Search Results (493)

The control of surface residual stress is paramount for ensuring the mechanical performance and longevity of machined GH2132 superalloy bolts. However, direct measurement of residual stress remains challenging. This study introduces a novel, efficient approach by establishing a quantitative correlation between Vickers hardness and residual stress based on the energy indentation method. The core hypothesis leverages the principle that residual stress modifies the indentation work; the difference in energy dissipation between stressed and stress-free states provides a direct measure of residual stress. A mathematical model relating hardness (HV) to residual stress (σ) was derived. To validate the model and unravel the underlying microstructural mechanisms, orthogonal cutting experiments were conducted. Comprehensive microstructural characterization using SEM, XRD, and metallography revealed a synchronous relationship between hardness and residual stress. Both properties increased concurrently with greater grain refinement and higher volume fraction/distribution density of carbides and γ’ phases, which impede dislocation motion and introduce micro-strain. The model predictions showed excellent agreement (R² = 92.5%) with X-ray diffraction measurements, confirming its reliability. Furthermore, the influence of cutting parameters (speed V_c, feed f, depth of cut a_p) on residual stress was analyzed. Cutting depth was identified as the most significant factor. An optimal parameter combination (V_c = 20 m × min⁻¹, f = 1 mm × rev⁻¹, a_p = 1.2 mm) was identified to maximize beneficial compressive residual stress, corresponding to the most refined microstructure. This work presents a validated, hardness-based model for residual stress assessment in GH2132 and provides a microstructure-guided pathway for optimizing machining processes to enhance component life. Full article

We investigate a stochastic coupled nonlinear Schrödinger (Manakov-type) system for option price and volatility wave fields within the Ivancevic adaptive-wave option-pricing paradigm, and derive exact wave families together with statistical diagnostics of the resulting dynamics. This system combines behavioral market effects with classical efficient-market dynamics and incorporates a controlled stochastic volatility component. Randomness in both the option price and volatility is incorporated via white noise, and a system of stochastic partial differential equations (PDEs) is developed that governs the joint evolution of option prices and stock price volatility. We derive advanced solutions of the proposed system using a newly created methodology. The obtained solutions are expressions of cnoidal, snoidal, dnoidal, hyperbolic, trigonometric, and exponential functions. The stochastic dynamical investigation, together with the statistical measures are presented. The autocorrelation function (ACF) of squared returns for the obtained analytical solutions is demonstrated to show distinct differences in second-order temporal dependence, while asymmetries in the temporal evolution of the fluctuations are depicted via leverage correlation (LC). The probability distribution function (PDF) dynamics of the soliton solutions illustrate prominent temporal variability and non-stationary statistical dynamics. Differences in dynamical coupling between the two components of the considered system are presented via phase velocity cross-correlation analysis and are supported by phase difference dynamics visualizations. The strength and structure of coupling between components are displayed via the amplitude cross-correlation function. Mean amplitude dynamics and variance as a function of noise intensity $\sigma$, provide a systematic influence of stochastic forcing on their energy and a quantitative measure of stochastic dispersion of soliton solutions. All the results are displayed in 3D and 2D graphs of the stochastics and statistical dynamics of the obtained solutions. Full article

Cemented carbides are essential in applications requiring exceptional hardness and wear resistance. However, the reliance on cobalt as a binder raises concerns related to cost, supply security, and health. High-entropy alloys (HEAs) are promising cobalt-free binders offering favorable mechanical properties and potential grain-growth control. This work presents a new approach for the development of Co-free WC-based cemented carbide employing an HEA binder designed through CALPHAD-guided simulations. An optimized composition corresponding to Al5Cr5Cu10Fe35Mn10Ni35 (at%) alloy is predicted to be FCC-dominant with minimal σ-phase formation and good compatibility with WC. A preliminary batch of powder of the proposed binder was produced by blending elemental powders, arc remelting, and ultrasonic atomization, yielding predominantly spherical particles with a dendritic microstructure. WC–HEA composites (WC–12 wt% HEA) were then prepared by ball milling, pressing, vacuum sintering, and sinter-HIP for a first evaluation of the microstructure and achievable hardness. The microstructure exhibited residual porosity without significant WC grain coarsening. XRD analyses showed the dominant presence of WC, along with FCC and M₃W₃C phases (M mainly Fe and Mn), indicating thermal interaction between the binder and WC. Despite these effects, the composite achieved a hardness of 1913 HV and retained a fine WC grain size (0.86 μm). The proposed design approach allowed the definition of a promising Co-free binder composition based on HEA with the expected microstructure, which will need further evaluation, especially aimed at investigating toughness properties as a function of the WC content. Full article

This study systematically investigated the chemical composition, microstructure, and mechanical properties of HR3C steel tubes that have been in service. The results indicate that, after nearly 70,000 h of operation, continuous lamellar M₂₃C₆ precipitates formed along grain boundaries in the HR3C steel, with needle-like or rod-like M₂₃C₆ phases extending from the grain boundaries into the grain interiors. Additionally, NbCrN and σ-phase precipitates were observed in the regions adjacent to the grain boundaries. Mechanical testing revealed a slight increase in hardness following service exposure, while the tensile strength remained largely unchanged; the yield strength, however, increased by approximately 15%. In contrast, the elongation at fracture decreased significantly—ductility declined by 64–73% relative to the as-received condition—and impact toughness dropped dramatically by 96%. These findings collectively indicate pronounced embrittlement of the HR3C steel after long-term service at 620 °C. Microstructural analysis confirms that the precipitation of M₂₃C₆ and σ phases is the primary contributor to the observed deterioration in toughness and ductility. Full article

Through-plane electronic transport in porous membrane electrode assembly (MEA) electrodes is governed by the three-dimensional (3D) connectivity of the conducting phase. Here, we quantify the role of the spanning-cluster fraction $P_{\infty }$, defined as the fraction of conducting-phase voxels that belong to the $z$-spanning connected component in a finite reconstructed volume, on effective conductivity using scanning electron microscopy (SEM)-informed 3D reconstructions of four archetypal morphologies: a granular catalyst layer (CL), labeled CL1; a fibrous gas diffusion layer (GDL), labeled GDL1; an open-cell foam (OCF); and a micro-fibrous non-woven (MFM), labeled MFM1. Each morphology is reconstructed on a $150\times 150\times 150$ voxel grid, and $z$-spanning connectivity is identified with a 26-neighbor flood-fill algorithm. Steady-state conduction is solved by a finite-volume method (FVM) with an imposed potential difference between the z-faces and no-flux lateral boundaries. Although all samples exhibit through-thickness connectivity, the normalized conductivity ${\sigma }_{\mathrm{e}\mathrm{f}\mathrm{f}}/{\sigma }_{\mathrm{b}\mathrm{u}\mathrm{l}\mathrm{k}}$ varies widely, from $\approx 0.134$ (MFM1) to $\approx 0.706$ (OCF). The corresponding ${(P}_{\infty },{\sigma }_{eff}/{\sigma }_{bulk})$ pairs are $\left(0.996,\approx 0.306\right)$ for CL1, $\left(0.999,\approx 0.303\right)$ for GDL1, $\left(0.997,\approx 0.706\right)$ for OCF, and $\left(0.901,\approx 0.134\right)$ for MFM1. OCF exhibits the highest response due to vertically coherent channels, whereas MFM1 underperforms due to laminated constrictions; CL1 and GDL1 lie in an intermediate regime with nearly isotropic skeletons. Overall, the results show that while a $z$-spanning connected component is required for measurable conduction, the magnitude of ${\sigma }_{\mathrm{e}\mathrm{f}\mathrm{f}}$ is dictated by percolating-skeleton quality (bottlenecks, cross-sectional constrictions, and pathway alignment) rather than phase amount alone. The proposed descriptors therefore enable percolation-aware screening metrics for designing and comparing MEA-relevant GDL and CL microstructures. Full article

Phase unwrapping is a critical step in interferometric imaging modalities such as holography and synthetic aperture radar, yet conventional analytical algorithms struggle in low signal-to-noise and high-speckle environments. This study presents an artificial intelligence (AI)-based phase-unwrapping framework using a Pix2Pix conditional generative adversarial network (cGAN). A model was designed for robustness under Rayleigh-distributed speckle noise and phase decorrelation, conditions representative of realistic interferometric measurements. Trained on synthetically generated wrapped–unwrapped phase pairs, the AI approach was compared against established analytical phase-unwrapping methods, a quality-guided unwrapping algorithm (Herraez)and a minimum-norm network-flow optimization method (Costantini). Quantitative evaluation using the root mean square error (RMSE), structural similarity index measure (SSIM), and a composite performance index demonstrated that the cGAN was superior under noisy conditions, successfully recovering phase information beyond its training noise range at $\sigma =10$, and accurately unwrapping phases up to $\sigma =20$. This was under a pure unwrapping performance analysis, utility performance was also tested comparing all images to clean noiseless phase. The Pix2Pix model also proved resilient to detector artifacts, despite not being explicitly trained on them, and its worst performance yielded RMSE and SSIM values of 0.089 and 0.927, respectively, with perfect values being 0 and 1. The proposed framework simultaneously unwraps and denoises the phase, offering a simple, open-source, and highly adaptable alternative for phase unwrapping in noisy interferometric systems. Future work will focus on extending the framework to experimental datasets. Full article

High-temperature self-lubricating materials with stable tribological performance across a wide temperature range are essential for advanced mechanical systems under extreme conditions. However, balancing mechanical strength and lubrication efficiency remains a key challenge. This study fabricated CoCrFeNiMox-Ni/MoS₂-Ag-Cr₂O₃ composites (x = 0.2, 0.5, 1) via spark plasma sintering, aiming to investigate the effect of Mo content on their microstructure, mechanical properties, and tribological behavior. Microstructural analysis showed that the as-sintered composites mainly consist of FCC phase, Cr₂O₃, Ag, and Ni/MoS₂. Increasing Mo content from 0.2 to 1 wt.% significantly promoted the formation of hard σ-phase intermetallics, leading to increased hardness (up to 546 HV) and yield strength (peaking at 502 MPa). Tribological tests at 25–800 °C indicated continuous lubrication behavior in all composites. The minimum friction coefficient was 0.23, and wear rates remained below 10⁻⁶ mm³/N·m. In the low-to-medium temperature range, lubrication was dominated by the synergistic effect of Ni/MoS₂ and Ag: Ni/MoS₂ formed low-shear-strength films, while Ag reduced surface adhesion. Meanwhile, the Mo solid solution strengthened and the σ-phase enhanced wear resistance by improving hardness and inhibiting plastic deformation. At high temperatures, tribochemical reactions generated lubricating films composed of oxides and molybdates, which maintained tribological performance by reducing direct contact between friction pairs. This study demonstrates that Mo-doped high-entropy alloy composites can serve as high-performance wide-temperature self-lubricating materials, providing a basis for designing “matrix-lubricant” systems for extreme-temperature applications. Full article

We used synchrotron X-ray diffraction (XRD) and ac-impedance spectroscopy (AIS) to uncover the structural and chemical modifications undergone by RbH₂PO₄ (RDP) at intermediate temperatures (150 °C < T < 300 °C) and investigate their relationship with RDP’s proton conductivity, σ. Nyquist plots collected on RDP samples sealed in a small volume (~50 mL) of dry air show a gradual increase in σ upon heating from 180 to 260 °C, but not the three-order-of-magnitude superprotonic jump observed in the Cs-based compound CsH₂PO₄ (CDP) within the same temperature range. Correspondingly, XRD measurements using synchrotron radiation (λ = 0.922 Å) on RDP crystalline powders sealed in a quartz capillary exhibit no evidence of a monoclinic-to-cubic superprotonic phase transition like the one observed in CDP. Instead, these temperature-resolved powder XRD patterns demonstrate that the intermediate-temperature RDP monoclinic phase (P2₁/m, a = 7.733 Å, b = 6.189 Å, c = 4.793 Å, and β = 109.21 deg) persists up to the melting point of the title compound. Our most significant finding comes from heating RDP under high pressure (P = 1 GPa), which leads to markedly different structural behavior. Indeed, our full profile refinements against XRD data collected on RDP crystals compressed at ~1 GPa show evidence of a polymorphic phase transition (at T_c = 300 °C) to a high-temperature cubic phase (Pm-3m, a = 4.784 Å) that is isomorphic with its CDP counterpart. This is significant, as it indicates that the superprotonic conduction in phosphate solid acids is not cation-specific, and a general highly efficient proton conduction mechanism is present in the high-temperature phases of these materials. Full article

In the context of three-dimensional (3D) point cloud modeling for pillar-type insulators during the “post-production–pre-use” phase, current methodologies encounter challenges in achieving a balance between cost-effectiveness, comprehensive coverage, and high precision. This study introduces a novel 3D point cloud modeling approach that utilizes multi-view two-dimensional (2D) LiDAR technology. This method employs three 2D LiDAR sensors positioned at 120° intervals to conduct layer-by-layer scanning, thereby capturing the surface point cloud data of insulators from various heights and perspectives. This approach effectively mitigates the impact of occlusion and facilitates comprehensive 360° data acquisition. Based on this foundation, the skirt structure characteristics of pillar-type insulators were extracted, and a point cloud registration and stitching algorithm, grounded in structural constraints, was developed to facilitate a high-precision 3D reconstruction. The experimental findings indicate that the proposed approach in this study demonstrates substantial improvements in modeling accuracy compared with the baseline methods. In repeated experiments, the proposed method in this study showed an average distance error with a mean (${\mu }_{\mathrm{MDE}}$) ± standard deviation ($\sigma$) of 1.15 ± 0.07, while the root mean square error had a mean (${\mu }_{\mathrm{RMS}}$) ± standard deviation ($\sigma$) of 1.26 ± 0.11. This method offers several advantages, including a straightforward structure, low system cost, and excellent point cloud continuity (1 mm). The maximum measurement error for the disc diameter was 2.986 mm, which satisfies the engineering application requirement of ±5 mm, thereby confirming the feasibility and practical utility of the method in the 3D modeling of pillar-type insulators. Full article

For those who did not follow the invention and development of enantioselective catalysis, this review introduces pertinent historical aspects of the field and presents the scientific concepts of asymmetric bio- and organocatalysis. They are powerful technologies applied in organic laboratories and industry. They realize chiral amplification by converting inexpensive achiral substrates and reagents into enantiomerically enriched products using readily recoverable solvents, if any are used. Racemic substrates can also be deracemized catalytically. More sustainable fabrications are now available that require neither toxic metallic species nor costly reaction conditions in terms of energy, atmosphere control, product purification, and safety. Nature has been the source of the first asymmetric catalysts (microorganisms, enzymes, alkaloids, amino acids, peptides, terpenoids, sugars, and their derivatives). They act as temporary chiral auxiliaries and lower the activation free energy of the reaction by altering the reaction mechanism. Reductions, oxidations, carbon-carbon and carbon-heteroatom bond-forming reactions are part of the process panoply. Asymmetric catalyzed multicomponent and domino reactions are becoming common. Typical modes of activation are proton transfers, hydrogen bonded complex formation, charged or uncharged acid/base pairing (e.g., σ-hole catalysts), formation of equilibria between achiral aldehydes and ketones with their chiral iminium salt or/and enamine intermediates, umpolung of aldehydes and ketones by reaction with N-heterocyclic carbenes (NHCs), phase transfer catalysis (PTC), etc. Often, the best enantioselectivities are observed with polyfunctional catalysts derived from natural compounds, but not always. They may combine to form chiral structures containing nitrogen, phosphorus, sulfur, selenium, and iodine functional moieties. Today, man-made enantiomerically enriched catalysts, if not enantiomerically pure, are available in both enantiomeric forms. Being robust, they are recovered and reused readily. Full article

We propose a low-complexity hybrid beamforming method for massive Multiple-Input Multiple-Output (MIMO) systems that is robust to Channel State Information (CSI) estimation errors. These errors stem from hardware impairments, pilot contamination, limited training, and fast fading, causing spectral-efficiency loss. However, existing hybrid beamforming solutions typically either assume near-perfect CSI or rely on greedy/black-box designs without an explicit mechanism to regularize the error-distorted singular modes, leaving a gap in unified, low-complexity, and theoretically grounded robustness. We unfold the Alternating Direction Method of Multipliers (ADMM) into a trainable Deep Learning (DL) network, termed DL-ADMM, to jointly optimize Radio-Frequency (RF) and baseband precoders and combiners. In DL-ADMM, the ADMM update mappings are learned (layer-wise parameters and projections) to amortize the joint RF/baseband optimization, whereas Regularized Singular Value Decomposition (RSVD) acts as an analytical regularizer that reshapes the observed channel’s singular values to suppress noise amplification under imperfect CSI. RSVD is integrated to stabilize singular modes and curb noise amplification, yielding a unified and scalable design. For ${\sigma }_e^2=0.1$, the proposed DL-ADMM-Reg achieves approximately 8–11 bits/s/Hz higher spectral efficiency than Orthogonal Matching Pursuit (OMP) at Signal-to-Noise Ratio (SNR) $=20$–40 dB, while remaining within <1 bit/s/Hz of the digital-optimal benchmark across both $(N_t,N_r)=(32,32)$ and $(64,64)$ settings. Simulations confirm higher spectral efficiency and robustness than OMP and Adaptive Phase Shifters (APSs). Full article

The microstructure evolution law and the changes in mechanical properties of 442 ferritic stainless steel after annealing treatment at different temperatures are systematically investigated. The results show that, as the annealing temperature increases, the cold-rolled 442 ferritic stainless steel successively undergoes the process of recovery, recrystallization and grain growth, with the microstructure gradually changing from a fibrous to recrystallized structure, and the secondary phases, such as the Nb(C, N) phase, σ phase and Laves phase, precipitate. In terms of mechanical properties, the tensile strength, yield strength and Vickers hardness gradually decrease, while the elongation after fracture gradually increases. When the annealing temperature reaches 800 °C, the material exhibits the optimal comprehensive mechanical properties. The yield strength, tensile strength and elongation reach 371 MPa, 534 MPa and 31%, respectively, and the hardness is 175 HV. The fracture mode of the sample is mainly ductile fracture. EBSD analysis indicates that the strong Brass {110}<112> texture existing in the cold-rolled state gradually weakens with the annealing process, and the {111}<110> texture strengthens, thereby reducing the influence of unfavorable textures. The research results provide theoretical basis and data support for microstructure regulation and performance optimization of 442 ferritic stainless steel. Full article

The formation and evolution of secondary phases, such as sigma (σ), chi (χ), Laves, carbides (M₂₃C₆), and nitrides (Cr₂N), have a fundamental impact on the corrosion resistance of stainless steels. These stages alter the matrix’s local chemistry, compromise the passive film’s quality, and promote micro-galvanic interaction, which enhances localized corrosion issues. The thermodynamic stability, precipitation kinetics, and corrosion consequences of secondary phases in austenitic, ferritic, duplex, and lightweight (Fe–Mn–Al–C) stainless-steel systems are thoroughly reviewed and discussed in this paper. Advances in high-resolution characterization techniques, such as TEM, EBSD, atom-probe tomography, and in situ synchrotron techniques, have made it possible to map corrosion problems caused by secondary phases at the nanoscale. Computational thermodynamics (CALPHAD, DICTRA, TC-PRISMA) and emerging machine-learning models now provide quantitative prediction of phase formation and dissolution. Strategies for mitigation through alloy design, thermal treatment, and surface engineering are summarized, together with additive-manufacturing approaches for microstructural tailoring. Finally, this review highlights the integration of multi-scale modeling and sustainable alloy design to ensure phase-stable, corrosion-resistant stainless steels that enhance asset integrity and infrastructure reliability as per Sustainable Development Goals. Full article

The steel industry contributes to approximately 7%–9% of global anthropogenic CO_2(g) emissions, with traditional blast furnace–basic oxygen furnace (BF–BOF) routes emitting up to 1.8 t_CO2 per ton of steel. In contrast, Electric Arc Furnace (EAF) steelmaking, especially when integrated with hydrogen direct-reduced iron (DRI), can reduce emissions by over 40%, positioning EAFs as a key enabler of low-carbon metallurgy. However, despite its lower direct emissions, the EAF process still depends on fossil carbon sources for slag foaming and FeO reduction, which are essential for arc stability and energy efficiency. Slag foaming plays a critical role in controlling the thermal efficiency of the EAF by shielding the electric arc, reducing radiative heat losses, and stabilizing the arc’s behavior. This review examines the mechanisms of slag foaming, discussed through empirical models that consider the foaming index (Σ) and slag foaming rate as critical parameters, and highlights the influence of physical properties such as slag viscosity, surface tension, and density on gas bubble retention. Also, the work embraces the potential use of alternative carbon sources including biochar, biomass, and waste-derived materials such as plastics and rubber to replace fossil-based reductants and foaming agents in EAF operations. Finally, it discusses the use of new materials with a biological base, such as nanocellulose, to serve as reactive templates for producing nanohybrid materials, containing both oxides, which can contribute to slag basicity (MgO and/or CaO, for example), together with a reactive carbonaceous phase, derived from the organic fiber’s thermal degradation, which could contribute to slag foaming, and could replace part of the fossil fuel charge to be employed in the EAF process. In this context, the development and characterization of renewable carbonaceous materials capable of simultaneously reducing FeO and promoting slag foaming are essential to achieving net-zero steel production and enhancing the sustainability of EAF-based steelmaking. Full article

To investigate whether tourism can act as a catalyst for regional economic convergence during the period 2000–2023, this study fills a critical gap in previous research by simultaneously examining the impact of tourism on economic disparities from both static stock and dynamic incremental perspectives, while accounting for spatial dependence. This study analyzes the economic convergence effects of tourism at the Chinese provincial and regional levels using σ convergence and the spatial Durbin model in a conditional β convergence framework. The results confirm the benefits that tourism brings to economic growth and convergence. Spatially, northeastern China exhibits stronger effects, followed by western and eastern China, in contrast to the relatively weaker impacts in central China. Structurally, its direct effect is more pronounced: the convergence effect is stronger for local areas than for neighboring areas. Temporally, the effect is most pronounced in the early (2000–2012) and late (2020–2023) phases, but becomes statistically insignificant in the intermediate period (2013–2019). By moving beyond the question of whether tourism drives growth to reveal for which regions it is most beneficial, this study offers a refined analytical perspective and actionable insights for achieving balanced regional development in China and other countries and regions at a comparable stage of development. The findings also highlight the potential of cultural heritage as a lever for sustainable and equitable regional growth, channeled through tourism. Full article