free-standing metal film. The meta-atom, composed of concentric disk and ring structures interconnected by narrow bridges, was fabricated by a mask-less direct laser ablation (DLA) technique to exhibit a pronounced transmission peak near a resonance frequency of 0.35 THz. We propose a novel spectral analysis framework that accounts for aperture-to-beam area mismatch suppressing non-resonant background contributions originating from edge diffraction and aperture discontinuities which are commonly encountered in subwavelength geometries. This technical analysis yields transmission spectra with improved accuracy providing good agreement with finite-difference time-domain (FDTD) simulations. A foundation for precise optical characterization of a single subwavelength size resonator is demonstrated paving the way for applications in quantum sensing, meta-surface design, and low-dimensional optoelectronic systems. Full article

This study presents a novel, satellite-based framework for quantifying the relative contribution of regional transport in biomass burning aerosol (BBA) formation over the South China Sea (SCS). We integrate the biomass burning emission (BBE) rates from potential source regions with a random forest regression model, which is driven by backward trajectory analysis. This approach isolates and evaluates the relative contribution from transported sources. The model demonstrates robust predictive skill for BBA concentrations (R² = 0.78 on an independent test set), using only transport-weighted BBE rates and meteorological data as inputs. Quantitative interpretation via SHAP (Shapley Additive exPlanations) analysis reveals nonlinear relationships and the distinct importance of transport-source features. A key finding is that BBE originating from northern Laos and Thailand contributes 21.23% to the predicted BBA concentrations over the SCS. Furthermore, there is a clear nonlinear positive correlation between the regional BBE rates and downwind BBA concentration, except for transport from Cambodia. Our results pinpoint that the impact of regional transport is paramount, governed by a combination of source emission intensity, transport duration, and trajectory pathway. This study establishes a satellite-driven methodology for attributing aerosol sources and clarifies the dominant controls on BBA concentration variability in a major maritime receptor region. Full article

As a prominent frontier in ultrafast laser–matter interaction, femtosecond laser filamentation holds great potential for atmospheric pollutant detection and remote sensing. However, its practical application in the open atmosphere is severely hampered by atmospheric turbulence, which induces beam wander, wavefront distortion, and intensity scintillations. In this study, we numerically investigated the propagation dynamics of femtosecond laser filaments in a turbulent medium and elucidated the underlying physical mechanisms. The results show that, compared to linear propagation, the nonlinear self-guiding effect inherent to filamentation effectively suppresses turbulence-induced beam wander. Furthermore, by employing vortex beams carrying orbital angular momentum (OAM), we significantly suppressed the stochastic generation of multiple filaments, thereby notably improving the stability of long-range filament propagation in complex atmospheric conditions. These findings provide new insights into the physical mechanisms and novel strategies for improving the robustness of laser filamentation technology in real-world turbulent environments. Full article

Femtosecond laser processing has become a powerful approach for high-precision micro- and nanofabrication in transparent materials, owing to its ultrashort pulse duration and minimized thermal effects. However, the limited predictability of processing depth remains a major obstacle to practical applications. Here, we present a morphology prediction framework for femtosecond Bessel laser processing of ZnS that integrates plasma physics modeling with deep learning. Through combined experimental measurements and plasma physics simulations, the influence of laser pulse energy on electron density evolution and material removal depth is systematically investigated. The results reveal the dominant roles of multiphoton ionization, avalanche ionization, and free-electron dynamics in deep-volume processing, and demonstrate the strong sensitivity of the processing morphology to the plasma distribution. Conventional plasma models can accurately reproduce the ablation diameter, yet exhibit significant limitations in predicting the processing depth. We propose a physics data-based framework for femtosecond Bessel beam processing, which integrates a depth-residual regression network conditioned on the peak electron density distribution to effectively learn and compensate for systematic modeling errors in plasma-based simulations. This strategy leads to excellent agreement between predicted and experimental processing depths and three-dimensional morphologies under various energy conditions. The model achieves a mean absolute error (MAE) of 4.9 nm at the pixel level for 3D crater reconstruction. Under rigorous crater-grouped cross-validation with Leave-One-Group-Out evaluation, the model achieves a mean R² of 0.74 across 8 independent craters, demonstrating reliable generalization to unseen energy conditions. These results demonstrate that incorporating physical priors into data-driven learning provides an effective pathway to overcoming accuracy limitations in modeling complex laser–matter interactions. This approach offers a reliable tool for quantitative prediction and parameter optimization in deep femtosecond laser processing of transparent materials and enabling highly controllable and reproducible micro- and nanofabrication for advanced photonic and three-dimensional optical applications. Full article

Quantum beams-including X-rays, synchrotron radiation, electrons, neutrons, ions, and ultrafast photon sources-are indispensable tools for probing the structure, dynamics, and electronic properties of matter. The excitation time scale ${\tau }_{exc}$ is defined operationally as the characteristic temporal interval governing externally imposed energy deposition events within the interaction volume, such as pulse duration, bunch spacing, or beam dwell time. Interpretation of beam–matter interactions has traditionally relied on steady-state or quasi-equilibrium assumptions, implicitly presuming that intrinsic material relaxation processes can accommodate externally imposed excitation. Recent advances in high-brightness synchrotron sources, X-ray free-electron lasers (XFELs), and pulsed electron beams increasingly operate in regimes where this assumption is strained, and systematic nonequilibrium effects, radiation damage, and irreversible transformations are reported even under routine experimental conditions. This work examines the role of time-scale mismatch between beam-driven energy deposition and intrinsic material relaxation as a governing constraint in beam–matter interactions. Analyzing the hierarchy of excitation, electronic relaxation, phonon coupling, and thermal diffusion time scales, the analysis introduces a dimensionless mismatch parameter $\mathsf{\Lambda }={\displaystyle \frac{{\tau }_{rel}}{{\tau }_{exc}}}$, which quantifies the competition between externally imposed excitation and intrinsic relaxation processes in beam–matter interactions. The resulting framework provides a unified physical interpretation of beam-induced damage, signal distortion, dose dependence, and nonlinear response across quantum beam modalities, framing these effects as consequences of forced nonequilibrium dynamics rather than technique-specific artifacts. Full article

This study proposes a principal component analysis-based convolutional neural network (PC-CNN) to correct atmospheric turbulence-induced aberrations. Unlike traditional Zernike polynomials (ZPs)-based methods (ZP-CNN), PC-CNN avoids mode aliasing and cross-coupling via the strict orthogonality of principal components (PCs). A coefficient magnification strategy is incorporated to further enhance efficacy, maximally preserving the intrinsic physical information within the PCs coefficients. A series of systematic experiments was conducted under conditions from weak to strong turbulence, characterized by $D/r_0$ from 1 to 25, where $D$ is the pupil diameter and $r_0$ is the atmospheric coherence length. Quantitative results show PC-CNN achieves a lower mean relative error (MRE) in coefficient prediction than ZP-CNN under equivalent conditions. It also yields a higher Strehl ratio, reduced speckles, and enhanced spot clarity while requiring fewer basis terms, demonstrating high stability and robustness in strong turbulence. These findings emphasize that basis function orthogonality and physically informed preprocessing are critical design principles for deep-learning-based wavefront sensor-less adaptive optics (AO), establishing a robust foundation for real-time intelligent AO systems in astronomy and free-space optical communications. Full article

We report on laser fusion research with nanotechnology-improved targets embedded in special polymers. The results of the last three years are reviewed here, including laser matter interaction craters, laser infrared breakdown spectroscopy, and Raman spectroscopy results, as well as a selected Thomson parabola image showing protons accelerated up to 300 keV. In this paper, we focus on proton acceleration and plasmonic enhancement mechanisms rather than on the direct demonstration of sustained fusion reactions. Full article

Atmospheric visibility nowcasting is vital for safety-critical operations but remains challenging due to complex atmospheric dynamics. We propose a compact stacking ensemble merging a multilayer perceptron (MLP) and gradient-boosted regression trees (GBRT). The model, trained on seven months of minute-scale resolution data with a variability-adaptive filter to suppress sensor noise, employs cross-validation. Results demonstrate that the ensemble achieves its peak performance in the operationally critical low-visibility regime (V < 5 km). This range is particularly significant as it encompasses the Category I and II (CAT I/II) operational thresholds defined by the World Meteorological Organization (WMO) for aviation and surface transportation safety. In this regime, the ensemble yields an R² of 0.82 and an MAE≈385 m, significantly outperforming single learners during rapid weather transitions. Conversely, in the high-visibility regime (V > 20 km), the explanatory power decreases (R² of 0.46) due to inherent forward-scattering sensor uncertainties and low aerosol concentrations. Despite these range-specific physical limitations, the model maintains high robustness with narrowly centered residuals. This efficient approach, utilizing cost-effective in situ sensors, is highly suitable for airport and road-weather applications and offers strong potential for multi-site scalability. Full article

This review serves as a conceptual and practical introduction to strong-field quantum electrodynamics (SFQED), written from the standpoint of experimental physicists. Rather than providing a comprehensive theoretical review, the paper focuses on the core ideas, terminology, and challenges in SFQED that are most relevant to experimental design and interpretation. The review serves as a first point of contact with the subject, bridging the gap between foundational theory and hands-on experimental investigations, and complementing more formal literature in the field. Full article