Neurodegenerative diseases, especially their early diagnosis, urgently require biomarkers capable of detecting tissue-level alterations before irreversible structural degeneration becomes clinically apparent. Pathological processes such as neuronal loss, demyelination, neuroinflammation, vascular dysfunction, and extracellular matrix remodeling can alter tissue mechanics, raising the possibility that mechanical changes may provide complementary information before overt atrophy becomes detectable. Magnetic resonance elastography (MRE) is currently the principal noninvasive technique for probing the mechanical state of brain tissue in vivo. However, MRE does not directly measure stiffness; it records harmonic displacement fields and infers mechanical parameters through constitutive assumptions and inverse reconstruction. This distinction is particularly important in the brain, where wave propagation is influenced by viscoelastic dissipation, structural anisotropy, fluid-solid coupling, geometric confinement, and complex boundary conditions. In this review, we examine brain MRE from a mechanics-centered perspective with a focus on its potential for developing mechanical biomarkers of neurodegenerative diseases. We first outline the forward-inverse problem framework underlying parameter estimation and the mechanical challenges imposed by the intracranial environment. We then review constitutive models and inverse reconstruction strategies, emphasizing observability, identifiability, reconstruction fidelity, and biomarker interpretation. Finally, we discuss opportunities and challenges in neurodegenerative disorders, with Parkinson’s disease as a representative example, and propose a hierarchical framework linking empirical contrast, mechanical interpretation, and biological validation. We argue that clinically meaningful mechanical biomarkers depend not on elastographic contrast alone, but on mechanically interpretable parameters supported by appropriate constitutive modeling, robust inversion, experimental validation, and multimodal integration.
Brain tissue failure can be described in terms of damage, strength, and fracture, which respectively characterize the initiation and accumulation of microstructural degradation, the loss of load-bearing capacity, and crack initiation and propagation. These failure processes are central to understanding traumatic brain injury. However, the failure of brain tissue has not yet been systematically synthesized. This review provides a comprehensive synthesis of the experimental methods, theoretical models, and mechanical parameters associated with brain tissue damage, strength, and fracture. Existing studies of brain tissue failure have primarily employed simple loading modes, particularly uniaxial tension and compression, whereas torsional and rotational loading remain insufficiently investigated. Meanwhile, current brain injury criteria largely rely on response variables, such as strain, strain rate, stress, and strain energy, predicted by finite element models that do not explicitly incorporate tissue failure. Consequently, these criteria have limited ability to characterize the initiation and evolution of brain tissue failure. Future research should develop brain injury criteria that explicitly incorporate damage, strength, and fracture properties. This review provides guidance for establishing a brain injury assessment framework grounded in tissue failure mechanics.
Electron correlation effects in 3d transition-metal compounds often play an important role in shaping their low-energy electronic properties. Here, using high-resolution angle-resolved photoemission spectroscopy, we investigate the electronic correlation behavior of CrSb, an altermagnetic material. Our measurements reveal the three-dimensional electronic structure of CrSb. A band splitting of about 1.1 eV is observed, consistent with previous reports. The comparison between experiment and density functional theory calculations shows renormalization factors of approximately 1.22–1.45 near the Fermi level, indicating moderate electron correlations. Notably, the renormalization is found to be momentum-dependent and band-dependent. These results provide insight into the role of electron correlations in altermagnetic CrSb and their reshaping of the low-energy electronic structure, which may be relevant for understanding its electronic properties and potential applications.
Quantum dot light-emitting diodes (QLEDs) with high external quantum efficiency (EQE) are attractive candidates for wearable integrated displays. However, the pursuit of high power efficiency—especially high EQE at low operating voltages-has long been hampered by interlayer charge injection barriers and inefficient intralayer charge transport. Here, we report a rational strategy to construct a submonolayer (sub-ML) quantum dot (QD) emissive layer (EML) using Langmuir-Blodgett assembly. We fabricate a closely packed monolayer of QD/NaYF4 hybrid nanoparticles (NPs), wherein each QD is precisely positioned to establish direct contact with both the electron transport layer (ETL) and hole transport layer (HTL), thus eliminating vertical charge transport between adjacent QDs. Critically, partial replacement of QDs with insulating NaYF4 NPs introduces localized electrostatic fields via embedded equivalent nanocapacitors, which substantially enhances the hole injection level. The sub-ML QLEDs realize efficient sub-bandgap-voltage emission, delivering a practical luminance of 585 cd m−2 at a low driving voltage of 1.8 V. These devices exhibit a maximum external power efficiency of 23.7
Ideal entangled states are crucial resources for quantum networks and quantum information processing. However, due to environmental noise, they inevitably degrade into mixed states, typically the Werner states. Purity is an important criterion for characterizing the quality of such realistic mixed states. Yet, efficient purity estimation remains a significant challenge without prior knowledge of the exact form of the initial ideal entangled states. In this work, we present a machine-learning-based protocol to quantify the purity of an arbitrary two-qubit Werner state without requiring knowledge of its underlying ideal form. Specifically, we train a fully connected neural network on theoretical data to achieve accurate purity estimation using only nine measurement bases-a significant reduction from the 16 bases in conventional quantum state tomography. Then this model is directly applied to estimate experimentally generated Werner states. The experimental results yield an average absolute error of 3.80
Piezo1 ion channels transduce mechanical forces through the flattening of their intrinsically curved transmembrane dome, a process modulated by the cortical cytoskeleton. Although experiments consistently show that the cytoskeleton elevates the channel’s half-activation tension—a phenomenon known as mechanoprotection—its mechanistic basis has remained unclear. Here, we develop a continuum framework that couples the elastic Piezo1 dome, the Helfrich lipid bilayer, and a two-dimensional tilted-spring model of the cortical cytoskeleton. Our results indicate that cytoskeletal coupling pre-flattens the dome, spatially confines the membrane footprint, and reduces the projected-area reservoir available for gating. These effects raise the half-activation tension from 1.90 to 3.15 mN/m, broaden the gating curve, and convert Piezo1 from a sharp binary switch into a graded mechanical rheostat, in quantitative agreement with single-channel measurements. Parameter-matched comparison with a vertical Winkler foundation model shows that the mechanoprotective shift is dominated by vertical cytoskeletal support, while lateral tension renormalisation provides only a secondary correction. This work offers a unified, energetics-based explanation for cytoskeletal mechanoprotection, highlighting how membrane-cytoskeleton coupling reshapes the energetic landscape of Piezo1 gating and expands its physiological dynamic range.
The interplay of spin, orbital, and spin-orbit coupling often gives rise to exotic phases of matter, including notably the quantum spin Hall (QSH) and quantum anomalous Hall (QAH) states. Achieving controllable switching between these phases in a single material platform holds great promise for future device applications. Here we predict FeNbX2 (X=Se, Te) as an appealing class of layered materials that can harbor thickness-dependent QSH and QAH states at elevated temperatures, with intriguing ferrimagnetic or altermagnetic characteristics. We first show that in the monolayer regime, both systems exhibit ferrimagnetism and behave as QAH insulators at room temperature. In contrast, the two systems behave distinctly differently in the multilayer regime. For FeNbSe2, the interlayer coupling is antiferromagnetic, and the materials exhibit layer-dependent topological properties, with the odd layers hosting ferrimagnetic QAH states of a constant Chern number C=2, while even layers hosting altermagnetic QSH states with a thickness-dependent spin Chern number Cs of ∣Cs∣=2 or higher. For FeNbTe2, the interlayer coupling is ferromagnetic, and the materials always exhibit QAH states with a thickness-dependent Chern number C⩾2. These findings offer unique materials platforms for achieving convertible QAH and QSH states for future dissipationless quantum devices.
We propose a linear model to quantify the feedthrough effect on the evolution of shock-accelerated fluid layers with arbitrary density stratification. The model is formulated explicitly in terms of the post-shock Atwood numbers of both interfaces, the dimensionless layer thickness, and the linear growth rates of a single shocked interface. Based on the concise framework, we first identify that feedthrough strength exhibits non-monotonic behavior with increasing dimensionless layer thickness within specific parameter ranges, which is contrary to widely accepted views. This non-monotonic behavior stems from competition between two physical mechanisms: the inertial response and the transport of perturbed velocity. The findings provide a theoretical foundation for optimizing the thickness and perturbation spectra of material layers in inertial confinement fusion capsule designs.
The pursuit of customizable magnetoelectric coupling in emerging altermagnets is significant for meeting diverse functional requirements in flexible, efficient, and low-power spintronic applications. However, a major challenge remains in simultaneously achieving switchable ferroelectric polarization and tunable magnetic order. Here, we propose that Janus altermagnetic bilayers offer a solution by enabling engineered control of magnetic order alongside intrinsic sliding ferroelectricity through Janus interface design. Using a Janus altermagnetic Mo2Cl3Br3 bilayer as a prototype, we find that selective ClCl or BrBr interface configurations yield distinct magnetoelectric coupling phenomena. The ClCl interface bilayer exhibits interlayer antiferromagnetic coupling and sliding ferroelectricity, where ferroelectric polarization reversal switches the band spin splitting and the spin polarization of the spin currents, but leaves the magneto-optical Kerr signal unchanged. In contrast, the BrBr interface bilayer shows interlayer ferromagnetic coupling and sliding ferroelectricity, where the magneto-optical Kerr signal is reversible upon ferroelectric polarization switching, while the spin splitting remains fixed. The Janus interface design in this work establishes a universal paradigm for on-demand magnetoelectric control and provides an alternative material platform for developing advanced spintronic logic and memory devices.
The conventional “trial-and-error” strategy for amorphous alloy composition development is inherently inefficient and resource-intensive, particularly in the context of complex multi-component systems where comprehensive phase diagrams are often unavailable. In this work, we introduce a machine learning (ML) driven iterative framework for the accelerated design of low-liquidus-temperature Ti-based amorphous alloys in multicomponent alloy systems. By targeting low-liquidus-temperature Ti-based alloys and selecting Cu as the key alloying element, we employ regression models to predict liquidus temperatures across 60 ternary and 11 quaternary systems. Through iterative incorporation of limited experimental feedback from prior prediction rounds, the model progressively improves its predictive accuracy and facilitates efficient identification of compositional regions associated with lower melting temperature. Using this framework, the optimal amorphous composition Ti60Cu11Ni19Zr10 is identified, exhibiting a low liquidus temperature of 1121 K. This methodology not only improves the reliability of the ML predictions with limited experimental iterations, but also provides a scalable and efficient paradigm for the future development of amorphous alloys with extensive design flexibility of properties.
We investigate an Unruh quantum Otto engine that employs a two-level Unruh-DeWitt detector as the working medium and is coupled to a massless scalar field. Going beyond the limitations of previous studies that focused solely on uniformly accelerated motion with linear coupling, this paper examines composite detector trajectories with four-acceleration and four-velocity components and investigates how quadratic coupling affects the behavior of quantum heat engine. We find that the four-velocity component modifies the two-point correlation function of the vacuum field, thereby altering the effective temperature predicted by the Unruh effect. The key finding is that, for quadratic coupling, the four-velocity component enhances the work output of the heat engine independent of the acceleration magnitude in the nonrelativistic limit, thereby overcoming the bottleneck in which linear coupling is effective only in low-acceleration regimes. Furthermore, the work output of the quadratically coupled heat engine consistently outperforms that of the linearly coupled one. We also show that, in the ultrarelativistic limit, relativistic corrections cause the detector to behave as a closed system, thereby producing no work. It is demonstrated that the quadratic coupling and the four-velocity component associated with the composite trajectory can serve as novel thermodynamic resources, thereby effectively expanding the operational potential of the heat engine.
The stringent requirements for ultrahigh gain, programmable beamforming, and multiband coexistence in 5G/6G extended millimeter-wave communications are fundamentally constrained by the intrinsic bandwidth-gain trade-off of conventional metasurface antennas. Here, we propose a metasurface antenna that integrates bound states in the continuum (BICs) with aperture coupling to improve field confinement, radiation control, and impedance matching. By exploiting symmetry-protected and quasi-BIC resonances with suppressed radiation leakage, the proposed architecture enables selective resonance excitation and controllable energy localization across 20–42 GHz. Through geometric optimization of quasi-BIC meta-atoms and parametric tuning of coupling apertures, the radiating metasurface achieves an ultrahigh Q-factor of 2.1×106, yielding a simulated peak gain of 22.4 dBi at 25.7 GHz and a maximum measured gain of 18.1 dBi, together with significantly improved return-loss performance compared with conventional designs. The results show that the combination of symmetrical perturbation and aperture-mediated coupling provides an effective route to balancing gain, frequency selectivity, and multiband operation. This work offers a promising strategy for high-efficiency and frequency-agile antennas in future wireless communication and sensing systems.
We have systematically studied the structural and electronic properties of a topological material NbNiTe5 under high pressure. The evolution of the normal state resistance shows a non-monotonic trend from 0.7 to 5.1 GPa, in accordance with the second-order transition along the inter-layer direction observed in X-ray diffraction and Raman spectra. At around 10 GPa, the sample starts amorphization, which is concurrent with the emergence of superconductivity. Upon further compression, the structural disorder enhances and the superconducting transition becomes clearer, suggesting that the superconductivity is modulated by the degree of disorder in NbNiTe5 under high pressure. Within 45.7 GPa, the superconducting transition temperature (Tc) slowly rises from 0.6 K at 9.5 GPa to 1.4 K at 45.7 GPa. Our findings extend the family of transition metal chalcogenide superconductors and shed new light on understanding superconductivity in disordered systems.
Resolving inconsistencies among historical photonuclear cross-section measurements is essential for reliable nuclear data evaluation. Significant discrepancies exist between the Livermore and Saclay datasets for the 127I(γ, n) reaction, leading to long-standing uncertainties in evaluated databases. In this work, a Bayesian neural network (BNN) framework is applied to assess the systematic consistency of existing experimental data. The evaluation predicts that Bergère et al. (1969) measurements are mutually consistent within uncertainty, whereas both Livermore Bramblett et al. (1966) and Berman et al. (1987) measurements exhibit a systematic underestimation of the cross section, while available (γ, 2n) data remain consistent across laboratories. To independently test this prediction, new high-precision measurements of the 127I(γ, n) cross section were performed at the SLEGS beamline using quasi-monochromatic γ rays produced via inverse Compton scattering. The new data, with total uncertainties below 4
The synergistic effects of the oxygen vacancies and Sr doping on the lattice dynamics of perovskite Ba1−xSrxSnO3−δ (x = 0, 0.03, 0.06, 0.10) are systematically investigated through neutron powder diffraction, Raman scattering, specific heat capacity, and thermal conductivity measurements, combined with thermal-transport theoretical calculations. The introduction of the defects induces local distortions and unique vibrational behaviors, and has been observed by different measurements. The existence of the oxygen vacancies activates several Raman spectra that are otherwise not active in vacancy-free BaSnO3, and the Sr doping further introduces more Raman excitations. On heat capacity, the defect-induced optical phonons enhance the intensity of the Boson peak and move the peak value to a lower temperature region. Furthermore, the defects not only significantly reduce the lattice thermal conductivities (κL) by lowering the sound velocity and strengthening the lattice anharmonicity, but also cause the temperature-independent κL due to the large contribution of the coherent thermal conductivity. This work establishes a structure-dynamics-transport relationship in the defect-engineered perovskite oxides and paves the path to achieve intrinsically low thermal conductivity in other materials.
