Browse Articles

Origin of spin–orbit torques and spin transport in Pt/Co/Cu/NiFe/Cu/capping multilayers

Spin–orbit torque (SOT) enables efficient current-driven control of magnetization, offering a promising pathway toward low-power spintronic devices. However, the origin of both damping-like (DL) and field-like (FL) SOTs and associated spin transport in complex multilayers remain unclear. Here, we investigated the dependence of SOT efficiencies on NiFe thickness within Ta/Pt/Co/Cu/xNiFe/Cu/Capping multilayers (x = 1–5 nm; Capping = Pt, Al, and SiO2). By employing a spin rotation geometry, the perpendicularly magnetized Pt/Co/Cu stacks serve as a spin source introducing an unconventional spin polarization orthogonal to the Oersted field, eliminating its contribution and enabling unambiguous extraction of SOTs using planar Hall and polar magneto-optic Kerr effect measurements. To distinguish bulk and interfacial contributions, we introduce a sample-area-normalized moment m = mNiFe/S, accounting for thickness-dependent magnetization and eliminating uncertainties arising from nominal thickness scaling and magnetic dead layers. We find that DL-SOT follows nearly linear 1/m scaling, consistent with rapid spin absorption at the Cu/NiFe interface but exhibits finite βSOT when 1/m approaches zero in both Pt- and Al-capped samples, indicating additional interfacial spin-current contributions at Cu/Pt and Cu/Al interfaces. In contrast, SiO2-capped samples show negligible interfacial contributions. Furthermore, FL-SOT deviates markedly from 1/m scaling, indicating a significantly longer spin dephasing length (∼1.7 nm) and thus more extended propagation of the spin currents responsible for FL-SOT in NiFe than for DL-SOT. Comparative capping-layer studies further corroborate this behavior through interface-dependent spin transport. Our findings clarify the origin of DL and FL torques and spin-transport processes, providing guidelines for engineering interfacial spin–orbit functionalities in ultrathin metallic heterostructures.

A method for collapse pressure analysis while drilling through fractured clay formations

SNAP-23 mediated vesicular trafficking in oligodendrocytes is necessary to maintain adult myelin integrity in mice

Spinel CoFe2O4/NiFe2O4 bilayer thin films with enhanced resistive switching and magnetic modulation

Designing multilayer resistive switching (RS) structure is an effective strategy to enhance resistive random-access memory performance. However, studies employing bilayer spinel oxides remain limited. In this work, CoFe2O4/NiFe2O4 bilayer thin films were fabricated via sol-gel spin-coating to enhance their resistive and magnetic modulation behaviors. Compared with single-layer devices, the bilayer devices show superior RS performance, including improved voltage uniformity, stable endurance, and reliable data retention, which is largely insensitive to the deposition sequence. As a result of the bilayer structural design, the devices demonstrate markedly enhanced switching behavior, attributed to the rupture of oxygen vacancy-type conductive filaments being confined to the interface of the bilayer film. Besides RS, the bilayer films exhibit magnetic anisotropy and their saturation magnetization is enhanced owing to an increase in oxygen vacancies. The results offer valuable insights into bilayer engineering in spinel oxides, with potential applications in non-volatile memory and multifunctional magnetoelectric devices.

Benchmarking engineered exchange interactions on NISQ hardware

Abstract Engineered exchange interactions, realized through the i SWAP and $$\sqrt{i\text {SWAP}}$$ gates, play a fundamental role in entangling operations for quantum algorithms, simulation of spin-exchange dynamics, and optimized qubit connectivity. In this work, we present hardware-aware implementations of the i SWAP and $$\sqrt{i\text {SWAP}}$$ gates tailored to superconducting quantum processors, along with comprehensive characterization using both quantum process tomography (QPT) and direct state measurements (DSM). QPT results show process fidelities of 97.32% ( i SWAP) and 98.02% ( $$\sqrt{i\text {SWAP}}$$ ) on quantum simulator, decreasing to 89.72% and 87.65% on quantum hardware, respectively. DSM on the $$|00\rangle$$ input state reveals that the i SWAP implementation achieves higher state preservation fidelity on hardware (93.53% vs. 92.44% for $$\sqrt{i\text {SWAP}}$$ ) but shows higher measured $$|11\rangle$$ population (2.26% vs. 0.38%). These results establish a benchmark for anisotropic exchange gates on noisy intermediate-scale quantum (NISQ) hardware and provide quantitative performance data to inform gate selection for quantum circuit design in the NISQ era.

Mechanistic basis of teichoic acid transport by a gatekeeper flippase

Abstract The cell wall is a complex structure that protects bacteria from environmental threats. Phosphocholine-containing teichoic acids are key cell wall biopolymers critical for host colonization, immune evasion, competence, and persistence in Streptococcus pneumoniae . The flippase TacF, a member of the multidrug/oligosaccharide-lipid/polysaccharide (MOP) superfamily, monitors the phosphocholine content of teichoic acids during transport, yet the underlying mechanism of this process remains unresolved. We present a cryo-EM structure of S. pneumoniae TacF in lipid nanodiscs. In vivo complementation assays and molecular dynamics simulations reveal key residues involved in teichoic acid recognition and transport, while coevolutionary and conservation analyses delineate common mechanistic elements among MOP flippases, indicating a shared mechanism for polyprenyl-diphosphate-linked oligosaccharide lipid transport. Our findings provide mechanistic insights into an essential flippase involved in S. pneumoniae pathogenesis and a potential drug target.

Mutual inductance sensing SQUID: Cryogenic microcalorimeter based on mutual inductance readout of superconducting temperature sensors

Superconducting microcalorimeters, such as superconducting transition-edge sensors and magnetic microcalorimeters, have emerged as state-of-the-art detectors for x-ray emission spectroscopy by combining near-unity quantum efficiency with excellent energy resolution. Despite these achievements, their resolving power has not yet reached the level required to rival modern wavelength-dispersive grating or crystal spectrometers. Here, we introduce a next-generation superconducting quantum interference device-based microcalorimeter concept that exploits the strong temperature dependence of the magnetic penetration depth of a superconductor operated close to its critical temperature. The resulting mutual inductance-based readout enables in situ tunable signal amplification, while inherently avoiding hysteretic effects that commonly limit superconducting sensors. Experiments with prototype devices demonstrate robust and reproducible operation over a wide temperature range. Based on our measurements and modeling, we project that, using an optimized absorber–sensor combination, an energy resolution below 100 meV (full width at half maximum) should be achievable for soft x-ray photons with energies below 800 eV. This approach therefore represents a promising pathway toward next-generation cryogenic detectors for high-precision x-ray emission spectroscopy.

Machine learning driven reservoir property modeling of the AEB-3E reservoir in the Berenice field Egypt

Abstract Accurate reservoir characterization in structurally complex fields is essential for optimizing hydrocarbon exploration and production. This study presents a detailed analysis of the AEB-3E reservoir within the Berenice Oil Field. It integrates well log and 3D seismic data using the EMBER (Ensemble Machine Learning for Better Estimation of Reservoir properties) workflow. Four wells provided composite logs, including gamma ray, density, neutron porosity, resistivity, and sonic measurements. These logs were quality-controlled, depth-matched, corrected, and upscaled to a 3D geological grid. Seismic-derived attributes were used to capture lateral heterogeneity and structural trends. They also enabled improved interwell property prediction in areas with sparse data. Petrophysical properties (shale volume, effective porosity, and water saturation) were modeled using EMBER. The method combines ensemble decision tree regression with embedded geostatistical features. This allows spatial continuity and stratigraphic relationships to be honored. Deterministic results indicate that the AEB-3E interval is predominantly clean. Average shale volume ranges from 0.11 to 0.18, effective porosity from 0.11 to 0.147, and water saturation from 0.28 to 0.45. Cross-sectional analysis from eastern and western parts of the field confirms lateral consistency. It also highlights localized variations controlled by structural features. Stochastic simulations were used to quantify uncertainty. They reveal low to moderate variability and indicate higher uncertainty near faults. The EMBER workflow significantly reduces modeling time and manual effort. At the same time, it delivers reliable and interpretable reservoir property predictions. Despite the limitation of only four wells, the methodology shows potential for application in similar geological settings. Overall, integrating machine learning with seismic and well data shows promise as an effective framework for reservoir evaluation, offering valuable insights to support informed decision-making and risk assessment in hydrocarbon development. However, the limited dataset warrants cautious interpretation, and further validation is recommended for broader application.

Thalamo-cortical synchrony shapes seizure expression in human temporal lobe epilepsy

Propagation of discharges inside a micro-channel within a lossy dielectric

Low temperature plasma interacting with porous dielectrics plays a significant role in the plasma-material industry. As plasma is capable of penetrating into a micro-channel and further inducing a forward ionization wave (FIW) and a restrike therein, their propagation properties remained poorly understood. This Letter establishes a 2D fluid model to study the propagation velocity of the two discharges inside the micro-channel within a lossy dielectric, highlighting its dependence on the permittivity (εr) and conductivity (σ) of the surrounding bulk dielectric. For the FIW, which propagated as a bulk streamer, increasing εr and σ reduced velocity; higher wall capacitance prolonged local charging times, while increased conductivity introduced resistive leakage that weakened the driving electric field. For the surface-hugging restrike, increased εr also slowed the restrike via capacitive loading, but its high plasma density rendered its velocity significantly less sensitive to permittivity changes than the FIW. Conversely, wall conductivity severely suppressed restrike speed and intensity by dissipating the accumulated surface charges essential for its propagation. Theoretical frameworks on the velocity of FIW and restrike were built, respectively, linking material properties and geometry to propagation properties.

Circular MIMO antenna with ML-based bandwidth and isolation prediction for 6G communications

Abstract The demanding requirements of next-generation 6G wireless systems necessitate the development of compact, wideband, and high-isolation terahertz (THz) MIMO antennas, while conventional full-wave electromagnetic optimization remains computationally expensive for complex multi-parameter designs. To overcome these challenges, this work introduces a compact circular MIMO antenna integrated with a machine learning (ML)-based framework for efficient performance prediction and design optimization. The proposed antenna consists of two co-oriented circular radiating elements, enhanced with a concentric ring and side stubs to improve impedance matching and broaden bandwidth. High inter-element isolation is achieved through the incorporation of isolation walls and an optimized partial ground structure. The polyimide-based antenna, with compact dimensions of 130 × 70 μm², operates at 5.55 THz and provides a wide bandwidth of 4.56–5.86 THz, a peak gain of 8.04 dB, a radiation efficiency of 85.64%, a diversity gain (DG) of 9.985 dB, and a total active reflection coefficient (TARC) below − 35 dB. To enable rapid performance estimation, an ML-based predictive model employing five supervised regression algorithms is trained using crucial geometrical parameters, including inner ring radius, feedline width, stub width, element spacing, and substrate height. Among the evaluated models, Cat Boost regression achieves the highest prediction accuracy, with R² scores of 97.05% for bandwidth and 92.56% for isolation. These results demonstrate that the proposed circular MIMO antenna, supported by ML-based predictive modeling, offers a promising solution for compact, high-performance antennas in 6G THz communication systems.

Fermi-surface diagnosis for topological superconductivity with s-wave-like pairing symmetries

Super-quadratic scaling anomaly in excitation power dependence of photoluminescence: Optically detected quasi-Fermi edge resonance

Standard model of electron–hole recombination predicts a sub-quadratic scaling of photoluminescence intensity as a function of the excitation power, which often helps clarify the underlying physical processes. This cardinal rule is defied in a system with discontinuous density-of-states (DOS). An anomalous super-quadraticity is observed in a superlattice-clad quantum well as quasi-Fermi edge crosses the DOS discontinuity. A DOS-dependent exponent, m(DOS), accounts for an unprecedented optically detected Fermi edge resonance that is observed.

Quantumness of hybrid systems under quantum noise

Photocatalytic reductive carboxylation of unactivated hydrazones with CO2

Extending Near‐Infrared Bioimaging Window Beyond 1500 nm

ABSTRACT Luminescent bioimaging has recently emerged as a crucial and indispensable tool for in vivo visualization and detection. Notably, due to significantly reduced photon scattering and minimal autofluorescence characteristic of the photon wavelength beyond 1500 nm, in vivo luminescent bioimaging offers enhanced capabilities, enabling the visualization of fine anatomical structures with superior tissue penetration depth and high spatial‐resolution. In this regard, this review highlighted the recent significant progress of extending the high‐resolution bioimaging window beyond 1500 nm and the corresponding novel luminescent materials. First, we systematically summarized the theoretical simulations that investigated potential superior bioimaging windows, including the 1500–1900 nm and 2100–2500 nm regions. Within such an extended region beyond 1500 nm, we then comprehensively concluded strategies for design of novel luminescent materials engineered for emission wavelength beyond 1500 nm, which included organic dyes with specific and large conjugated structures, quantum dots with size‐tunable emission spectra, lanthanide‐based nanocrystals, and complexes emitted from f‐f transitions. Subsequently, pioneering opportunities that exhibited superior in vivo bioimaging performance for biomedical applications were analyzed across volumetric bioimaging and wide‐field multiplexed bioimaging. Despite these promising achievements, opportunities for next‐generation bioimaging windows and challenges in clinical translation were objectively discussed at last.

Dimensional crossover-induced polarization boost in layered perovskite Ca2Nb2O7 under high pressure

In recent years, dimensional engineering in perovskites has attracted considerable interest as an effective strategy to tailor their optical and electrical properties. However, conventional chemical doping inherently modifies the material composition, complicating the fundamental understanding of intrinsic dimensional effects on perovskite properties. Here, taking the layered perovskite Ca2Nb2O7 as a model, we showcase the observation of a mechanical pressure-induced dimensional transition from a two-dimensional layered perovskite to a denser three-dimensional defective perovskite structure across a distinct two-phase coexistence region ranging from 12 to 18 GPa, employing in situ high-pressure x-ray diffraction experiments in conjunction with first-principles calculations. Notably, our comprehensive high-pressure single-crystal property measurements reveal that the structural densification results in approximately a 20% bandgap reduction, over two orders of magnitude decrease in the electrical transport anisotropy factor, and a threefold enhancement of ferroelectric remanent polarization. Our results establish a viable approach for achieving composition-preserving dimensional transitions in perovskites and provide key insights into the structure–property evolution of layered perovskites under extreme pressure.

Risk prevention and control evaluation and optimization strategy for digital transformation in oil and gas production management areas

High energy triplet-state manipulation via temperature-responsive twisted hetero-annulation systems

Abstract Precisely controlling excited-state transitions is vital for advanced technologies, particularly for sensitive, long-lived triplet states. Unlike traditional research focused on the lowest-energy excited triplet state (T 1 ), this work focuses on the high-energy triplet state (T n ), which presents significant challenges due to its short-lived and elusive nature. By implementing an annulation strategy to tailor the size, geometry, and electronic properties of the π-conjugated systems, we achieve precise control over two distinct T n -mediated pathways, T n → S 1 → S 0 and T n → T 1 → S 0 transitions, using temperature as an external trigger. This approach enables temperature-modulated blue-to-red afterglow, characterized by an exceptionally high energy gap of up to 0.76 eV between the delayed fluorescence and phosphorescence. It offers an elegant solution to resolving the key challenge in T n manipulation, providing a blueprint for developing next-generation responsive organic semiconductors with tailored excited-state behavior.

Niobium nitride grown on III-nitrides by MBE: Epitaxial growth and phase transitions, prospects for hybrid superconductor/metal/semiconductor heterostructures on silicon

III–N nitride semiconductor materials have made remarkable breakthroughs in the fields of optoelectronics and electronics using epitaxial heterostructures combining binary and ternary alloys (GaN, AlN, InGaN, and AlGaN). Nowadays, it is interesting to study the epitaxial integration of other nitride materials to implement new functionalities in III-nitride semiconductors. Niobium nitride (NbN), usually deposited by sputtering, is a well-established superconducting metal; therefore, it is interesting to investigate NbN epitaxy and its integration with III-nitrides. Using ammonia-molecular beam epitaxy (MBE-NH3), the epitaxial growth of NbN thin films on aluminum nitride (AlN) grown on silicon is studied. Three different phases, δ-NbN, the superconducting cubic-phase, β-Nb2N, the niobium-rich hexagonal phase, and ε-NbN, the 1:1 stoichiometric hexagonal phase, are stabilized. Phases are characterized ex situ by x-ray diffraction, atomic force microscopy, and scanning transmission electron microscopy. The three phases are also well identified in situ by specific reflection high energy electron diffraction patterns, which allow to study phase transitions during annealing. An experimental phase diagram is established. Various heterostructures combining III-N materials and NbN are epitaxially grown on silicon substrates, demonstrating the potential of this system for fabricating epitaxial hybrid superconductor/metal/semiconductor heterostructures.