Search Results (625)

Blade Tip Timing (BTT) is an essential non-contact technique for monitoring vibrations in rotating machinery, but its practical accuracy is often degraded by noise, undersampling, and spectral leakage. This paper proposes a multi-stage robust Bayesian high-resolution identification framework that systematically addresses these challenges. A recursive digital algorithm based on Kalman filtering estimates the rotational speed without requiring once-per-revolution probes, effectively suppressing sensor noise. An attention-enhanced dynamic convolutional autoencoder then generates channel-specific window functions to minimize spectral leakage. The core identification algorithm extracts phases via all-phase FFT and employs sub-bin interpolation to overcome the resolution limitation of conventional FFT. A Tukey-biweight-based robust aggregation strategy is used to suppress the influence of abnormal or unequal-quality sensor channels during multi-channel phase fusion. A Bayesian prior distribution over the vibration order guides the estimation toward physically plausible values under noisy conditions. Finally, a coarse-to-fine multi-stage search strategy drastically reduces computational burden while preserving accuracy. Experiments on a rotor-blade test bench at constant and variable speeds show that the method reduces the noise floor by about 60 dB, achieves a maximum frequency identification error of 7.84%, and accelerates the search by approximately 48.6% compared to exhaustive search. The proposed method provides a reliable and efficient solution for blade health monitoring. Full article

Gem-quality garnets exhibit significant potential for U-Pb geochronological applications due to their advantageous characteristics, including high closure temperatures (750–850 °C), optical transparency, chemical homogeneity, and low inclusion content. This study focuses on the gem-quality yellow-green grossular garnet variety (commonly termed Mali garnet), a unique gemstone exclusively occurring in contact metamorphic deposits of Western Africa’s Republic of Mali. Despite its mineralogical significance, fundamental aspects, including precise age determination and chromophore mechanisms of Mali garnet, remain poorly constrained. Here, we conducted standard gemological characterization, spectroscopic analyses (UV–Vis, FTIR, and Raman), electron probe microanalysis (EPMA), micro-X-ray fluorescence (μ-XRF) elemental mapping, and in situ trace element and laser ablation U-Pb geochronological analysis on Mali garnets. The spectral data and chemical composition studies reveal that the coloration of Malian garnets is primarily attributed to the presence of iron and chromium. Our U-Pb geochronological results yield a crystallization age of 197 ± 3 Ma for the Mali garnet samples. The robustness of garnet U-Pb systems in preserving crystallization ages through multiple thermal events supports their application to Precambrian polymetamorphic terranes, where zircon systems are frequently reset. Full article

Triboelectric nanogenerators (TENGs) offer a promising route for self-powered microscale energy harvesting, yet their design optimisation remains empirically challenging due to the complex interplay of material surface physics, device geometry and operating mode. In this work, we present an integrated framework that combines atomic force microscopy (AFM) characterisation, COMSOL Multiphysics 6.0 finite element simulation and physics-informed conditional variational autoencoder (cVAE) to predict and optimise TENG output performance. Four polymer dielectric materials, HDPE, LDPE, TPU, and PMMA, were characterised via Kelvin Probe Force microscopy (KPFM) for work function, surface potential and surface roughness. Surface charge density was calculated from measured KPFM potential using the parallel plate capacitor model and used as a boundary condition in COMSOL Multiphysics simulations for contact-separation and lateral sliding TENG mode for dielectric film thicknesses of 50 µm and 100 µm. The simulated open circuit voltage (Voc) and short circuit charge (Qsc) across gap distances up to 150 mm formed the training dataset for a cVAE model with eight physicochemical condition features. The trained model demonstrated strong reconstruction accuracy (R² ≥ 0.94) and enables generative prediction across unseen design spaces. Results reveal that the LDPE/TPU pair at 50 µm thickness consistently achieves the highest electric outputs in both modes, and the sliding mode yields 25–30% higher voltages than the contact separation mode across all material pairs. This study provides a transferable data-efficient methodology for accelerating TENG material and geometry optimisation. Full article

Separating heavy oil from solid matrices is still difficult, mainly because of the strong adhesion between oil and solids and the intrinsically high viscosity of heavy oil, both of which restrict efficient large-scale recovery. Ionic liquids have recently attracted attention as additives that may improve extraction performance. In this work, [Bmim][ClO₄] was introduced into toluene- and xylene-based solvent systems to examine its role in heavy-oil recovery. The extraction performance was evaluated through recovery yield, viscosity, contact angle, oil–solid interaction force, and SARA composition. Molecular dynamics simulation was also carried out to probe how [Bmim][ClO₄] interacts with heavy-oil components and how it influences the diffusion of saturates, aromatics, resins, and asphaltenes. The results show that the addition of [Bmim][ClO₄] improved heavy-oil recovery in every solvent system tested, with the highest value, 89.31 wt%, obtained in the toluene system. At the same time, the ionic liquid lowered the viscosity, reduced the contact angle, and weakened the adhesion between the oil phase and the solid surface, all of which favored oil–solid separation. SARA analysis further indicated that the extraction of heavier fractions, especially resins and asphaltenes, became more pronounced after adding the ionic liquid. Simulation results suggest that [Bmim][ClO₄] interacts more strongly with resins and asphaltenes, disrupts their associated structures, and facilitates mass transfer. Taken together, these results suggest that [Bmim][ClO₄] improves heavy-oil extraction by altering interfacial behavior and loosening the aggregated structure of heavy fractions, which may be useful for future process optimization. Full article

Cellulose ether, like hypromellose (HM), is an extremely versatile material that is widely used in pharmaceutical products as film coatings. To modify the surface properties of HM films, additives are routinely included during the film formulation process, which are typically hydrophobic lubricants or hydrophilic plasticizers. Plasticizers increase the flexibility and reduce the brittleness of the film. The first goal of this study is to demonstrate that plasticization of HM films by low-molecular-weight (400 g∙mol⁻¹) polyethylene glycol (PEG) allows tuning adhesion and friction properties of HM films, both at nano- and macroscales. Surface morphology, surface energy, nano/macro adhesion, and nano/macro friction coefficient were studied by atomic force microscopy (AFM) in adhesion or friction modes at the nanoscale, wettability, and probe-tack adhesion, as well as pin-on-disk friction experiments at the macroscale. The results show that the addition of PEG decreases the Young’s modulus and the Tg of HM-plasticized films while increasing their strain at break and surface energy. The macroadhesion force increases from 9 to 90 mN by the addition of 40% w/w of PEG, whereas the macrofriction coefficient is reduced by 50%. The hypothesis of insertion of plasticizer molecules in HM chains’ nano-domains is evidenced and explains these results. The second goal of this study is to investigate nanoscale versus macroscale correlation of adhesion and friction properties and the role of adhesion in friction experiments. The results show, first, that the evolution of the adhesion energy at the macroscale as a function of adhesion energy at the nanoscale is linear. On the contrary, a high friction coefficient at the nanoscale corresponds to a low friction coefficient at the macroscale and vice versa, showing a first linear decrease for PEG contents ranging from 0 to 30% (w/w) and the second linear decrease, less pronounced, is observed for PEG contents ranging from 30 to 40% (w/w). The hypothesis of a difference in contact pressure applied on the probe at both scales, as well as HM-PEG surface phase separation at a high PEG content (>30% w/w), is proposed to explain this difference. The variations in friction coefficients are linear according to the PEG plasticizer content and suggest its lubricant role in HM-Plasticized films. Finally, the interplay between adhesion and friction, in friction experiments, is evidenced and appears dominant at the nanoscale. Full article

Diamond-like carbon (DLC) coatings are widely used as protective and self-lubricating surfaces in metal–metal contacts. Their frictional behavior is governed by the formation and evolution of carbon-rich transfer layers (TLs), which can be tailored through functionalization with carbon nanomaterials. Recent studies have shown that graphene sheets (GSs) and nanodiamonds (NDs) act synergistically to achieve ultra-low friction in microrough (~0.2 μm) metal–DLC contacts under dry N₂ at a 1 N load. Here, we probe how this lubrication mechanism evolves with increasing load from 1 to 10 N—corresponding to local contact pressures up to ~11–16 GPa—respectively, in dry N₂ and humid air conditions. Ball-on-disk experiments are performed on an industrial hydrogenated DLC coating sliding against stainless-steel. In dry N₂, GS–ND functionalization yields a low and stable coefficient of friction across the entire load range, reaching a minimum of about 0.05. In humid air, higher friction levels are observed across all loads (CoF ~0.10–0.15), accompanied by oxidation-driven modifications of both wear debris and the counterface contact region, with oxygen content increasing by more than a factor of three compared to dry N₂. Detailed microscopy and spectroscopy analyses indicate that enhanced lubricity in dry N₂ arises from TLs incorporating GSs, NDs, and nanoscroll-like structures, whereas humid air promotes interfacial amorphization and oxidation, leading to load-insensitive friction and boundary lubrication effects through physisorbed water molecules. Full article

Monitoring of blade vibrations in turbomachinery equipped with ferromagnetic blades is currently performed using the Blade Tip-Timing (BTT) non-contact technique. To reduce measurement uncertainty on time samples, BTT systems require measurement probes to meet high dynamic performance requirements. Anisotropic magnetoresistive (AMR) sensors have recently gained interest for this application owing to their high sensitivity to magnetic flux variations and robustness in harsh, contaminated environments. However, a thorough dynamic characterization of AMR-based BTT probes remains largely unexplored, representing a critical gap in next-generation industrial measurement systems. This work presents a custom-designed signal conditioning circuit tailored for AMR-based BTT measurements, alongside a systematic methodology for characterizing its dynamic performance. The circuit is modeled as a block diagram, from which transfer functions are derived analytically and validated experimentally, providing a rigorous and reproducible framework for probe dynamic assessment. The complete instrumentation chain is then tested on a low-speed rotor test bench in a BTT configuration. Results reveal a fundamental sensitivity–bandwidth trade-off: satisfying the cutoff frequency requirement imposed by BTT applications inherently reduces signal gain below the threshold needed to resolve individual blade-passage events. This finding isolates the key design bottleneck for AMR-based BTT probes and provides quantitative guidance for future optimization of both sensor and circuit design toward industrial tip-timing deployment. Full article

Surface Acoustic Wave (SAW) technology, long central to analog signal processing and RF filtering, is undergoing a major renewal. Driven by advances that decouple SAWs from traditional piezoelectric materials and fixed-function devices, the field is gaining unprecedented control over acoustic, optical, and electronic interactions at the micro and nanoscale. This review synthesizes these developments across four fronts: new physical mechanisms for SAW manipulation, emerging material platforms, ranging from thin films to 2D systems, along with reconfigurable device architectures and circuits, and the expanding landscape of applications they enable. Optical methods are reshaping how SAWs are generated and controlled, bypassing the limits of conventional electromechanical coupling. Coherent optical excitation of high-Q SAW cavities via Brillouin-like optomechanical interactions now grants access to modes in non-piezoelectric substrates such as diamond and silicon, while on-chip SAW excitation in photonic waveguides through backward stimulated Brillouin scattering opens new integrated sensing routes. In parallel, magneto-acoustic experiments have revealed nonreciprocal SAW diffraction from resonant scattering in magnetoelastic gratings. On the device side, ZnO thin-film transistors integrated on LiNbO₃ exploit acoustoelectric coupling to realize voltage-tunable phase shifters; UHF Z-shaped delay lines achieve high sensitivity in a compact footprint; and parametric synthesis of wideband, multi-stage lattice filters targets 5G-class performance. Atomistic simulations show that SAW propagation in 2D MXene films can be engineered via surface terminations, while aerosol jet printing and SAW-assisted particle patterning provide agile, cleanroom-light fabrication of microfluidic and magnetic components. These advances enable applications ranging from hybrid quantum systems and quantum links to lab-on-a-chip particle control, SBS-based and UHF sensing, reconfigurable RF front-ends, and soft robotic actuators based on patterned magnetic composites. At the same time, optical techniques offer non-contact probes of dissipation, and MXenes and other emerging materials open new regimes of acoustic control. Conclusively, they are transforming SAW technology into a versatile, programmable platform for mediating complex interactions in next-generation electronic, photonic, and quantum systems. Full article

In Coiled Tubing (CT) acoustic telemetry, the reliability of surface signal reception is severely challenged by the “contact dead zone” of traditional probes and complex nonstationary environmental noise. To address these issues, this paper proposes a hardware-software integrated solution for high-fidelity signal extraction. In terms of hardware, a novel pickup probe based on the micro-lever principle is developed. By utilizing a pivoted lever structure with an optimized arm ratio of 2.6 to 1 and a full pressure-balanced mechanism, the design physically overcomes the contact dead zone inherent in traditional pressure-compensating probes and effectively isolates low frequency common-mode interference through a lateral floating architecture. In terms of software, a joint denoising model combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and wavelet thresholding is proposed. A cross-correlation coefficient criterion is introduced to adaptively screen intrinsic mode functions and eliminate residual fluid turbulence noise. Field experiments on a 1500 ft full-scale circulation loop demonstrate that the proposed probe improves the detection sensitivity of the radial breathing mode by approximately 20.6 dB compared to the baseline, while effectively eliminating stick-slip friction noise during dynamic tripping. Furthermore, the joint algorithm increases the Signal to noise Ratio by an additional 16.9 dB under typical pumping conditions of 0.5 bpm, with a normalized cross-correlation exceeding 0.96. These results verify that the proposed method effectively solves the bottleneck of weak signal detection in deep wells, providing robust technical support for CT telemetry operations. Full article

A nonlinear ultrasonic time-domain identification method based on chaos sensitivity was proposed in this study. The Duffing chaotic system was introduced into the weak second harmonic identification to realize early detection and quantitative evaluation of fatigue damage in U71Mn steel. First, to ensure the reliability of nonlinear ultrasonic testing, a probe-pressure monitoring device was designed. Through pressure-stability experiments, 16 N was determined as the optimal pressure, which effectively suppresses contact nonlinearity interference and ensures coupling stability. Subsequently, the Duffing chaos detection system was established. The signal-system frequency-matching problem was resolved through time-scale transformation. Simultaneously, the issue of unknown initial phases was resolved using phase traversal compensation. Based on the chaotic system’s sensitivity to specific frequency signals and immunity to noise, the amplitudes of the fundamental wave and second harmonics in the target signals were quantified to calculate the nonlinear coefficient. Experimental results demonstrate that the proposed method can extract these amplitudes directly in the time domain, thereby effectively overcoming the spectral leakage inherent in traditional frequency-domain methods. The nonlinear coefficient of U71Mn steel exhibits a “double-peak” characteristic as fatigue damage increases. Specifically, the first peak appears at approximately 50% of fatigue life, while the second occurs at approximately 80%. This phenomenon is closely correlated with the distinct stages of internal fatigue crack propagation, reflecting a complex damage-evolution mechanism. This study not only provides a novel method for the precise extraction of weak nonlinear signals but also establishes a critical theoretical and experimental foundation for accurate fatigue life prediction for U71Mn rail steel. Full article

The Yechangping super-large porphyry–skarn deposit is a key component of the East Qinling molybdenum metallogenic belt, central China. Magnetite is widely developed across all mineralization stages of this deposit, yet its systematic geochemical evolution and prospecting significance remain poorly constrained. This study presents in situ major- and trace-element analyses of magnetite via electron probe microanalysis (EPMA), laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), and elemental mapping, to unravel the ore-forming hydrothermal evolution and establish reliable prospecting indicators. Four magnetite generations are identified based on petrography and paragenetic relationships: late skarn stage (Mt1), oxide stage (Mt2 and Mt3), and polymetallic sulfide stage (Mt4). Magnetite has total iron contents (TFeO, total Fe calculated as FeO) of 82.72–95.46 wt.% (values above the 93 wt.% stoichiometric limit of pure magnetite stem from minor oxidation), with dominant isovalent Fe³⁺ and Al³⁺ lattice substitution supported by a significant negative Fe–Al correlation. Systematic stage-dependent geochemical variations are observed: Mt1 has the highest Ti (mostly >1500 ppm), V and Cr, while Mt2–Mt4 show progressive Ti depletion (mostly <100 ppm), recording continuous cooling of the hydro-thermal system. V and Cr contents decrease markedly from Mt1 to Mt3, with secondary enrichment in Mt4; Mo concentrations peak in Mt2 (average 5.06 ppm), coupled with elevated chalcophile metalloid Te, As, Pb and Bi. Elemental mapping results show that K occurs as discrete hotspots, which may be mainly derived from feldspar microinclusions, rather than lattice substitution in magnetite. These geochemical fingerprints record a transition from high-temperature magmatic–hydrothermal fluids to late contact-metasomatic fluids, with evolving fluid–rock interaction and oxygen fugacity. Our results demonstrate that magnetite chemistry is a reliable tool for discriminating mineralization stages and vectoring prospecting targets in porphyry–skarn Mo–W systems. Full article

Photocatalytic H₂O₂ synthesis emerges as a promising green substitute for the energy-intensive anthraquinone process, yet its efficiency is limited by rapid charge recombination and limited surface active sites in bulk polymeric semiconductors. Herein, we report a topology-directed strategy to tailor the interlayer interactions of graphitic carbon nitride (g-C₃N₄), yielding ultrathin nanosheets with optimized electronic structures. The resulting catalyst exhibits an exceptional H₂O₂ production rate of 1.34 mmol g⁻¹ h⁻¹ under visible light, surpassing bulk g-C₃N₄ by a factor of 2.48. Water contact angle measurements confirm the superior hydrophilicity of the engineered nanosheets, facilitating interfacial mass transfer, while in situ FTIR and EPR spectroscopies unravel that the abundant exposed active sites optimize the adsorption configuration of the key *OOH intermediate and promote the generation of •O₂⁻ and •OH radicals. Regarding charge transfer dynamics, in situ EPR trapping experiments and Kelvin probe force microscopy (KPFM) reveal that the attenuated interlayer coupling induces a robust internal electric field, effectively suppressing carrier recombination and prolonging the exciton lifetime by a factor of 1.249. This work establishes a quantitative structure–activity relationship between interlayer engineering and exciton dynamics, offering a reliable protocol for the rational design of high-performance molecular photocatalysts. Full article

Texture characteristics are critical quality evaluation indicators for soft foods. Traditional texture profile analysis (TPA) relies on probe–sample contact and may cause irreversible structural damage, limiting its application in nondestructive or online detection. In this study, a non-contact and nondestructive Controlled Airflow–Laser Texturemeter (CAFLT) system was developed to achieve rapid multi-parameter texture characterization. The system integrates programmable airflow loading with laser displacement sensing to implement a TPA-like double-cycle loading protocol, simultaneously acquiring time–applied airflow pressure (T–AP) and time–displacement (T–D) responses. Gelatin–maltose composite gels with graded Bloom strengths (CL50–CL250) were used as model samples. Texture-related descriptors were extracted using a dual-curve feature framework and compared with traditional TPA measurements. The CAFLT system produced a double-peak response pattern resembling that of traditional TPA and showed clear monotonic trends with increasing gel strength. Hardness_CAFLT exhibited a strong correlation with the reference TPA hardness value (r = 0.97). In addition, Gumminess_CAFLT showed a positive association with traditional gumminess (r = 0.87), but should be interpreted within the CAFLT-specific loading framework. Multivariate principal coordinates analysis further demonstrated clear multivariate discrimination among samples. Additionally, the time-domain descriptor t_Peak1 showed a strong power-law relationship with Bloom strength ($R^2=0.96$), indicating enhanced sensitivity to mechanical differences under small-deformation conditions. Overall, the CAFLT system provides a feasible approach for non-contact, nondestructive, and quantitative texture evaluation of soft foods, and shows strong potential for real-time quality monitoring and intelligent food inspection. Full article

Visual perception is fundamental to robotic manipulation for recognizing objects, goals, and contextual details. Third-person cameras provide global views but can miss contact-rich interactions and require calibration. Wrist-mounted egocentric cameras reduce these limitations but introduce occlusion, motion blur, and partial observability, which complicate visuomotor learning. Furthermore, existing perception modules that rely solely on pixels or fuse imagery with proprioception as flat vectors do not explicitly model structured scene representations in dynamic egocentric views. To address these challenges, a multi-slot attention fusion encoder for egocentric manipulation is introduced. Learnable slot queries extract localized visual features from image tokens, and Feature-wise Linear Modulation (FiLM) conditions each slot on the robot’s joint states, producing a structured slot-based latent representation that adapts to viewpoint and configuration changes without requiring object labels or external camera priors. The resulting structured slot-based latent representation is used as input to a Soft Actor–Critic (SAC) agent, which achieves a higher mean cumulative return than pixel-only CNN/DrQ and state-only baselines on a ManiSkill3 egocentric manipulation task. Probing experiments and real-camera evaluation further show that the learned representation remains stable under egocentric viewpoint shifts and partial occlusions, indicating robustness in practical manipulation settings. Full article

The meshing contact performance of spiral bevel gears is critical for transmission accuracy and service life but is inevitably influenced by manufacturing deviations. Existing tooth contact analysis (TCA) and lubrication-related studies for spiral bevel gears are mostly based on ideal theoretical tooth surfaces, failing to reflect the actual meshing state of as-machined gears with inherent machining deviations, and have poor robustness for complex deviated spatial surfaces. To accurately assess the actual meshing state, this paper proposes a novel contact performance analysis method based on a high-precision digital tooth surface reconstructed from one-dimensional probe measurement data. Unlike traditional TCA methods that rely on complex principal curvature calculations, this approach eliminates the mounting distance parameter by simplifying the meshing coordinate system, and employs a variable-radius cylindrical cutting method combined with a binary search algorithm to determine the instantaneous contact ellipse, effectively reducing computational complexity and improving solution robustness for deviated tooth surfaces. Experimental validation demonstrates that the digital tooth surface achieves a reconstruction accuracy of 2.6 × 10⁻⁵ mm. Furthermore, the method accurately predicts the contact pattern location and transmission error, with a discrepancy of only 4.7% compared to theoretical design values, which is highly consistent with the no-load rolling test results. This study confirms that the proposed method effectively reflects the actual meshing condition of machined gears, providing a practical theoretical foundation for the high-quality manufacturing and control of spiral bevel gears. Meanwhile, the high-fidelity contact characteristics of as-machined tooth surfaces output by this method can provide reliable input boundaries for thermoelastohydrodynamic lubrication (TEHL) simulation, friction loss prediction and anti-scuffing design of spiral bevel gears considering machining deviations. Full article