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It is generally accepted that the Universe is dominated by dark energy but the different methods to measure the Hubble constant disagree, giving origin to what is known as the "Hubble tension”. The authors demonstrate that the sole reduction of the sound horizon is not sufficient to fully resolve the Hubble tension.
By using 2D materials heterostructures it is possible to exploit the properties of both materials at the interface, for instance, spin-dependent transport for application in spintronic devices. Here, using a heterostructure of MoTe2/Graphene the authors demonstrate a proximity induced spin-galvanic effect which can be controlled by the gate voltage.
Excitons are quasiparticles consisting of an electron-hole pair and can be used to study many-body phenomenon. Here, the authors demonstrate on-demand quantum confinement of long-lived interlayer excitons in WS2/WSe2 heterostructures deposited on nanopatterned substrates.
The recent discovery of superconducting nickelates has reignited interest in these materials and whether they can shed light on the mechanism of unconventional superconductivity in the cuprates. Here, the authors use first principles calculations to investigate the f electrons and magnetic ordering effects in the infinite layer nickelates and elaborate on the role of the cuprate-like 3dx2-y2 band.
Synchronization phenomena, where coupled oscillators coordinate their behavior, are ubiquitous in physics, biology, and neuroscience. In this work the authors investigate a framework of coupled topological signals where oscillators are defined both on the nodes and the links of a network, showing that this leads to new topologically induced explosive transitions.
Network approaches are key to understand epidemic spreading, inherently driven by human mobility patterns and constrained by transport systems. In this work, the authors develop a country distance framework to capture the spread of COVID-19 on top of the airline network, analyzing the effectiveness of mobility restrictions in the presence of multiple outbreaks and suggesting strategies for optimized coordinated travel restrictions.
Magnetic Weyl semimetals, such as Co3Sn2S2, are ideal to realise anomalous transport properties based on the Berry curvature in the specific electronic bands and are expected to be useful for topological spintronics. Here, the authors investigate the bulk and surface conduction channels of Co3Sn2S2 determining the relationship between the film thickness and surface conductance.
Fano resonance is an important phenomenon for optical devices particularly for application in switching and sensing. Here, the authors demonstrate theoretically and experimentally that Fano resonances can be induced using the polarization dependent properties of stacked wire-grid metallic metasurfaces.
Doped-fullerenes are a class of organic superconductors where disorder can be used to tune the superconducting temperature as well as the presence of subgap excitations such as Yu-Shiba-Rusinov states. Here, the authors investigate how structural disorder and non-magnetic impurities affect the superconductivity of Rb-doped fullerenes and what information this can provide about the underlying mechanisms.
Although acoustic and optical tweezers are widely used, it is challenging to create a 3D trap with a simple set-up. Here, acoustic vortex streaming is combined with radiation force to realise 3D trapping of particles in a fluid.
Quantum simulators are becoming an established method to help investigate and unpack the complexities of a many-body system and understand how it evolves over time. Here, using the 5-qubit IBM cloud computer the authors simulate the evolution of a protein complex and show that the energy-transfer behaviour is consistent with theoretical expectations.
Higher-order contagion models capture opinion dynamics and adoption of behavior in social networks. In this paper, the authors propose a mathematical framework able to accurately characterize the phase diagram of these contagion processes in social higher-order networks.
The experimental observation of plasmon-polaritons in charge-neutral bilayer graphene sparked interest for plasmonic and superconducting devices. Here, simulations predict that plasmon-polaritons possessing either transverse magnetic or electric polarization arise under an applied magnetic field in charge-neutral monolayer graphene.
Higher order topological systems add an additional layer of complexity to the bulk boundary correspondence by being able to sustain additional modes such as corner and hinge states. Here, the authors use electrical circuits to realize a two-dimensional quasicrystalline quadrupole topological insulator without discrete translational symmetry and observe localized corner modes.
The superposition of two layers of graphene or hBN at an angle gives rise to interesting geometrical structures, named Moiré superlattice, that has been intensively studied recently. The authors report on experimental data and simulations for twisted h-BN/AB-stacked tetralayer graphene heterostructures, finding that band gaps appear because of Fermi surface nesting due to the specific angle used.
The control and manipulation of spin waves holds promise for miniaturized radio-frequency spintronic devices. The authors demonstrate electrical access to the dynamics of magnetic vortices in confined geometries: a new avenue of research for future applications exploiting super high frequency behaviour for microwave communications and computing applications.
Symmetry is key for magnetism of molecules as well as other nanostructures. Here, the authors tune the magnetic moment of a metal-organic molecule deposited on NbSe2 via the adsorption symmetry, and observe a non-collinear intramolecular spin-spin interaction.
The Faraday effect describes the rotation of polarised light when exposed to a magnetic field projected along the direction of the light source. Here, the authors investigate the enhancement of this phenomenon in a magneto-plasmonic nanostructure and clarify the underlying physical mechanisms.
Building quantum computers typically requires substantial engineering efforts to achieve precise control on qubits and quantum gates. Here, the authors introduce an architecture based on reservoir computing and machine learning to realize efficient quantum operations without resorting to full optimization of the control parameters.
