Search Results (480)

Carbon deposits on the crown of engine pistons can markedly reduce combustion efficiency and shorten service life. Conventional cleaning techniques often fail to simultaneously ensure a high carbon removal efficiency and maintain optimal surface integrity. To enable efficient and precise carbon removal, this study proposes the application of hybrid laser cleaning—combining continuous-wave (CW) and pulsed lasers—to piston carbon deposit removal, and employs response surface methodology (RSM) for multi-objective process optimization. Using the N52B30 engine piston as the experimental substrate, this study systematically investigates the combined effects of key process parameters—including CW laser power, pulsed laser power, cleaning speed, and pulse repetition frequency—on surface roughness (Sa) and carbon residue rate (RC). Plackett–Burman design was employed to identify significant factors, the method of the steepest ascent was utilized to approximate the optimal region, and a quadratic regression model was constructed using Box–Behnken response surface methodology. The results reveal that the Y-direction cleaning speed and pulsed laser power exert the most pronounced influence on surface roughness (F-values of 112.58 and 34.85, respectively), whereas CW laser power has the strongest effect on the carbon residue rate (F-value of 57.74). The optimized process parameters are as follows: CW laser power set at 625.8 W, pulsed laser power at 250.08 W, Y-direction cleaning speed of 15.00 mm/s, and pulse repetition frequency of 31.54 kHz. Under these conditions, the surface roughness (Sa) is reduced to 0.947 μm, and the carbon residue rate (RC) is lowered to 3.67%, thereby satisfying the service performance requirements for engine pistons. This study offers technical insights into the precise control of the hybrid laser cleaning process and its practical application in engine maintenance and the remanufacturing of end-of-life components. Full article

This study focuses on the analysis of soliton solutions within the framework of the time-fractional cubic–quintic nonlinear Schrödinger equation (TFCQ-NLSE), a powerful model with broad applications in complex physical phenomena such as fiber optic communications, nonlinear optics, optical signal processing, and laser–tissue interactions in medical science. The nonlinear effects exhibited by the model—such as self-focusing, self-phase modulation, and wave mixing—are influenced by the combined impact of the cubic and quintic nonlinear terms. To explore the dynamics of this model, we apply a robust analytical technique known as the sub-ODE method, which reveals a diverse range of soliton structures and offers deep insight into laser pulse interactions. The investigation yields a rich set of explicit soliton solutions, including hyperbolic, rational, singular, bright, Jacobian elliptic, Weierstrass elliptic, and periodic solutions. These waveforms have significant real-world relevance: bright solitons are employed in fiber optic communications for distortion-free long-distance data transmission, while both bright and dark solitons are used in nonlinear optics to study light behavior in media with intensity-dependent refractive indices. Solitons also contribute to advancements in quantum technologies, precision measurement, and fiber laser systems, where hyperbolic and periodic solitons facilitate stable, high-intensity pulse generation. Additionally, in nonlinear acoustics, solitons describe wave propagation in media where amplitude influences wave speed. Overall, this work highlights the theoretical depth and practical utility of soliton dynamics in fractional nonlinear systems. Full article

We describe the output characteristics of a compact, passively Q-switched, diode-end-pumped (Er/Yb):Glass laser operating in a multi-pulse burst mode. Such operation enables much higher optical efficiency and larger output of total energy than possible with conventional solitary pulse emissions. The laser generated a 15-pulse burst of pulses at 1.5 μm with a combined energy of 5.8 mJ. Measurements of pulse energies, spatial mode characteristics, output beam divergence, and impact of thermal effects in the (Er/Yb):Glass are described. These results are compared to predictions of a numerical simulation using a finite-difference beam propagation method (FD-BPM) that incorporates thermal effects caused by distributed local heating in the glass. We show good agreement between the measured and simulated laser output characteristics. Full article

Achieving targeted nanoparticle (NP) size and concentration combinations in Pulsed Laser Ablation in Liquid (PLAL) remains a challenge due to the highly nonlinear relationships between laser processing parameters and NP properties. Despite the promise of PLAL as a surfactant-free, scalable synthesis method, its industrial adoption is hindered by empirical trial-and-error approaches and the lack of predictive tools. The current literature offers limited application of machine learning (ML), particularly recommender systems, in PLAL optimization and automation. This study addresses this gap by introducing a ML-based recommender system trained on a 3 × 3 design of experiments with three replicates covering variables, such as fluence (1.83–1.91 J/cm²), ablation time (5–25 min), and laser scan speed (3000–3500 mm/s), in producing magnesium nanoparticles from powders. Multiple ML models were evaluated, including K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Random Forest, and Decision trees. The DT model achieved the best performance for predicting the NP size with a mean percentage error (MPE) of 10%. The XGBoost model was optimal for predicting the NP concentration attaining a competitive MPE of 2%. KNN and Cosine similarity recommender systems were developed based on a database generated by the ML predictions. This intelligent, data-driven framework demonstrates the potential of ML-guided PLAL for scalable, precise NP fabrication in industrial applications. Full article

This paper focuses on the switching and modulation techniques of terahertz waves, develops ${\mathrm{VO}}_2$ thin-film materials with an SPP structure, and uses terahertz time-domain spectroscopy (THz-TDS) to study the semiconductor–metal phase transition characteristics of ${\mathrm{VO}}_2$ thin films, especially the photoinduced semiconductor–metal phase transition characteristics of silicon-based ${\mathrm{VO}}_2$ thin films. The optical modulation characteristics of silicon-based ${\mathrm{VO}}_2$ thin films to terahertz waves under different light excitation modes, such as continuous light irradiation at different wavelengths and femtosecond pulsed laser irradiation, were analyzed. Combining the optical modulation characteristics of silicon-based ${\mathrm{VO}}_2$ thin films with the filtering characteristics of SPP structures, composite structures of ${\mathrm{VO}}_2$ thin films with metal hole arrays, composite structures of ${\mathrm{VO}}_2$ thin films with metal block arrays, and silicon-based ${\mathrm{VO}}_2$ microstructure arrays were designed. The characteristics of this dual-function device were tested experimentally. The experiment proves that the ${\mathrm{VO}}_2$ film material with an SPP structure has a transmission rate dropping sharply from 32% to 1% under light excitation; the resistivity changes by more than six orders of magnitude, and the modulation effect is remarkable. By applying the SPP structure to the ${\mathrm{VO}}_2$ material, the material can simultaneously possess modulation and filtering functions, enhancing its optical performance in the terahertz band. Full article

Near-infrared (NIR) photoelectric synaptic devices show great potential in studying NIR artificial visual systems integrating excellent optical characteristics and bionic synaptic plasticity. However, NIR synapses based on transition metal dichalcogenides (TMDCs) suffer from low stability and poor environmental performance. Thus, an environmentally friendly NIR synapse was fabricated based on lanthanide-doped upconversion nanoparticles (UCNPs) and two-dimensional (2D) WSe₂ via solution spin coating technology. Biological synaptic functions were simulated successfully through 975 nm laser regulation, including paired-pulse facilitation (PPF), spike rate-dependent plasticity, and spike timing-dependent plasticity. Handwritten digital images were also recognized by an artificial neural network based on device characteristics with a high accuracy of 97.24%. In addition, human and animal identification in foggy and low-visibility surroundings was proposed by the synaptic response of the device combined with an NIR laser and visible simulation. These findings might provide promising strategies for developing a 24/7 visual response of humanoid robots. Full article

Recently, the urgency of combating antibiotic-resistant bacteria, viruses, and other pathogens has dramatically increased. With the development of nanotechnology, significant hopes are placed on nanoparticles with antimicrobial properties. The efficiency of such materials can be significantly enhanced through light-activated processes. In this study, we prepared composite ZnO-Ag nanoparticles and tested their ability to inhibit Staphylococcus aureus bacteria. The composite ZnO-Ag nanoparticles were fabricated using pulsed laser ablation of Zn and Ag targets in water using a nanosecond pulsed laser. During antibacterial tests, light-enhanced activation of the nanoparticles was achieved using low-power near UV (375 nm) and blue visible (410 nm) LED irradiation. For comparison, similar laser-fabricated ZnO nanoparticles were also tested. The combined use of nanoparticles and LED irradiation significantly increased the generation of reactive oxygen species. As a result, low nanoparticle concentrations (0.05 g/L) and low-power LED irradiation (0.17–0.22 W) significantly reduced the concentration of Staphylococcus aureus bacteria, including experiments with visible light irradiation. Compared to their ZnO counterparts, the use of ZnO-Ag composite particles led to an additional increase in antimicrobial activity. Full article

Laser weld quality remains a critical priority across nearly all industries. However, identifying optimal laser parameter sets continues to be highly challenging, often relying on costly, time-consuming trial-and-error experiments. This difficulty is largely attributed to the severe fluctuations and instabilities inherent in laser welding, particularly keyhole instabilities. This study examines the impact of laser power modulation parameters, which, when properly applied, have been found effective in controlling and minimizing process instabilities. The investigated parameters include different pulse shapes (sinusoidal and cosinusoidal) and their associated characteristics, namely frequency (100–800 Hz) and amplitude (1000–4000 W). The impact of these modulation parameters on keyhole mode laser spot welding performance in aluminum is investigated. Using a Taguchi experimental design, a series of tests were developed, focusing on eight key welding responses, including keyhole dimensions, mean temperature, and the variability of instability-inducing forces and related factors affecting process stability. Grey relational analysis (GRA) combined with analysis of variance (ANOVA) is applied to identify the optimal combinations of laser parameters. The results indicate that low amplitude (1000 W), low to intermediate frequencies (100–400 Hz), and cosinusoidal waveforms significantly enhance weld quality by improving process stability and balancing penetration depth. Among the factors, amplitude has the greatest impact, accounting for over 50% of the performance variation, followed by frequency and pulse shape. The findings provide clear guidance for optimizing laser welding parameters to achieve stable, high-quality aluminum welds. Full article

This fundamental understanding of molecular structure–NLO property relationships provides critical design principles for next-generation optical limiting materials, quantum photonic devices, and ultrafast nonlinear optical switches, addressing the growing demand for high-performance organic optoelectronic materials in laser protection and photonic computing applications. In this study, it was observed that selenophene-incorporated fused D-A-D architectures exhibit a remarkable enhancement in two-photon absorption characteristics. By strategically modifying the heteroatomic composition of the Y6-derived fused-ring core, replacing thiophene (BDS) with selenophene (BDSe), the optimized system achieves unprecedented NLO performance. BDSe displays a nonlinear absorption coefficient (β) of 3.32 × 10⁻¹⁰ m/W and an effective two-photon absorption cross-section (σ_TPA) of 2428.2 GM under 532 nm with ns pulse excitation. Comprehensive characterization combining Z-scan measurements, transient absorption spectroscopy, and DFT calculations reveals that the heavy atom effect of selenium induces enhanced spin–orbit coupling, optimized intramolecular charge transfer dynamics and stabilized excited states, collectively contributing to the superior reverse saturable absorption behavior. It is believed that this molecular engineering strategy establishes critical structure–property relationships for the rational design of organic NLO materials. Full article

Background: Diode lasers are valuable in oral surgery due to their excellent hemostasis, minimum post-operative pain, and minimally invasive procedures. A dual-wavelength diode laser in dentistry combines two distinct wavelengths, typically 450 nm and 808 nm, to provide a versatile approach to soft tissue procedures. This ex vivo study investigated the quantity of thermal effects of a dual-wavelength diode laser on porcine lingual mucosa to determine the optimal laser parameters for oral soft tissue biopsies and to improve the reliability of histological evaluation. The presence of thermal damage in the prelesional margins may compromise the diagnostic accuracy, particularly in cases of suspected malignancy. Methods: Thirty-six porcine lingual mucosa samples were excised using a diode laser (Wiser 3, Doctor Smile) in continuous wave (CW) and pulsed wave (PW) modes at average powers of 2, 3, and 4 W. The samples, preserved in 5% buffered formalin, underwent histological evaluation to measure epithelial and connective tissue damage. Results: The study demonstrated variable thermal effects depending on the laser mode and power settings. Minimal epithelial damage (0.62 mm) was observed at 2 W CW, while maximum damage (3.12 mm) occurred at 4 W pulsed wave (PW). Connective tissue exhibited slightly greater damage than epithelial tissue, with minimal damage (0.53 mm) at 4 W CW and maximum damage (3.19 mm) at 4 W pulsed wave (PW). Statistical analyses were performed using t-tests and ANOVA and revealed significant differences in tissue damage between certain groups, highlighting the impact of laser parameters on thermal effects. Conclusions: The dual-wavelength diode laser seems to have good surgical properties and is suitable for managing complex clinical cases. Although the low power average showed minimal thermal damage, for the importance of the diagnosis of suspected lesions of malignancy, a 2 mm prelesional margin should be maintained. Full article

LiDAR is a technology that uses laser pulses to measure an object’s distance, an essential technology for Advanced Driver Assistance Systems (ADASs). However, it can be affected by adverse weather environments that may reduce the safety of ADASs. This paper proposes a convolutional neural network that utilizes lightweight network nodes with multiple repetitions instead of the traditional large-scale model. The proposed approach reduces the parameter size, and a consistent pre-processing method is designed to control the input parameters of the network. This process reduces the data size while retaining sufficient features for neural network training. The method was tested on a LiDAR system, demonstrating its ability to run on simple embedded systems and be deployed in heavy rain environments for real-time processing. The proposed convolutional neural Repetitive Lightweight Feature-preserving Network (RLFN) for LiDAR noise filtering demonstrates significant potential for generalization across various adverse weather conditions and environments. This paper discusses the theoretical and practical aspects of the model’s generalization capabilities. By theoretical justification, the design of our model incorporates several key features that enhance its ability to generalize: (1) Adaptive pre-processing—The adaptive pre-processing method standardizes input sizes while preserving essential features. This ensures that the model can handle varying data distributions and noise patterns, making it robust to different types of adverse weather conditions. (2) Inception and residual structures—The use of inception modules and residual connections allows the model to capture multi-scale features and maintain gradient flow, respectively. These structures are known for their robustness and ability to generalize well across different tasks and datasets. (3) Lightweight network design: The lightweight nature of the network, combined with repetitive loops, ensures efficient computation without sacrificing performance. This design is particularly beneficial for deployment on embedded systems, which often have limited computational resources. As verified by testing on a dataset, WADS, the RLFN demonstrated 98.53% accuracy, with a 96.31% F1 score. Full article

Reducing surface roughness can enhance the mechanical properties of processed materials. The variation law of the aluminum alloy surface roughness induced by continuous-nanosecond combined laser (CL) with different continuous laser power densities and laser delay is investigated experimentally. A back propagation neural network (BPNN) coupled with a sparrow search algorithm (SSA) is employed to predict surface roughness. The nanosecond laser energy density, continuous laser power density and laser delay are input parameters, while the surface roughness is output parameter. The lowest surface roughness is achieved with completely paint film removed by the CL while the nanosecond laser energy density is 1.99 J/cm², the continuous laser power density is 2118 W/cm² and the laser delay is 1 ms. Compared to the original target and the target irradiated by nanosecond pulse laser (ns laser), the reductions in the surface roughness are 20.62% and 12.00%, respectively. The SSA-BPNN model demonstrates high prediction accuracy, with a correlation coefficient (R²) of 0.98628, root mean square error (RMSE) of 0.024, mean absolute error (MAE) of 0.020 and mean absolute percentage error (MAPE) of 1.30% on the test set. These results indicate that the SSA-BPNN demonstrates higher-precision surface roughness prediction with limited experimental data than BPNN. Furthermore, the findings confirm that the CL can effectively reduce surface roughness. Full article

This study elucidates the dynamic tribo-mechanical response of laser-cladded FeNiCr-B₄C metal matrix composite (MMC) coatings on AISI 1040 steel substrate, unraveling the intricate interplay between microstructural features and phase transformations. A multi-faceted approach, employing high-resolution scanning electron microscopy (SEM) and advanced X-ray diffraction/Raman spectroscopy techniques, provided a comprehensive characterization of the coatings’ behavior under mechanical and scratch testing, shedding light on the mechanisms governing their wear resistance. Specifically, microstructural analysis revealed uniform coatings with a columnar structure and controlled defect density, showcasing an average thickness of 250 ± 20 μm and a transition zone of 80 ± 10 μm. X-ray diffraction and Raman spectroscopy confirmed the presence of α-Fe (Im-3m), γ-FeNiCr (Fm-3m), Fe₂B (I-42m), and B₄C (R-3m) phases, highlighting the successful incorporation of B₄C reinforcement. The addition of 5 and 7 wt.% B₄C significantly increased microhardness, showing enhancements up to 201% compared to the B₄C-free FeNiCr coating and up to 351% relative to the AISI 1040 steel substrate, respectively. Boron carbide addition promoted a synergistic strengthening effect between the in situ formed Fe₂B and the retained B₄C phases. Furthermore, scratch test analysis clarified improved wear resistance, excellent adhesion, and a tailored hardness gradient. These findings demonstrated that optimized short-pulsed laser cladding, combined with moderate B₄C reinforcement, is a promising route for creating robust, high-strength FeNiCr-B₄C MMC coatings suitable for demanding engineering applications. Full article

Laser engraving is a widely used technique for bamboo applications. However, the literature lacks a recommended standard roughness parameter or a numerical comparison between samples for a quantitative assessment of surface texture post-engraving. In this research, we systematically evaluated well-known 2D roughness parameters typically used for metal parts and measured them for laser bamboo engraving under various laser conditions. We utilized a pulsed laser with a 450 nm wavelength and 5.5 W power to engrave bamboo specimens, examining the surface roughness at different speed and power combinations. We utilized all available parameters with calibrated equipment to compare the results and identify the most relevant ones for characterizing the final texture with sufficient resolution for bamboo specimens. Unlike existing studies, which were limited by testing materials and laser conditions, we propose only two 2D measurement parameters that can be evaluated and compared across different materials and engraving lasers, not just under specific conditions. These selected combinations of R and P parameters can determine the quality of the engraved surface with a single measurement. In this paper the Rz parameter is proposed as the quantitative parameter to characterize the engraving. Full article

The development of bioactive coatings for metallic implants is essential to enhance osseointegration and improve implant longevity. In this study, composite thin films based on bioactive glass and melittin were synthesized using the matrix-assisted pulsed laser evaporation technique and deposited onto titanium substrates. The coatings were characterized using physicochemical analysis methods, including scanning electron microscopy, atomic force microscopy, contact angle measurements, Fourier transform infrared spectroscopy, energy-dispersive X-ray spectroscopy, and electrochemical impedance spectroscopy. Simulated body fluid immersion tests were also conducted to assess bioactivity over time. Scanning electron microscopy and atomic force microscopy revealed dense, irregular surface textures with nanoscale features and an average roughness of ~120 nm, favorable for cell adhesion. Contact angle measurements showed a significant shift from hydrophobic (~95° for bare titanium) to moderately hydrophilic (~62° for the bioglass and melittin coating) surfaces, indicating improved biocompatibility. Electrochemical impedance spectroscopy demonstrated enhanced corrosion resistance in simulated body fluid, with the coating exhibiting a ~45% decrease in impedance magnitude after 12 h of immersion, compared to only 4% for bare titanium. Fourier transform infrared spectroscopy and energy-dispersive X-ray spectroscopy analyses confirmed the progressive formation of a carbonated apatite layer after 7 days of simulated body fluid exposure, suggesting high bioactivity and osteoconductive potential. The combined effects of bioactive glass and melittin in the thin film structure offer promising applications in orthopedic and dental implants, enhancing both biological performance and structural integrity. Full article