Search Results (427)

The strategic balance between strength and electrical conductivity in Al-Mg-Si alloys is a critical challenge that must be overcome to enable their widespread adoption as viable alternatives to copper conductors in power transmission systems. To address this, the present study comprehensively investigates model alloys with Mg/Si ratios ranging from 1.0 to 2.0. A multi-faceted experimental approach was employed, combining tailored thermo-mechanical treatments (solution treatment, cold drawing, and isothermal annealing) with comprehensive microstructural characterization techniques, including electron backscatter diffraction (EBSD) and scanning electron microscopy (SEM). The results elucidate a fundamental competitive mechanism governing property optimization: excess Mg atoms concurrently contribute to solid-solution strengthening via the formation of Cottrell atmospheres around dislocations, while simultaneously enhancing electron scattering, which is detrimental to conductivity. A critical synergy was identified at the Mg/Si ratio of 1.75, which promotes the dense precipitation of fine β″ phase while facilitating extensive recovery of high dislocation density. Furthermore, EBSD analysis confirmed the development of a microstructure comprising 74.1% high-angle grain boundaries alongside a low dislocation density (KAM ≤ 2°). This specific microstructural configuration effectively minimizes electron scattering while providing moderate grain boundary strengthening, thereby synergistically achieving an optimal balance between strength and electrical conductivity. Consequently, this work elucidates the key quantitative relationships and competitive mechanisms among composition (Mg/Si ratio), processing parameters, microstructure evolution, and final properties within the studied Al-xMg-0.5Si alloy system. These findings establish a clear design guideline and provide a fundamental understanding for developing high-performance aluminum-based conductor alloys with tailored Mg/Si ratios. Full article

Distributed acoustic sensing (DAS) systems have been widely employed in oil and gas resource exploration, pipeline monitoring, traffic and transportation, structural health monitoring, hydrophone usage, and perimeter security due to their ability to perform large-scale distributed acoustic measurements. Conventional DAS relies on Rayleigh backscattering (RBS) from standard single-mode fibers (SMFs), which inherently limits the signal-to-noise ratio (SNR) and sensing robustness. Ultra-weak fiber Bragg grating (UWFBG) arrays can significantly enhance backscattering intensity and thereby improve DAS performance. This review provides a comprehensive overview of recent advances in UWFBG arrays for high-performance DAS. We introduce major inscription techniques for UWFBG arrays, including the drawing tower grating method, ultraviolet (UV) exposure through UV-transparent coating fiber technologies, and femtosecond laser direct writing methods. Furthermore, we summarize the applications of UWFBG arrays in DAS systems for the enhancement of RBS intensity, suppression of fading, improvement of frequency response, and phase noise compensation. Finally, the prospects of UWFBG-enhanced DAS technologies are discussed. Full article

For hot-rolled ferritic stainless steels with preferential α-fiber texture, the strong α-fiber texture is retained after annealing, greatly affecting the texture and plastic formability during the subsequent cold-rolling process. For optimizing the texture of hot-rolled steels toward the favorable γ-fiber type, it is essential to control the annealing temperature in the annealing process. To investigate the evolution of the microstructure, texture, and properties of hot-rolled ferritic stainless steel with preferential α-fiber orientation, a series of annealing tests was performed at the lab scale at 800, 840, 880, 910, 930, and 950 °C for 3 min. The microstructure, texture, and grain boundary characteristics of the tested samples were analyzed using optical microscopy (OM) and electron back-scattered diffraction (EBSD). The mechanical properties and plastic strain ratio (r-value) were determined through universal tensile testing. The results show that at temperatures above 840 °C, more than 93% of recrystallization occurs, leading to significant microstructural refinement. The α-fiber texture intensity typically diminishes with rising temperature, whereas the γ-fiber texture initially weakens during the early stages of recrystallization (below 840 °C) and subsequently exhibits a slight increase at higher temperatures. The improved formability of the material is mainly attributed to microstructural refinement and texture refinement, as reflected by the I(γ)/I(α) texture intensity ratio. At an annealing temperature of 930 °C, the I(γ)/I(α) ratio peaks at 0.85, static toughness is maximized, the strain-hardening exponent (n) reaches a high value of 0.28, and the maximum average plastic strain ratio ($\overline{r}$) is 0.96. This result represents the optimum balance between mechanical properties and formability, making it suitable for subsequent cold-rolling. Full article

A novel multi-wavelength differential absorption lidar operating at 1.5 μm band is proposed and theoretically analyzed for simultaneous remote sensing of vertical profiles of H₂¹⁶O, HD¹⁶O, and the isotopic ratio δD. The spectral band is compatible with mature, commercially available fiber-optic components, ensuring practical implementability. By employing the 1976 U.S. Standard atmosphere and considering the temperature dependence of H₂¹⁶O, the systematic error induced by a +1 K temperature uncertainty within the 2 km altitude is limited to 0.81% through appropriate absorption line selection. Simulations of atmospheric backscattered signals with a time resolution of 30 min and a range resolution of 120 m show that random error remains below 0.16% up to 2 km. The simultaneous retrieval errors of H₂¹⁶O and HD¹⁶O mixing ratio profiles at 2 km are 0.13 g/kg (3.19%) and 1.69 × 10⁻⁴ g/kg (18.02%), respectively, from which the δD is successfully and reliably retrieved. The results provide essential technical guidance for implementing high-resolution, isotopologue-resolved lidar observations in atmospheric science. Full article

Laser-based remote sensing (lidar) is a proven technique for detecting atmospheric features such as clouds and aerosols as well as for determining their vertical distribution with high accuracy. Even simple elastic backscatter lidars can distinguish clouds from aerosols, and accurate knowledge of their vertical location is essential for air quality assessment, hazard avoidance, and operational decision-making. However, daytime lidar measurements suffer from reduced signal-to-noise ratio (SNR) due to solar background contamination. Conventional processing approaches mitigate this by applying horizontal and vertical averaging, which improves SNR at the expense of spatial resolution and feature detectability. This work presents a deep learning-based framework that enhances lidar SNR at native resolution and performs fast layer detection and cloud–aerosol discrimination. We apply this approach to ICESat-2 532 nm photon-counting data, using artificially noised nighttime profiles to generate simulated daytime observations for training and evaluation. Relative to the simulated daytime data, our method improves peak SNR by more than a factor of three while preserving structural similarity with true nighttime profiles. After recalibration, the denoised photon counts yield an order-of-magnitude reduction in mean absolute percentage error in calibrated attenuated backscatter compared with the simulated daytime data, when validated against real nighttime measurements. We further apply the trained model to a full month of real daytime ICESat-2 observations (April 2023) and demonstrate effective layer detection and cloud–aerosol discrimination, maintaining high recall for both clouds and aerosols and showing qualitative improvement relative to the standard ATL09 data products. As an alternative to traditional averaging-based workflows, this deep learning approach offers accurate, near real-time data processing at native resolution. A key implication is the potential to enable smaller, lower-power spaceborne lidar systems that perform as well as larger instruments. Full article

Distributed Acoustic Sensing (DAS) transforms conventional optical fibres into large-scale acoustic sensor arrays. While existing telecommunication cables are increasingly considered for DAS-based monitoring, their performance depends strongly on cable construction and strain transfer efficiency. In this study, the relative DAS signal amplitudes of three commercial telecommunication optical cables were experimentally compared using a benchtop Rayleigh backscattering-based interrogator under controlled laboratory conditions. By maintaining a constant temperature and ensuring no additional strain changes from the outside environment, we guaranteed that only strain-induced variations from acoustic excitations were measured. The results show clear differences in signal amplitude and signal-to-noise ratio (SNR) among the tested cables. The Microcable consistently produced the highest spatial peak amplitude (up to 0.029 a.u.) and SNR (up to 79), while the Duct cable reached 0.00268 a.u. with mean SNR ≈ 32. The Anti-Rodent cable showed low signal amplitude (0.0018 a.u.) but exhibited a high mean SNR (≈111) driven by an exceptional low noise floor in one of the runs. These findings reflect the variations in mechanical coupling between the fibre core and external perturbations and provide practical insights into the suitability of different telecom cable types for DAS applications, supporting informed choices for future deployments. Full article

To address the issues of low efficiency and poor reliability associated with current manufacturing processes like welding for T-shaped tubes in aircraft, this study proposes a die-less rotary drawing forming process for T-shaped tubes. Finite element simulations combined with experiments were conducted to investigate the influence of four key process parameters-pre-hole size, preheating temperature, feed rate, and rotary drawing speed-on the rotary drawing forming of thin-walled 5A02 aluminum alloy T-shaped tubes. Variations in the effective branch height and wall thickness reduction ratio under different combinations of process parameters were explored. The optimal combination of process parameters was determined based on simulated orthogonal experiments. The optimized results were experimentally validated, and their pressure resistance limit was analyzed through pressure tests. Finally, the microstructural changes in the rotary-drawn material were analyzed using Electron Backscatter Diffraction (EBSD) tests. The results demonstrate that the proposed die-less rotary drawing forming process enables the local integrated forming of thin-walled 5A02 aluminum alloy T-shaped tubes. A branch height exceeding 3.5 mm and a maximum wall thickness reduction ratio of approximately 18% were achieved. The pressure withstand limit reaches 7 MPa, satisfying the engineering requirement of 6.4 MPa. Full article

Background: Practical, quantitative ultrasound-based tools for measuring hepatic steatosis are needed in everyday MASLD care. We evaluated a new multiparametric quantitative ultrasound (QUS) platform that integrates ultrasound-guided fat fraction (UGFF), attenuation coefficient (AC), backscatter coefficient (BSC), and signal-to-noise ratio (SNR), using Controlled Attenuation Parameter (CAP) as the reference and examining the effect of breathing. Methods: In a prospective single-center study, adult patients underwent same-day liver QUS and FibroScan. QUS measurements were performed during breath-hold and during normal breathing. Regions of interest were placed in right-lobe parenchyma 2 cm below the capsule, avoiding vessels. Primary outcomes were correlation with CAP and ROC performance at CAP cutoffs for S1 (≥230 dB/m), S2 (≥275 dB/m), and S3 (≥300 dB/m). Results: QUS was feasible in almost all examinations. UGFF, BSC, and SNR were consistent across breathing conditions, while AC was slightly higher during normal breathing. UGFF showed strong correlation with CAP and high accuracy for detecting steatosis. Across grades, AUCs were around 0.89–0.91, with cutoffs (UGFF ≈ 4% for ≥S1 and ≈11% for ≥S3). Conclusions: Multiparametric QUS provides reliable liver fat quantification that aligns closely with CAP and remains robust in practice whether patients hold their breath or breathe normally. These findings support UGFF as a practical, reliable point-of-care alternative for liver fat quantification that can be embedded in routine ultrasound in real time. Validation against MRI-PDFF or histology and multicenter studies will further define cutoffs and generalizability. Full article

The parameters of the austempering process play a crucial role in governing the microstructure and mechanical properties of carbide-free bainitic (CFB) steel. In this study, a CFB steel was austempered at temperatures close to its martensite start (Ms = 372 °C) temperature to investigate the bainitic transformation kinetics, microstructure, and mechanical properties. To identify the optimal strength–ductility combination, austempering was carried out at 360 °C, 380 °C, and 400 °C for comparison. The results show that austempering slightly below Ms (360 °C) produces the highest yield-to-tensile strength ratio and a good strength–ductility balance. Dilatometry curves indicate that the onset of bainite transformation occurs fastest when austempering slightly below Ms. The stronger transformation driving force and the presence of athermal martensite are the primary reasons. The enhanced thermodynamic driving force and increased nucleation density promote the formation of a larger amount of bainitic laths. Electron backscatter diffraction (EBSD) analysis reveals that the retained austenite blocks are finest after austempering at 360 °C, which helps alleviate the ductility loss associated with the reduction in retained austenite content. Full article

With the growing demand for durable and corrosion-resistant materials in advanced Li-ion battery cases, super duplex stainless steels (SDSSs) have emerged as promising candidates due to their excellent mechanical and electrochemical properties. This study aims to investigate how the ferrite and austenite phase balance in SDSS EN 1.4501 affects the microstructural and electrochemical behavior of Ag coatings, tailored for next-generation battery enclosure applications. Ag coatings were deposited to PVD (to 1 μm) on SDSS EN 1.4501 substrates with varying ferrite (from 32 vol.% to 70 vol.%) and austenite ratios (from 56 vol.% to 30 vol.%) to evaluate the influence of phase balance on coating performance. Microstructural analysis was performed using field emission scanning electron microscopy (FE-SEM, mag x 1000), electron backscatter diffraction (EBSD), and X-ray diffraction (XRD, from 20° to 80°), which provided insights into surface morphology, crystallographic texture, and phase distribution. Electrochemical characteristics were assessed through open circuit potential (OCP), and potentiodynamic polarization in a simulated corrosive environment. The results showed that a balanced duplex microstructure promoted superior Ag coating adhesion, grain refinement, and uniform phase distribution. Furthermore, the electrochemical analyses indicated enhanced corrosion resistance and passivation layer stability in volume fraction balanced substrates, as evidenced by more noble OCP values (form −0.06 V to −0.11 V), and potentiodynamic polarization value (higher corrosion potential (from 0.08 V to 0.10 V), and lower corrosion current densities (from 3 × 10⁻⁷ A/cm² to 4 × 10⁻⁷ A/cm²)). These findings demonstrate that optimizing the phase balance in SDSS is critical for achieving high-performance Ag coated surfaces, offering significant potential for durable and corrosion-resistant Li ion battery casing applications. Full article

Ti-Nb alloys have been under active consideration for superelastic applications in biomedical devices due to their superior biocompatibility compared to NiTi. However, these alloys have been found to be highly sensitive to processing conditions, with many studies measuring different transformation temperatures for the same alloy composition. Several processing factors, including heat treatment times, temperatures and cooling rates, have been investigated. However, the effect of the rolling ratio on superelastic properties has not yet been systematically considered. In this study, samples of Ti-24Nb-4Zr-8Sn (wt%) with varied cold rolling reduction ratios were produced, and the superelastic properties were characterised. After the heat treatment, all samples were found to be predominantly in the metastable cubic β phase, with a small, non-varying volume fraction of the ω phase also present. Electron backscattered diffraction was utilised to measure the resulting texture and grain size in each sample, and these values were correlated to the superelastic properties. Full article

For growth control of graphene, observation techniques, particularly those allowing in situ imaging during synthesis, are essential. Scanning electron microscopy (SEM) is a conventional surface observation method capable of in situ imaging of graphene segregation or growth in chemical vapor deposition, as well as ex situ imaging of synthesized materials. However, secondary electron (SE) emission from graphene is not fully understood, and the contrast formation mechanism of the monolayer material remains unclear. This review summarizes the SEM imaging of graphene, with a focus on SE contrast mechanisms under different conditions. The monolayer graphene layer does not greatly affect SE emission. Its SE contrast is brought from the charging effect, oxidation effect, or attenuation effect of backscattered electron (BSE) from the substrate. Characteristics of SE detectors, such as energy window, acceptance angle, and detected SE/BSE ratio, also contribute to the graphene contrast formation. Full article

The increasing demand for sustainable and multifunctional materials in radiation shielding and optical applications has driven research toward utilizing natural and waste-derived reinforcements in polymer matrices. However, achieving effective attenuation performance across different radiation types using eco-friendly fillers remains a significant challenge. In this study, polyvinyl chloride (PVC)/Polystyrene (PSt) blend composites (1:1 weight ratio) were reinforced with powdered snail shell (SSP) as a biogenic additive, aiming to enhance their shielding and optical performance. Composites containing 5%, 10%, 20%, and 30% SSP (w/v) were fabricated and characterized. Key parameters including linear attenuation coefficient (LAC), mass attenuation coefficient (MAC), mean free path (MFP), half-value layer (HVL), and effective atomic number (Zeff) were measured using a variable-energy X-ray source (13.37–59.54 keV) and ULEGe detector. Fast neutron shielding performance and theoretical values for build-up factor (EBF) and macroscopic neutron cross-sections were also calculated. The results showed a marked improvement in X-ray attenuation with increasing SSP content (SSP30 > SSP20 > SSP10 > SSP5), while neutron shielding declined due to the high oxygen content of SSP. Among the tested samples, the SSP30 composite exhibited the highest X-ray attenuation efficiency, whereas the SSP5 composition showed the greatest enhancement in optical reflectance and neutron absorption, indicating optimal performance in these respective tests. Additionally, 5% SSP incorporation improved optical reflectance by 12%, indicating enhanced photon backscattering at the material surface. This behavior contributes to improved gamma shielding efficiency by reducing photon penetration and enhancing surface-level attenuation. These findings highlight the potential of snail shell-based fillers as low-cost, sustainable reinforcements in multifunctional polymer composites. Full article

The Laser Doppler Vibrometer (LDV) is widely used in precision vibration measurement due to its non-contact nature and high accuracy. However, when measuring non-cooperative targets, the internal stray light in the LDV interferes with the target’s return light, creating competition with the reference light, a phenomenon known as interference competition. This issue is particularly prominent in integrated transceiver LDV systems, where the backscattered light from the lens can be comparable in intensity to the target’s return light, significantly degrading phase extraction accuracy and limiting the LDV’s applicability. To address this challenge, this paper proposes a noise suppression algorithm based on the In-phase and Quadrature (IQ) demodulation. The algorithm uses the power spectrum within each frame’s relevant frequency band as an evaluation metric and employs the Three-point Probe Extremum Localization (3P-PEL) method to estimate the amplitude and phase of the stray light interference with the reference light in real time. This enables the accurate extraction of the interference signal between the measurement light and the reference light. Both simulations and experiments validate the effectiveness of the proposed method. The simulation results demonstrate that when the stray-to-measurement power ratio is below 0.25, the proposed algorithm can suppress spurious signals induced by multi-beam interference by more than 25 dB, while experimental results show it can reduce such signals below the LDV’s noise floor in various motion scenarios. The proposed algorithm holds potential applications in laser interferometry and effectively enhances LDV measurement accuracy. Full article

Distributed acoustic sensing (DAS) systems enable real-time monitoring of physical events across extended areas using optical fiber that detects vibrations through changes in backscattered light patterns. In perimeter security applications, these systems must accurately distinguish between legitimate activities and potential security threats by analyzing complex spatio-temporal data patterns. However, the high dimensionality and noise content of raw DAS data presents significant challenges for effective feature extraction and event classification, particularly when computational efficiency is required for real-time deployment. Traditional approaches or current machine learning methods often struggle with the balance between information preservation and computational complexity. This study addresses the critical need for efficient and accurate feature extraction methods that can identify informative signal components while maintaining real-time processing capabilities in DAS-based security systems. Here we show that wavelet packet decomposition (WPD) combined with a cascaded machine learning approach achieves 98% classification accuracy while reducing computational load through intelligent channel selection and preliminary filtering. Our modified peak signal-to-noise ratio metric successfully identifies the most informative frequency bands, which we validate through comprehensive neural network experiments across all possible WPD channels. The integration of principal component analysis with logistic regression as a preprocessing filter eliminates a substantial portion of non-target events while maintaining high recall level, significantly improving upon methods that processed all available data. These findings establish WPD as a powerful preprocessing technique for distributed sensing applications, with immediate applications in critical infrastructure protection. The demonstrated gains in computational efficiency and accuracy improvements suggest broad applicability to other pattern recognition challenges in large-scale sensor networks, seismic monitoring, and structural health monitoring systems, where real-time processing of high-dimensional acoustic data is essential. Full article