368 papers
This study demonstrates that a robust nanofabrication protocol restoring atomic-scale purity to epitaxial graphene on SiC enables the emergence of macroscopic excitonic coherence in HMTP overlayers, revealing a Davydov-split vibronic manifold where a dark excitonic branch dominates radiative relaxation via a polaron-mediated pathway.
This paper introduces an all-optical imaging modality that achieves 100-attosecond temporal and 200-nanometer spatial resolution to directly visualize the spatiotemporal evolution and vector electric field lines of light within a widefield transmission microscope, enabling the observation of ultrafast scattering dynamics and pulse broadening in MoTe2 that are inaccessible via standard simulations.
This study demonstrates that intercalating bismuthene beneath zero-layer graphene on SiC(0001) creates a robust, protected platform for quantum spin Hall edge states that exhibit enhanced electronic correlations, establishing a tunable system for investigating correlated topological physics.
This paper demonstrates the successful fabrication of surface acoustic wave devices using niobium nitride (NbN) interdigitated transducers, which enable a 16-fold modulation of wave transmission by exploiting the material's sharp superconducting-to-normal state transition at cryogenic temperatures to overcome limitations of traditional metal electrodes in quantum applications.
First-principles calculations reveal that monolayer CrSBr exhibits significant thermal anisotropy (with a ratio of approximately 1.8) driven by phonon velocities and lifetimes, which can be further tuned by controlling flake size to suppress long mean free path phonons.
This study demonstrates that bulk single crystals of the triplet superconductor uranium ditelluride (UTe) exhibit an intrinsic, electrically controllable memory effect where magnetic-field-tuned current pulses switch the material between metastable states of distinct critical currents, offering a promising pathway for ultralow-power superconducting memory in cryogenic computing.
This paper theoretically demonstrates that Majorana Cooper pair boxes connected by normal-metal leads can realize tunable XY and Dzyaloshinskii--Moriya spin couplings via gate-controlled RKKY interactions, establishing them as a versatile platform for engineered quantum spin systems.
This paper systematically investigates spin control mechanisms in a planar silicon hole quantum dot using g-matrix formalism to map Rabi frequency dependence on magnetic field orientation, identifying optimal operating conditions for fast, coherent, and noise-resilient electrical qubit manipulation.
This study systematically demonstrates that optimizing gate-stack engineering—specifically through higher ALD temperatures for AlO, the use of HfO for defect passivation, and poly-Si gates—significantly enhances carrier mobility and reduces charge noise in SiMOS quantum devices, thereby establishing a clear pathway toward scalable, high-fidelity silicon spin-qubit platforms.
This review summarizes recent advances in 2D van der Waals superconductor heterostructures, highlighting how their unique interfaces enable the engineering of emergent quantum states and unconventional superconducting phenomena for applications in next-generation quantum technologies.
This study demonstrates that CVD-synthesized alloy MoSSe, characterized by an intrinsic out-of-plane electric field that extends exciton lifetimes and enhances charge separation, enables high-sensitivity photodetection with tunable response speeds via the photogating effect.
This study utilizes optically detected magnetic resonance to reveal that CsPbI perovskite nanocrystals exhibit an exceptionally long millisecond-scale electron spin relaxation time at 1.6 K, attributing the observed temperature dependence to a two-LO-phonon Raman process and low-field nuclear field fluctuations.
This paper theoretically demonstrates that DC current bias can control chaos in the magnetization dynamics of perpendicular magnetic tunnel junctions, while thermal fluctuations actively induce noise-driven chaotic behavior, offering a foundation for brain-inspired spintronic computing.
This paper derives hydrodynamic equations for a viscous two-component electron fluid formed by Zeeman splitting in ultra-pure 2D nanostructures, calculating relaxation rates and friction effects to explain magnetotransport phenomena in inclined magnetic fields.
This paper demonstrates that an angled electrostatic barrier in tilted Dirac/Weyl semimetals enables purely electrostatic valley filtering by exploiting valley-dependent refraction and reflection, a mechanism validated through a generalized transfer-matrix formalism and simulated valley-resolved trajectories.
By integrating local electromagnetic shielding and low-pass filtering directly at the cryogenic scan head, this study overcomes dynamical Coulomb blockade limitations to achieve a record energy resolution of 3.7 μeV, thereby revealing a novel coupling between atomic-scale Josephson currents and macroscopic cavity quantum electrodynamics modes.
This theoretical study establishes unconventional -wave magnets as a versatile platform for realizing a rich landscape of emergent superconducting phases, including topological superconductivity with Majorana edge modes, Bogoliubov Fermi surfaces, and the superconducting diode effect, within a unified microscopic framework.
This paper presents a scalable, monolithic fabrication strategy that integrates high-stress silicon nitride membranes with distributed Bragg reflectors using dry processing techniques, achieving self-aligned optomechanical cavities with high optical finesse and mechanical quality factors while eliminating the alignment and stability bottlenecks of conventional methods.
This study demonstrates that quantum phase transitions significantly enhance the charging power of non-integrable quantum batteries based on the Axial Next-Nearest-Neighbor Ising model, contrasting with integrable cases where such effects are absent at short timescales and providing insights for the design of practical many-qubit energy storage devices.
This paper introduces a physics-informed neural network framework that enables the unsupervised learning of effective Hamiltonian parameters directly from experimental transport data in solid-state quantum simulators, utilizing an autoencoder architecture with a physics-decoder to ensure physically meaningful representations, robust generalization, and noise resilience.