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Charge-4e transport could be useful for realizing parity-protected superconducting qubits. In this work, the authors demonstrated the controlled conversion between charge-2e dominated and charge-4e dominated supercurrent in a superconducting quantum interference device (SQUID) fabricated in an InAs two-dimensional electron gas proximitized by the vicinity to an epitaxial Al layer.
The reduced dimensions of 2D materials increase the strength of electron-electron correlations and hence they can be used as a platform to engineer exotic physical states such as Dirac semimetals. Here, using first-principles calculations, the authors investigate the mechanical properties of β12-B5H3, as well as possible Dirac semimetal and phonon-mediated superconducting phases.
The common probes for cold atoms systems are typically global and do not provide direct information on the local spatial structure of states, limiting the insight on disordered and quasiperiodic systems. The authors demonstrate a local probe able to distinguish metallic and insulating states in an energy-resolved manner.
Materials hosting magnetic rare-earth ions sitting on a two-dimensional triangular lattices are ideal candidates to realize spin liquid states. In this work, the authors synthesize a high-quality single crystal sample of an erbium based triangular lattice compound, that exhibits a mixture of ferromagnetic and antiferromagnetic behaviour.
In this work, metasurface-based perfect vortex beams (MPVBs) featuring topological charges (TCs) of −32 and 16 have been successfully manufactured. As one of the tremendous phenomena in quantum mechanics, the fancy optical eraser experiment by integrating these MPVBs has also been successfully demonstrated in this study.
The study of frustrated magnet systems has unveiled a range of novel physical phenomena and continues to attract interest for in fields such as quantum spin liquid theory and high-temperature superconductors. Here, the authors use ab-initio calculations and a spin-wave analysis to demonstrate that an order-from-disorder phenomenon contributes to the columnar antiferromagnet ordering of BaCoS2.
Narrow-gap semiconductors with gate-controllable spin-splitting provide an ideal platform for novel spintronic and topological devices. The authors observe a large spontaneous spin-splitting energy, reaching 18 meV and widely tunable by a gate voltage, in an InAs quantum well that is magnetically proximitized by a ferromagnetic semiconductor (Ga,Fe)Sb.
Migdal-Eliashberg theory is a method for describing conventional superconductors. Here, the authors present an implementation that goes beyond the widely used constant density of states approximation by accommodating scattering processes beyond the Fermi surface, and they show its importance in two classes of near room temperature superhydrides.
Impedance theory grants insight to design metasurfaces for controlling acoustic fields, but such theory imposes great limitation on boundary conditions. The authors propose a generalized acoustic impedance theory connecting arbitrarily conservative acoustic fields, and design a beam splitter as an example of power flow processing.
A Chern insulator has non-trivial bulk topology with a quantized Chern number defined in the Brillouin zone (BZ), yielding robust gapless edge states. The authors introduce the concept of reduced Chern number, defined in subregions of the BZ, and construct a family of Chern dartboard insulators with quantized reduced Chern numbers but trivial bulk topology, exhibiting distinct pseudospin textures.
The turbulent skin friction drag at the solid/liquid interface results in high electric energy consumption when conveying liquids through hydraulic networks, and efficient drag reduction strategies are still unavailable. The authors coat a channel with a magnetic fluid film and achieve almost complete wall drag reduction of up to 90% across laminar and turbulent flow regimes.
Most physics-informed deep learning models alleviate the poor generalizability of pure data-driven models by minimizing residuals of the governing equations. In this work the authors propose to leverage physics priors, by embedding the discretized governing equations into the neural network architecture, which improves the generalizability and long-term prediction accuracy.
Reservoir computing (RC) is energy-efficient due to its simple architecture, allowing physical implementation. The proposed self-modulated RC extends the capabilities of RC, exhibiting superior learning abilities and complex dynamics, including attention and chaos, while retaining the advantage of physical implementation.
One main obstacle of flow cytometry techniques is the inability to image internal structure of live cells on the go, posing challenges in deciphering their biological mechanisms. To overcome this limit, the authors devise a light-sheet-based multichannel, multisheet and multicolor volume imaging cytometry for interrogating cells flowing simultaneously through microfluidic channels.
Understanding the thermal transport properties of tungsten nitrides formed on the divertor surface of the tokamak is crucial, as they will be subjected to continuous heat flux. In this article, the authors theoretically calculated the influence of vacancy defects on the electrical and thermal conductivities of tungsten nitrides, providing an understanding of the mechanism behind the effects in terms of electron behavior.
Generating ultrashort spectrally tunable pulses via high-harmonic generation (HHG) enables matching the excitation photon energy to the characteristic resonance of the sample to study its ultrafast dynamics. The authors demonstrate a compact continuously tunable high-intensity VUV HHG source that can rival state-of-the-art seeded FELs.
Harmonics produced when structured lasers hit isotropic targets inherit the topological properties of the driver, but such properties become more complex in the case of anisotropic nonlinear targets. The authors exploit topological analysis of the high-order harmonics to spatially resolve the information about the nonlinear response of the target.
Twisted bilayer systems have become a popular tool to investigate strongly correlated phenomenon but such a setup can also be relevant on the macroscopic scale. Here, the authors investigate the transport of macroscopic magnetic particles between magnetic square patterns and consider the impact the twist angle and the impact of disorder exerts on the observed behaviour.
Understanding the material strength of heterogeneously wetted granular systems is challenging due to the presence of a liquid modifying the interaction between the grains. Here, the authors investigate the mechanical properties of grains wetted with silicon oil finding an increase in Young’s modulus and an increased resistance to compressive strains.
Discovering nonlinear differential equations from empirical data is a significant challenge, often requiring manual parameter tuning. This paper introduces a machine learning method integrating denoising techniques, sparse regression, and bootstrap confidence intervals, which shows consistent accuracy in identifying 3D dynamical systems with moderate data size and high signal quality.
