Search Results (463)

Fast radio bursts (FRBs) are millisecond-duration radio transients of extragalactic origin, while ultra-high-energy cosmic rays (UHECRs; $E\gtrsim {10}^{18}$ eV) remain among the most important unresolved problems in astroparticle physics. This review examines the viability of FRBs and their central engines as sources of UHECRs within a comprehensive multi-messenger framework. We summarize the observational constraints on UHECR source populations imposed by the energy spectrum, nuclear composition, anisotropy measurements, diffuse $\gamma$-ray background, and high-energy neutrino observations, which, together, favor source classes capable of accelerating heavy nuclei with hard injection spectra, modest cosmological evolution, and sufficiently high source densities. We then review the current landscape of FRB progenitor and engine models, including magnetars, supramassive neutron stars, compact-object mergers, and accretion-powered systems, emphasizing their energetics, environments, and particle-acceleration capabilities through relativistic shocks, magnetic reconnection, magnetar wind nebulae, and direct electromagnetic acceleration by ultra-relativistic FRB pulses. We discuss how these scenarios are constrained by neutrino and $\gamma$-ray observations from IceCube, KM3NeT, and Fermi-LAT, as well as by large-scale UHECR anisotropy measurements from the Pierre Auger Observatory and Telescope Array. Finally, we examine the observational tests that will become possible in the coming decade through large samples of localized FRBs, composition-resolved UHECR measurements, next-generation neutrino observatories, and wide-field $\gamma$-ray facilities. We emphasize that FRB dispersion and rotation measures provide unique probes of the baryonic and magnetic environments relevant for UHECR acceleration and propagation, enabling a new form of multi-messenger tomography of cosmic-ray source environments and allowing the FRB–UHECR connection to become a quantitatively testable astrophysical framework. Full article

Single-crystal diamond (SCD) is an ideal substrate material for semiconductor devices due to its extremely wide bandgap and exceptionally high thermal conductivity. However, diamond’s extreme hardness and chemical inertness pose challenges for the fabrication of ultra-smooth surfaces. Traditional polishing processes are not only inefficient but also prone to introducing subsurface defects, which severely degrade device performance. To address the above issues, this study proposes a hybrid polishing process combining reactive ion etching (RIE) surface modification with chemical mechanical polishing (CMP), which enables low-loss atomic-level processing of SCD. The study found that RIE treatment induces lattice disorder on the diamond surface, forming a sp²-hybridized amorphous carbon-modified layer. Compared to the sp³ structure of native diamond, this modified layer has lower hardness and is easier to remove. We conducted the verification of the optimized process using high-quality single-crystalline diamond (SCD) samples with an initial surface roughness Ra of 0.68 nm. Under the optimized RIE parameters (substrate bias power: 200 W, etching time: 600 s, gas flow ratio of Ar:O₂:CF₄ = 40:50:10), the surface roughness Ra was reduced to as low as 0.35 nm after 2 h of CMP treatment. Furthermore, systematic characterization of the SCD’s as-received surface, RIE-modified surface, and CMP-treated surface was performed using Raman spectroscopy and X-ray photoelectron spectroscopy (XPS), elucidating the “etching modification–mechanical removal” polishing mechanism. Full article

Objectives: This study evaluated the influence of shade, irradiance, and exposure time on the depth of cure (DoC) of a simplified bulk-fill universal composite (Tetric plus Fill) and a conventional composite (Filtek Supreme Ultra). Methods: The 80% bottom-to-top hardness ratio was used as an empirical cutoff, and 99% confidence intervals were calculated using R version 4.5.1. Results: The DoC values were material and protocol dependent. Tetric plus Fill reached the 80% threshold to depths ranging from 3.5 to 4.5 mm, depending on shade and exposure protocol. All the Tetric plus Fill and the Filtek Supreme Ultra products reached their manufacturer’s claimed DoC depth with both 3 s extra-high and 10 s high exposures from the Bluephase PowerCure. Significance: This study highlights the importance of following the manufacturers’ recommendations for increment thickness and exposure time and of recognizing that shade and material formulation influence DoC. Full article

Ultra-High Molecular Weight Polyethylene (UHMWPE), the standard base material in ski manufacturing, offers excellent gliding performance but exhibits limited mechanical and scratch resistance on hard and icy snow conditions. In this work, stainless steel is proposed as a mechanically robust alternative, and its inherently higher friction against snow is addressed through surface engineering. The snow friction behavior of 301H stainless steel surfaces decorated with fishbone-like microstructures combined with Laser-Induced Periodic Surface Structures (LIPSSs) was investigated using a custom-built snow tribometer. Several pattern designs, with different pitch distances and depths, were engraved using femtosecond laser pulse irradiation. We conducted morphological, physical, and chemical investigations through microscopy, static contact angle measurements, and X-ray Photoelectron Spectroscopy analyses. Results indicate that the gliding performance is not directly related to the modifications in surface chemistry and wetting behavior of the samples but is affected by the geometry and orientation with respect to the sliding direction of the specific micro- and nano-features. Overall, we achieved friction coefficient values comparable to those found in UHMWPE with a fast and economically sustainable single-step laser-texturing process. This approach allows the industrial up-scaling of the fishbone-texture design to real-size alpine ski prototypes. Full article

Cardiovascular diseases impose a substantial global health burden and often require timely detection, creating strong demand for real-time electrocardiogram (ECG) monitoring on resource-constrained devices. Although portable single-lead wearable ECG devices are valuable for daily monitoring, their diagnostic performance is limited by spatial information loss and hardware constraints. Moreover, conventional lightweight models lack interpretable analysis beyond coarse classification. This study proposes an edge–cloud collaborative ECG-assisted analysis method combining lightweight ECG model distillation with large language models. At the algorithmic level, a cross-lead distillation framework transfers knowledge from a 12-lead InceptionTime–Transformer teacher to an ultra-lightweight single-lead student via a hybrid loss integrating hard-label, temperature-scaled soft-label, and auxiliary multi-label objectives. At the system level, a three-layer architecture integrates edge-side real-time screening with cloud-side report generation through a LoRA-fine-tuned Qwen3-8B model. Experiments on PTB-XL show that, under 123.7× parameter compression and 12-to-1 lead reduction, the student retains 92.8% of the teacher’s Macro-F1 and 94.7% of its AUC-ROC. After 8-bit integer (INT8) quantization, the TFLite file is 20.8 KB; QEMU-based Cortex-M4 simulation shows approximately 63.0 KB SRAM usage and 11.6 ms latency, suggesting potential on-device deployment under simulated conditions. Validation on physical hardware—including power consumption, BLE latency, and motion artifacts—remains necessary. Full article

Cooperative localization for multi-Unmanned Aerial Vehicle (UAV) systems in GPS-degraded environments is often compromised by ideal-communication or uniform-quality assumptions. This paper proposes Quality-Aware Distributed State Estimation (QA-DSE), which combines three operational quality factors—freshness (Age of Information), accuracy (covariance trace), and link reliability (packet loss and channel noise)—into a single multiplicative score $q_{ij}$, modulated by a bounded history-consistency factor based on velocity-propagated self-trajectory continuity. A dual-constraint AND-gate on AoI and covariance trace excludes jointly degraded neighbors, while admitted neighbors are fused through a quality-squared information-matrix update under a stated bounded residual cross-correlation assumption, with an adaptive Covariance-Intersection fallback when the assumption is stressed. Under explicit observability, bounded-noise, bounded-quality, joint-connectivity, and bounded residual cross-correlation assumptions, we establish mean-square bounded error, exponential convergence at a rate inherited from the Kalman update operator, $O\left(n^3+nm\right)$ per-step complexity, Bounded-Input Bounded-Output (BIBO) stability, soft attenuation of single-axis faults (Theorem 4), and hard exclusion under joint AoI–covariance violation (Theorem 5). Under a Ultra-Wideband (UWB)-style cooperative-observation model, Monte Carlo experiments across five scenarios show 74.08–74.24% position- Root Mean Square Error (RMSE) reductions over Covariance Intersection, with the relative advantage held within 73.04–74.24% as the fleet scales from 3 to 50 UAVs; QA-DSE remains within 8.1% of an idealized no-cooperation single-vehicle Kalman filter, demonstrating graceful degradation rather than improvement above that floor. Per-step Central Processing Unit (CPU) time scales from 0.09 ms (5 UAVs) to 0.31 ms (50 UAVs); embedded validation is left to future work. Full article

Sensor technologies have been increasingly recognized as a cornerstone for advancing tumor diagnostics amid the global health challenge posed by cancer. Traditional diagnostic methods are often constrained by inherent tumor heterogeneity, while liquid biopsy has emerged as a transformative minimally invasive alternative, with biosensors playing a pivotal role in its clinical translation. This review summarizes the progress of tumor diagnostic biosensors, focusing on electrochemical and fluorescent sensors. Electrochemical sensors excel in quantitative precision, miniaturization, and point-of-care (POCT) applicability, enabling ultra-sensitive detection of biomarkers such as circulating tumor cells, circulating tumor DNA, and exosomes through nanomaterial modification and signal amplification strategies. Fluorescent sensors, meanwhile, offer superior multiplexing capability and in situ imaging performance, which are further enhanced by novel nanomaterials. Additionally, it covers other promising sensor types including Surface-Enhanced Raman Scattering, microfluidic, photoelectrochemical, field-effect transistor, and clustered regularly interspaced short palindromic repeats and CRISPR-associated proteins-based sensors. Current research efforts are concentrated on multiplexed detection, point-of-care integration, and translation toward higher-order clinical functions such as cancer subtype discrimination, risk stratification, and prognosis. Future directions will focus on multimodal integration, intelligent data analysis, and prospective clinical validation against hard endpoints to facilitate the implementation of precision oncology. Full article

To address the critical challenges of edge chipping and poor processing quality in sapphire precision dicing, this paper proposes a femtosecond laser filamentation-guided dicing technology. By systematically investigating the influence of pulse overlap rate, energy, and scan counts on damage evolution, the physical differences between 343 nm UV and 515 nm visible lasers in suppressing plasma shielding and breaking through processing saturation limits are revealed. The results indicate that an extremely high pulse overlap rate (>98%) significantly inhibits lateral energy dissipation and drives the efficient propagation of the filament deep along the optical axis; furthermore, the 343 nm laser demonstrates superior removal rates and localisation compared to the 515 nm laser. Using super-resolution imaging, the precision cleavage cross-section is clearly categorised into four evolutionary stages: general ablation, filament ablation, transition, and mechanical cleavage. To mitigate morphological degradation induced by multiple scans, a sacrificial-layer-assisted strategy is innovatively proposed to achieve spatial damage transfer and in situ self-polishing, effectively eliminating longitudinal damage striations and residual stress-induced hackles. Finally, taper-free, high-precision separation of 1 mm × 450 μm micro-units is successfully achieved on a 220-μm-thick sapphire wafer. This technology not only achieves ultra-low-loss dicing but also establishes a highly efficient, contamination-free in situ characterisation paradigm for buried structures in hard and brittle materials. Full article

The Vertical Shaft Machine (VSM) method is increasingly used in ultra-deep prefabricated shafts. However, as its application extends into hard ground, existing segment designs still largely follow soft soil experiences, resulting in insufficient material utilization and poor economic efficiency. Based on the first VSM shaft in South China, this study establishes a refined finite element model validated by field monitoring and subsequently constructs a structural response database. A GA-XGBoost surrogate model combined with the SHAP method quantifies the contributions of key parameters—concrete strength, rebar diameter, and steel plate thickness—to shaft structural stress. Following the optimization objective of reducing material consumption while maintaining the overall structural performance of the original design, an optimization scheme for Ring 0 reinforcement is proposed. Results show that SHAP analysis effectively identifies the contribution ranking of each parameter to the structural response: for Ring 0, concrete strength contributes the most while rebar diameter shows low sensitivity; for the cutting edge ring, steel plate thickness and concrete strength contribute significantly, whereas tie bars show the lowest sensitivity. After optimization of Ring 0, reinforcement consumption per linear meter of segment is reduced by 43.43 kg, and steel content decreases by 57.91 kg/m³. Verification confirms that the stress distribution remains largely unchanged and crack width meets specification limits. Tie bars in the cutting edge ring play an irreplaceable structural role during concrete pouring and should not be directly optimized. The proposed scheme reduces material consumption while ensuring structural safety, offering a reference for optimizing VSM shaft segment structures in hard ground conditions. Full article

Lithium niobate (LiNbO₃, LN) single crystal is widely used in optoelectronic fields due to its excellent performance. However, its low hardness, high brittleness, and strong anisotropy lead to low processing efficiency and poor surface quality. Hydrophilic fixed abrasive lapping technology was adopted for the thinning of LN wafers in this research. The effects of lapping pressure on the thinning process were investigated comprehensively in terms of the material removal rate (MRR), surface quality, and subsurface damage (SSD). The results show that lapping pressure exerted a significant influence on the machining performance. High pressure contributed to improving the MRR but aggravated surface roughness (Ra) and SSD. With low pressure, material removal was dominated by ductile removal machining, with fine scratches as the main damage form, which was favorable for obtaining low Ra and low SSD. The root mean square (RMS) of the acoustic emission (AE) signal rose with the increase in pressure, increasing slowly in the ductile removal regime and rising abnormally in the brittle removal regime. It was positively correlated with the MRR and SSD and can be used as an in situ monitoring indicator. After a comprehensive comparison of five groups of experiments, 7 kPa was determined to be the optimal lapping pressure, with the following corresponding parameters: wafer speed: 100 rpm; lapping table speed: 80 rpm; slurry flow rate: 100 mL/min; eccentricity: 60 mm; soft lapping pad; abrasive mass fraction: 50%; and lapping time: 5 min. Under these conditions, the Ra value was approximately 30 nm, the MRR exceeded 1 μm/min, and SSD was as low as 3.3 μm, realizing the synergistic optimization of high-efficiency and low-damage machining. It provides a favorable foundation for the subsequent processing of LN substrates, such as ultra-precision polishing, thin-film transfer, and bonding. Full article

Surface hardness is a fundamental parameter influencing wear resistance, durability, and the interaction of occlusal ramps with opposing enamel during orthodontic treatment. Five commercially available materials (Harmonize, Leone F3172-01, Transbond™ XT, Band and Build LC, and Ultra Band-Lok) and one experimental material (Composite RK-F10) were evaluated for bite ramps. Twelve standardized specimens (n = 2 per material) were prepared using EVA molds and polymerized according to manufacturers’ instructions or internal protocols. Vickers microhardness (HV) was measured following ASTM E384-16 using a 500 g load, 20 s dwell time, and ten indentations per specimen. Load dependence was assessed (25–2000 g). Surface morphology was analyzed by SEM, and cytotoxicity of eluates was evaluated on dental pulp stem cells (DPSCs) and monocyte/macrophage cell lines using CCK-8 assays (ISO 7405, ISO 10993). Significant differences in hardness were observed among materials (p < 0.05). Harmonize (64.5 ± 1.6 HV), Band and Build LC (64.4 ± 1.9 HV), and Ultra Band-Lok (64.1 ± 2.0 HV) showed the highest values, whereas Transbond™ XT exhibited the lowest value (53.7 ± 6.0 HV). Composite RK-F10 demonstrated intermediate hardness and good cytocompatibility. SEM analysis revealed differences in surface homogeneity and filler distribution. Overall, the materials exhibited distinct mechanical and biological profiles. The combined Vickers microhardness, short-term (24 h) cytotoxicity, and SEM data provide an integrated preliminary in vitro characterization of materials for bite ramps. The observed differences contribute to a comparative description of their physico-biological behavior. Full article

Achieving efficient and high-quality surface processing of single-crystal diamond (SCD) remains challenging due to its extreme hardness and chemical inertness. Traditional Fenton-based slurries using H₂O₂ suffer from poor stability, safety risks, and strict acidic pH requirements. In this study, peroxymonosulfate (PMS) is introduced as an alternative oxidant to develop a novel chemical mechanical polishing (CMP) slurry for SCD. Compared with H₂O₂, PMS exhibits higher stability and generates sulfate radicals (SO₄⁻·) with stronger oxidation capability when activated by Fe²⁺. The proposed slurry achieves efficient material removal over a wider pH range (2–6). Under optimal conditions (pH = 3), a maximum material removal rate (MRR) of 676 nm/h is obtained, along with an ultra-smooth surface (Sa = 0.177 nm in the measuring area of 868 × 868 μm²). Notably, the slurry maintains high MRR (>400 nm/h) even under weakly acidic conditions (pH 5–6). XPS and radical quenching experiments confirm that continuous generation of reactive radicals promotes surface oxidation and stable material removal. This work provides a stable and efficient CMP slurry for SCD with enhanced pH adaptability. Full article

Ultra-low-expansion (ULE) glass serves as a critical material in high-precision optical devices and semiconductor manufacturing; however, its inherent hardness and brittleness pose significant challenges for machining processes. During the diamond cutting of ULE glass, severe tool wear emerges as the primary factor limiting machined quality, which not only shortens tool life but also prolongs subsequent polishing time, thereby increasing processing costs and hindering sustainable manufacturing. To address this challenge, in situ laser assisted diamond cutting (LADC) has emerged as a promising technique for the sustainable machining of difficult-to-machine materials. In this study, for achieving sustainable machining of ULE glass, the effects of cutting speed on surface roughness and tool wear were systematically investigated. To determine the optimal parameter combination for minimizing surface roughness and tool wear simultaneously, an integrated optimization approach combining artificial neural network (ANN) and non-dominated sorting genetic algorithm II (NSGA-II) was employed. The experimental results indicated that a spindle speed of 2900 rpm and a feed speed of 1.1 mm/min was ascertained as the optimum combination to attain the desired outcomes for in situ LADC of ULE glass. Under the optimum machining parameters, in situ LADC resulted in a 70.08% reduction in surface roughness and 61.24% reduction in tool wear compared to conventional diamond cutting (CDC). This study demonstrates that in situ LADC can be recognized as a promising sustainable machining technique for machining of ULE glass. Full article

Hard ceramic coatings are essential for extending the performance of metal parts under the extreme heat and stress found in aerospace and defense environments. There is a major knowledge gap regarding this topic in the current literature. While there has been significant research on individual fabrication methods or specific coating materials separately, no previous review has combined experimental lifecycle data with a broad computational design approach that covers the entire design-to-deployment process. This review fills that gap by offering a unified roadmap from integrated computational materials engineering (ICME) to machine learning (ML). This roadmap speeds up the rational design of coatings for next-generation aerospace systems. The practical importance of this framework is its clear use in gas turbine engine qualification, hypersonic vehicle thermal protection, and landing gear surface engineering. It can cut down on experimental trial-and-error cycles by allowing ML-guided composition screening and condition-based maintenance through digital twin integration. The main ceramic material systems, tungsten carbide (WC), boron nitride (BN), boron carbide (B₄C), silicon carbide (SiC), alumina (Al₂O₃), and zirconia (ZrO₂), are examined for their protective roles in aerospace-grade alloys. A key contribution is the multiscale computational framework that includes density functional theory, molecular dynamics, finite element analysis, and ML-driven inverse design. Together, these methods improve predictions for thermal breakdown, multi-axial stress responses, and coating lifetime. Future research should focus on ultra-high-temperature ceramics, multifunctional self-healing coatings, and surface engineering methods driven by data. Full article

Hard carbon has been widely recognized as the most commercially viable anode material for sodium-ion batteries (SIBs); however, its inherently low initial Coulombic efficiency (ICE), typically 60–90%, remains a critical bottleneck constraining practical full-cell deployment. While extensive research has addressed ICE optimization, existing reviews have predominantly focused on individual precursor types or isolated strategies, lacking a unified cross-precursor comparative framework. This review systematically deconstructs the complete causal continua—from chemical composition through carbonization trajectories and microstructural evolution to ultimate ICE outcomes—across five major precursor categories: biomass, synthetic resins, pitches, coal-based materials, and saccharides. An “SSA-closed pore–defect” three-parameter trade-off framework is proposed to elucidate the microstructural origins of precursor-dependent ICE divergences. Cross-categorical benchmarking reveals that resin-based precursors achieve the highest ICE (95%) through ultra-low specific surface area and extensive closed porosity, pitch-based systems deliver the most consistent ICE distribution (86–91%), and coal-derived carbons are confined to the lowest tier (78–85%). The differentiated efficacy of carbonization conditions and post-treatment strategies across precursor types is critically evaluated, demonstrating that optimal process selection is inextricably linked to precursor taxonomy. Building upon these analyses, a precursor selection decision roadmap targeting three application-specific ICE thresholds is constructed, providing actionable guidance for matching precursor–process combinations to industrial requirements. The comparative framework is grounded in 25 representative studies selected through explicit inclusion criteria (detailed in the Introduction), and its predictive utility is illustrated for emerging precursor candidates beyond the five canonical categories. This cross-precursor perspective offers a systematic reference for accelerating the commercialization of hard carbon anodes in SIBs. Full article